quantitative research methods types

Educational resources and simple solutions for your research journey

What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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What Is Quantitative Research? Types, Characteristics & Methods

Step into the fascinating world of quantitative research , where numbers reveal extraordinary insights!

By gathering and studying data in a systematic way, quantitative research empowers us to understand our ever-changing world better. It helps understand a problem or an already-formed hypothesis by generating numerical data. The results don’t end here, as you can process these numbers to get actionable insights that aid decision-making.

You can use quantitative research to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, etc. The research is conducted on a larger sample population to draw predictive, average, and pattern-based insights.

Here, we delve into the intricacies of this research methodology, exploring various quantitative methods, their advantages, and real-life examples that showcase their impact and relevance.

Ready to embark on a journey of discovery and knowledge? Let’s go!

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to test theories about customer attitudes and behaviors. It helps researchers gather and analyze data systematically to gain valuable insights and draw evidence-based conclusions about customer preferences and trends.

Researchers use online surveys, questionnaires , polls , and quizzes to question a large number of people to obtain measurable and bias-free data.

In technical terms, quantitative research is mainly concerned with discovering facts about social phenomena while assuming a fixed and measurable reality.

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision is going to work out.

Let’s consider an example.

Suppose your qualitative analysis shows that your customers are looking for social media-based customer support. In that case, quantitative analysis will help you see how many of your customers are looking for this support.

If 10% of your customers are looking for such a service, you might or might not consider offering this feature. But, if 40% of your regular customers are seeking support via social media, then it is something you just cannot overlook.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

1. Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

As you can see, all the variables are measurable. This ensures that the research is in-depth and provides less erroneous data for reliable, actionable insights.

2. Sample Size

No matter what data analysis methods for quantitative research are being used, the sample size is kept such that it represents the target market.

As the main aim of the research methodology is to get numerical insights, the sample size should be fairly large. Depending on the survey objective and scope, it might span hundreds of thousands of people.

3. Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in result interpretation. Any bias can ruin the fairness of the entire process and defeats the purpose of research.

4. Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

5. Predictive Outcomes

Quantitative analysis of data can also be used for estimations and prediction outcomes. You can construct if-then scenarios and analyze the data for the identification of any upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools that come with advanced analytics capabilities.

Types of Quantitative Research Methods

Quantitative research is usually conducted using two methods. They are-

• Primary quantitative research methods
• Secondary quantitative research methods

1. Primary quantitative research methods

Primary quantitative research is the most popular way of conducting market research. The differentiating factor of this method is that the researcher relies on collecting data firsthand instead of relying on data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects, which are:

1.1. Techniques & Types of Studies:

• Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

They help them understand their customers, products, and other brand offerings in a proper manner.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys.

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys are of two types:

• Cross-Sectional Surveys Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.
• Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.
• Longitudinal Surveys Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes.
• Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

Surveys can be distributed via various channels. Some of the most popular ones are listed below:

• Email: Sending surveys via email is a popular and effective method. People recognize your brand, leading to a higher response rate. With ProProfs Survey Maker’s in-mail survey-filling feature, you can easily send out and collect survey responses.
• Embed on a website: Boost your response rate by embedding the survey on your website. When visitors are already engaged with your brand, they are more likely to take the survey.
• Social media: Take advantage of social media platforms to distribute your survey. People familiar with your brand are likely to respond, increasing your response numbers.
• QR codes: QR codes store your survey’s URL, and you can print or publish these codes in magazines, signs, business cards, or any object to make it easy for people to access your survey.
• SMS survey: Collect a high number of responses quickly with SMS surveys. It’s a time-effective way to reach your target audience.

1.2. Correlational Research:

Correlational research aims to establish relationships between two or more variables.

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

1.3. Causal-Comparative Research:

Causal-comparative research, also known as quasi-experimental research, seeks to determine cause-and-effect relationships between variables.

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to draw conclusions.

Causal-comparative research is useful in situations where it’s not ethical or feasible to conduct true experiments.

Examples of questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

1.4. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

2. Data collection methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls.

2.1Sampling methods:

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

2.2Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

• Simple random sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
• Stratified random sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
• Cluster sampling: Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
• Systematic sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

2.3Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

• Convenience sampling: The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them.
• Consecutive sampling: Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
• Quota sampling: With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
• Snowball sampling: This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
• Judgmental sampling: In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

3. Data analysis techniques

To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

• SWOT Analysis: This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
• Conjoint Analysis: This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
• Cross-tabulation: A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
• TURF Analysis: Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.
• By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error, you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

2. Secondary Quantitative Research Methods

• Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.
• Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports.
• Secondary quantitative research plays a crucial role in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Advantages of Quantitative Research Methods

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings.

Here are some core benefits this research methodology offers.

Direct Result Comparison

As the studies can be replicated for different cultural settings and different times, even with different groups of participants, they tend to be extremely useful. Researchers can compare the results of different studies in a statistical manner and arrive at comprehensive conclusions for a broader understanding.

Replication

Researchers can repeat the study by using standardized data collection protocols over well-structured data sets. They can also apply tangible definitions of abstract concepts to arrive at different conclusions for similar research objectives with minor variations.

Large Samples

As the research data comes from large samples, the researchers can process and analyze the data via highly reliable and consistent analysis procedures. They can arrive at well-defined conclusions that can be used to make the primary research more thorough and reliable.

Hypothesis Testing

This research methodology follows standardized and established hypothesis testing procedures. So, you have to be careful while reporting and analyzing your research data , and the overall quality of results gets improved.

Proven Examples of Quantitative Research Methods

Below, we discuss two excellent examples of quantitative research methods that were used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time.

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases.

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages.

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations.

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns.

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days.

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
• People felt massive digitization to be a resourceful yet demanding aspect as they have to adapt every day.
• Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

8 Best Practices to Conduct Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Define Research Objectives

There can be many ways to collect data via quantitative research methods that are chosen as per the research objective and scope. These methods allow you to build your own observations regarding any hypotheses – unknown, entirely new, or unexplained. 

You can hypothesize a proof and build a prediction of outcomes supporting the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

Below, we explore quantitative research methods and discuss some examples to enhance your understanding of them.

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, and that includes a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether it’s experimental, quasi-experimental, or correlational. You also need to pay attention to the sample size and sampling technique such that it represents the target population accurately.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Implement data collection protocols to minimize errors and bias during data gathering. Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings in a clear and unbiased manner. Avoid making unwarranted causal claims, especially in correlational studies. Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data. Comply with ethical guidelines and gain approval from a governing body if necessary.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid research method by opting for the methods that are most relevant to your objective.

This understanding comes gradually as you learn how to implement the correct combination of qualitative and quantitative research methods for your research projects. For the best results, we recommend investing in smart, efficient, and scalable research tools that come with delightful reporting and advanced analytics to make every research initiative a success.

These software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.

So, choose the best research partner, create the right research plan, and gather insights that drive sustainable growth for your business.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Quantitative research methods

a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time. The goal of gathering this quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Quantitative research techniques include experiments and surveys. 

SAGE Research Methods Videos

What are the strengths of quantitative research.

Professor Norma T. Mertz briefly discusses qualitative research and how it has changed since she entered the field. She emphasizes the importance of defining a research question before choosing a theoretical approach to research.

This is just one segment in a series about quantitative methods. You can find additional videos in our SAGE database, Research Methods: 

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Home » Types of Quantitative Research Methods

Types of Quantitative Research Methods

Quantitative Research is essentially about numbers and statistical analysis. It’s how we turn observations into data that we can measure and analyze in a structured way. This type of research allows us to quantify behaviors, opinions, or phenomena and translate that into figures and statistics that can be used to uncover patterns, test theories, or make predictions.

Table of Contents

Now, let’s explore the main types of Quantitative Research Methods:

Imagine you have a big jar of jellybeans, and you want to know which color is the most popular. So, you ask everyone in your class to pick their favorite color. That’s like doing a survey! You’re asking lots of people questions to gather lots of number-based information or data.

Experiments

This is like doing a science experiment in class. You might change one thing, like the amount of sunlight a plant gets, to see how it affects something else, like how tall the plant grows. You’re trying different things and watching what happens, keeping track of the results with numbers.

Observational Studies

Imagine you’re bird watching, and you’re writing down every time a bird comes to the feeder and what type it is. You’re not changing anything; you’re just watching and noting down numbers and facts.

Longitudinal Studies

Think of this like taking a picture of the same tree every day for a whole year. You’re trying to see how the tree changes over time, from winter to spring, and all the way back to winter again. You’re collecting data over a long period to see trends or changes.

Cross-Sectional Studies

This is like taking a single snapshot of a crowded park. You’re getting a lot of information all at once, but just for one point in time. It’s like a group photo that tells you who was there at that moment, but not what happens before or after.

Correlational Studies

Now, imagine you noticed that the taller your classmates are, the bigger their shoe size. You start to think there’s a connection between height and shoe size. So, you measure everyone’s height and shoe size to see if taller people really do have bigger feet, looking for a pattern or correlation.

Each of these tools helps you collect different kinds of number-based information for solving different kinds of mysteries. Just like a detective, you need to pick the right tool for the job to find the clues you need!

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Types of Quantitative Research | An Absolute Guide for Beginners

It does not matter which discipline a person belongs to. You have to come across the quantitative research data once or multiple times in life. Most people often come across one or multiple questionnaires or surveys. 

Let’s take an example of quantitative research. A survey is conducted to know how much time a shopkeeper takes to attend to the customer. And how many times he/she walks into the shop. 

Here, the survey is conducted using different questions. Such as how much time the shopkeeper takes to attend to the customer. And how many times the customer comes in the shop, etc.

The aim of these surveys is to draw the most relevant analytical conclusions. That helps in understanding the targeted audience. 

There are various types of quantitative research that a company uses. It is used to understand the product’s demand within the market. 

In this blog, we have given the necessary details about what quantitative research is. And its types. 

So, let’s move on to the details. 

What is quantitative research?

Table of Contents

Quantitative research is one of the systematic techniques. It is used to collect the data using the sampling for quantitative methods. For example , online polls , questionnaires, and online surveys. You can seamlessly integrate them into your WordPress website using a versatile WordPress voting plugin , providing an interactive and engaging way to collect valuable insights from your audience.

The data is collected from both existing and potential users and represented numerically. 

Quantitative research also used to measure the variables, analyze, and register the relationships between the variable studies with a numerical system’s help.  

In quantitative research, the information is collected via structured research. And the outcomes reflect or represent the population. 

Where we use quantitative research?

Quantitative researchers use different tools. The tools are used to collect numeric data in terms of numbers and statistics. This data is represented in non-textual forms, such as charts, figures, and tables. 

Moreover, the researchers can take the non-numerical data to examine the information.

Quantitative research is using in several areas, such as:

• gender studies, 
• demography,
• community health,
• psychology,
• education, and so on.

What are the 5 types of quantitative research?

Survey research.

The survey is one of the primary statistic methods. It is used for different types of quantitative research. The aim of this is to provide a comprehensive description of the characteristics of the specific population or group. 

Both large and small organizations also apply the offline and online survey research method. This helps to know their users and understand the product and merchandise views.

There are numerous methods to manage survey research. It can be done on the phone, in person, or by email or mails.

Descriptive research

It describes the present status of all the selected or identified variables. The basic objective of descriptive research is to describe and evaluate the people’s present status, conditions, settings, or events.

Descriptive research is considered to be one of the important types of quantitative research.

The most common descriptive questions start with the “How much..,” “what is the…,” “what is a percentage of…,” and these kinds of questions. 

Let’s take an example of this survey. An Exit poll is a descriptive survey that includes questions like: “ Which candidate will win this election?” 

Moreover, the demographic segmentation survey might be like this: “ How many students between the 18-25 age do study at night?”

Experimental research

This is one of the types of quantitative research, as its name suggests that it is based on single or multiple theories. 

It terms to be the true experiments that utilize the scientific technique to verify the cause-effect relations within the group of variables. 

Therefore, more than one theory is used to conduct the particular research. An example of experimental research is “ the effect of the particular dose and treatment on breast cancer.”

The use of experimental research can be implemented in various fields. And these fields are sociology, physics, biology, chemistry, medicine, and so on.

Correlational research

It is used for establishing relationships among two close entities. And determining the relational impact on each other.  

For such cases, a researcher requires a minimum of two different groups. Additionally, this research approaches and recognizes the patterns and trends in the data without going far into the observation to analyze various trends and patterns.

An example of correlational research is the correlation between self-esteem and intelligence.

Suppose your favorite ice-cream truck has a specific jingle, and the truck is coming to your area. The more would be the sound of the jingle, the more closer the ice-cream truck would be. 

But, if two ice-cream trucks are coming in your area, you can easily know which sounds are from your favorite ice-cream truck. This is what you are not taught in your classroom, but you can relate the fact in your mind on your own. 

Moreover, it depends on your intelligence that you can quickly recognize without anybody’s help. This is what the correlation research method is.

Causal-comparative research

It is one of the scientific methods that apply to summarize the cause and effect equations among different variables. In causal relationships, a single variable is based on the complementary experimental variable.

The experimenters do not manipulate the independent variable. But, the impact of independent variables over the dependent variables can be measured in causal-comparative research.

Let’s take some examples. The impact of divorce of the parents on their children. The impact of sports activity on the participants, and so on.

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Why do I select different types of quantitative research over qualitative research?

It has been seen that quantitative research prefers over qualitative research. The reasons for it can be like quantitative research can be done fast, scientific, acceptable, objective, and focused. 

Apart from this, there are several reasons to select different types of quantitative research. Let’s check each of them one by one.

Deal with the larger sample data

The types of quantitative research results depend on the large sample size. This sample size represents the population. The lager is the sample size; the more valid results will be drawn.

The types of quantitative research results depend on the large sample size. This sample size represents the population. The lager is the sample size; the more valid results will be drawn. 

Control-sensitive

It has been seen that researchers have more control over the data collection methods. This data is also different from the experiments.

Researchers use different types of quantitative research. It is used to establish facts, make predictions, and test the previous hypotheses. 

The relatable aims for finding evidence that may or may not support an existing hypothesis. By testing and validating the constructed theories, it can give reasons why a phenomenon has occurred.

Generalizable

A project can generalize the concepts more accurately. It also analyzes the casual relationship, and predicts results.  Moreover, the findings can generalize when the selection processes are designed. And the sample represents the population study.

Arrange in simple analytical ways

The data is being collected in the form of statistics and numbers. Further, it is arranged in charts, tables, figures, or another non-textual form.

The methods of data collection that use a quantitative research method are comparatively quick (such as telephone interviews). Some companies even use the best landline phone service for more professionalism and speed. Some companies even use a special telephone service for more professionalism and speed.  Moreover, the data analysis is also comparatively less time-consuming (as it does use statistical software).

Consistent with data

Using the different types of quantitative research, you can easily get data. This data is reliable, precise, and consistent, numerical, and quantitative.

More structured

The researchers use different tools to get structured quantitative researched data. The tools can be equipment or questionnaires for collecting numerical data.

The repeatable and replica methods are usually done in research studies. This leads to high reliability.

Decision-making

The data taken from quantitative research like demographics, market size, and user preferences can help provide information on business decisions.

So, what are the methods of quantitative research?

The quantitative research method features objective calculations and mathematical, statistical, or numerical analysis. The data is collected by questionnaires, polls, and surveys for analysis. 

The quantitative research method mainly focuses on collecting numerical data. This data generalize across the set of people so that a specific phenomenon can be explained.

Researchers who use the quantitative research method try to identify and separate the variables. These variables separate within a study framework, seek relationships, correlation, and casualties. 

After this, the quantitative researchers try to control the system in which the information is being gathered. This helps in avoiding the variables’ risk by which the accurate relationships can be identified.

What is the methodology for the quantitative research designs?

The structure of various types of quantitative research depends on the scientific method. 

This utilizes deductive reasoning, in which the researchers: find out the hypothesis. Gather the information. Uses it for further investigation to prove whether the hypothesis is true or not. Once the analysis is done, share your summary.  

Therefore, a basic procedure is followed for the quantitative research design:

• Make the observations related to something, which is new and unexplained. Analyzing the present theory that is surrounded by issues or problems.
• Hypothesizing the observations’ explanations.
• Predict the result depends on the hypothesis studies by formulating the plan for your prediction test control.
• Gather and process the information. If the prediction is right, move to the next step; otherwise, return to step 2. Get new hypotheses that depend on the new knowledge and situations.
• Finally, verify the findings of the sample on different factors. Make the conclusions. Represent the outcomes in a well-structured manner to your audience.

Now, let’s check your knowledge regarding types of quantitative research!

Now, you have studied different types of quantitative research. Let’s check what you have learned. 

Take a quiz regarding the different quantitative research. Select the correct quantitative research type to the given statement. 

• Do people think working from home is a great option to enhance the employee’s productivity with longer commutes?
• Descriptive Research
• Experimental Research
• Correlational Research
• Causal-Comparative Research

• How frequently do employees get the chance of traveling for a holiday?

• What is the primary difference between senior citizens and Millennials regarding smartphone usage?

• How has the Covid-19 modified profession for white-collar employees?

• Does the management method of the car shop owners foretell the work satisfaction of car sales associates?

There are various types of quantitative research . And researchers use different kinds of scientific tools to collect numeric data. 

It has been observed that the quantitative research survey questions are must. So that the participants can have an easy and effective medium to responses. 

Hope you easily understand the details mentioned in the blog. If you still have any queries, comment in the below section, and we will help you in the best possible way. Are you looking for statistics help for students ? Get the best help from our experts to clear all your doubts.

Frequently Asked Questions

Where is quantitative research used.

The main use of quantitative research is for quantifying the problem by forming numerical data. (Moreover, this data must transform into useful statistics.) The quantitative research is used to quantify opinions, attitudes, behaviors, and another defined variable. By this, you can generalize the results from a greater population sample.

What are the steps in quantitative research?

There are 11 steps to following in Quantitative Research, and these are:

Theory.  Hypothesis.  Research design.  Operationalizing concepts.  Selection of a research site(s).  Selection of respondents.  Data collection.  Processing data. Data analysis. Findings and conclusions. Writing the findings in a well-structured manner.

What are the 7 characteristics of quantitative research?

The 7 major characteristics of quantitative research methods are as follows:

Practice Standardized Research Instruments.  Contain Measurable Variables. Data representation in Graphs, Tables, or Figures. Use a Repeatable Method.  Work on Measuring Devices. Allows a Normal Population Distribution. Can Predict Outcomes.

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Types of Quantitative Research

Introduction to Quantitative Research

Quantitative research is outlined as a scientific investigation of phenomena by gathering quantitative information and activity applied mathematics, or procedure techniques.

Quantitative research is a research method where you gather and analyze numerical data to understand and explain various phenomena. The different types of quantitative research are survey, descriptive, experiential, correlational, and causal-comparative.

It focuses on using mathematical and statistical techniques to understand and investigate the subject. This is why the collected information must be in numerical form.

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Different Types of Quantitative Research

The following are the different types of Quantitative research types with the description of each.

1. Survey Research

Survey Research is one of the most common types of quantitative research techniques. In this research method, you can use surveys to collect numerical data from a particular group or population. After that, you can analyze the data and explain the characteristics of the group. Both small and large organizations typically employ it for a proper understanding of their customers and to understand the merchandise and product views. It is of two types:

• Cross-sectional: In a cross-sectional survey, you collect information from a large group of people at a specific time. These are mostly useful in retail stores, education, etc.
• Longitudinal: In a longitudinal survey, you collect information from a small group of people over a long period of time, usually years. These are useful in medical trials and applied sciences.

2. Descriptive Research

Descriptive research aims to explain and understand the current state of things like people, places, conditions, or events. Here, the researcher’s goal is to gather general observational data without exploring the reasons behind them. Also, you don’t need to begin with a hypothesis; instead, you gather data first and then create a hypothesis if needed. Importantly, descriptive research doesn’t try to prove or disprove the hypothesis. It is mainly about creating a research hypothesis.

3. Experimental Research

Experimental research, as the name suggests, uses the scientific method to establish the cause-effect relationship among a group of variables using experiments. Researchers can use multiple theories to conduct this research. The major components of experimental research are:

• A comparison group of participants who are randomly selected and assigned to experimental and control groups.
• An independent variable, referred to as the experimental variable that can be applied to the experimental group.
• A dependent variable, referred to as the effect or posttest variable that can be measured in an identical manner for all groups.

4. Correlational Research

Correlational research establishes a relationship between two close entities and determines how one impacts the other. For this, a researcher needs at least two separate groups. This type of research will recognize trends and patterns in data, but it does not go so far in its analysis to observe the different patterns.

5. Casual-Comparative Research

Causal-comparative research is employed to conclude the cause-effect equation between two or more variables, where one variable depends on the opposite experimental variable. The experimenter does not manipulate the independent variable and then measures the effects of the independent variable on the dependent variable.

Types of Quantitative Research Infographic

Here is a detailed infographic explaining the different types of quantitative research.

Characteristics

Here are the common characteristics of different types of quantitative research.

1. Numerical Data: Quantitative research primarily deals with numerical data, which can be quantified and measured. Researchers use structured instruments like surveys, experiments, or observations to collect this data.

2. Large Sample Sizes: Quantitative research typically involves large sample sizes to ensure statistical validity and generalize findings to a broader population. This allows for greater confidence in the results.

3. Statistical Analysis: Statistical analysis plays a central role in quantitative research. Researchers use statistical methods to analyze and interpret the data, identify patterns and relationships, and draw conclusions. Common statistical techniques include regression analysis, correlation analysis, t-tests, ANOVA, and chi-squared tests.

4. Structured Research Design: Quantitative research often employs a structured and pre-defined research design. Researchers develop hypotheses, select variables, and design experiments or surveys before data collection begins. This structured approach helps maintain consistency and rigor in the research process.

5. Generalizability: Quantitative research often aims to generalize findings to a larger population. When conducted correctly, quantitative studies can provide insights that are applicable beyond the specific sample studied.

6. Closed-Ended Questions: Surveys and questionnaires used in quantitative research often employ closed-ended questions with predetermined response options. This facilitates data analysis and allows for comparisons between participants.

Here is a step-by-step process of performing different types of quantitative research.

Step 1: Clearly define your research problem or question. Step 2: Conduct a literature review to identify relevant studies and theories related to your research topic. Step 3: Develop specific hypotheses or research questions that you aim to answer through your research. Step 4: Decide on the research design (e.g., experimental, cross-sectional, longitudinal) and your target population, and select a representative sample. Also, develop a data collection plan and choose appropriate data collection methods (e.g., surveys, experiments, observations). Step 5: Choose valid and reliable measurement instruments (e.g., questionnaires, scales) to collect data. Make sure to test your measurement tools before actual research. Step 6: Administer your selected data collection methods to your sample population. Step 7: Select appropriate statistical techniques (e.g., descriptive statistics, inferential statistics) based on your research design and hypotheses. Analyze the data to test your hypotheses or answer your research questions. Step 8: Interpret the statistical findings and summarize them to determine if they support or refute your hypothesis. Step 9: Prepare a research report or paper that includes the research process, methodology, results, and conclusions to share your findings.

Following are some of the common uses of various types of quantitative research.

Advantages and Disadvantages

Some of the key advantages and disadvantages of types of quantitative research are:

Final Thoughts

Quantitative research is an effective method that helps us build statistically reliable insights across numerous fields. However, researchers should select the appropriate quantitative approach considering the limitations and benefits of each method. Moreover, new emerging technologies like artificial intelligence (AI) can simplify data collection, analysis, and visualization of research outcomes.

Frequently Asked Questions (FAQs)

Q1. What are the methods of data analysis in quantitative research? Answer: Quantitative research uses different ways to analyze data. It uses descriptive stats, like averages and spreads, to summarize data. It also uses inferential statistics, like tests and regression, to check ideas and connections. For more complex situations, it uses multivariate methods like ANOVA and factor analysis to look at how different things interact. Researchers often use special software like SPSS or R to do these analyses.

Q2. How to interpret data in quantitative research? Answer: Interpreting quantitative data involves looking at the statistical results and assessing the present relationships or trends. You must also discuss if the data is applicable in both theoretical and practical sense. It is also crucial to present results clearly with tables and graphs and think about any problems or biases in collecting data. When interpreting, stay objective and let the data guide you, avoiding making guesses or assumptions.

Q3. What is quantitative vs. qualitative research? Answer: Quantitative research involves collecting and analyzing numerical data to uncover patterns, relationships, and trends. It relies on structured surveys, experiments, and statistical methods to draw objective conclusions. In contrast, qualitative research focuses on exploring the depth and meaning of phenomena through non-numerical data, such as interviews, observations, and open-ended questions. It aims to understand the underlying context and subjective experiences.

Recommended Articles

This is a guide to Types of Quantitative Research. Here, we also discuss the introduction and different types of quantitative research, which include survey, descriptive, experimental research, etc. You may also have a look at the following articles to learn more –

• Types of Research Methodology
• Quantitative Research Examples
• Descriptive Research
• Advantages and Disadvantages of Qualitative Research

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Home » Quantitative Data – Types, Methods and Examples

Quantitative Data – Types, Methods and Examples

Table of Contents

Quantitative Data

Definition:

Quantitative data refers to numerical data that can be measured or counted. This type of data is often used in scientific research and is typically collected through methods such as surveys, experiments, and statistical analysis.

Quantitative Data Types

There are two main types of quantitative data: discrete and continuous.

• Discrete data: Discrete data refers to numerical values that can only take on specific, distinct values. This type of data is typically represented as whole numbers and cannot be broken down into smaller units. Examples of discrete data include the number of students in a class, the number of cars in a parking lot, and the number of children in a family.
• Continuous data: Continuous data refers to numerical values that can take on any value within a certain range or interval. This type of data is typically represented as decimal or fractional values and can be broken down into smaller units. Examples of continuous data include measurements of height, weight, temperature, and time.

Quantitative Data Collection Methods

There are several common methods for collecting quantitative data. Some of these methods include:

• Surveys : Surveys involve asking a set of standardized questions to a large number of people. Surveys can be conducted in person, over the phone, via email or online, and can be used to collect data on a wide range of topics.
• Experiments : Experiments involve manipulating one or more variables and observing the effects on a specific outcome. Experiments can be conducted in a controlled laboratory setting or in the real world.
• Observational studies : Observational studies involve observing and collecting data on a specific phenomenon without intervening or manipulating any variables. Observational studies can be conducted in a natural setting or in a laboratory.
• Secondary data analysis : Secondary data analysis involves using existing data that was collected for a different purpose to answer a new research question. This method can be cost-effective and efficient, but it is important to ensure that the data is appropriate for the research question being studied.
• Physiological measures: Physiological measures involve collecting data on biological or physiological processes, such as heart rate, blood pressure, or brain activity.
• Computerized tracking: Computerized tracking involves collecting data automatically from electronic sources, such as social media, online purchases, or website analytics.

Quantitative Data Analysis Methods

There are several methods for analyzing quantitative data, including:

• Descriptive statistics: Descriptive statistics are used to summarize and describe the basic features of the data, such as the mean, median, mode, standard deviation, and range.
• Inferential statistics : Inferential statistics are used to make generalizations about a population based on a sample of data. These methods include hypothesis testing, confidence intervals, and regression analysis.
• Data visualization: Data visualization involves creating charts, graphs, and other visual representations of the data to help identify patterns and trends. Common types of data visualization include histograms, scatterplots, and bar charts.
• Time series analysis: Time series analysis involves analyzing data that is collected over time to identify patterns and trends in the data.
• Multivariate analysis : Multivariate analysis involves analyzing data with multiple variables to identify relationships between the variables.
• Factor analysis : Factor analysis involves identifying underlying factors or dimensions that explain the variation in the data.
• Cluster analysis: Cluster analysis involves identifying groups or clusters of observations that are similar to each other based on multiple variables.

Quantitative Data Formats

Quantitative data can be represented in different formats, depending on the nature of the data and the purpose of the analysis. Here are some common formats:

• Tables : Tables are a common way to present quantitative data, particularly when the data involves multiple variables. Tables can be used to show the frequency or percentage of data in different categories or to display summary statistics.
• Charts and graphs: Charts and graphs are useful for visualizing quantitative data and can be used to highlight patterns and trends in the data. Some common types of charts and graphs include line charts, bar charts, scatterplots, and pie charts.
• Databases : Quantitative data can be stored in databases, which allow for easy sorting, filtering, and analysis of large amounts of data.
• Spreadsheets : Spreadsheets can be used to organize and analyze quantitative data, particularly when the data is relatively small in size. Spreadsheets allow for calculations and data manipulation, as well as the creation of charts and graphs.
• Statistical software : Statistical software, such as SPSS, R, and SAS, can be used to analyze quantitative data. These programs allow for more advanced statistical analyses and data modeling, as well as the creation of charts and graphs.

Quantitative Data Gathering Guide

Here is a basic guide for gathering quantitative data:

• Define the research question: The first step in gathering quantitative data is to clearly define the research question. This will help determine the type of data to be collected, the sample size, and the methods of data analysis.
• Choose the data collection method: Select the appropriate method for collecting data based on the research question and available resources. This could include surveys, experiments, observational studies, or other methods.
• Determine the sample size: Determine the appropriate sample size for the research question. This will depend on the level of precision needed and the variability of the population being studied.
• Develop the data collection instrument: Develop a questionnaire or survey instrument that will be used to collect the data. The instrument should be designed to gather the specific information needed to answer the research question.
• Pilot test the data collection instrument : Before collecting data from the entire sample, pilot test the instrument on a small group to identify any potential problems or issues.
• Collect the data: Collect the data from the selected sample using the chosen data collection method.
• Clean and organize the data : Organize the data into a format that can be easily analyzed. This may involve checking for missing data, outliers, or errors.
• Analyze the data: Analyze the data using appropriate statistical methods. This may involve descriptive statistics, inferential statistics, or other types of analysis.
• Interpret the results: Interpret the results of the analysis in the context of the research question. Identify any patterns, trends, or relationships in the data and draw conclusions based on the findings.
• Communicate the findings: Communicate the findings of the analysis in a clear and concise manner, using appropriate tables, graphs, and other visual aids as necessary. The results should be presented in a way that is accessible to the intended audience.

Examples of Quantitative Data

Here are some examples of quantitative data:

• Height of a person (measured in inches or centimeters)
• Weight of a person (measured in pounds or kilograms)
• Temperature (measured in Fahrenheit or Celsius)
• Age of a person (measured in years)
• Number of cars sold in a month
• Amount of rainfall in a specific area (measured in inches or millimeters)
• Number of hours worked in a week
• GPA (grade point average) of a student
• Sales figures for a product
• Time taken to complete a task.
• Distance traveled (measured in miles or kilometers)
• Speed of an object (measured in miles per hour or kilometers per hour)
• Number of people attending an event
• Price of a product (measured in dollars or other currency)
• Blood pressure (measured in millimeters of mercury)
• Amount of sugar in a food item (measured in grams)
• Test scores (measured on a numerical scale)
• Number of website visitors per day
• Stock prices (measured in dollars)
• Crime rates (measured by the number of crimes per 100,000 people)

Applications of Quantitative Data

Quantitative data has a wide range of applications across various fields, including:

• Scientific research: Quantitative data is used extensively in scientific research to test hypotheses and draw conclusions. For example, in biology, researchers might use quantitative data to measure the growth rate of cells or the effectiveness of a drug treatment.
• Business and economics: Quantitative data is used to analyze business and economic trends, forecast future performance, and make data-driven decisions. For example, a company might use quantitative data to analyze sales figures and customer demographics to determine which products are most popular among which segments of their customer base.
• Education: Quantitative data is used in education to measure student performance, evaluate teaching methods, and identify areas where improvement is needed. For example, a teacher might use quantitative data to track the progress of their students over the course of a semester and adjust their teaching methods accordingly.
• Public policy: Quantitative data is used in public policy to evaluate the effectiveness of policies and programs, identify areas where improvement is needed, and develop evidence-based solutions. For example, a government agency might use quantitative data to evaluate the impact of a social welfare program on poverty rates.
• Healthcare : Quantitative data is used in healthcare to evaluate the effectiveness of medical treatments, track the spread of diseases, and identify risk factors for various health conditions. For example, a doctor might use quantitative data to monitor the blood pressure levels of their patients over time and adjust their treatment plan accordingly.

Purpose of Quantitative Data

The purpose of quantitative data is to provide a numerical representation of a phenomenon or observation. Quantitative data is used to measure and describe the characteristics of a population or sample, and to test hypotheses and draw conclusions based on statistical analysis. Some of the key purposes of quantitative data include:

• Measuring and describing : Quantitative data is used to measure and describe the characteristics of a population or sample, such as age, income, or education level. This allows researchers to better understand the population they are studying.
• Testing hypotheses: Quantitative data is often used to test hypotheses and theories by collecting numerical data and analyzing it using statistical methods. This can help researchers determine whether there is a statistically significant relationship between variables or whether there is support for a particular theory.
• Making predictions : Quantitative data can be used to make predictions about future events or trends based on past data. This is often done through statistical modeling or time series analysis.
• Evaluating programs and policies: Quantitative data is often used to evaluate the effectiveness of programs and policies. This can help policymakers and program managers identify areas where improvements can be made and make evidence-based decisions about future programs and policies.

When to use Quantitative Data

Quantitative data is appropriate to use when you want to collect and analyze numerical data that can be measured and analyzed using statistical methods. Here are some situations where quantitative data is typically used:

• When you want to measure a characteristic or behavior : If you want to measure something like the height or weight of a population or the number of people who smoke, you would use quantitative data to collect this information.
• When you want to compare groups: If you want to compare two or more groups, such as comparing the effectiveness of two different medical treatments, you would use quantitative data to collect and analyze the data.
• When you want to test a hypothesis : If you have a hypothesis or theory that you want to test, you would use quantitative data to collect data that can be analyzed statistically to determine whether your hypothesis is supported by the data.
• When you want to make predictions: If you want to make predictions about future trends or events, such as predicting sales for a new product, you would use quantitative data to collect and analyze data from past trends to make your prediction.
• When you want to evaluate a program or policy : If you want to evaluate the effectiveness of a program or policy, you would use quantitative data to collect data about the program or policy and analyze it statistically to determine whether it has had the intended effect.

Characteristics of Quantitative Data

Quantitative data is characterized by several key features, including:

• Numerical values : Quantitative data consists of numerical values that can be measured and counted. These values are often expressed in terms of units, such as dollars, centimeters, or kilograms.
• Continuous or discrete : Quantitative data can be either continuous or discrete. Continuous data can take on any value within a certain range, while discrete data can only take on certain values.
• Objective: Quantitative data is objective, meaning that it is not influenced by personal biases or opinions. It is based on empirical evidence that can be measured and analyzed using statistical methods.
• Large sample size: Quantitative data is often collected from a large sample size in order to ensure that the results are statistically significant and representative of the population being studied.
• Statistical analysis: Quantitative data is typically analyzed using statistical methods to determine patterns, relationships, and other characteristics of the data. This allows researchers to make more objective conclusions based on empirical evidence.
• Precision : Quantitative data is often very precise, with measurements taken to multiple decimal points or significant figures. This precision allows for more accurate analysis and interpretation of the data.

Advantages of Quantitative Data

Some advantages of quantitative data are:

• Objectivity : Quantitative data is usually objective because it is based on measurable and observable variables. This means that different people who collect the same data will generally get the same results.
• Precision : Quantitative data provides precise measurements of variables. This means that it is easier to make comparisons and draw conclusions from quantitative data.
• Replicability : Since quantitative data is based on objective measurements, it is often easier to replicate research studies using the same or similar data.
• Generalizability : Quantitative data allows researchers to generalize findings to a larger population. This is because quantitative data is often collected using random sampling methods, which help to ensure that the data is representative of the population being studied.
• Statistical analysis : Quantitative data can be analyzed using statistical methods, which allows researchers to test hypotheses and draw conclusions about the relationships between variables.
• Efficiency : Quantitative data can often be collected quickly and efficiently using surveys or other standardized instruments, which makes it a cost-effective way to gather large amounts of data.

Limitations of Quantitative Data

Some Limitations of Quantitative Data are as follows:

• Limited context: Quantitative data does not provide information about the context in which the data was collected. This can make it difficult to understand the meaning behind the numbers.
• Limited depth: Quantitative data is often limited to predetermined variables and questions, which may not capture the complexity of the phenomenon being studied.
• Difficulty in capturing qualitative aspects: Quantitative data is unable to capture the subjective experiences and qualitative aspects of human behavior, such as emotions, attitudes, and motivations.
• Possibility of bias: The collection and interpretation of quantitative data can be influenced by biases, such as sampling bias, measurement bias, or researcher bias.
• Simplification of complex phenomena: Quantitative data may oversimplify complex phenomena by reducing them to numerical measurements and statistical analyses.
• Lack of flexibility: Quantitative data collection methods may not allow for changes or adaptations in the research process, which can limit the ability to respond to unexpected findings or new insights.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is quantitative data? How to collect, understand, and analyze it

A comprehensive guide to quantitative data, how it differs from qualitative data, and why it's a valuable tool for solving problems.

• Key takeaways
• What is quantitative data?
• Examples of quantitative data
• Difference between quantitative and qualitative data
• Characteristics of quantitative data
• Types of quantitative data
• When should I use quantitative or qualitative research?
• Pros and cons of quantitative data
• Collection methods

Quantitative data analysis tools

• Return to top

Data is all around us, and every day it becomes increasingly important. Different types of data define more and more of our interactions with the world around us—from using the internet to buying a car, to the algorithms behind news feeds we see, and much more. 

One of the most common and well-known categories of data is quantitative data or data that can be expressed in numbers or numerical values. 

This guide takes a deep look at what quantitative data is , what it can be used for, how it’s collected, its advantages and disadvantages, and more. 

Key takeaways: 

Quantitative data is data that can be counted or measured in numerical values.

The two main types of quantitative data are discrete data and continuous data.

Height in feet, age in years, and weight in pounds are examples of quantitative data. 

Qualitative data is descriptive data that is not expressed numerically. 

Both quantitative research and qualitative research are often conducted through surveys and questionnaires. 

What is quantitative data? 

Quantitative data is information that can be counted or measured—or, in other words, quantified—and given a numerical value.

Quantitative data is used when a researcher needs to quantify a problem, and answers questions like “what,” “how many,” and “how often.” This type of data is frequently used in math calculations, algorithms, or statistical analysis. 

In product management, UX design, or software engineering, quantitative data can be the rate of product adoption (a percentage), conversions (a number), or page load speed (a unit of time), or other metrics. In the context of shopping, quantitative data could be how many customers bought a certain item. Regarding vehicles, quantitative data might be how much horsepower a car has. 

What are examples of quantitative data? 

Quantitative data is anything that can be counted in definite units and numbers . So, among many, many other things, some examples of quantitative data include: 

Revenue in dollars

Weight in kilograms or pounds

Age in months or years

Distance in miles or kilometers

Time in days or weeks

Experiment results

Website conversion rates

Website page load speed

What is the difference between quantitative and qualitative data? 

There are many differences between qualitative and quantitative data —each represents very different data sets and are used in different situations. Often, too, they’re used together to provide more comprehensive insights.

As we’ve described, quantitative data relates to numbers ; it can be definitively counted or measured.  Qualitative data, on the other hand, is descriptive data that are expressed in words or visuals. So, where quantitative data is used for statistical analysis, qualitative data is categorized according to themes. 

Examples of qualitative vs. quantitative data

As mentioned above, examples of quantitative data include distance in miles or age in years. 

Qualitative data, however, is expressed by describing or labeling certain attributes, such as “chocolate milk,” “blue eyes,” and “red flowers.” In these examples, the adjectives chocolate, blue, and red are qualitative data because they tell us something about the objects that cannot be quantified. 

Further reading: The differences between categorical and quantitative Data and examples of qualitative data

Characteristics of quantitative data 

Quantitative data is made up of numerical values has numerical properties, and can easily undergo math operations like addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using math techniques. 

Specific types of quantitative data

All quantitative data can be measured numerically, as shown above. But these data types can be broken down into more specific categories, too.

There are two types of quantitative data: discrete and continuous . Continuous data can be further divided into interval data and ratio data. 

Discrete data

In reference to quantitative data, discrete data is information that can only take certain fixed values. While discrete data doesn’t have to be represented by whole numbers, there are limitations to how it can be expressed. 

Examples of discrete data:

The number of players on a team

The number of employees at a company

The number of items eggs broken when you drop the carton

The number of outs a hitter makes in a baseball game

The number of right and wrong questions on a test

A website's bounce rate (percentages can be no less than 0 or greater than 100)

Discrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, as shown below.

Continuous data 

Continuous data , on the other hand, can take any value and varies over time. This type of data can be infinitely and meaningfully broken down into smaller and smaller parts. 

Examples of continuous data:

Website traffic

Water temperature

The time it takes to complete a task

Because continuous data changes over time, its insights are best expressed with a line graph or grouped into categories, as shown below.

Continuous data can be further broken down into two categories: interval data and ratio data. 

Interval data

Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. Interval data is always expressed in numbers where the distance between two points is standardized and equal. These numbers can also be called integers. 

Examples of interval data include temperature since it can move below and above 0.

Ratio data has all the properties of interval data, but unlike interval data, ratio data also has a true zero. For example, weight in grams is a type of ratio data because it is measured along a continuous scale with equal space between each value, and the scale starts at 0.0.

Other examples of ratio data are weight, length, height, and concentration. 

Interval data vs. ratio data

Ratio data gets its name because the ratio of two measurements can be interpreted meaningfully, whereas two measurements cannot be directly compared with intervals.

For example, something that weighs six pounds is twice as heavy as something that weighs three pounds. However, this rule does not apply to interval data, which has no zero value. An SAT score of 700, for instance, is not twice as good as an SAT score of 350, because the scale does not begin at zero.

Similarly, 40º is not twice as hot as 20º. Saying uses 0º as a reference point to compare the two temperatures, which is incorrect.

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When should I use quantitative or qualitative research? 

Quantitative and qualitative research can both yield valuable findings, but it’s important to choose which type of data to collect based on the nature and objectives of your research. 

When to use quantitative research

Quantitative research is likely most appropriate if the thing you are trying to study or measure can be counted and expressed in numbers. For example, quantitative methods are used to calculate a city’s demographics—how many people live there, their ages, their ethnicities, their incomes, and so on. 

When to use qualitative research

Qualitative data is defined as non-numerical data such as language, text, video, audio recordings, and photographs. This data can be collected through qualitative methods and research such as interviews, survey questions, observations, focus groups, or diary accounts. 

Conducting qualitative research involves collecting, analyzing, and interpreting qualitative non-numerical data (like color, flavor, or some other describable aspect). Methods of qualitative analysis include thematic analysis, coding, and content analysis.

If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis.

To use our city example from above, determining why a city's population is happy or unhappy—something you would need to ask them to describe—requires qualitative data. 

In short: The goal of qualitative research is to understand how individuals perceive their own social realities. It's commonly used in fields like psychology, social sciences and sociology, educational research, anthropology, political science, and more. 

In some instances, like when trying to understand why users are abandoning your website, it’s helpful to assess both quantitative and qualitative data. Understanding what users are doing on your website—as well as why they’re doing it (or how they feel when they’re doing it)—gives you the information you need to make your website’s experience better. 

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What are the pros and cons of quantitative data? 

Quantitative data is most helpful when trying to understand something that can be counted and expressed in numbers. 

Pros of quantitative data: 

Quantitative data is less susceptible to selection bias than qualitative data.

It can be tested and checked, and anyone can replicate both an experiment and its results.

Quantitative data is relatively quick and easy to collect. 

Cons of quantitative data: 

Quantitative data typically lacks context. In other words, it tells you what something is but not why it is.

Conclusions drawn from quantitative research are only applicable to the particular case studied, and any generalized conclusions are only hypotheses.

How do you collect quantitative data? 

There are many ways to collect quantitative data , with common methods including surveys and questionnaires. These can generate both quantitative data and qualitative data, depending on the questions asked. 

Once the data is collected and analyzed, it can be used to examine patterns, make predictions about the future, and draw inferences. 

For example, a survey of 100 consumers about where they plan to shop during the holidays might show that 45 of them plan to shop online, while the other 55 plan to shop in stores. 

Questionnaires and surveys 

Surveys and questionnaires are commonly used in quantitative research and qualitative research because they are both effective and relatively easy to create and distribute. With a wide array of simple-to-use tools, conducting surveys online is a quick and convenient research method. 

These research types are useful for gathering in-depth feedback from users and customers, particularly for finding out how people feel about a certain product, service, or experience. For example, many e-commerce companies send post-purchase surveys to find out how a customer felt about the transaction — and if any areas could be improved. 

Another common way to collect quantitative data is through a consumer survey, which retailers and other businesses can use to get customer feedback, understand intent, and predict shopper behavior . 

Open-source online datasets 

There are many public datasets online that are free to access and analyze. In some instances, rather than conducting original research through the methods mentioned above, researchers analyze and interpret this previously collected data in the way that suits their own research project. Examples of public datasets include: 

The Bureau of Labor Statistics Data

The Census Bureau Data

World Bank Open Data

The CIA World Factbook  

Experiments

An experiment is another common method that usually involves a  control group  and an  experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

Controlled experiments,  A/B tests , blind experiments, and many others fall under this category.

With large data pools, a survey of each individual person or data point may be infeasible. In this instance, sampling is used to conduct quantitative research. Sampling is the process of selecting a representative sample of data, which can save time and resources. There are two types of sampling: random sampling (also known as probability sampling) and non-random sampling (also known as non-probability sampling). 

Probability sampling allows for the randomization of the sample selection, meaning that each sample has the same probability of being selected for survey as any other sample. 

In non-random sampling, each sample unit does not have the same probability of being included in the sample. This type of sampling relies on factors other than random chance to select sample units, such as the researcher’s own subjective judgment. Non-random sampling is most commonly used in qualitative research. 

Typically, data analysts and data scientists use a variety of special tools to gather and analyze quantitative data from different sources. 

For example, many web analysts and marketing professionals use Google Analytics (pictured below) to gather data about their website’s traffic and performance. This tool can reveal how many visitors come to your site in a day or week, the length of an average session, where traffic comes from, and more. In this example, the goal of this quantitative analysis is to understand and optimize your site’s performance. 

Google Analytics is just one example of the many quantitative analytics tools available for different research professionals. 

Other quantitative data tools include…

Microsoft Excel

Microsoft Power BI

Apache Spark

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Frequently asked questions about quantitative data

Is quantitative data objective.

Quantitative researchers do everything they can to ensure data’s objectivity by eliminating bias in the collection and analysis process. However, there are factors that can cause quantitative data to be biased.

For example, selection bias can occur when certain individuals are more likely to be selected for study than others. Other types of bias include reporting bias , attrition bias , recall bias , observer bias , and others. 

Who uses quantitative data?

Quantitative research is used in many fields of study, including psychology, digital experience intelligence , economics, demography, marketing, political science, sociology, epidemiology, gender studies, health, and human development. Quantitative research is used less commonly in fields such as history and anthropology. 

Many people who are seeking advanced degrees in a scientific field use quantitative research as part of their studies.

What is quantitative data in statistics?

Statistics is a branch of mathematics that is commonly used in quantitative research. To conduct quantitative research with statistical methods, a researcher would collect data based on a hypothesis, and then that data is manipulated and studied as part of hypothesis testing, proving the accuracy or reliability of the hypothesis.

Is quantitative data better than qualitative data?

It depends on the researcher’s goal. If the researcher wants to measure something—for example, to understand “how many” or “how often,”—quantitative data is appropriate. However, if a researcher wants to learn the reason behind something—to understand “why” something is—qualitative research methods will better answer these questions.

Further reading: Qualitative vs. quantitative data — what's the difference?

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Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

Categorical & quantitative variables both provide vital info about a data set. But each is important for different reasons and has its own pros/cons.

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

Discover how just-in-time data, explained by Lane Greer, enhances customer insights and decision-making beyond real-time analytics.

Jordan Morrow shares how AI-driven decision-making can revolutionize your business by harnessing data and enhancing your decision-making processes.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Qualitative vs. Quantitative Data: Key Differences, Methods, and Examples

Jul 22, 2024 · 8 min read

“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” But how do we uncover these stories?

It starts with understanding the difference between qualitative and quantitative data.

Whether you are conducting research or developing a product , knowing the differences is key to better understanding your data and your customers.

In this article, we will study the differences between qualitative and quantitative data, examine their collection methods, look at real-world examples, and show how they work together to provide a complete picture and guide strategic decisions.

In this article

What is qualitative data, what is quantitative data, qualitative vs quantitative data: key differences, qualitative vs quantitative data: collection methods, qualitative vs quantitative data: examples, when is qualitative data useful vs. quantitative data is useful, qualitative vs quantitative data: benefits and limitations, why choose openreplay for your qualitative and quantitative data collection needs.

Qualitative data , derived from the term “quality,” is non-numerical information that describes the characteristics, attributes, or properties of a subject. It uses words to detail experiences, thoughts, feelings, behaviors, and interactions.

This type of data provides personal insights, helping us understand the reasons behind people’s actions and opinions. It is subjective and open to interpretation in different ways.

Quantitative data , derived from the term “quantity,” is numerical information that describes quantities and measurements. It uses numbers to present amounts, frequencies, and statistics.

This type of data provides clear and factual information by capturing measurable details. It is objective and less open to interpretation.

Qualitative and quantitative data serve different purposes, use distinct collection and analysis methods, and reveal different types of information.

1. What are the primary purposes of qualitative vs. quantitative data?

The primary purpose of qualitative data is to understand experiences, behaviors, motivations, and perspectives. It is meant to answer questions like “why?”, “how?” and “what are the underlying reasons?”

On the other hand, the primary purpose of quantitative data is to provide numerical information that can be measured and quantified. It answers questions like “how many?”, “how often?” and “what is the relationship between variables?”

2. How do their data collection methods differ?

Qualitative data collection methods gather descriptive information to understand experiences and perspectives. Methods include interviews, focus groups, and session replays.

Quantitative data is collected through methods that focus on measuring and counting to produce numerical data. Methods include surveys, data analytics tools, and experiments.

3. What types of data are used in each?

When you hear qualitative data , think words, non-numerical , often involving the five senses. Examples include text, audio, video, images, and artifacts.

When you hear quantitative data , think numbers , precise, countable, and measurable information. Examples include percentages, statistics, measures, and counts.

4. How to analyze qualitative vs. quantitative data

Since qualitative and quantitative data differ, so do the ways of analyzing them. Raw data can’t tell us much until it is analyzed using suitable methods. Think of raw data as a gold mine- analyzed data is the gold we extract.

Qualitative data analysis methods:

Qualitative data analysis relies on decoding text, observations, and narratives to identify patterns and themes. Common methods include:

• Thematic analysis: finding recurring themes in data.
• Content analysis: categorizing text into predefined categories.
• Narrative analysis: analyzing stories to understand experiences.
• Observational analysis: studying behaviors in natural settings.

Quantitative data analysis methods:

Quantitative data analysis relies on decoding numbers and making sense of them through statistical methods. Common methods include:

• Descriptive statistics: summarizing data using measures like mean and median.
• Inferential statistics: making predictions or inferences from sample data.
• Data visualization: creating charts and graphs to represent data.
• Statistical software: using specialized software for statistical analysis.
• Correlation and regression analysis: studying relationships between variables.

Everything that collects non-numerical data, such as words and observations, is a means of collecting qualitative data . By contrast, anything that produces numbers is a method of collecting quantitative data.

According to the book Educational Research: Quantitative, Qualitative, and Mixed Approaches , qualitative and quantitative data collection methods each bring valuable perspectives.

We collect qualitative data using a variety of methods, including:

• Interviews, or surveys with open-ended questions: allow for detailed, descriptive answers.
• Focus groups: gather diverse perspectives on a specific topic through small group discussions.
• Unstructured observations: observe behaviors and interactions in their natural setting.
• Case studies: conduct in-depth examinations of individual or group experiences.

We collect quantitative data through the following methods:

• Experiments: manipulate one or more variables to observe the effect on another variable.
• Surveys with closed-ended questions : use structured questionnaires to collect quantifiable data.
• Structured observations: define specific categories or behaviors and quantify the observations.

Modern collection methods

Traditionally, qualitative and quantitative data collection methods could be done on paper. Paper surveys and written case studies were standard. However, with the rise of digital tools, data collection has been revolutionized, and so have the methods.

In the context of digital products, some qualitative and quantitative data methods are:

• Session replay (qualitative): visually recreate how users interact with a website or mobile application.
• Analytics tools (quantitative): automatically gather product and website usage data.
• A/B testing (quantitative): compare two versions of a webpage or app feature to determine which performs better.
• Usability testing (both): assess how easily users can navigate and use your product.

Choosing the “right” method

• Determine if you need to understand behaviors/experiences or measure quantities.
• Identify the data type to choose between qualitative (non-numerical) and quantitative (numerical) data collection methods.
• Assess the audience and context to select a method suitable for your target population and study environment.
• Combine multiple methods if needed to gain a complete understanding.

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Understanding qualitative and quantitative data becomes easier with specific, real-world examples. Here’s a table to show different examples:

Qualitative data can often be coded quantitatively. In other words, almost all qualitative data can be assigned meaningful numerical values.

For example, many users may be abandoning a product. Using session replay , we can find that those churning users are experiencing problems with a button that is causing dead clicks and rage clicks… These sessions can be categorized and quantified into numbers, providing a complete picture of user behavior and the issues impacting their experience.

Qualitative data is useful for exploring new topics, understanding behaviors, capturing emotions, generating hypotheses, and testing assumptions. Quantitative data , on the other hand, is useful for testing hypotheses, measuring variables, identifying patterns, making predictions, and generalizing results.

To ask which is “better” or more “valuable” is to ignore their intimate relationship. Depending on the context, both are useful and can complement each other. Combining the two can give a clearer picture of the situation.

In product development, especially for an early-stage product or company, you typically lack sufficient quantitative data to make informed decisions. At this stage, qualitative data is very important as it helps you understand user behaviors and experiences, which is essential for identifying and addressing early user needs and frustrations.

As your product or company stabilizes and scales, you will start to have more user interactions and can rely on quantitative data to measure variables, identify patterns, and make predictions. By integrating qualitative data with quantitative data , you can make more sense of user behavior puzzles and user experience flaws.

So, the cherry on top is when quantitative data is backed up by qualitative information, resulting in an overview that combines statistical reliability with rich, contextual understanding.

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Let’s break down the benefits and limitations of qualitative and quantitative data. By looking at each type separately, we can see their unique strengths and weaknesses.

Benefits of qualitative data

• Captures the human element and emotions behind behaviors and decisions.
• Offers rich context to explain patterns observed in quantitative data.
• Allows for exploration of unexpected topics that emerge during data collection.

Limitations of qualitative data

• Data is subjective and open to interpretation, which can lead to bias.
• Findings are often not generalizable to a larger population due to small, non-random samples.
• Data collection and analysis are time-intensive and resource-heavy.
• It often focuses on a limited scope or specific context, which may miss broader trends.

Benefits of quantitative data

• Results can be generalized to larger populations due to large, random samples.
• Provides numerical data that is objective and easy to analyze.
• Data collection and analysis can be faster and less resource-intensive.

Limitations of quantitative data

• Provides limited insights into underlying reasons, emotions, and motivations.
• It may overlook the context behind the numbers, missing nuances and subtleties.
• Predefined response options can limit the richness of the data collected.

Interestingly, when you combine qualitative and quantitative , you can often overcome many limitations and amplify the benefits.

OpenReplay gives your data a clear and convincing voice, helping you uncover the stories behind the numbers.

With OpenReplay, you can capture detailed session replays that let you see your product through your users’ eyes. Visualize user interactions, identify underlying issues, and understand the human element behind every click, error, and user path. This qualitative data , combined with quantitative metrics, provides a deep understanding of user behavior.

You can make sense of your product’s quantitative data with OpenReplay’s analytics tools. Track conversion funnels, map user journeys, and analyze heatmaps and trends. This quantitative data helps you identify friction points and ensure your improvements lead to better digital experiences.

From early development to scaling, OpenReplay supports every stage of your product lifecycle. Integrating session replays with detailed analytics allows you to validate findings, explore underlying reasons, and make data-driven decisions that enhance your product.

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6 types of market research surveys and how to create one

Continuous improvement is what makes you stand out from other players in your respective industry and helps you engage your target audience.

The preferences of your customers change over time, along with their buying behaviors and market dynamics. To survive the cutthroat competition, it’s vital to constantly improve your solutions.

But how do you identify the changes that need to be made or find where there’s room for improvement? Conducting a market research survey may help.

When it comes to improving your solutions, the best source of information is your current or potential customers. So, reaching out to them will help you access relevant insights that steer you in the right direction.

Market research surveys enable you to collect extensive feedback from your customers. They allow you to explore behavioral dynamics and extract actionable insights from customer experiences.

This makes it easier for you to improve your products and promote them to the right audience in an efficient way.

There are different types of market research surveys. You can go with one or several that pique your interest.

In this article, we’ll explore different types of market research surveys and discuss a step-by-step process to create one.

So, without further ado, let’s get started.

6 types of market research surveys

There are a variety of market research surveys that you can leverage to collect useful information and make data-driven decisions when it comes to improving your products and offering seamless experiences. We’ll be discussing the six of the best.

Online surveys

Online surveys enable you to reach out to your target audience or customers anywhere in the world and collect the required information from them. People only need an internet connection to participate in the survey and share their experiences or preferences with you.

There are a number of solutions that you can leverage to create an online survey. You can either choose to go with free solutions such as Google Forms for simple surveys, or a Google Forms alternative , as well as other premium form builders for more advanced design features.

The tools will generate a survey link for you, which can be easily accessed by the chosen participants.

This survey type is ideal for exploring the changing preferences of your target audience, collecting feedback about your products, and assessing customer satisfaction.

Telephone/virtual call surveys

If you want to access in-depth insights and detailed feedback from your audience, conducting a market research survey via telephone or virtual calls may be your best bet.

Telephone surveys are generally preferred to reach out to participants who are a little less tech-savvy and not acquainted with modern communication methods.

For example, if your audience comprises elderly individuals, reaching out to them via phone may be the best strategy to get useful information. However, you can choose to conduct a survey through a virtual call if the participants prefer using modern communication solutions.

This survey method is best suited when you ask open-ended questions, facilitating your access to detailed responses and valuable insights for decision-making.

Face-to-face surveys

Conducting telephone or virtual call surveys is an effective way to gather useful information by asking open-ended questions. However, you may come across problems like a low response rate.

This is the reason why many resort to face-to-face surveys and personally reach out to the participants. Plus, interacting with the participants face-to-face ensures a high response rate, especially when your survey is lengthy and requires more time to complete.

Face-to-face surveys provide you with the opportunity to connect with your audience on a deeper level. The method enables you to probe for more detailed answers that can help you devise efficient business strategies .

Mobile surveys

Mobile surveys encourage participants to submit their responses using mobile devices. People can respond to your questions using their smartphones or tablets.

Nowadays, people are always on-the-go and use mobile devices to access useful information or interact with their preferred brands.

So, optimizing your surveys with mobile-first experience in mind is an efficient way of collecting relevant insights and required information from your current or potential customers.

Mail/email surveys

People may have diverse preferences when it comes to contacting their preferred brands. However, 60% of people prefer to be contacted by businesses via email .

Hence, conducting email surveys may be an efficient way to engage the intended audience and collect the required information from the participants.

You can ask questions directly in the email sent to the recipients or add a survey link in the email body. It’s one of the most cost-effective ways to gather insightful data at a mass level.

Leveraging social media is one of the most efficient strategies to explore the changing behavioral dynamics of your audience and market trends .

There are over 5 billion social media users worldwide , enabling you to cast a wider net and reach out to your target audience anywhere in the world.

Polls are one of the most common types of market research surveys that brands use to collect useful information from their respective audiences through social media . They generally comprise a single question paired with a variety of responses to choose from.

Polls are best suited to find out what the majority of your audience wants or to know their opinion. For example, you can ask your audience about a particular feature they would like your solution to offer or identify areas for improvement.

You can leverage polls to engage your target audience with ease and gather useful insights from them in no time.

How to create a market research survey

Now that we have discussed different types of market research surveys, let’s dive into the process of creating one that can help you access relevant insights.

Define your objectives

The first step to creating a market research survey is to clearly define your objectives. The goal here is to collect useful data. So, it’s best that you know what to do with it.

For example, you are planning to launch an online project management solution and want to conduct a market research survey to make informed decisions.

In this case, your survey objective may be to know what your target audience expects from an ideal online project management solution.

They must have come across different alternatives in the industry. So, they can share meaningful insights with you that can help you outmaneuver the competition by offering better project management features or capabilities.

Know your audience

Once you have set clear goals for the survey, the next step is to identify an audience best suited to participate in the survey.

Carrying forward the aforementioned example, if your goal is to launch a feature-rich project management solution, your target audience may comprise project managers, entrepreneurs, and small business owners.

Encouraging them to participate in your survey will help you get acquainted with their needs or preferences . As a result, you may be able to design a project management solution that offers a seamless user experience and helps you establish lasting customer relationships.

Craft engaging questions

After identifying your target audience, you should move on to the next step, which is designing the market research survey.

If your goal is to launch a feature-rich online project management solution, you should ask questions that pave the way for you to come up with capabilities superior to the competing products.

Your questions should be clear, concise, relevant, and easy to understand. Your objective is to collect useful information from the participants, not confuse them.

Furthermore, you should refrain from designing lengthy surveys with unending questions, as it will severely affect participation or response rates.

Choose the right survey method

It’s not necessary to stick to a single survey method when it comes to collecting information from your participants. You can leverage different types of surveys to gather relevant insights.

To see which method works for you, you can test them all and go with the one that gives you the best results.

Let’s say your audience comprises project managers, entrepreneurs, and small business owners. These people are less likely to respond to polls, telephone surveys, and emails. Furthermore, you may have to make an appointment to meet with them face-to-face. 

Similarly, if you're conducting ux research to design successful products, you'll probably need to schedule user interviews or card sorting sessions to find out what makes sense to your users.

So, online or mobile surveys may help you engage the intended audience. However, the selection of the right survey method may vary from one scenario to another.

Analyze and interpret data

Once you’ve gathered responses from your participants, the next step is to analyze and interpret the data collected. There are a variety of data analysis solutions that you can leverage to analyze the data and draw conclusions.

The software you choose to go with and the analysis technique depend on the complexity of your potential findings.

If you simply want to discover the most sought-after project management software features by participants, you can simply choose to go with Excel. However, if you want to run complex analysis, you may want to consider solutions like Python, SPSS, Minitab, R, SAS, and more.

Once you’ve analyzed the data, the next step is to interpret the results and make data-driven decisions.

Final thoughts

In this article, we discussed different market research survey types and how you can create a market research survey to gather useful information from your target audience.

Conducting a market research survey helps you get acquainted with the preferences of your current or potential customers and stay tuned to market trends.

The findings from these surveys pave the way for you to make smart decisions and stand out in the competitive landscape .

So, if you’ve been planning to conduct a market research survey, the information laid out in this article may help.

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What is qualitative research? Approaches, methods, and examples

Updated 23 Jul 2024

Students in social sciences frequently seek to understand how people feel, think, and behave in specific situations or relationships that evolve over time. To achieve this, they employ various techniques and data collection methods in qualitative research allowing for a deeper exploration of human experiences. Participant observation, in-depth interviews, and other qualitative methods are commonly used to gather rich, detailed data to uncover key aspects of social behavior and relationships. What is qualitative research? This article will answer this question and guide you through the essentials of this methodology, including data collection techniques and analytical approaches.

Qualitative research definition and significance 

This inquiry method is helpful for learners interested in how to conduct research . It focuses on understanding human behavior, experiences, and social phenomena from the perspective of those involved. What does qualitative mean? It uses non-numerical data, such as interviews, observations, and textual analysis, to understand people’s feelings, thoughts, and actions.

Where and when is it used?

Qualitative analysis is crucial in education, healthcare, social sciences, marketing, and business. It helps gain detailed insights into behaviors, experiences, and cultural phenomena. This approach is fundamental during exploratory phases, for understanding complex issues, and when context-specific insights are required. By focusing on depth over breadth, this approach is often employed when researchers seek to explore complex issues, understand the context of a phenomenon, or investigate things that are not easily quantifiable. It uncovers rich, nuanced data essential for developing theories and evaluating programs.

Why is qualitative research important in academia?

• It sheds light on complex phenomena and human experiences that quantitative methods may overlook.
• This method offers contextual understanding by studying subjects in their natural environments, which is crucial for grasping real-world complexities.
• It adapts flexibly to evolving study findings and allows for adjusting approaches as new ideas emerge.
• It collects rich, detailed data through interviews, observations, and analysis, offering a comprehensive view of the exploration topic.
• Qualitative research studies focus on new or less explored areas, helping to identify key variables and generate hypotheses for further study.
• This approach focuses on understanding individuals' perspectives, motivations, and emotions, essential in fields like sociology, psychology, and education.
• It supports theory development by providing empirical data that can create new theories and frameworks (you may read about “What is a conceptual framework?” and learn about other frameworks on the EduBirdie website).
• It improves practices in fields such as education and healthcare by offering insights into practitioners' and clients' needs and experiences.

The difference between qualitative and quantitative studies

Now that you know the answer to “Why is qualitative data important?”, let’s consider how this method differs from quantitative. Both studies represent two main types of research methods. The qualitative approach focuses on understanding behaviors, experiences, and perspectives using interviews, observations, and analyzing texts. These studies are based on reflexivity and aim to explore complexities and contexts, often generating new ideas or theories. Researchers analyze data to find patterns and themes, clarifying the details. However, findings demonstrated in the results section of a research paper may not apply broadly because they often use small, specific groups rather than large, random samples.

Quantitative studies, on the other hand, emphasize numerical data and statistical analysis to measure variables and relationships. They use methods such as surveys, experiments, or analyzing existing data to collect structured information. The goal is quantifying phenomena, testing hypotheses, and determining correlations or causes. Statistical methods are used to analyze data, identifying patterns and significance. Quantitative studies produce results that can be applied to larger populations, providing generalizable findings. However, they may lack the detailed context that qualitative methods offer.

The approaches to qualitative research 

To better understand the answer to “What is qualitative research?”, it’s necessary to consider various approaches within this methodology, each with its unique focus, implications, and functions. 

1. Phenomenology.

This theory aims to understand and describe the lived experiences of individuals regarding a particular phenomenon. 

Peculiarities:

• Focuses on personal experiences and perceptions.
• Seeks to uncover the essence of a phenomenon.
• Uses in-depth interviews and first-person accounts.

Example: Studying the experiences of people living with chronic illness to understand how it affects their daily lives.

2. Ethnography.

The approach involves immersive, long-term observation and participation in particular cultural or social contexts. 

• Provides a deep understanding of cultural practices and social interactions.
• Involves participant observation and fieldwork.
• Researchers often live within the community they are studying.

Example: Observing and participating in the daily life of a rural village to understand its social structure and cultural practices.

3. Grounded theory.

This approach seeks to develop a research paper problem statement and theories based on participant data.

• Focuses on creating new theories rather than analyzing existing ones.
• Uses a systematic process of data collection and analysis.
• Involves constant comparison and coding of data.

Example: Developing a theory on how people cope with job loss by interviewing and analyzing the experiences of unemployed individuals.

4. Case study.

Case studies involve an in-depth examination of a single case or a small number of cases.

• Provides detailed, holistic insights.
• Can involve individuals, groups, organizations, or events.
• Uses multiple data sources such as interviews, observations, and documents.

Example: One of the qualitative research examples is analyzing a specific company’s approach to innovation to understand its success factors.

5. Narrative research.

This methodology focuses on the stories and personal interpretations of individuals.

• Emphasizes the chronological sequence and context of events.
• Seeks to understand how people make sense of their experiences.
• Uses interviews, diaries, and autobiographies.

Example: Collecting and analyzing the life stories of veterans to understand their experiences during and after military service.

6. Action research.

This theoretical model involves a collaborative approach in which researchers and participants work together to solve a problem or improve a situation.

• Aims for practical outcomes and improvements.
• Involves cycles of planning, acting, observing, and reflecting.
• Often used in educational, organizational, and community settings.

Example: Teachers collaborating with researchers to develop and test new teaching approaches to improve student engagement.

7. Discourse analysis.

It examines language use in texts, conversations, and other forms of communication.

• Focuses on how language shapes social reality and power dynamics.
• Analyzes speech, written texts, and media content.
• Explores the underlying meanings and implications of language.

Example: Analyzing political speeches to understand how leaders construct and convey their messages to the public.

Each of these examples of qualitative research offers unique tools and perspectives, enabling researchers to delve deeply into complex issues and gain a rich understanding of the issue they study.

Qualitative research methods

Various techniques exist to explore phenomena in depth and understand the complexities of human behavior, experiences, and social interactions. Some key methodologies that are commonly used in different sciences include several approaches.

Unstructured interviews;

These are informal and open-ended, designed to capture detailed narratives without imposing preconceived notions. Researchers typically start with a broad question and encourage interviewees to share their stories freely.

Semi-structured interviews;

They involve a core set of questions that allow researchers to explore topics deeply, adapting their inquiries based on responses received. This method of qualitative research design aims to gather rich, descriptive information, such as understanding what qualities make a good teacher.

Open questionnaire surveys;

They differ from closed-ended surveys in that they seek opinions and descriptions through open-ended questions. They allow for gathering diverse viewpoints from a larger group than one-on-one interviews would permit.

Observation;

It relies on researchers' skills to observe and interpret unbiased behaviors or activities. For instance, in education research, observation might track how students stay focused and manage distractions, recorded through field notes taken during or shortly after the observation.

Keeping logs and diaries;

This involves participants or researchers documenting daily activities or study contexts. Participants might record their social interactions or exercise routines, giving detailed data for later analysis. Researchers may also maintain diaries to document study contexts, helping to explain findings and other information sources.

All types of qualitative research have their strengths for gathering detailed information and exploring the social, cultural, and psychological aspects of exploration topics. Learners often use several methods (triangulation) to confirm their findings and deepen their understanding of complex subjects. If you need assistance choosing the most appropriate method to explore, feel free to contact our website, as we offer essays for sale and support with academic papers. 

Advantages and disadvantages of the qualitative research methodology

This approach has unique strengths, making it valuable in many sciences. One of the primary advantages of qualitative research is its ability to capture participants' voices and perspectives accurately. It is highly adaptable, allowing researchers to modify the technique as new questions and ideas arise. This flexibility allows researchers to investigate new ideas and trends without being limited to set methods from the start. While this approach has many strengths, it also has significant drawbacks. A research paper writer faces practical and theoretical limitations when analyzing and interpreting data. Let’s consider all the pros and cons of this methodology in detail.

Strengths of qualitative research:

• Adaptability: Data gathering and analysis can be adjusted as new patterns or ideas develop, ensuring the study remains relevant and responsive.
• Real-world contexts: Research often occurs in natural conditions, providing a more authentic understanding of phenomena and describing the particularities of human behavior and interactions.
• Rich insights: Detailed analysis of people’s feelings, perceptions, and experiences can be useful for designing, testing, or developing systems, products, and services.
• Innovation: Open-ended responses allow experts to discover new problems or opportunities, leading to innovative ideas and approaches.

Limitations of qualitative research:

• Unpredictability: Real-world conditions often introduce uncontrolled factors, making this approach less reliable and difficult to replicate.
• Bias: The qualitative method relies heavily on the researcher’s viewpoint, leading to subjective interpretations. This makes it challenging to replicate studies and achieve consistent results.
• Limited applicability: Small, specific samples give detailed information but limit the ability to generalize findings to a broader population. Conclusions about the qualitative research topics may be biased and not representative of the wider population.
• Time and effort: Analyzing qualitative data is time-consuming and labor-intensive. While software can help, much of the analysis must be done manually, requiring significant effort and expertise.

So, qualitative methodology offers significant benefits, such as adaptability, real-world context, rich insights, and fostering innovation. However, it also presents challenges like unpredictability, bias, limited applicability, or time- and labor-intensive. Understanding these pros and cons helps researchers make informed decisions about when and how to effectively utilize various types of qualitative research designs in their studies.

Final thoughts

Qualitative research provides a valuable understanding of complicated human experiences and social situations, making it a strong tool in various areas of study. Despite its challenges, such as unreliability, subjectivity, and limited generalizability, its strengths in flexibility, natural settings, and generating meaningful insights make it an essential approach. If you are one of the students looking to incorporate qualitative methodology into their academic papers, EduBirdie is here to help. Our experts can guide you through the process, ensuring your work is thorough, credible, and impactful.

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• Published: 22 July 2024

CXCL5 impedes CD8 + T cell immunity by upregulating PD-L1 expression in lung cancer via PXN/AKT signaling phosphorylation and neutrophil chemotaxis

• Dantong Sun 1   na1 ,
• Lipin Tan 2   na1 ,
• Yongbing Chen 1   na1 ,
• Qiang Yuan 3   na1 ,
• Kanqiu Jiang 1 ,
• Yangyang Liu 4 ,
• Yuhang Xue 5 ,
• Jinzhi Zhang 1 ,
• Xianbao Cao 1 ,
• Minzhao Xu 1 ,
• Yang Luo 1 ,
• Zhonghua Xu 1 ,
• Zhonghen Xu 1 ,
• Weihua Xu 1 &
• Mingjing Shen   ORCID: orcid.org/0000-0002-7918-0803 1  

Journal of Experimental & Clinical Cancer Research volume  43 , Article number:  202 ( 2024 ) Cite this article

291 Accesses

Metrics details

Lung cancer remains one of the most prevalent cancer types worldwide, with a high mortality rate. Upregulation of programmed cell death protein 1 (PD-1) and its ligand (PD-L1) may represent a key mechanism for evading immune surveillance. Immune checkpoint blockade (ICB) antibodies against PD-1 or PD-L1 are therefore widely used to treat patients with lung cancer. However, the mechanisms by which lung cancer and neutrophils in the microenvironment sustain PD-L1 expression and impart stronger inhibition of CD8 + T cell function remain unclear.

We investigated the role and underlying mechanism by which PD-L1 + lung cancer and PD-L1 + neutrophils impede the function of CD8 + T cells through magnetic bead cell sorting, quantitative real-time polymerase chain reaction (RT-PCR), western blotting, enzyme-linked immunosorbent assays, confocal immunofluorescence, gene silencing, flow cytometry, etc. In vivo efficacy and safety studies were conducted using (Non-obeseDiabetes/severe combined immune deficiency) SCID/NOD mice. Additionally, we collected clinical and prognostic data from 208 patients who underwent curative lung cancer resection between 2017 and 2018.

We demonstrated that C-X-C motif chemokine ligand 5 (CXCL5) is markedly overexpressed in lung cancer cells and is positively correlated with a poor prognosis in patients with lung cancer. Mechanistically, CXCL5 activates the phosphorylation of the Paxillin/AKT signaling cascade, leading to upregulation of PD-L1 expression and the formation of a positive feedback loop. Moreover, CXCL5 attracts neutrophils, compromising CD8 + T cell-dependent antitumor immunity. These PD-L1 + neutrophils aggravate CD8 + T cell exhaustion following lung cancer domestication. Combined treatment with anti-CXCL5 and anti-PD-L1 antibodies significantly inhibits tumor growth in vivo.

Conclusions

Our findings collectively demonstrate that CXCL5 promotes immune escape through PD-L1 upregulation in lung cancer and neutrophils chemotaxis through autocrine and paracrine mechanisms. CXCL5 may serve as a potential therapeutic target in synergy with ICBs in lung cancer immunotherapy.

Introduction

Lung cancer remains the most prevalent cancer globally and the leading cause of cancer-related death. Over 2 million new diagnoses and 1.7 million deaths are reported annually, with numbers continuing to rise [ 1 ]. Approximately 85% of the diagnosed lung cancer cases are non-small cell lung cancer (NSCLC), with adenocarcinoma and squamous carcinoma comprising over 50% and 30% of the cases, respectively. Recently, immune checkpoint blockade (ICB) therapies targeting programmed cell death protein 1 (PD-1) and its ligand 1 (PD-L1) have shown considerable efficacy in clinical settings. Of note, PD-1/PD-L1 blocking antibodies enhance endogenous antitumor immunity, greatly benefiting a subset of patients with lung cancer through ICB [ 2 , 3 ]. However, most patients exhibit poor responses to ICBs due to distinct primary or adaptive resistance that develops during treatment [ 4 , 5 ]. Therefore, further understanding of the molecular mechanisms underlying PD-L1 expression in lung cancer is necessary for improving the clinical effect of PD-L1/PD-1 therapy [ 6 , 7 ].

Chemokines are small, secreted proteins that signal through cell surface G protein-coupled chemokine receptors. They are best known for their ability to promote the migration of cells, most notably leukocytes. Consequently, chemokines play a central role in the development and homeostasis of the immune system, influencing either protective or destructive immune responses. Accumulating evidence validates the role of chemokines in regulating the cancer-immunity cycle of lung cancer. For instance, C-C motif ligand 7 (CCL7) recruits dendritic cells to promote antitumor immunity and facilitate anti-PD-1 immunotherapy by promoting T cell expansion [ 8 ]. Conversely, high expression of CCL5 correlates with poor prognosis, accumulation of immunosuppressive regulatory T cells (Tregs), and impaired CD8 effector function in patients with lung adenocarcinoma [ 9 ]. In addition, C-X-C motif chemokine ligand 9 (CXCL9) and CXCL10 are critical for CD8 + T cell infiltration into the tumor site to facilitate a productive antitumor response [ 10 ].

Among all chemokines, CXCL5 stands out as one of the most important in the tumor microenvironment (TME). Indeed, various malignant tumor types, including NSCLC [ 11 ], exhibit high levels of CXCL5 expression compared to para-carcinoma or normal tissues. CXCL5 binds to its receptor, C-X-C motif chemokine receptor 2 (CXCR2), and the CXCL5/CXCR2 axis directly promotes angiogenesis, tumor growth, and metastasis via the ERK/Snail pathway or the AKT/GSK3β/β-catenin pathway [ 12 , 13 , 14 , 15 ]. Importantly, CXCL5 has been shown to exert chemotactic effects on diverse immune subsets, such as neutrophils, myeloid-derived suppressor cells (MDSCs), and macrophages [ 16 , 17 ]. Blockade of the CXCL5/CXCR2 signaling axis can increase the sensitivity of immunotherapy and delay tumor progression. However, the biological implications of CXCL5 in regulating PD-1/PD-L1 signaling and antitumor immunity in lung cancer remain largely unclear.

In this study, we observed marked overexpression of CXCL5 in lung cancer cells, which compromised CD8 + T cell-dependent antitumor immunity. We further elucidated that high PD-L1 expression in cancer cells resulted from enhanced phosphorylation of the Paxillin (PXN)/AKT signaling pathway triggered by CXCL5 stimulation of CXCR2 in a dose-dependent manner. Moreover, this CXCL5- p -PXN/AKT-PD-L1 signaling cascade constituted a positive feedback loop. Given the considerable expression of CXCR2 in neutrophils, we also investigated the paracrine role of CXCL5 in neutrophils. CXCL5-treated neutrophils further increased PD-L1 expression in lung cancer cells by releasing (granulocyte-macrophage colony-stimulating factor (GM-CSF). Moreover, we demonstrated that PD-L1 + neutrophils, after co-culture with lung cancer cells, exacerbated the CD8 + T cell exhaustion process. In addition, we investigated the influence of CXCL5 on the survival of patients with lung cancer, which revealed a positive correlation between CXCL5 levels and poor prognosis. Consistently, the combination of anti-CXCL5 and anti-PD-L1 treatment significantly inhibited tumor growth in vivo. Our findings collectively demonstrate that CXCL5 overexpression promotes immune escape and predicts a poor outcome in patients with lung cancer, indicating that CXCL5 may serve as a potential therapeutic target that could synergize with ICBs.

Materials and methods

Human samples.

A total of 208 patients diagnosed with lung cancer at the Second Affiliated Hospital of Soochow University between 2017 and 2018 were enrolled in this study. Paraffin-embedded tumor tissues were collected from the Department of Pathology. None of the patients in this study received systemic treatment before sample collection. Patients were followed up for survival or lung cancer-related death every three months for five successive years. The study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Soochow University, and written informed consent was obtained from all patients prior to participation.

Cell culture

Human lung cancer cells, including lung adenocarcinoma (LAUD) and Lung squamous cell carcinoma (LUSC) (A549 and H226), and the normal lung epithelial cell line BEAS-2B were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). A549 and H226 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin at 37℃. All cells were authenticated using the short tandem repeat method and were checked for mycoplasma contamination.

Isolation of tumor-infiltrating neutrophils (TINs) from lung cancer tissues

Fresh lung cancer tissues were washed three times with (phosphate buffer saline (PBS)containing 1% FBS before being minced into small pieces. The specimens were collected in RPMI-1640 medium containing collagenase IV (1 mg/mL) and deoxyribonuclease I (10 mg/mL) and dissociated using the MACS Dissociator (Miltenyi BioTech). Dissociated cell suspensions were further incubated for 1 h at 37℃ under continuous rotation. The cell suspensions were then filtered through a 40-µm cell strainer (Biologix). TINs were purified using anti-Custer of differentiation 66b (CD66b) magnetic beads (Miltenyi BioTech). The purity of the TINs was evaluated by flow cytometry using an anti-CD66b antibody and exceeded 90%.

Isolation of neutrophils and CD8 + T cells from peripheral blood mononuclear cells (PBMCs)

PBMCs from healthy donors were isolated by density-gradient centrifugation using Ficoll-Paque Plus. Blood neutrophils were harvested after the lysis of red blood cells using a lysis solution. CD66b + neutrophils and CD8 + T cells were purified from PBMCs using anti-CD66b (Miltenyi Biotec) and anti-CD8 (Miltenyi BioTech) magnetic beads. The sorted cells were used unless their purity exceeded 90%.

In vitro neutrophil-CD8 + T cell co-culture system

In a three-day incubation, bead-purified peripheral CD8 + T cells (1 × 10 5 cells/well in 96-well plates) were co-cultured with A549 or H226 “educated” neutrophils isolated from PBMCs. For co-stimulation experiments, 96-well flat-bottom culture plates were coated overnight with anti-CD3 antibody (10 µg/mL, Abcam). Neutrophils and CD8 + T cells were cultured at a 2:1 ratio in 200 µL RPMI-1640 medium containing rIL-2 (20 IU/mL, Sino Biological), anti-CD3 (10 µg/mL, Abcam), and anti-CD28 (2 µg/mL, Abcam) antibodies, with or without a human anti-PD-L1 neutralizing antibody (4 µg/mL, Abcam). When indicated, co-culture experiments were performed using Transwell plates with a pore size of 0.4 μm (Corning). After the three-day incubation period, the cells were harvested for further analysis.

Cell transfection

To knock down (KD) specific target genes, we plated the cells at a density of 5 × 10 5 cells/mL and transfected them with specific siRNA duplexes using the Lipofectamine 3000 Transfection reagent (Invitrogen) according to the manufacturer’s instructions. SiRNAs were provided by General Biol (Anhui, China). The oligonucleotide sequences of the siRNAs were as follows: Control siRNA: 5′-GGAGCGAGATCCCTCCAAAAT-3′, 3′-GGCTGTTGTCATACTTCTCATGG-5′; CXCL5 siRNA: 5′-CUGAAGAACGGGAAGGAAATT-3′, 3′-UUUCCUUCCCGUUCAGTT-5′; CXCR2 siRNA: 5′-CCUCAAGAUUCUAGCUAUATT-3′, 3′-UAUAGCUAGAAUCUUGAGGTT-5′; PD-L1 siRNA: 5′-TGGCATTTGCTGAACGCATTT-3′, 3′-TGCAGCCAGGTCTAATTGTTTT-5′.

Flow cytometry

To assess apoptosis, we double-stained lung cancer cells with annexin V-FITC and propidium iodide (BD Biosciences, USA), with or without CD8 + T cell co-culture, following the manufacturer’s instructions and under specific experimental conditions. Staining was assessed using the Cyto-FLEX Flow Cytometer (Beckman Coulter, USA). To assess the proliferation of CD8 + T cells under different conditions, we performed flow cytometric analysis following standard protocols. CD8 + T cell suspensions, with or without neutrophils, were stained in vitro with fluorochrome-conjugated antibodies and matched-isotype control antibodies. Red cells were lysed with an ammonium chloride solution, and samples were incubated with the Live/Dead Fixable Dead Cell Staining Kit (Invitrogen) prior to staining to allow for the identification of live cells. The stained cells were subsequently analyzed by multicolor flow cytometry.

Quantitative real-time PCR (qRT-PCR)

Total RNA was extracted from lung cancer cells using the Trizol reagent (Invitrogen), and first-strand cDNA was reversed-transcribed using the All-in-One cDNA Synthesis SuperMix (E047-01 A, Novoprotein, China). qPCR was performed using the Advanced SYBR Green Supermix and the CFX Connect RT System (BioRad) to examine gene expression. The data obtained for each gene were normalized to the expression of GAPDH. The primer sequences used were as follows: GAPDH: forward, 5′-GAGAAGTATGACAACAGCCTCAA-3′ and reverse, 3′-GCCATCACGCCACAGTTT-5′; PD-L1: forward, 5′-GGAAATTCCGGCAGTGTACC-3′.

and reverse, 3′-GAAACCTCCAGGAAGCCTCT-5′; CXCL5: forward, 5′-CAATCTTCGCTCCTCCAATC-3′ and reverse, 3′-CTCCTTGCGTGGTCTGTAAA-5′; CXCR2: forward, 5′-ACACGCACACTGACCCAGAA-3′ and reverse, 3′-CGTGAATCCGTAGCAGAACA-5′.

Western blot analysis

Lung cancer cells were lysed using the RIPA buffer on ice for 10 min. The lysates were centrifuged at 12,000 × g for 10 min at 4℃, and the supernatants were collected for protein concentration determination. Total proteins were separated on a 10% SDS-PAGE gel and transferred to a PVDF membrane (Millipore). The blot was incubated with appropriate primary antibodies at 4℃ overnight. Proteins were quantified through densitometric analysis and normalized to GAPDH.

Enzyme-linked immunosorbent assays (ELISA)

Chemokines and cytokines produced by lung cancer cells or neutrophils were detected using human chemokine and cytokine ELISA kits (EK158, EK180, EK182; MultiSciences, China) following the manufacturer’s instructions.

The sections were deparaffinized with xylene, rehydrated in 100%, 85%, and 70% ethanol for 10 min, quenched for endogenous peroxidase activity with 3% hydrogen peroxide, and subjected to antigen retrieval in 0.5 mM EDTA buffer (pH 8.0) by heating in a microwave for 20 min. The sections were allowed to cool naturally to room temperature and then stained with various antibodies diluted in PBS containing 1% BSA, followed by incubation at room temperature for over 6 h. Immunostaining was performed, and subsequently, the sections were counterstained with hematoxylin for 5 min prior to coverslipping.

For H&E staining, mouse tissues were fixed with 4% paraformaldehyde, embedded in paraffin, fully dewaxed, hydrated, and stained with H&E. A random visual field was selected under a light microscope to observe the pathomorphological changes in each mouse tissue sample.

Neutrophils and CD8 + T cell chemotaxis assay

A cell migration experiment was conducted using a Transwell system consisting of a polycarbonate membrane with a 5.0 μm pore size (5.0 μm, Corning). Neutrophils and CD8 + T cells were washed twice, resuspended in serum-free medium, and added to the top chamber, while conditioned medium was added to the bottom chamber. After 24 h of culture, the cells at the bottom of the chamber were collected and fixed with 4% paraformaldehyde. The number of neutrophils and CD8 + T cells that passed through the membrane was quantified using the Cyto-FLEX Flow Cytometer for 30 s.

Confocal immunofluorescence

Frozen sections of tumor cells, neutrophils, and CD8 + T cells were fixed with 4% paraformaldehyde, followed by incubation with 5% goat serum for 1 h at room temperature to prevent nonspecific antibody binding. The sections were then incubated with primary antibodies overnight at 4℃. Secondary antibodies were used at a concentration of 5 µg/mL, followed by staining with DAPI. Stained sections were imaged using the LSM 880 Confocal Microscope (Zeiss, Jena, Germany).

Tumor models and in vivo treatments .

A549 cells were cultured, harvested, and suspended in PBS. For flank injections, a total of 0.2 mL containing 5 × 10 5 cells were injected subcutaneously into the flank of six- to eight-week-old NOD/SCID mice (Hunan, China). The mice were housed in groups of five under specific pathogen-free conditions with unlimited access to food and water. Ten days after injection, the mice were treated with anti-CXCL5 and anti-PD-L1 antibodies. Tumor growth was assessed using the following formula: (length × width 2 )/2. All experimental protocols and animal care were approved by the Institutional Review Board of The Second Affiliated Hospital of Soochow University.

Bioinformatics analysis

The Cancer Genome Atlas (TCGA) project, jointly initiated by the National Cancer Institute and the National Human Genome Research Institute in 2006, currently studies a total of 36 cancer types. TCGA utilizes large-scale sequencing-based genomic analysis technology, through extensive collaboration, to understand the molecular mechanisms of cancer, enhance scientific understanding of the molecular basis of cancer onset, and enhance our ability to diagnose, treat, and prevent cancer. Ultimately, it aims to create comprehensive maps detailing all changes in the cancer genome. We downloaded the RNA-seq data and survival characteristics data of patients with lung cancer from TCGA database for further research. Subsequent data analysis was conducted using R Studio 4.0.0. R Studio is a programming language and software environment with powerful data processing and analysis functions, including basic sequence analysis, molecular evolution, comparative genomics, protein structure comparison and prediction, and computer-aided drug design. Bioconductor, based on the R Studio environment, serves as a tool for visualizing, annotating, processing, analyzing, and collecting biological information, consisting of a series of R extension packages. The EdgeR and Ggplot2 R software packages were employed to identify genes with differential expression, using |log2FC| > 2 and FDR < 0.05 as selection criteria.

Statistical analysis

All data are presented as means ± standard error of the mean (SEM), unless otherwise indicated. Statistical analyses were performed using a two-tailed Student’s t -test in GraphPad Prism software (GraphPad Inc., CA, USA), except for the analysis of the association of CXCL5 with the survival of patients with lung cancer, which utilized IBM SPSS Statistics 23.0. A p-value < 0.05 was considered statistically significant.

Autocrine CXCL5 from lung cancer compromises antitumor immunity via PD-L1 upregulation and CD8 + T cell migration inhibition

Initially, we investigated whether lung cancer cells produce distinct chemokine profiles compared to non-cancerous cells. To this end, we performed ELISA to detect various chemokines secreted by normal tracheal epithelial cells, BEAS-2B, and lung cancer cell lines, A549/H226. We found that CXCL5 secretion was significantly increased in lung cancer cells, as evidenced by heatmap analysis (Fig.  1 A). Specific quantitative results of CXCL5 secretion by A549/H226 were also obtained (Supplementary Fig. 1A). To further investigate the biological functions of CXCL5 in lung cancer immunosurveillance, we established lung cell lines with either normal control(NC)or KD-CXCL5 using the Lipofectamine 3000 transient transfection method (Fig.  1 C). The concentration of CXCL5 was determined through ELISA (Fig.  1 B). Subsequently, we assessed the sensitivity of NC and KD-CXCL5 cells to CD8 + T cell-mediated killing. Flow cytometric analysis revealed that CXCL5 KD cells showed increased apoptosis when co-cultured with CD8 + T cells, indicating that CXCL5 KD could indeed enhance sensitivity to CD8 + T cell-mediated killing (Fig.  1 D). Similarly, the application of IgG and anti-CXCL5 neutralizing antibodies showed consistent trends (Supplementary Fig. 1D). Notably, CXCL5 KD did not affect the apoptosis of lung cancers without co-culture with CD8 + T cells (Supplementary Fig. 1B).

Autocrine CXCL5 from lung cancer compromises antitumor immunity via PD-L1 upregulation and inhibition of CD8 + T cell migration. (A) ELISA was performed to detect various chemokines secreted by BEAS-2B cells and lung cancer cell lines A549/H226. (B) The NC and KD-CXCL5 lung cancer cell lines A549/H226 were established through CXCL5 plasmid transfection, with CXCL5 levels examined by western blotting. (C) ELISA was performed to detect CXCL5 secretion by NC and KD-CXCL5 cells of A549/H226. (D) The apoptotic rates of NC and KD-CXCL5 A549/H226 cells were measured by flow cytometry for 6 h co-culture with CD8 + T cells (tumor cells: CD8 + T cells = 1:1). (E) Representative images of NC and KD-CXCL5 A549/H226 cells. Immunofluorescence was utilized to assess PD-L1 expression in each type of cell. (F) Western blotting was performed to analyze PD-L1 expression in response to CXCL5 in a dose-dependent manner. (G) To evaluate the autocrine effect of CXCL5, we established NC and KD-CXCR2 A549/H226 cell lines. PD-L1 expression was analyzed in NC and KD-CXCR2 A549/H226 cells with or without CXCL5 stimulation. (H) A Transwell assay was performed to examine the chemotaxis of CD8 + T cells to NC and KD-CXCL5 cells and NC and KD-PD-L1 A549/H226 cells, respectively. The data represent at least three independent experiments and are presented as the mean ± SEM. NS, not significant; * p  < 0.05; ** p  < 0.01; *** p  < 0.001

Given the critical role of PD-L1 in mediating immune escape, we measured the expression of PD-L1 in NC A549 or H226 cells, as well as those cell lines with CXCL5 KD. Immunofluorescence staining showed a significant decrease in PD-L1 expression in lung cancer cells when CXCL5 expression was knocked down (Fig.  1 E). Furthermore, we validated the dose-dependent increase in PD-L1 expression in response to CXCL5 (Fig.  1 F). Subsequently, we characterized the role of CXCR2 in mediating PD-L1 expression. To this end, we established A549 or H226 cell lines with either control or CXCR2 siRNA (Supplementary Fig. 1E). Indeed, we found that CXCL5-triggered PD-L1 expression was blocked when CXCR2 expression was knocked down (Fig.  1 G). The mRNA data is shown in Supplementary Fig. 1C. To further elucidate the role of the CXCL5-PD-L1 axis in mediating CD8 + T cell infiltration, we conducted a chemotaxis experiment and found that KD-CXCL5 or KD-PD-L1 indeed enhanced the chemotaxis of CD8 + T cells (Fig.  1 H), which may represent an important mechanism regulating antitumor immunity in lung cancer. Conversely, the introduction of exogenous CXCL5 inhibited the chemotaxis of CD8 + T cells (Supplementary Fig. 1F).

Positive feedback loop of the CXCL5-p-PXN/AKT-PD-L1 signaling cascade contributes to CD8 + T cell-mediated immune evasion by lung cancer cells

Bioinformatics analysis revealed that multiple genes are associated with the CXCL5/PD-L1 pathway. According to the cutoff criteria, 27 differentially expressed genes (DEGs) were identified (Fig.  2 A). The heatmap(Fig.  2 B) displayed that the genes PRKDC , MELK , and PXN were positively correlated with the expression of interacting genes, The results of the data statistics are shown in Supplementary Fig. 1G, wherein JAK/AKT/MELK and other genes are associated with PD-L1, according to known articles. To further characterize the regulatory relationship of CXCL5 and the PXN/AKT signaling pathway, we measured the activity of the PXN/AKT pathway through a western blotting assay. We found that CXCL5 KD significantly decreased the phosphorylation of both PXN and AKT in A549 or H226 cells (Fig.  2 C). In addition, the application of a CXCL5 neutralizing antibody validated this finding (Fig.  2 D), while CXCL5 exhibited the opposite trend (Fig.  2 E). The small-molecule inhibitor 6-B345TTQ disrupted the interaction of PXN and α4 integrin, thereby interfering with α4 integrin signaling. CXCL5 stimulation did not improve PD-L1 expression upon 6-B345TTQ treatment (Fig.  2 F). Together, the AKT inhibitor LY-294,002 exerted the similar effect (Fig.  2 G). Notably, PD-L1 KD also decreased the phosphorylation of both PXN and AKT in A549 or H226 cells (Fig.  2 H). Moreover, CXCL5 was measured in NC and PD-L1 KD lung cancer cells through ELISA (Fig.  2 I) and IF (Supplementary Fig. 1K). In addition, flow cytometric analysis elucidated that inhibiting the PXN pathway with the small-molecule inhibitor 6-B345TTQ enhanced the killing of lung cancer cells by CD8 + T cells (Fig.  2 J). As a control, 6-B345TTQ treatment did not exacerbate the apoptosis of lung cancer cells without CD8 + T cell co-culture (Supplementary Fig. 1I). Furthermore, a Transwell assay demonstrated that 6-B345TTQ treatment enhanced the chemotaxis of CD8 + T cells (Fig.  2 K). In conclusion, CXCL5 promoted PD-L1 expression through the phosphorylated PXN/AKT pathway. Furthermore, KD-PD-L1 inhibited the phosphorylation of the PXN/AKT pathway and also inhibited the secretion of CXCL5, suggesting a positive feedback regulatory loop modulating CXCL5 or PD-L1 expression via the PXN and AKT signaling cascade. Additionally, survival analysis revealed that high PXN expression was associated with a poor prognosis in patients with lung cancer (Supplementary Fig. 1H).

Positive feedback loop of the CXCL5-p-PXN/AKT-PD-L1 signaling cascade contributes to CD8 + T cell-dependent immunity escape in lung cancer cells. (A) Bioinformatics analysis using TCGA database revealed multiple genes associated with the CXCL5/PD-L1 pathway. (B) The heatmap displays clustering information for the correlated genes obtained through multinomial logistic regression. (C) Western blot was performed to analyze the activity and phosphorylation of the PXN/AKT pathway in NC and KD-CXCL5 cells. (D) Western blot was performed to analyze the activity and phosphorylation of the PXN/AKT pathway in cells treated with IgG and anti-CXCL5 antibodies. (E) Western blot was performed to analyze the activity and phosphorylation of the PXN/AKT pathway in NC and CXCL5-treated cells. (F, G) To demonstrate that PD-L1 was upregulated by CXCL5 stimulation via phosphorylation of PXN and AKT, we examined PD-L1 expression through western blotting with or without 6-B345TTQ and LYG294002 treatment under different CXCL5 conditions. (H) Phosphorylation of PXN and AKT in NC and PD-L1 KD A549/H226 cells was determined through western blotting. (I) CXCL5 concentrations in NC and PD-L1 KD lung cancer cells as determined through ELISA, which also decreased the phosphorylation of PXN/AKT in A549 and H226 cells. (J) The apoptotic rates of A549/H226 cells with or without 6-B345TTQ treatment were measured by flow cytometry for 6 h co-culture with CD8 + T cells (tumor cells: CD8 + T cells = 1:1). (K) A Transwell assay demonstrated that 6-B345TTQ treatment enhances the chemotaxis of CD8 + T cells. Data represent at least three independent experiments and are presented as the mean ± SEM. NS, not significant; * p  < 0.05; ** p  < 0.01; *** p  < 0.001

Neutrophil-derived GM-CSF induces the phosphorylation of the PXN/AKT pathway to promote PD-L1 expression in lung cancer

Given that neutrophils express CXCR2, we hypothesized that lung cancer-secreted CXCL5 may attract neutrophils in peripheral blood mononuclear cells (PBMCs) to enter the TME (Fig.  3 A). To this end, we established a co-culture system with neutrophils and lung cancer cells. The expression of PD-L1 in lung cancer significantly increased when co-cultured with neutrophils (Fig.  3 B). Moreover, after co-culture of neutrophils and lung cancer cells, the killing of lung cancer cells by CD8 + T cells was inhibited, with more cancer cells surviving (Fig.  3 C). Subsequently, we investigated the mechanism by which PD-L1 expression was upregulated in cancer cells co-cultured with neutrophils. Neutrophils are known to secrete various proteins, including GM-CSF, Bone morphogenic protein-2 (BMP2), Vascular endothelial growth factor A (VEGFA), Transforming growth factor-β1 (TGFβ-1), and S100A8, which are likely to regulate PD-L1. We found that GM-CSF had the most significant effect on promoting the expression of PD-L1 in lung cancer cells (Fig.  3 D). The western blot data is shown in Fig.  3 E. Immunofluorescence imaging analysis further validated that GM-CSF administration increased the expression of PD-L1 in lung cancer cells (Fig.  3 F). The upregulation of PD-L1 by GM-CSF was concentration-dependent, as shown in Supplementary Fig. 1J. Notably, neutrophil-derived GM-CSF could also induce the phosphorylation of the PXN/AKT pathway to promote PD-L1 expression (Fig.  3 G, H). Flow cytometric analysis validated the role of GM-CSF in mediating the killing of lung cancer cells by CD8 + T cells (Fig.  3 I). Likewise, antagonizing neutrophil-derived GM-CSF could enhance CD8 + T cell chemotaxis (Fig.  3 J).

Neutrophil-derived GM-CSF induces the phosphorylation of the PXN/AKT pathway to promote PD-L1 expression in lung cancer. (A) A Transwell assay was performed to assess the chemotaxis of neutrophils with or without CXCL5 stimulation. (B) A549/H226 cells were co-cultured with or without neutrophils for 24 h, followed by immunofluorescence analysis of PD-L1 expression on A549/H226 cells. (C) A549/H226 cells were pretreated with or without neutrophils for 24 h and then co-cultured with CD8 + T cells. After 6 h, the apoptosis of A549/H226 cells was analyzed through flow cytometry. (D) RT-PCR was conducted to assess the mRNA expression of PD-L1 after treatment with neutrophils and antibodies against BMP2, VEGFA, TGFβ1, GM-CSF, and S100AB. (E) Western blot was performed to analyze PD-L1 expression in neutrophils and anti-GM-CSF antibody-treated cells. (F) Immunofluorescence was utilized to analyze the expression of PD-L1 on A549/H226 cells with or without stimulation of GM-CSF for 12 h. (G) After GM-CSF stimulation for 12 h, PXN and AKT signaling and phosphorylation in A549/H226 cells were examined through western blotting. (H) To demonstrate that GM-CSF upregulates PD-L1 via PXN and AKT phosphorylation, we assessed PD-L1 expression through western blotting with or without 6-B345TTQ treatment under different GM-CSF conditions. (I) A549/H226 cells were pretreated with or without neutrophils and anti-GM-CSF antibody for 24 h and then co-cultured with CD8 + T cells. After 6 h, the apoptosis of A549/H226 cells was analyzed through flow cytometry. (J) A Transwell assay demonstrated that anti-GM-CSF treatment can reverse the inhibition of chemotaxis in CD8 + T cells induced by neutrophils. Data represent at least three independent experiments and are presented as the mean ± SEM. NS, not significant; * p  < 0.05; ** p  < 0.01; *** p  < 0.001

Lung cancer cell-educated PD-L1 + neutrophils promote CD8 + T cell exhaustion

Lung cancer environments may contribute to the activated immunosuppressive phenotype of neutrophils. Consistent with our hypothesis, neutrophils significantly upregulated PD-L1 expression compared to non-co-cultured neutrophils (Fig.  4 A). To further elucidate this effect, we isolated TINs from fresh lung cancer tissues. Compared to lung cancer-educated neutrophils from PBMCs, TINs expressed higher PD-L1 levels (Supplementary Fig. 2A). In addition to PD-L1, lung cancer cells significantly upregulated CD54, another granulocyte activation marker (Supplementary Fig. 2F). Subsequently, we investigated the potential impacts of recruited neutrophils on the functionality of CD8 + T cells. The presence of cancer-educated neutrophils significantly promoted the apoptosis of CD8 + T cells, which was partially rescued by blocking PD-L1 (Fig.  4 B). We then measured the proliferation of CD8 + T cells (Fig.  4 C). As expected, the proliferation of CD8 + T cells was severely impaired by cancer-educated neutrophils. The expression of diverse effector cytokines, including TNF-α, IFN-γ, granzyme-β, and perforin, was also suppressed by neutrophils. Blocking PD-L1 could, at least in part, rescue the decreased effector cytokine release (Fig.  4 D, E). As controls, the proliferation, apoptosis, and the release of Tumor necrosis factor-α (TNF-α) and Interferon-γ (IFN-γ) by CD8 + T cells did not exhibit alterations following co-culture with “non-educated” neutrophils (Supplementary Fig. 2B–E). Collectively, these observations suggest that PD-L1 expression on neutrophils likely triggers a significant functional decline in CD8 + T cells. The expression of classical exhaustion markers PD-1 and TIM-3 also increased in the presence of PD-L1 + neutrophils, which reduced when PD-L1 signaling was blocked (Fig.  4 F and Supplementary Fig. 2G). Furthermore, in the chemotaxis experiment, inhibition of PD-L1 signaling rescued the decreased chemotaxis of CD8 + T cells triggered by PD-L1 + neutrophils (Fig.  4 G).

Lung cancer cell-educated PD-L1 + neutrophils promote CD8 + T cell exhaustion. (A) Immunofluorescence was utilized to analyze PD-L1 expression on neutrophils stimulated with or without A549/H226 cells for 24 h. Neutrophils were pretreated with or without anti-PD-L1 antibody for 12 h before co-culturing with CD8 + T cells for 72 h. Flow cytometry was conducted to analyze the apoptosis (B) and proliferation (C) of CD8 + T cells. ELISA was performed to assess the concentrations of TNF-α, IFN-γ (D), granzyme-β, and perforin (E) in CD8 + T cell media. Immunofluorescence was utilized to analyze the expression of PD-1 and TIM-3 on CD8 + T cells, with representative images shown (F). (G) A Transwell assay demonstrated that anti-PD-L1 treatment can reverse the inhibition of chemotaxis of CD8 + T cells induced by neutrophils. Data represent at least three independent experiments and are presented as the mean ± SEM. NS, not significant; * p  < 0.05; ** p  < 0.01; *** p  < 0.001

Dual blockade of CXCL5 and PD-L1 inhibits lung cancer progression with good biological safety in vivo

We evaluated the potential therapeutic effects of dual blockade of CXCL5 and PD-L1 in vivo. For this purpose, the SCID/NOD mouse tumor-bearing model (Fig.  5 A) was employed to assess the CD8 + T cell-dependent antitumor effects of CXCL5 and PD-L1 neutralizing antibodies. The SCID/NOD mice were injected with neutrophils and CD8 + T cells via the tail vein, followed by weekly injections of either anti-CXCL5 antibody or anti-PD-L1 antibody injections over a 3-week period subsequent to tumor formation using the A549 cell line. Representative tumors from each group are shown in Fig.  5 B. As expected, adoptive transfer of T cells significantly delayed tumor growth (Fig.  5 C), whereas the administration of neutrophils greatly reduced the tumor control of T cells. Of note, administration of blocking antibodies against either CXCL5 or PD-L1 improved therapeutic efficacy, with the combination of CXCL5 and PD-L1 blocking antibodies demonstrating superior tumor control. Moreover, similar results were observed in terms of distinct tumor weights (Fig.  6 D). Consistently, the expressions of CXCL5, p -PXN, and PD-L1 decreased with CXCL5 or PD-L1 dual blockade, while the infiltration of CD8 + T cells was enhanced (Fig.  6 E). Importantly, the administration of CXCL5 or PD-L1 blockade antibodies demonstrated good safety, as indicated by H&E staining of the heart, liver, lung, kidney, and intestinal tissues, which showed no obvious damage due to the treatment modality (Fig.  6 F).

Dual blockade of CXCL5 and PD-L1 inhibits lung cancer progression with good biological safety in vivo. (A) A SCID/NOD mouse tumor-bearing model was employed to assess the CD8 + T cell-dependent antitumor effects of anti-CXCL5 and anti-PD-L1 neutralizing antibodies, with neutrophils and CD8 + T cells injected via the tail vein. The lung cancer cell line A549 was employed for tumor formation. (B) The tumors were excised and photographed six weeks after tumor cell injection. Tumor weight (C) and volume (D) calculations for each group are presented. (E) Representative images of immunohistochemical staining of CXCL5, p -PXN, PD-L1, and CD8 validating in vitro results. (F) H&E staining of heart, liver, lung, kidney, and intestinal tissues was conducted to assess the biological safety of anti-CXCL5 or anti-PD-L1 blockade antibodies. Data represent at least three independent experiments and are presented as the mean ± SEM. NS, not significant; * p  < 0.05; ** p  < 0.01; *** p  < 0.001

High CXCL5 and PD-L1 expression in lung cancer is associated with poor patient survival. Representative patients who underwent lung cancer resection were chosen from the cohort. (A) Immunohistochemical staining of CXCL5, p -PXN, PD-L1, GM-CSF, CD66b, CD8, PD-1, and TIM-3 from representative patients is shown. (B) The quantity of CXCL5 predicts the prognosis of patients with lung cancer, and the combined levels of CXCL5 and PD-L1 further predict the prognosis of patients with lung cancer. (C) Autocrine and paracrine effects of CXCL5 derived from lung cancer, leading to higher expression of PD-L1 on either lung cancer or neutrophils. Dual blockade of CXCL5 and PD-L1 inhibits lung cancer progression through CD8 + T cell immunity with good biological safety in vivo

High CXCL5 and PD-L1 expression in lung cancer is associated with poor patient survival

To elucidate the influence of key molecules on clinical outcomes, we performed immunohistochemical analysis of tumor tissue micro-arrays (TMAs) and quantified. In a cohort comprising primary tumors from 208 patients with lung cancer, we measured the number of CD66b + neutrophils and the expression of CXCL5, p -PXN, PD-L1, GM-CSF, CD8, PD-1, and T cell immunoglobulin-3 (TIM-3). Representative cases are shown in Fig.  6 A. Patients with lung cancer exhibiting higher CXCL5 expression displayed greater CD66b + neutrophil infiltration, lower CD8 + T cell infiltration, and higher levels of p -PXN, PD-L1, GM-CSF, PD-1, and TIM-3 expressions. Conversely, patients with lung cancer exhibiting lower CXCL5 expression displayed less CD66b + neutrophil infiltration, greater CD8 + T cell infiltration, and lower levels of p -PXN, PD-L1, GM-CSF, PD-1, and TIM-3 expressions. We performed survival analysis for high and low levels of CXCL5 and PD-L1 expressions and observed a significant association of high CXCL5 or PD-L1 expression with significantly decreased overall survival (OS) and recurrence-free survival (RFS) (OS: p  < 0.001; RFS: p  < 0.001) (Fig.  6 B and Supplementary Fig. 2H). The log-rank tests revealed that patients with CXCL5 - PD-L1 - exhibited the best prognoses, whereas patients with CXCL5 + PD-L1 - or CXCL5 - PD-L1 + demonstrated moderate prognoses, and patients with CXCL5 + PD-L1 + exhibited the worst prognoses (OS: p  < 0.01; RFS: p  < 0.01) (Fig.  6 B).

Discussions

Tumor cells may employ multiple mechanisms to evade immune surveillance. In this study, we demonstrated the elevated expression of CXCL5 in lung cancer and explored its autocrine and paracrine roles. We found that high CXCL5 expression is associated with a poor prognosis and a decreased survival curve in lung cancer. In addition, combined utilization of anti-CXCL5 and anti-PD-L1 yielded the most effective outcome in controlling lung cancer, with convinced biological safety.

Chemokines play an instructive role in orchestrating the immune responses to tumors by influencing the cellular composition [ 18 ] and landscape of the TME [ 19 , 20 ]. For instance, the production of CXCL9 and CXCL10 [ 21 , 22 , 23 ] is critical for attracting cytotoxic T cells and Th1 cells, which are required for mounting an effective antitumor immunity. Conversely, certain chemokines can recruit immunosuppressive subsets that facilitate tumor growth. For instance, CCL2 has been reported to recruit Tregs [ 24 , 25 ], tumor-associated macrophages [ 26 ], as well as MDSCs [ 27 , 28 ]. In particular, the CCL2/CCR2 axis has been well documented to mediate Treg recruitment and accumulation across gliomas [ 29 ] and diverse types of tumors [ 30 ]. In this regard, cancer cell-derived chemokines are crucial in shaping the infiltration pattern of either cancer-fighting effector cells or cancer-promoting immune subsets. Our study revealed that lung cancer cells generate high levels of CXCL5, promoting cancer escape or suppressing antitumor immunity through either autocrine or paracrine mechanisms. Upregulation of PD-L1 is a key mechanism mediating immune escape by binding to PD-1 on tumor-fighting CD8 + T cells. In this study, we demonstrated that tumor-secreted CXCL5 induces phosphorylation of the PXN/AKT pathway, leading to PD-L1 upregulation in lung cancer and enabling the evasion of immune surveillance by CD8 + T cells. Drawing on the understanding that MAPK/ERK signaling, JAK/STAT3 signaling, and interleukin-1b secretion [ 31 , 32 , 33 ] are implicated in PD-L1 expression, our group is the first to report the regulatory role of PXN/AKT phosphorylation in PD-L1 upregulation.

As one of the most abundant leukocytes in the immune system, accumulating evidence [ 34 , 35 , 36 ] suggests that neutrophils play an essential role in cancer progression and metastasis by releasing key inflammatory mediators, or cytokines. Of note, neutrophils have also been suggested to exert antitumor/antimetastatic effects in certain contexts [ 37 ]. Notably, neutrophils display a high degree of heterogeneity, displaying diverse activation states that elicit either antitumor or protumor functions. This is influenced by the distinct spectrum and levels of immunomodulatory mediators produced by cancer cells as well as cancer-associated cells within the TME. Our previous study as well as that of Antoine Schernberg [ 38 , 39 ] demonstrated that neutrophils serve as a prognostic biomarker, indicating poor survival in locally advanced lung cancer. Neutrophils in the cancer environment can impede CD8 + T cell-dependent immune surveillance through various mechanisms, including Kras mutation, SETD2 deficiency-induced H3K36me3 methylation, miR-146a delivery, and the production of NO, GM-CSF, and arginase-1 [ 40 , 41 , 42 , 43 , 44 ]. Notably, we observed that lung cancer cells can induce the expression of PD-L1 in neutrophils, and these educated PD-L1 + neutrophils can notably impair CD8 + T cell functionality. Further investigations are required to elucidate the mechanism by which lung cancer cell-educated neutrophils trigger CD8 + T cell function exhaustion beyond the PD-L1/PD-1 signaling axis.

ICBs exert considerable therapeutic effects in a minority of patients in clinical settings [ 45 , 46 ], highlighting the significant need to improve therapeutic efficacy through combined treatment modalities. Given the association between high CXCL5 expression levels and poor prognosis in patients with lung cancer and the autocrine and paracrine roles of CXCL5 in mediating notable immunosuppression and immune evasion, we investigated the combined treatment of lung cancer with dual blockade of CXCL5 and PD-L1. This approach was found to be safe and to improve CD8 + T cell antitumor immunity [ 47 , 48 , 49 , 50 ]. These findings may provide valuable insights for the development of novel therapeutic strategies aimed at improving response rates to ICBs through the concurrent blockade of CXCL5 and PD-L1 in vivo.

In summary, our study revealed that CXCL5 overexpression can upregulate PD-L1 via PXN/AKT phosphorylation in lung cancer. Furthermore, CXCL5 chemotactic neutrophils not only induce the release of GM-CSF, which activates the PXN/AKT signaling pathway, but also lead to CD8 + T cell functional exhaustion, ultimately inhibiting their antitumor ability. These findings, coupled with clinical sample analysis, suggest that CXCL5 may be a promising target for alleviating immunosuppression and could serve as a potential therapeutic target in synergy with ICBs.

In this study, we found that autocrine CXCL5 by lung cancer improves its PD-L1 expression. In addition, paracrine CXCL5 attracts neutrophils into the lung cancer microenvironment. Mechanistically, CXCL5 activates the phosphorylation of the Paxillin/AKT signaling cascade, leading to upregulation of PD-L1 and the formation of a positive feedback loop. Furthermore, PD-L1 + neutrophils contribute to the progression of CD8 + T cell exhaustion. Inhibition of CXCL5-Paxillin/AKT-PD-L1 axis can reverse the CD8 + T cell depended immunity in vitro and in vivo. CXCL5 may serve as a potential therapeutic target in synergy with existed ICBs in lung cancer immunotherapy.

Data availability

Data are available upon reasonable request.

Abbreviations

1 Programmed cell death protein 1

L1 Programmed cell death protein ligand 1

Immune checkpoint blockade

Cluster of differentiation 8

Quantitative real-time polymerase chain reaction

Non-obeseDiabetes/severe combined immune deficiency

C-X-C motif chemokine ligand 5

Non-small cell lung cancer

C-C motif ligand 7

C-C motif ligand 5

Regulatory T cells

C-X-C motif chemokine ligand 9

Tumor microenvironment

Chemokine receptor 2

Myeloid-derived suppressor cells

Granulocyte-macrophage colony-stimulating factor

Adenocarcinoma

Lung squamous cell carcinoma

Fetal bovine serum

Tumor infiltrated neutrophils

Custer of differentiation 66b

Peripheral blood mononuclear cells

The Cancer Genome Atlas

Standard error of the mean

Bone morphogenic protein-2

Vascular endothelial growth factor A

Transforming growth factor-β1

Tumor necrosis factor-α

Interferon-γ

T cell immunoglobulin-3

Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. 2018;68:394–424.

PubMed   Google Scholar  

Brahmer J, et al. Nivolumab versus Docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med. 2015;373:123–35.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Borghaei H, et al. Nivolumab versus Docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015;373:1627–39.

Zappasodi R, Merghoub T, Wolchok JD. Emerging concepts for immune checkpoint blockade-based combination therapies. Cancer Cell. 2018;33:581–98.

Xia L, Liu Y, Wang Y. PD-1/PD-L1 blockade therapy in advanced non-small-cell lung cancer: current status and future directions.Oncologist. 2019;24:S31–41.

Chen Y, Liu Q, Chen Z, Wang Y, Yang W, Hu Y, et al. PD-L1 expression and tumor mutational burden status for prediction of response to chemotherapy and targeted therapy in non-small cell lung cancer. J Exp Clin Cancer Res. 2019;38(1):193.

Article   PubMed   PubMed Central   Google Scholar  

Clark CA, Gupta HB, Sareddy G, Pandeswara S, Lao S, Yuan B, et al. Tumor-intrinsic PD-L1 signals regulate cell growth, pathogenesis, and autophagy in ovarian cancer and melanoma. Cancer Res. 2016;76(23):6964–74.

Zhang M, Yang W, Wang P, Deng Y, Dong YT, Liu FF, Huang R, Zhang P, Duan YQ, Liu XD, Lin D, Chu Q, Zhong B. CCL7 recruits cDC1 to promote antitumor immunity and facilitate checkpoint immunotherapy to non-small cell lung cancer. Nat Commun. 2020;11(1):6119.

Melese ES, Franks E, Cederberg RA, Harbourne BT, Shi R, Wadsworth BJ, Collier JL, Halvorsen EC, Johnson F, Luu J, Oh MH, Lam V, Krystal G, Hoover SB, Raffeld M, Simpson RM, Unni AM, Lam WL, Lam S, Abraham N, Bennewith KL, Lockwood WW. CCL5 production in lung cancer cells leads to an altered immune microenvironment and promotes tumor development. Oncoimmunology. 2021;11(1):2010905.

Jie X, Chen Y, Zhao Y, Yang X, Xu Y, Wang J, Meng R, Zhang S, Dong X, Zhang T, Yang K, Xu S, Wu G. Targeting KDM4C enhances CD8 + T cell mediated antitumor immunity by activating chemokine CXCL10 transcription in lung cancer. J Immunother Cancer. 2022;10(2):e003716.

Pold M, Zhu LX, Sharma S, Burdick MD, Lin Y, Lee PP, et al. Cyclooxygenase-2-dependent expression of angiogenic CXC chemokines ENA-78/CXC Ligand (CXCL) 5 and interleukin-8/CXCL8 in human non-small cell lung cancer. Cancer Res. 2004;64(5):1853–60.

Article   CAS   PubMed   Google Scholar  

Huang Z, Zhang M, Chen G, Wang W, Zhang P, Yue Y, et al. Bladder cancer cells interact with vascular endothelial cells triggering EGFR signals to promote tumor progression. Gut. 2019;54(5):1555–66.

CAS   Google Scholar  

Chen C, Xu ZQ, Zong YP, Ou BC, Shen XH, Feng H, et al. CXCL5 induces tumor angiogenesis via enhancing the expression of FOXD1 mediated by the AKT/NF-kappaB pathway in colorectal cancer. Cell Death Dis. 2019;10(3):178.

Wang L, Shi L, Gu J, Zhan C, Xi J. CXCL5 regulation of proliferation and migration in human non-small cell lung cancer cells. J Physiol Biochem. 2018;74(2):313–24.

Zhao J, Ou B, Han D, Wang P, Zong Y, Zhu C, Liu D, Zheng M, Sun J, Feng H, Lu A. Tumor-derived CXCL5 promotes human colorectal cancer metastasis through activation of the ERK/Elk-1/Snail and AKT/GSK3β/β-catenin pathways. Mol Cancer. 2017;16(1):70.

Haider C, Hnat J, Wagner R, Huber H, Timelthaler G, GrubingerM, et al. Transforming growth factor-beta and Axl Induce CXCL5 and Neutrophil Recruitment in Hepatocellular Carcinoma. Hepatology. 2019;69(1):222–36.

Gil-Bernabe AM, Ferjancic S, Tlalka M, Zhao L, Allen PD, Im JH, et al. Recruitment of monocytes/macrophages by tissue factor-mediated coagulation is essential for metastatic cell survival and premetastatic niche establishment in mice. Blood. 2012;119(13):3164–75.

Portella L, Bello AM, Scala S. CXCL12 Signaling in the Tumor Microenvironment. Adv Exp Med Biol. 2021;1302:51–70.

Märkl F, Huynh D, Endres S, Kobold S. Utilizing chemokines in cancer immunotherapy. Trends Cancer. 2022;8(8):670–82.

Article   PubMed   Google Scholar  

Song Na,Cui Kai,Zeng Liqun. Advance in the role of chemokines/chemokine receptors in carcinogenesis: focus on pancreatic cancer[. J] Eur J Pharmacol. 2024;967:176357.

Yamada T, Fujiwara N, Kubota N, Matsushita Y, Nakatsuka T, Kurosaki S, Minami T, Tateishi R, Ichida A, Arita J, Hasegawa K, Koike K, Fujishiro M, Nakagawa H. Lenvatinib recruits cytotoxic GZMK + CD8 T cells in hepatocellular carcinoma. Hepatol Commun. 2023;7(8):e0209.

Middleton MR, McAlpine C, Woodcock VK, Corrie P, Infante JR, Steven NM, Evans TRJ, Anthoney A, Shoushtari AN, Hamid O, Gupta A, Vardeu A, Leach E, Naidoo R, Stanhope S, Lewis S, Hurst J, O’Kelly I, Sznol M, Tebentafusp. A TCR/Anti-CD3 Bispecific Fusion protein targeting gp100, potently activated Antitumor Immune responses in patients with metastatic melanoma. Clin Cancer Res. 2020;26(22):5869–78.

Kamei Momo,Matsuo Kazuhiko,Yoshida Yusuke et al. Intratumoral delivery of a highly active form of XCL1 enhances antitumor CTL responses through recruitment of CXCL9-expressing conventional type-1 dendritic cells.[J].Int J Cancer, 2024, undefined: undefined.

Cervantes-Villagrana RD, Albores-García D, Cervantes-Villagrana AR, García-Acevez SJ. Tumor-induced neurogenesis and immune evasion as targets of innovative anti-cancer therapies. Signal Transduct Target Ther. 2020;5(1):99.

Peng Q, Nowocin A, Ratnasothy K, Smith RA, Smyth LA, Lechler RI, Dorling A, Lombardi G. Inhibition of thrombin on endothelium enhances recruitment of regulatory T cells during IRI and when combined with adoptive Treg transfer, significantly protects against acute tissue injury and prolongs allograft survival. Front Immunol. 2023;13:980462.

Shi L, Wang J, Ding N, Zhang Y, Zhu Y, Dong S, Wang X, Peng C, Zhou C, Zhou L, Li X, Shi H, Wu W, Long X, Wu C, Liao W. Inflammation induced by incomplete radiofrequency ablation accelerates tumor progression and hinders PD-1 immunotherapy. Nat Commun. 2019;10(1):5421.

Takacs GP, Kreiger CJ, Luo D, Tian G, Garcia JS, Deleyrolle LP, Mitchell DA, Harrison JK. Glioma-derived CCL2 and CCL7 mediate migration of immune suppressive CCR2+/CX3CR1 + M-MDSCs into the tumor microenvironment in a redundant manner. Front Immunol. 2023;13:993444.

Li BH, Jiang W, Zhang S, Huang N, Sun J, Yang J, Li ZF. The spleen contributes to the increase in PMN-MDSCs in orthotopic H22 hepatoma mice. Mol Immunol. 2020;125:95–103.

Möckel D, Patricia, et al. CCL2 chemokine inhibition primes the tumor vasculature for improved nanomedicine delivery and efficacy[. J] J Control Release. 2024;365:358–68.

Ko Kyung-Pil,Huang Yuanjian,Zhang Shengzhe et al. Key genetic determinants driving esophageal squamous cell carcinoma initiation and Immune Evasion.[J].Gastroenterology, 2023, 165: 613–e62820.

Xu R, Liu X, Li A, Song L, Liang J, Gao J, Tang X. c-Met up-regulates the expression of PD-L1 through MAPK/NF-κBp65 pathway. J Mol Med (Berl). 2022;100(4):585–98.

Shen M, Xu Z, Xu W, Jiang K, Zhang F, Ding Q, Xu Z, Chen Y. Inhibition of ATM reverses EMT and decreases metastatic potential of cisplatin-resistant lung cancer cells through JAK/STAT3/PD-L1 pathway. J Exp Clin Cancer Res. 2019;38(1):149.

Hirayama A, Tanaka K, Tsutsumi H, Nakanishi T, Yamashita S, Mizusaki S, Ishii Y, Ota K, Yoneshima Y, Iwama E, Okamoto I. Regulation of PD-L1 expression in non-small cell lung cancer by interleukin-1β. Front Immunol. 2023;14:1192861.

Linde IL, Prestwood TR, Qiu J, Pilarowski G, Linde MH, Zhang X, Shen L, Reticker-Flynn NE, Chiu DK, Sheu LY, Van Deursen S, Tolentino LL, Song WC, Engleman EG. Neutrophil-activating therapy for the treatment of cancer. Cancer Cell. 2023;41(2):356–e37210.

Quail DF, Amulic B, Aziz M, Barnes BJ, Eruslanov E, Fridlender ZG, Goodridge HS, Granot Z, Hidalgo A, Huttenlocher A, Kaplan MJ, Malanchi I, Merghoub T, Meylan E, Mittal V, Pittet MJ, Rubio-Ponce A, Udalova IA, van den Berg TK, Wagner DD, Wang P, Zychlinsky A, de Visser KE, Egeblad M, Kubes P. Neutrophil phenotypes and functions in cancer: a consensus statement. J Exp Med. 2022;219(6):e20220011.

Xiong S, Dong L, Cheng L. Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol. 2021;14(1):173.

Giese MA, Hind LE, Huttenlocher A. Neutrophil plasticity in the tumor microenvironment. Blood. 2019;133(20):2159–67.

Schernberg A, Mezquita L, Boros A, Botticella A, Caramella C, Besse B, Escande A, Planchard D, Le Péchoux C, Deutsch E. Neutrophilia as prognostic biomarker in locally advanced stage III lung cancer. PLoS ONE. 2018;13(10):e0204490.

Shen M, Jiang K, Sui Y, Xu Z, Cui H, Wang Y, Zhang H, Xu Z, Xu W, Ding Q, Chen Y. Characterization of CD66b and its relationship between immune checkpoints and their synergistic impact in the prognosis of surgically resected lung adenocarcinoma. Lung Cancer. 2021;160:84–91.

Michaeli J, Shaul ME, Mishalian I, Hovav AH, Levy L, Zolotriov L, Granot Z, Fridlender ZG. Tumor-associated neutrophils induce apoptosis of non-activated CD8 T-cells in a TNFα and NO-dependent mechanism, promoting a tumor-supportive environment. Oncoimmunology. 2017;6(11):e1356965.

Yang M, Wang B, Hou W, Yu H, Zhou B, Zhong W, Liu Z, Li J, Zeng H, Liu C, Qin H, Lin T, Huang J. Negative effects of Stromal Neutrophils on T Cells Reduce Survival in Resectable Urothelial Carcinoma of the bladder. Front Immunol. 2022;13:827457.

Chen R, Coleborn E, Bhavsar C, Wang Y, Alim L, Wilkinson AN, Tran MA, Irgam G, Atluri S, Wong K, Shim JJ, Adityan S, Lee JS, Overwijk WW, Steptoe R, Yang D, Wu SY. miR-146a inhibits ovarian tumor growth in vivo via targeting immunosuppressive neutrophils and enhancing CD8 + T cell infiltration. Mol Ther Oncolytics. 2023;31:100725.

Niu N, Shen X, Zhang L, Chen Y, Lu P, Yang W, Liu M, Shi J, Xu D, Tang Y, Yang X, Weng Y, Zhao X, Wu LM, Sun Y, Xue J. Tumor Cell-intrinsic SETD2 Deficiency Reprograms neutrophils to Foster Immune escape in pancreatic tumorigenesis. Adv Sci (Weinh). 2023;10(2):e2202937.

Feng M, Wang F, Liu X, Hao T, Zhang N, Deng M, Pan Y, Kong R. Neutrophils as key regulators of tumor immunity that restrict immune checkpoint blockade in liver cancer. Cancer Biol Med. 2023;20(6):421–37.

CAS   PubMed   PubMed Central   Google Scholar  

Wang J, Xiaomin, et al. Gut microbiota: a double-edged sword in immune checkpoint blockade immunotherapy against tumors[. J] Cancer Lett. 2024;582:216582.

Article   CAS   Google Scholar  

Zhijie, et al. KLF5 inhibition potentiates anti-PD1 efficacy by enhancing CD8 T-cell-dependent antitumor immunity. [J] Theranostics. 2023;13:1381–400. Liu Zhou,Gao.

Xie XH, Wang YJ, Lei W, Gao HJ, Duan YJ, Hou X. [CXCL5 inhibits tumor immune of lung cancer via modulating PD1/PD-L1 signaling]. Zhonghua Zhong Liu Za Zhi. 2022;44(5):382–8. Chinese.

CAS   PubMed   Google Scholar  

Simoncello F, Piperno GM, Caronni N, Amadio R, Cappelletto A, Canarutto G, Piazza S, Bicciato S, Benvenuti F. CXCL5-mediated accumulation of mature neutrophils in lung cancer tissues impairs the differentiation program of anticancer CD8 T cells and limits the efficacy of checkpoint inhibitors. Oncoimmunology. 2022;11(1):2059876.

Chen J, Lin Z, Liu L, Zhang R, Geng Y, Fan M, Zhu W, Lu M, Lu L, Jia H, Zhang J, Qin LX. GOLM1 exacerbates CD8 + T cell suppression in hepatocellular carcinoma by promoting exosomal PD-L1 transport into tumor-associated macrophages. Signal Transduct Target Ther. 2021;6(1):397.

Huang Q, Wu X, Wang Z, Chen X, Wang L, Lu Y, Xiong D, Liu Q, Tian Y, Lin H, Guo J, Wen S, Dong W, Yang X, Yuan Y, Yue Z, Lei S, Wu Q, Ran L, Xie L, Wang Y, Gao L, Tian Q, Zhou X, Sun B, Xu L, Tang Z, Ye L. The primordial differentiation of tumor-specific memory CD8 + T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell. 2022;185(22):4049–e406625.

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Acknowledgements

We thank Dr. Hongxia Cui and Yiqun Sui from the Department of Pathology Medicine at the Second Affiliated Hospital of Soochow University for their invaluable assistance in reviewing the paraffin sections.

This work was supported by the Technological Innovation Project of CNNC Medical Industry Co. Ltd (grant no. ZHYLYB2021007), the Suzhou Science and Technology Foundation (grant no. SKJY2021077, SKY2022166, and SKYD2023117), the Hospital Internal Research Foundation (grant no. SDFEYBS2009, SDFEYJGL2101 and SDFEYHL2255), the National Key Laboratory of Radiation Medicine and Auxiliary Protection (grant no. GZK12023032), the National Key Laboratory of Neural and Tumorous Drug Development (grant no. SIML202301), the Jiangsu Key Research and Social Development Project (grant no. BE2020653), the Gusu Health Talent Program of Suzhou (grant no. GSWS2021020, GSWS2023045 and GSWS2022147), the National Natural Science Foundation of China (grant no. 82172076), the Key Scientific Program of Jiangsu Provincial Health Commission (grant no. ZD2021033), and the Project of Medical New Technology Assistance of the Second Affiliated Hospital of Soochow University (grant no. 23ZL004 and 23ZL012).

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Dantong Sun, Lipin Tan, Yongbing Chen and Qiang Yuan contributed equally to this work.

Authors and Affiliations

Department of Thoracic and Cardiac Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China

Dantong Sun, Yongbing Chen, Kanqiu Jiang, Jinzhi Zhang, Xianbao Cao, Minzhao Xu, Yang Luo, Zhonghua Xu, Zhonghen Xu, Weihua Xu & Mingjing Shen

Department of nursing administration, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China

Department of interventional medicine, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China

Department of Vascular Surgery, Hospital of Zhangjiagang, Suzhou, 215600, China

Yangyang Liu

Department of Thoracic Surgery, Hospital of Yancheng, Yancheng, 224000, China

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Contributions

MJ.S., and WH.X. conceived the study and designed the project. DT.S., YB.C. and Q.Y. performed most of the experiments. JZ.Z., XB.C. MZ.X. and Y.L. helped to perform a part of experiments. LP.T. collected clinical samples. YY.L. analyzed data obtained from the experiments. YH.X. did bioinformatic analysis. KQ.J., ZH.X. and ZH.X. wrote the manuscript and prepared figures. MJ.S., as the guarantor for the overall content, approved and supervised the project. All authors reviewed the final manuscript.

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Correspondence to Mingjing Shen .

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The biopsy specimens were obtained under protocols approved by the ethics committees of The Second Affiliated Hospital of Soochow University (Approval Number: JD-HG-2020-09) and informed consent was obtained from all patients. All animals were maintained in the specific pathogen-free barrier facility in the Animal Center of The Second Affiliated Hospital of Soochow University. All animal experiments were approved by the Animal Ethical and Experimental Committee of the The Second Affiliated Hospital of Soochow University, and were performed in accordance with the Guide for the Care and Use of Laboratory Animals.

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Sun, D., Tan, L., Chen, Y. et al. CXCL5 impedes CD8 + T cell immunity by upregulating PD-L1 expression in lung cancer via PXN/AKT signaling phosphorylation and neutrophil chemotaxis. J Exp Clin Cancer Res 43 , 202 (2024). https://doi.org/10.1186/s13046-024-03122-8

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Received : 22 April 2024

Accepted : 09 July 2024

Published : 22 July 2024

DOI : https://doi.org/10.1186/s13046-024-03122-8

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