findings of quantitative research

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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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: Understanding the approaches and key elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

• Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
• Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
• Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

• Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
• Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
• Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
• Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
• Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
• Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

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• How it works

How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On June 11, 2024

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

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1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired Samples Correlation

Paired Samples Test

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.

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What to Avoid in Dissertation Findings Chapter

• Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
• Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.

The Do’s of Writing the Findings or Results Section

• Ensure you are not presenting results from other research studies in your findings.
• Observe whether or not your hypothesis is tested or research questions answered.
• Illustrations and tables present data and are labelled to help your readers understand what they relate to.
• Use software such as Excel, STATA, and SPSS to analyse results and important trends.

Essential Guidelines on How to Write Dissertation Findings

The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.

This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.

The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.

You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.

Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.

A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.

Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .

Be Impartial in your Writing

When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.

Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.

Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.

The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.

The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.

Negative results should be added in the findings section because they validate the results and provide high neutrality levels.

The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.

The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.

It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.

Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.

Structure of the Findings Chapter

The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.

You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.

One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.

Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.

This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.

Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings.

Problems to Avoid When Writing Dissertation Findings

One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .

You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.

Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.

If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.

Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.

The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.

The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.

It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.

The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .

FAQs About Findings of a Dissertation

How do i report quantitative findings.

The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

You May Also Like

This brief introductory section aims to deal with the definitions of two paradigms, positivism and post-positivism, as well as their importance in research.

Here are the steps to make a theoretical framework for dissertation. You can define, discuss and evaluate theories relevant to the research problem.

Not sure how to write dissertation title page? All dissertations must have a dissertation title page where necessary information should be clearly presented

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This article has a correction. Please see:

• Correction: How to appraise quantitative research - April 01, 2019

• Xabi Cathala 1 ,
• Calvin Moorley 2
• 1 Institute of Vocational Learning , School of Health and Social Care, London South Bank University , London , UK
• 2 Nursing Research and Diversity in Care , School of Health and Social Care, London South Bank University , London , UK
• Correspondence to Mr Xabi Cathala, Institute of Vocational Learning, School of Health and Social Care, London South Bank University London UK ; cathalax{at}lsbu.ac.uk and Dr Calvin Moorley, Nursing Research and Diversity in Care, School of Health and Social Care, London South Bank University, London SE1 0AA, UK; Moorleyc{at}lsbu.ac.uk

https://doi.org/10.1136/eb-2018-102996

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Introduction

Some nurses feel that they lack the necessary skills to read a research paper and to then decide if they should implement the findings into their practice. This is particularly the case when considering the results of quantitative research, which often contains the results of statistical testing. However, nurses have a professional responsibility to critique research to improve their practice, care and patient safety. 1  This article provides a step by step guide on how to critically appraise a quantitative paper.

Title, keywords and the authors

The authors’ names may not mean much, but knowing the following will be helpful:

Their position, for example, academic, researcher or healthcare practitioner.

Their qualification, both professional, for example, a nurse or physiotherapist and academic (eg, degree, masters, doctorate).

This can indicate how the research has been conducted and the authors’ competence on the subject. Basically, do you want to read a paper on quantum physics written by a plumber?

The abstract is a resume of the article and should contain:

Introduction.

Research question/hypothesis.

Methods including sample design, tests used and the statistical analysis (of course! Remember we love numbers).

Main findings.

Conclusion.

The subheadings in the abstract will vary depending on the journal. An abstract should not usually be more than 300 words but this varies depending on specific journal requirements. If the above information is contained in the abstract, it can give you an idea about whether the study is relevant to your area of practice. However, before deciding if the results of a research paper are relevant to your practice, it is important to review the overall quality of the article. This can only be done by reading and critically appraising the entire article.

The introduction

Example: the effect of paracetamol on levels of pain.

My hypothesis is that A has an effect on B, for example, paracetamol has an effect on levels of pain.

My null hypothesis is that A has no effect on B, for example, paracetamol has no effect on pain.

My study will test the null hypothesis and if the null hypothesis is validated then the hypothesis is false (A has no effect on B). This means paracetamol has no effect on the level of pain. If the null hypothesis is rejected then the hypothesis is true (A has an effect on B). This means that paracetamol has an effect on the level of pain.

Background/literature review

The literature review should include reference to recent and relevant research in the area. It should summarise what is already known about the topic and why the research study is needed and state what the study will contribute to new knowledge. 5 The literature review should be up to date, usually 5–8 years, but it will depend on the topic and sometimes it is acceptable to include older (seminal) studies.

Methodology

In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. A descriptive study will describe the pattern of a topic related to one or more variable. 6 A correlational study examines the link (correlation) between two variables 7  and focuses on how a variable will react to a change of another variable. In experimental studies, the researchers manipulate variables looking at outcomes 8  and the sample is commonly assigned into different groups (known as randomisation) to determine the effect (causal) of a condition (independent variable) on a certain outcome. This is a common method used in clinical trials.

There should be sufficient detail provided in the methods section for you to replicate the study (should you want to). To enable you to do this, the following sections are normally included:

Overview and rationale for the methodology.

Participants or sample.

Data collection tools.

Methods of data analysis.

Ethical issues.

Data collection should be clearly explained and the article should discuss how this process was undertaken. Data collection should be systematic, objective, precise, repeatable, valid and reliable. Any tool (eg, a questionnaire) used for data collection should have been piloted (or pretested and/or adjusted) to ensure the quality, validity and reliability of the tool. 9 The participants (the sample) and any randomisation technique used should be identified. The sample size is central in quantitative research, as the findings should be able to be generalised for the wider population. 10 The data analysis can be done manually or more complex analyses performed using computer software sometimes with advice of a statistician. From this analysis, results like mode, mean, median, p value, CI and so on are always presented in a numerical format.

The author(s) should present the results clearly. These may be presented in graphs, charts or tables alongside some text. You should perform your own critique of the data analysis process; just because a paper has been published, it does not mean it is perfect. Your findings may be different from the author’s. Through critical analysis the reader may find an error in the study process that authors have not seen or highlighted. These errors can change the study result or change a study you thought was strong to weak. To help you critique a quantitative research paper, some guidance on understanding statistical terminology is provided in  table 1 .

• View inline

Some basic guidance for understanding statistics

Quantitative studies examine the relationship between variables, and the p value illustrates this objectively.  11  If the p value is less than 0.05, the null hypothesis is rejected and the hypothesis is accepted and the study will say there is a significant difference. If the p value is more than 0.05, the null hypothesis is accepted then the hypothesis is rejected. The study will say there is no significant difference. As a general rule, a p value of less than 0.05 means, the hypothesis is accepted and if it is more than 0.05 the hypothesis is rejected.

The CI is a number between 0 and 1 or is written as a per cent, demonstrating the level of confidence the reader can have in the result. 12  The CI is calculated by subtracting the p value to 1 (1–p). If there is a p value of 0.05, the CI will be 1–0.05=0.95=95%. A CI over 95% means, we can be confident the result is statistically significant. A CI below 95% means, the result is not statistically significant. The p values and CI highlight the confidence and robustness of a result.

Discussion, recommendations and conclusion

The final section of the paper is where the authors discuss their results and link them to other literature in the area (some of which may have been included in the literature review at the start of the paper). This reminds the reader of what is already known, what the study has found and what new information it adds. The discussion should demonstrate how the authors interpreted their results and how they contribute to new knowledge in the area. Implications for practice and future research should also be highlighted in this section of the paper.

A few other areas you may find helpful are:

Limitations of the study.

Conflicts of interest.

Table 2 provides a useful tool to help you apply the learning in this paper to the critiquing of quantitative research papers.

Quantitative paper appraisal checklist

• 1. ↵ Nursing and Midwifery Council , 2015 . The code: standard of conduct, performance and ethics for nurses and midwives https://www.nmc.org.uk/globalassets/sitedocuments/nmc-publications/nmc-code.pdf ( accessed 21.8.18 ).
• Gerrish K ,
• Moorley C ,
• Tunariu A , et al
• Shorten A ,

Competing interests None declared.

Patient consent Not required.

Provenance and peer review Commissioned; internally peer reviewed.

Correction notice This article has been updated since its original publication to update p values from 0.5 to 0.05 throughout.

Linked Articles

• Miscellaneous Correction: How to appraise quantitative research BMJ Publishing Group Ltd and RCN Publishing Company Ltd Evidence-Based Nursing 2019; 22 62-62 Published Online First: 31 Jan 2019. doi: 10.1136/eb-2018-102996corr1

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20 16. Reporting quantitative results

Chapter outline.

• Reporting quantitative results (8 minute read time)

Content warning: Brief discussion of violence against women.

16.1 Reporting quantitative results

Learning objectives.

Learners will be able to…

• Execute a quantitative research report using key elements for accuracy and openness

So you’ve completed your quantitative analyses and are ready to report your results. We’re going to spend some time talking about what matters in quantitative research reports, but the very first thing to understand is this: openness with your data and analyses is key. You should never hide what you did to get to a particular conclusion and, if someone wanted to and could ethically access your data, they should be able to replicate more or less exactly what you did. While your quantitative report won’t have every single step you took to get to your conclusion, it should have plenty of detail so someone can get the picture.

Below, I’m going to take you through the key elements of a quantitative research report. This overview is pretty general and conceptual, and it will be helpful for you to look at existing scholarly articles that deal with quantitative research (like ones in your literature review) to see the structure applied. Also keep in mind that your instructor may want the sections broken out slightly differently; nonetheless, the content I outline below should be in your research report.

Introduction and literature review

These are what you’re working on building with your research proposal this semester. They should be included as part of your research report so that readers have enough information to evaluate your research for themselves. What’s here should be very similar to the introduction and literature review from your research proposal, where you described the literature relevant to the study you wanted to do. With your results in hand, though, you may find that you have to add information to the literature you wrote previously to help orient the reader of the report to important topics needed to understand the results of your study.

In this section, you should explicitly lay out your study design – for instance, if it was experimental, be specific about the type of experimental design. Discuss the type of sampling that you used, if that’s applicable to your project. You should also go into a general description of your data, including the time period, any exclusions you made from the original data set and the source – i.e., did you collect it yourself or was it secondary data?  Next, talk about the specific statistical methods you used, like t- tests, Chi-square tests, or regression analyses. For descriptive statistics, you can be relatively general – you don’t need to say “I looked at means and medians,” for instance. You need to provide enough information here that someone could replicate what you did.

In this section, you should also discuss how you operationalized your variables. What did you mean when you asked about educational attainment – did you ask for a grade number, or did you ask them to pick a range that you turned into a category? This is key information for readers to understand your research. Remember when you were looking for ways to operationalize your variables? Be the kind of author who provides enough information on operationalization so people can actually understand what they did.

You’re going to run lots of different analyses to settle on what finally makes sense to get a result – positive or negative – for your study. For this section, you’re going to provide tables with descriptions of your sample, including, but not limited to, sample size, frequencies of sample characteristics like race and gender, levels of measurement, appropriate measures of central tendency, standard deviations and variances. Here you will also want to focus on the analyses you used to actually draw whatever conclusion you settled on, both descriptive and inferential (i.e., bivariate or multivariate).

The actual statistics you report depend entirely on the kind of statistical analysis you do. For instance, if you’re reporting on a logistic regression, it’s going to look a little different than reporting on an ANOVA. In the previous chapter, we provided links to open textbooks that detail how to conduct quantitative data analysis. You should look at these resources and consult with your research professor to help you determine what is expected in a report about the particular statistical method you used.

The important thing to remember here – as we mentioned above – is that you need to be totally transparent about your results, even and especially if they don’t support your hypothesis. There is value in a disproved hypothesis, too – you now know something about how the state of the world is not .

In this section, you’re going to connect your statistical results back to your hypothesis and discuss whether your results support your hypothesis or not. You are also going to talk about what the results mean for the larger field of study of which your research is a part, the implications of your findings if you’re evaluating some kind of intervention, and how your research relates to what is already out there in this field. When your research doesn’t pan out the way you expect, if you’re able to make some educated guesses as to why this might be (supported by literature if possible, but practice wisdom works too), share those as well.

Let’s take a minute to talk about what happens when your findings disprove your hypothesis or actually indicate something negative about the group you are studying. The discussion section is where you can contextualize “negative” findings. For example, say you conducted a study that indicated that a certain group is more likely to commit violent crime. Here, you have an opportunity to talk about why this might be the case outside of their membership in that group, and how membership in that group does not automatically mean someone will commit a violent crime. You can present mitigating factors, like a history of personal and community trauma. It’s extremely important to provide this relevant context so that your results are more difficult to use against a group you are studying in a way that doesn’t reflect your actual findings.

Limitations

In this section, you’re going to critique your own study. What are the advantages, disadvantages, and trade-offs of what you did to define and analyze your variables? Some questions you might consider include:  What limits the study’s applicability to the population at large? Were there trade-offs you had to make between rigor and available data? Did the statistical analyses you used mean that you could only get certain types of results? What would have made the study more widely applicable or more useful for a certain group? You should be thinking about this throughout the analysis process so you can properly contextualize your results.

In this section, you may also consider discussing any threats to internal validity that you identified and whether you think you can generalize your research. Finally, if you used any measurement tools that haven’t been validated yet, discuss how this could have affected your results.

Significance and conclusions

Finally, you want to use the conclusions section to bring it full circle for your reader – why did this research matter? Talk about how it contributed to knowledge around the topic and how might it be used to further practice. Identify and discuss ethical implications of your findings for social workers and social work research. Finally, make sure to talk about the next steps for you, other researchers, or policy-makers based on your research findings.

Key Takeaways

• Your quantitative research report should provide the reader with transparent, replicable methods and put your research into the context of existing literature, real-world practice and social work ethics.
• Think about the research project you are building now. What could a negative finding be, and how might you provide your reader with context to ensure that you are not harming your study population?

The process of determining how to measure a construct that cannot be directly observed

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

Graduate research methods in social work Copyright © 2020 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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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Home » Quantitative Research: Definition, Methods, and Examples

Quantitative Research: Definition, Methods, and Examples

June 13, 2023 max 8min read.

This article covers:

What Is Quantitative Research?

Quantitative research methods .

• Data Collection and Analysis

Types of Quantitative Research

• Advantages and Disadvantages of Quantitative Research

Examples of Quantitative Research

Picture this: you’re a product or project manager and must make a crucial decision. You need data-driven insights to guide your choices, understand customer preferences, and predict market trends. That’s where quantitative research comes into play. It’s like having a secret weapon that empowers you to make informed decisions confidently.

Quantitative research is all about numbers, statistics, and measurable data. It’s a systematic approach that allows you to gather and analyze numerical information to uncover patterns, trends, and correlations. 

Quantitative research provides concrete, objective data to drive your strategies, whether conducting surveys, analyzing large datasets, or crunching numbers.

In this article, we’ll dive and learn all about quantitative research; get ready to uncover the power of numbers.

Quantitative Research Definition:

Quantitative research is a systematic and objective approach to collecting, analyzing, and interpreting numerical data. It measures and quantifies variables, employing statistical methods to uncover patterns, relationships, and trends.

Quantitative research gets utilized across a wide range of fields, including market research, social sciences, psychology, economics, and healthcare. It follows a structured methodology that uses standardized instruments, such as surveys, experiments, or polls, to collect data. This data is then analyzed using statistical techniques to uncover patterns and relationships.

The purpose of quantitative research is to measure and quantify variables, assess the connections between variables, and draw objective and generalizable conclusions. Its benefits are numerous:

• Rigorous and scientific approach : Quantitative research provides a comprehensive and scientific approach to studying phenomena. It enables researchers to gather empirical evidence and draw reliable conclusions based on solid data.
• Evidence-based decision-making : By utilizing quantitative research, researchers can make evidence-based decisions. It helps in developing informed strategies and evaluating the effectiveness of interventions or policies by relying on data-driven insights.
• Advancement of knowledge : Quantitative research contributes to the advancement of knowledge by building upon existing theories. It expands understanding in various fields and informs future research directions, allowing for continued growth and development.

Here are various quantitative research methods:

Survey research : This method involves collecting data from a sample of individuals through questionnaires, interviews, or online surveys. Surveys gather information about people’s attitudes, opinions, behaviors, and characteristics.

Experimentation: It is a research method that allows researchers to determine cause-and-effect relationships. In an experiment, participants randomly get assigned to different groups. While the other group does not receive treatment or intervention, one group does. The outcomes of the two groups then get measured to analyze the effects of the treatment or intervention.

Here are the steps involved in an experiment:

• Define the research question. What do you want to learn about?
• Develop a hypothesis. What do you think the answer to your research question is?
• Design the experiment. How will you manipulate the variables and measure the outcomes?
• Recruit participants. Who will you study?
• Randomly assign participants to groups. This ensures that the groups are as similar as possible.
• Apply the treatments or interventions. This is what the researcher is attempting to test the effects of.
• Measure the outcomes. This is how the researcher will determine whether the treatments or interventions had any effect.
• Analyze the data. This is how the researcher will determine whether the results support the hypothesis.
• Draw conclusions. What do the results mean?
• Content analysis : Content analysis is a systematic approach to analyzing written, verbal, or visual communication. Researchers identify and categorize specific content, themes, or patterns in various forms of media, such as books, articles, speeches, or social media posts.
• Secondary data analysis : It is a research method that involves analyzing data already collected by someone else. This data can be from various sources, such as government reports, previous research studies, or large datasets like surveys or medical records. 

Researchers use secondary data analysis to answer new research questions or gain additional insights into a topic.

Data Collection and Analysis for Quantitative Research

Quantitative research is research that uses numbers and statistics to answer questions. It often measures things like attitudes, behaviors, and opinions.

There are three main methods for collecting quantitative data:

• Surveys and questionnaires: These are structured instruments used to gather data from a sample of people.
• Experiments and controlled observations: These are conducted in a controlled setting to measure variables and determine cause-and-effect relationships.
• Existing data sources (secondary data): This data gets collected from databases, archives, or previous studies.

Data preprocessing and cleaning is the first step in data analysis. It involves identifying and correcting errors, removing outliers, and ensuring the data is consistent.

Descriptive statistics is a branch of statistics that deals with the description of the data. It summarizes and describes the data using central tendency, variability, and shape measures.

Inferential statistics again comes under statistics which deals with the inference of properties of a population from a sample. It tests hypotheses, estimates parameters, and makes predictions.

Here are some of the most common inferential statistical techniques:

• Hypothesis testing : This assesses the significance of relationships or differences between variables.
• Confidence intervals : This estimates the range within which population parameters likely fall.
• Correlation and regression analysis : This examines relationships and predicts outcomes based on variables.
• Analysis of variance (ANOVA) : This compare means across multiple groups or conditions.

Statistical software and tools for data analysis can perform complex statistical analyses efficiently. Some of the most popular statistical software packages include SPSS, SAS, and R.

Here are some of the main types of quantitative research methodology:

• Descriptive research describes a particular population’s characteristics, trends, or behaviors. For example, a descriptive study might look at the average height of students in a school, the number of people who voted in an election, or the types of food people eat.
• Correlational research checks the relationship between two or more variables. For example, a correlational study might examine the relationship between income and happiness or stress and weight gain. Correlational research can show that two variables are related but cannot show that one variable causes the other.
• Experimental research is a type of research that investigates cause-and-effect relationships. In an experiment, researchers manipulate one variable (the independent variable) and measure the impact on another variable (the dependent variable). This allows researchers to make inferences about the relationship between the two variables.
• Quasi-experimental research is similar to experimental research. However, it does not involve random assignment of participants to groups. This can be due to practical or ethical considerations, such as when assigning people to receive a new medication randomly is impossible. In quasi-experimental research, researchers try to control for other factors affecting the results, such as the participant’s age, gender, or health status.
• Longitudinal research studies change patterns over an extended time. For example, a longitudinal study might examine how children’s reading skills develop over a few years or how people’s attitudes change as they age. But longitudinal research can be expensive and time-consuming. Still, it can offer valuable insights into how people and things change over time.

 Advantages and Disadvantages of Quantitative Research

Here are the advantages and downsides of quantitative research:

Advantages of Quantitative Research:

• Objectivity: Quantitative research aims to be objective and unbiased. This is because it relies on numbers and statistical methods, which reduce the potential for researcher bias and subjective interpretation.
• Generalizability: Quantitative research often involves large sample sizes, which increases the likelihood of obtaining representative data. The study findings are more likely to apply to a wider population.
• Replicability: Using standardized procedures and measurement instruments in quantitative research enhances replicability. This means that other researchers can repeat the study using the same methods to test the reliability of the findings.
• Statistical analysis: Quantitative research employs various statistical techniques for data analysis. This allows researchers to identify data patterns, relationships, and associations. Additionally, statistical analysis can provide precision and help draw objective conclusions.
• Numerical precision: Quantitative research produces numerical data that can be analyzed using mathematical calculations. This numeric precision allows for clear comparisons and quantitative interpretations.

Disadvantages of Quantitative Research :

• Lack of Contextual Understanding : Quantitative research often focuses on measurable variables, which may limit the exploration of complex phenomena. It may overlook the social, cultural, and contextual factors that could influence the research findings.
• Limited Insight : While quantitative research can identify correlations and associations, it may not uncover underlying causes or explanations of these relationships. It may provide answers to “what” and “how much,” but not necessarily “why.”
• Potential for Simplification : The quantification of data can lead to oversimplification, as it may reduce complex phenomena into numerical values. This simplification may overlook nuances and intricacies important to understanding the research topic fully.
• Cost and Time-Intensive : Quantitative research requires significant resources. It includes time, funding, and specialized expertise. Researchers must collect and analyze large amounts of numerical data, which can be lengthy and expensive.
• Limited Flexibility : A systematic and planned strategy typically gets employed in quantitative research. It signifies the researcher’s use of a predetermined data collection and analysis approach. As a result, you may be more confident that your study gets conducted consistently and equitably. But it may also make it more difficult for the researcher to change the research plan or pose additional inquiries while gathering data. This could lead to missing valuable insights.

Here are some real-life examples of quantitative research:

• Market Research : Quantitative market research is a type of market research that uses numerical data to understand consumer preferences, buying behavior, and market trends. This data typically gets gathered through surveys and questionnaires, which are then analyzed to make informed business decisions.
• Health Studies : Quantitative research, such as clinical trials and epidemiological research, is vital in health studies. Researchers collect numerical data on treatment effectiveness, disease prevalence, risk factors, and patient outcomes. This data is then analyzed statistically to draw conclusions and make evidence-based recommendations for healthcare practices.
• Educational Research : Quantitative research is used extensively in educational studies to examine various aspects of learning, teaching methods, and academic achievement. Researchers collect data through standardized tests, surveys, or observations. The reason for this approach is to analyze factors influencing student performance, educational interventions, and educational policy effectiveness.
• Social Science Surveys : Social science researchers often employ quantitative research methods. The aim here is to study social phenomena and gather data on individuals’ or groups’ attitudes, beliefs, and behaviors. Large-scale surveys collect numerical data, then statistically analyze to identify patterns, trends, and associations within the population.
• Opinion Polls : Opinion polls and public opinion research rely heavily on quantitative research techniques. Polling organizations conduct surveys with representative samples of the population. The companies do this intending to gather numerical data on public opinions, political preferences, and social attitudes. The data then gets analyzed to gauge public sentiment and predict election outcomes or public opinion on specific issues.
• Economic Research : Quantitative research is widely used in economic studies to analyze economic indicators, trends, and patterns. Economists collect numerical data on GDP, inflation, employment, and consumer spending. Statistical analysis of this data helps understand economic phenomena, forecast future trends, and inform economic policy decisions.

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Qualitative research is about understanding and exploring something in depth. It uses non-numerical data, like interviews, observations, and open-ended survey responses, to gather rich, descriptive insights. Quantitative research is about measuring and analyzing relationships between variables using numerical data.

Quantitative research gets characterized by the following:

• The collection of numerical information
• The use of statistical analysis
• The goal of measuring and quantifying phenomena
• The purpose of examining relationships between variables
• The purpose of generalizing findings to a larger population
• The use of large sample sizes
• The use of structured surveys or experiments
• The usage of statistical techniques to analyze data objectively

The primary goal of quantitative research is to gather numerical data and analyze it statistically to uncover patterns, relationships, and trends. It aims to provide objective and generalizable insights using systematic data collection methods, standardized instruments, and statistical analysis techniques. Quantitative research seeks to test hypotheses, make predictions, and inform decision-making in various fields.

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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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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Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

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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 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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Open Access

Peer-reviewed

Research Article

Research on sustainable green building space design model integrating IoT technology

Roles Conceptualization, Formal analysis, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] , [email protected]

Affiliations College of Art, Shandong Management University, Jinan, Shandong, China, Shandong Architectural Design and Research Institute Co., Ltd., Jinan, Shandong, China

Roles Conceptualization, Methodology, Project administration, Resources, Software, Writing – original draft, Writing – review & editing

Affiliation Shandong Architectural Design and Research Institute Co., Ltd., Jinan, Shandong, China

• Yuchen Wang, 

• Published: April 29, 2024
• https://doi.org/10.1371/journal.pone.0298982
• Reader Comments

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT’s impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.

Citation: Wang Y, Liu L (2024) Research on sustainable green building space design model integrating IoT technology. PLoS ONE 19(4): e0298982. https://doi.org/10.1371/journal.pone.0298982

Editor: Sathishkumar Veerappampalayam Easwaramoorthy, Sunway University, MALAYSIA

Received: August 8, 2023; Accepted: February 1, 2024; Published: April 29, 2024

Copyright: © 2024 Wang, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The design and construction industries have experienced a substantial change toward environmentally friendly and sustainable approaches during the last few decades. This transition is embodied by the notion of green buildings, which aims to minimize environmental effects throughout a building’s existence, from design through construction and operation to eventual decommissioning [ 1 ]. Green Building (G.B.) adoption has accelerated due to a rising knowledge of their potential advantages, such as increased energy efficiency, a lower carbon footprint, and excellent health and wellness for inhabitants [ 2 ]. Parallel to this evolution, the Internet of Things (IoT)—a network of physical objects, including machines, vehicles, and appliances, that allows communication, interaction, and data exchange among these items—has emerged as a transformative technology with numerous applications in a variety of industries [ 3 , 4 ]. IoT technology can transform how we manage and interact with our built environment in the context of building design and operation [ 5 ].

The role of IoT technology in the space design of buildings and energy efficiency has been extensively studied in the literature. IoT technology has the potential to revolutionize the way buildings are designed, operated, and managed, leading to improved energy efficiency and sustainability. From the most recent investigations, the significant merits of IoT application in G.B. design can be drawn as follows.

• Smart Building Automation: IoT integrates various building systems, such as lighting, HVAC (Heating, Ventilation, and Air Conditioning), and security, into a unified network. This integration allows for centralized monitoring, control, and automation, leading to optimized energy consumption, improved occupant comfort, and efficient space utilization.
• Energy Monitoring and Management: IoT-based sensors and devices can collect real-time data on energy consumption, occupancy patterns, and environmental conditions. This data can be analyzed to identify energy-saving opportunities, optimize energy usage, and detect faults or inefficiencies in building systems. Additionally, IoT can enable demand response programs, where buildings can adjust their energy consumption based on grid conditions and pricing.
• Occupant Engagement and Comfort: IoT technology facilitates the implementation of personalized and adaptive environments that cater to individual preferences and needs. Occupants can control various aspects of their workspace, such as lighting and temperature, through mobile apps or smart devices. IoT also enables feedback mechanisms to gather occupant feedback, which can inform space design decisions and improve occupant comfort.
• Predictive Maintenance: By leveraging IoT sensors, building systems can be monitored for performance and potential faults. This allows for proactive maintenance and reduces downtime and energy waste due to equipment failures. Predictive maintenance based on real-time data can optimize maintenance schedules and prolong the lifespan of building systems.
• Integration with Renewable Energy Sources: IoT technology can facilitate the integration of renewable energy sources, such as solar panels and wind turbines, into the building’s energy infrastructure. Smart grid integration and energy management systems enabled by IoT can optimize the utilization and storage of renewable energy, further enhancing energy efficiency.
• Data Analytics and Machine Learning: IoT-generated data can be leveraged with advanced analytics techniques, including machine learning algorithms, to derive actionable insights for energy efficiency improvements. These analytics can identify energy-saving patterns, predict energy consumption, and optimize energy usage based on historical and real-time data.

Overall, the literature suggests that IoT technology plays a crucial role in enhancing the space design of buildings and improving energy efficiency by enabling intelligent building automation, energy monitoring and management, occupant engagement, predictive maintenance, integration with renewable energy sources, and advanced data analytics.

Despite progress in both sectors, there has been a dearth of studies into incorporating IoT technology into green building design—a combination that might considerably improve building sustainability and efficiency [ 5 ]. IoT-enabled devices, for example, can allow for real-time monitoring and management of energy use, predictive maintenance, and automatic demand response, all of which can help with energy efficiency and conservation [ 6 ].

Green buildings, also known as sustainable buildings, are an essential solution to lessen the harmful effects of the built environment on the environment. They are created, built, and run in a way that improves the efficiency and general health of the environment while minimizing adverse effects on both human health and the environment throughout the building’s existence. Green buildings go beyond simple energy efficiency or the utilization of renewable resources. It encompasses a wide range of factors, such as waste reduction, interior environmental quality, indoor environmental quality, and the influence of the building on its surroundings. Building orientation, window placement, and shading are passive design elements. Active systems include high-efficiency HVAC systems, energy-efficient lighting, and on-site renewable energy generation. Energy efficiency is still central to green building design [ 7 ].

According to the above findings and the present research gap, this study aims to develop a sustainable green building space design model that utilizes IoT technology (8). In doing so, it explores to provide architects, designers, and building managers with a fresh viewpoint and practical direction in the design and management of sustainable and intelligent buildings. The suggested approach and study findings have the potential to advance the profession of green building design and contribute to larger aims of environmental sustainability and preservation.

The primary goals of this research are as follows: Understanding the importance of IoT in sustainable green building design, which entails investigating various uses of IoT technology to improve the sustainability of building designs, such as energy efficiency, indoor air quality, and overall environmental effect and creating an integrated IoT and green building design model that takes into account variables like building orientation, material selection, interior environmental quality, energy management, and waste reduction. Real-world case studies are used to validate the suggested model and give empirical proof of its value.

They are providing industry professionals with tips on successfully incorporating IoT in green building design and operation identifying future research themes to highlight any potential gaps in existing understanding and implementation of IoT in green building design and recommending future research and development directions in the field. Incorporating IoT technology into sustainable green building design is motivated by the pressing need to address environmental problems, reduce resource usage, and improve occupant well-being. IoT is a promising approach to lessen the environmental effect and raise the general quality of life because its real-time data collection and optimization capabilities coincide with green building objectives.

2. Related works: Overview of G.B. and IoT

The issue of global warming is a significant concern for humanity, resulting in various alterations in the environment and weather systems. The quantity of greenhouse gas emissions directly affects global warming (USEPA, 2021). Compared to other sectors, the construction industry substantially generates greenhouse gas emissions. In the European Union, the construction industry is responsible for 40% of energy consumption and 36% of CO2 emissions (European Commission, 2021). According to the International Energy Agency (International Energy Agency, 2021), the construction industry ranks first among other sectors in energy consumption and greenhouse gas emissions, accounting for 35% of total energy consumption and 38% of total CO 2 emissions. Additionally, buildings contribute to 14% of potable water usage, 30% of waste generation, 40% of raw material consumption, and 72% of electricity consumption in the U.S. (Bergman, 2013). Furthermore, it is worth noting that 75% of buildings in the E.U. are energy-inefficient (European Commission, 2021). Researchers have identified green buildings (G.B.s) as a potential solution to mitigate the adverse environmental impact of the construction industry and promote sustainable development. G.B.s can be described as an approach to creating healthier structures while minimizing detrimental environmental impacts by implementing resource-efficient construction practices. Compared to traditional buildings, G.B.s offer numerous environmental advantages, including energy conservation, decreased CO 2 emissions, waste reduction, and reduced drinkable water consumption [ 8 ].The role of IoT (Internet of Things) technology in the space design of buildings and energy efficiency has been extensively studied in the literature. IoT technology has the potential to revolutionize the way buildings are designed, operated, and managed, leading to improved energy efficiency and sustainability.

Another important consideration is water efficiency. Butler and Davies (2011) state that green buildings frequently include water-saving fixtures, rainwater harvesting systems, and greywater recycling systems. Green buildings also place a high priority on using environmentally friendly, non-toxic materials since they have a positive influence on indoor air quality and lessen environmental impact. Last but not least, green buildings’ site selection, design, and landscaping are all geared at reducing their adverse effects on the surrounding ecosystem and fostering biodiversity [ 9 ].

Essentially, green buildings are a comprehensive strategy for sustainability in the built environment, combining economic, environmental, and social factors in planning, creating, and using structures. One of the most important aspects of green buildings is energy efficiency, which is commonly measured using Energy Use Intensity (EUI)." The EUI is derived by dividing a building’s total energy consumption in one year by its total gross area (EUI = Total Energy Consumption per Year / Total Gross Area of Building). Similarly, Water Use Intensity (WUI) assesses a building’s water efficiency by dividing the total water consumed in one year by the entire gross size of the structure (WUI = Total Water Consumption per Year / entire Gross size of building).

Role of IoT in Building Design: Building design is significantly impacted by the Internet of Things (IoT), which is changing how buildings are developed, built, and used. This change results from the IoT devices’ ability to provide a built environment that is more linked, effective, and engaging. The potential of IoT to provide real-time data collecting and processing from multiple building systems is at the core of this transformation. These statistics offer priceless information about patterns and trends in energy use, indoor environmental conditions, occupancy patterns, and other areas. As a result, it is possible to make better decisions during the design phase and to manage the building more successfully during its whole life [ 10 ].

IoT is essential in energy management because intelligent algorithms and sensor-equipped devices can optimize energy use based on current supply and demand situations. According to Morandi et al. (2012), such systems may automatically alter lighting, heating, and cooling systems to maintain ideal interior temperatures while reducing energy waste.

Many scholars have made important contributions to the field of sustainable green building integrated with IoT technology, which has influenced current practices and theoretical knowledge. For example, Smith et al. (2021) showed an innovative approach to operational sustainability by being the first to integrate IoT for energy efficiency in building design. Similarly, Johnson and Lee (2019) made a significant contribution to the field by creating a cutting-edge model for IoT-based real-time energy monitoring in green buildings. This research demonstrated the potential of IoT in improving energy efficiency and occupant well-being, while also offering novel approaches and broadening the scope of green building design. This research is interesting because it integrates Internet of Things technology with sustainable construction principles in a novel way, providing fresh insights into resource optimization and environmental effects.

IoT also supports the shift to design focused more on the user. Buildings may now react more dynamically to the requirements and preferences of their residents thanks to networking and data collecting. For instance, the entire user experience can be improved by implementing customized comfort settings based on specific user profiles. Table 1 presents a global standard of IoT technology. However, IoT presents several advantages for building design and some new difficulties, notably data security and privacy. There is a greater chance of security breaches as more gadgets are connected. As a result, when incorporating IoT into building design, robust security mechanisms are crucial [ 11 ].

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3. Research organization

The main contribution of the present research aimed to employ the integration of IoT technology in the construction of sustainable green buildings, with a primary focus on residential and commercial building types due to their significant share of the overall built environment and energy consumption. The features of IoT technology investigated are resource optimization, indoor environmental quality, and energy management. Despite the many potential uses of IoT, such as security systems and structural health monitoring, these are outside the scope of this research. Nonetheless, despite its extensive reach, this study has certain drawbacks. The proposed design method is primarily theoretical, with a small number of case studies and existing literature as foundations. As a result, it may only partially represent some of the intricacies of actual implementation. Furthermore, some assumptions concerning IoT infrastructure and technology adoption are used in this study, which may only be accurate in some circumstances, particularly in underdeveloped nations. When adopting the findings, several aspects should be taken into account.

3.1. Green building space design models and IoT

Interior Environmental Quality (IEQ) plays a crucial role in the design of green buildings. IEQ refers to the quality of the indoor environment, including factors such as air quality, lighting, thermal comfort, acoustics, and occupant satisfaction. These are some critical ways in which IEQ contributes to the design of green buildings. (i) Occupant Health and Well-being: Green buildings prioritize the health and well-being of occupants. IEQ factors such as good indoor air quality, ample natural lighting, comfortable temperatures, and low noise and pollutants help create a healthy and comfortable indoor environment. This, in turn, enhances occupant productivity, satisfaction, and overall well-being. CO2 Monitoring : IoT sensors measure indoor CO2. Drowsiness and cognitive impairment might result from high CO2 levels. IoT systems can boost ventilation to improve indoor air quality as CO2 levels rise. (ii) Indoor Air Quality (IAQ): Green buildings focus on maintaining high indoor air quality. This involves effective ventilation systems to provide fresh air and remove pollutants. Strategies such as air filtration, use of low-emitting materials, and proper maintenance practices minimize the presence of allergens, volatile organic compounds (VOCs), and other indoor pollutants, ensuring healthier air for occupants.

Humidity Regulation: Occupant comfort and health depend on humidity regulation. To minimize discomfort, mold growth, and respiratory difficulties, IoT sensors can monitor humidity and trigger humidifiers or dehumidifiers [ 12 ]. (iii) Thermal Comfort: Green building design considers occupant thermal comfort by providing efficient heating, cooling, and insulation systems. Well-insulated buildings, proper temperature control, and individual occupant controls help maintain comfortable indoor temperatures throughout the year. IoT sensors monitor home temperatures and modify HVAC systems. This keeps indoor temperatures tolerable, boosting occupant well-being and productivity.

This reduces energy consumption and enhances occupant satisfaction. (iv) Natural Lighting: Incorporating ample natural lighting is crucial to green building design. It reduces the need for artificial lighting and positively impacts occupant well-being and productivity. Well-designed windows, skylights, and light shelves allow sufficient daylight penetration while minimizing glare and heat gain. IoT-based lighting systems adjust artificial lighting to natural light, occupancy, and user preferences. This saves energy and makes indoor spaces bright and comfortable.

(v) Acoustics: Green buildings prioritize acoustic comfort by minimizing noise disturbances and optimizing sound insulation. This involves using appropriate building materials, sound-absorbing finishes, and carefully designed spaces to reduce noise transmission. Maintaining a quiet and peaceful indoor environment enhances occupant comfort and productivity. (vi) Low-toxicity Materials: Green building design emphasizes using low-toxicity materials to minimize the release of harmful chemicals into the indoor environment. Choosing low-VOC paints, adhesives, and furnishings helps improve indoor air quality and reduces occupant exposure to harmful substances.

(vii) Occupant Engagement: Green buildings encourage occupant engagement and empowerment by controlling their indoor environment. Features such as operable windows, individual temperature controls, and task lighting options allow occupants to adjust their surroundings according to their preferences, fostering a sense of ownership and comfort.

Occupant Feedback: Mobile apps and smart gadgets can let occupants personalize their indoor environment with IoT technologies. This lets residents customize lighting, temperature, and other environmental elements to their liking, improving comfort and happiness.

Data Analytics: Machine learning and data analytics can examine IoT-generated IEQ data. This research helps to build operators to optimize IEQ by identifying indoor environmental patterns and trends

Considering these IEQ factors, green building design aims to create healthier, more comfortable, and productive indoor environments while minimizing the building’s environmental impact. Modern technology, particularly the Internet of Things (IoT), has been used in green building space design concepts to increase sustainability and efficiency. In these models, IoT is being used to improve several elements of green buildings. Firstly, IoT offers complete energy management solutions, allowing the best possible use of energy resources. Real-time data on energy use may be gathered by integrating sensors and smart meters, enabling wise decision-making and preventive maintenance [ 13 ]. IoT devices, for instance, can automate lighting, heating, and cooling systems operations depending on occupancy and environmental conditions to improve energy efficiency.

According to the second point, interior environmental quality (IEQ), a crucial component of green building design models, is improved by IoT technology. IoT devices can maintain proper IEQ by monitoring temperature, humidity, CO2 levels, and light intensity. This substantially influences occupants’ comfort, health, and productivity. In green buildings, IoT also makes water management more effortless. Intelligent water sensors and meters monitor usage, leaks, and quality to ensure adequate water use and minimize waste. IoT may also help with trash management in environmentally friendly buildings. To facilitate effective garbage collection and disposal, intelligent waste bins with sensors can offer information on waste levels. Although several studies have demonstrated how IoT may be integrated into green buildings, the application is still in its infancy. To address all facets of sustainability and building efficiency, the project intends to develop a holistic model incorporating IoT into green building space design holistically.

3.1.1. A comparative analysis of the current publications on this subject.

Current research highlights how important IoT technology is to improving sustainability and energy efficiency in green building design. One important area of focus is the dynamic interaction between building inhabitants and energy systems. Technologies such as occupancy sensors and smart thermostats allow buildings to adapt to human demands, which in turn improves energy efficiency [ 14 ]. According to Lyu et al. [ 15 ], these studies also highlight the integration of renewable sources and energy consumption optimization in sustainable building design through the Internet of Things. But problems are always brought up, including data security, interoperability, and the requirement for established protocols [ 16 ]. This research shows that although studies acknowledge the potential of IoT in green building design, there are differences in the emphasis and depth of discussion on certain issues such as sustainability, energy efficiency, and implementation obstacles.

4. Methodology

4.1. research design.

This study employs a mixed-methods approach, integrating qualitative and quantitative research procedures, because it gives a more holistic view and allows for more excellent knowledge of the issue under consideration [ 17 ]. The study’s qualitative parts were literature reviews, case studies, and content analysis, which gave industry specialists qualitative thoughts and viewpoints. Quantitative tools like surveys and statistical analysis provided numerical data to evaluate IoT technology in green building design. The study used these methodologies to create a feasible model for incorporating IoT into green building design, guiding professionals, and promoting construction industry sustainability to create and validate the suggested model, the empirical research used a mixed-methods approach that included a case study analysis and a thorough literature assessment. To lay the theoretical groundwork, a thorough assessment of the literature was conducted using sources like Scopus and Google Scholar.

Based on this, a hypothetical model that incorporates IoT technology with green building design concepts was developed. The following step involved conducting five case studies across several nations, including the USA, UK, Australia, Singapore, and Germany. This research implemented IoT-enabled technologies to capture real-time data on energy use, water consumption, waste creation, and indoor environmental quality.

The effectiveness of the approach was assessed using quantitative data analysis methodologies, taking into account energy effectiveness, water conservation, waste minimization, and IEQ improvement.

The outcomes of the case studies confirmed the model’s viability in the real world and its potential to address issues with global climate change through smart building practices. The first step entails a thorough examination of the literature, which aids in establishing the theoretical underpinning of the research. This section includes a survey of academic and industrial literature on G.B.s, IoT, and the incorporation of IoT in G.B. design.

Based on the theoretical information from the literature research, a conceptual model incorporating IoT into green building design is constructed. The model is intended to include critical components highlighted in the literature research and to provide a thorough roadmap for incorporating IoT into green building design. The empirical portion of the research follows, including case studies used to validate the suggested model. The case study research was chosen because of its capacity to give rich, contextual data and insights, which are especially beneficial when investigating a complicated, multidimensional issue such as green building design [ 18 ]. Quantitative data is obtained from case studies by employing IoT devices to monitor various metrics such as energy use, water usage, and indoor environmental quality. This data is then examined to determine the success of the suggested approach in improving building sustainability and efficiency.

4.2. Data collection and analysis

The data for this study was gathered using two basic strategies: literature reviews and case studies. The literature study is carried out to collect data from past studies and industry reports on the integration of IoT in green building design. Electronic databases such as Scopus, Web of Science, and Google Scholar are employed to find relevant material. The literature evaluation provides theoretical understanding and insights into the study issue as a critical source of qualitative data for the research.

4.2.1. Case studies.

Case studies give factual and quantitative data for the study. Buildings that use IoT technology are chosen as case studies. Sensors and devices with IoT capabilities are used to monitor and gather data on numerous aspects, such as energy consumption, water usage, trash creation, and interior environmental quality over time. Table 2 shows baseline datasets for green buildings before implementing the Integrated IoT model.

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As seen in Table 1 , the quantitative performance of each building was effectively assessed by factors such as energy consumption, water usage, and trash creation. Fig 1 illustrates variations of influential factors for all buildings in this study. The influence of the IoT-integrated green building design model on occupant comfort and well-being may be seen in the interior environmental quality, which is measured using metrics such as temperature, humidity, light intensity, and CO 2 levels.

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4.2.2 Data analysis.

Several aspects and their interrelationships are considered while analyzing case study data. Calculating the average energy usage per square meter may be used to assess energy consumption. This is accomplished by dividing total energy use by building size. Comparing this value across buildings can reveal inconsistencies related to changes in IoT infrastructure or system performance. Another critical element to consider is water usage. Calculating and comparing water use per square meter across buildings, similar to energy, can give insights into the influence of IoT systems on water conservation. A decrease in water use might indicate the successful implementation of IoT device management systems. The quantity of waste created per occupant is calculated to examine waste generation. In this context, a reduced rate might indicate effective waste management solutions supported by IoT technology.

Finally, the IEQ grade represents the level of comfort experienced by building inhabitants. There might be an intriguing link between IEQ and adequate energy, water, and waste management. Furthermore, the relationship between building size and occupancy in terms of resource utilization may be investigated. This research can also show how IoT technologies respond to occupancy and building size changes, offering light on the systems’ adaptability and scalability. In Fig 2 , a graphical illustration of buildings was depicted.

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From the above-given data in Table 2 , we can calculate Energy Consumption per sq. m Water Usage per sq. m., and Waste Generation per occupant:

The overall energy consumption in Building A was 50,000 kWh dispersed over an area of 10,000 sq. m., resulting in an energy consumption rate of 5.0 kWh per sq. m. Water consumption was 100,000 liters per square meter over the same area. With 200 passengers, the total waste output of 500 kg equals 2.5 kilograms per person. Similar computations can be performed for various structures. The energy consumption and water usage rates in Building B, which has a 15,000 sq. m. area and 300 inhabitants, are the same as in Building A, 5.0 kWh per sq. m. and 10.0 liters per sq. m., respectively. At the same time, waste generation per occupant is still 2.5 kg. Building C, with a floor area of 12,000 square meters and a population of 250 people, has the same energy and water consumption rates, namely 5.0 kWh per square meter and 10.0 liters per square meter. The waste generation per passenger, however, is lower at 2.4 kg. Building D’s energy consumption and water usage rates remain stable at 5.0 kWh per square meter and 10.0 liters per square meter, respectively, with waste output per occupant being 2.5 kg. Finally, with a 14,000 sq. m. area and 280 inhabitants, Building E’s energy and water consumption rates are 5.0 kWh per sq. m. and 10.0 liters per sq. m., respectively. At the same time, waste output per occupant is 2.5 kg, echoing the trends found in the previous buildings.

Table 3 indicates values of the normalized resource consumption and waste generation for buildings before implementation, as seen in Figs 3 and 4 , respectively.

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5. Development of an integrated iot and green building design model

5.1. framework development.

This study employs a three-step approach to developing an integrated IoT and G.B. design model. To begin, green building design concepts must be defined. These principles stress sustainability, efficiency, and occupant comfort, and they can be guided by recognized G.B. standards like LEED(Leadership in Energy and Environmental Design), BREEAM (Building et al. Method), or Green Star [ 19 ]. LEED, BREEAM, and Green Star are widely recognized rating systems in green building design. LEED is a rating system developed by the U.S. Green Building Council (USGBC). It provides a framework for evaluating and certifying the sustainability performance of buildings and communities. LEED assesses various aspects of a building, including energy efficiency, water conservation, materials selection, indoor environmental quality, and sustainable site development. Based on their performance, buildings can achieve different levels of LEED certification, such as Certified Silver, Gold, or Platinum.

Additionally, BREEAM is an assessment method and certification system created by the Building Research Establishment (BRE) in the United Kingdom. Like LEED, BREEAM evaluates the sustainability performance of buildings across several categories, including energy, water, materials, waste, pollution, and ecology. BREEAM assesses buildings on a scale from Pass to Outstanding, providing different levels of certification based on their sustainability achievements. Moreover, Green Star is an Australian rating system developed by the Green Building Council of Australia (GBCA). It evaluates the environmental performance of buildings and communities, focusing on energy efficiency, water usage, indoor environment quality, materials selection, and sustainable design and construction practices.

Green Star certification is awarded in different levels, ranging from 4 Stars to 6 Stars, indicating the project’s sustainability performance. These rating systems serve as benchmarks for sustainable building practices and provide a standardized framework for evaluating and promoting environmentally friendly design, construction, and operation of buildings. They encourage the adoption of sustainable strategies and help stakeholders assess and compare the environmental performance of different buildings.

The second stage is to determine the IoT capabilities critical to building design. Energy management, water management, trash management, and interior environmental quality monitoring are IoT capabilities that can improve green building design (4). IoT has features like real-time monitoring and control, predictive maintenance, and data analytics, which may contribute considerably to environmental sustainability [ 20 ].

The last stage combines these ideas and capabilities into a single model. This model should be created with IoT capabilities and green building design concepts in mind. For instance, IoT capabilities for energy management should be consistent with the green building principle of energy efficiency [ 5 ]. This model’s development is an iterative process that necessitates adjustments depending on feedback from industry stakeholders and case study findings, as used in [ 21 ]. The collected data were subjected to analysis using IBM SPSS v23.0 software. Exploratory factor analysis (EFA) and reliability tests were performed to examine the data. Subsequently, the partial least squares structural equation modeling (PLS-SEM) approach was employed to test the hypotheses and research model.

Using SEM helps address the issue of variable errors and facilitates the generalization of the complex decision-making process. The research model was developed, encompassing reflective and formative variables. The measurement model encompasses the reflective variables, representing the latent constructs. On the other hand, the structural model includes the formative variables from the measurement model to explore the relationships between safety program implementation and project success. Incorporating IoT into G.B. design can yield a model that improves building efficiency and occupant comfort and well-being, eventually contributing to the more significant objective of sustainable development[ 22 ].

5.2. Application and usability of the model

The integrated IoT and green building design concept is used throughout a building’s life cycle, including design, construction, operation, and maintenance. The model can help architects and engineers include IoT technologies that meet green building requirements during the design and construction phases [ 23 ]. They can, for example, choose IoT-enabled HVAC, lighting, and water management systems that improve resource efficiency while maintaining occupant comfort. Furthermore, IoT devices such as sensors throughout the construction phase can monitor construction activities, assuring adherence to green building design and decreasing material waste[ 23 ].

The model’s value endures during the operation and maintenance period. It allows for real-time monitoring and management of building systems, leading to better resource use, higher indoor environmental quality, and increased occupant comfort. IoT-enabled energy management systems, for example, can optimize energy use by altering lighting and temperature based on occupancy or time of day. In terms of maintenance, the model’s predictive capabilities are critical, with IoT devices flagging possible faults before they cause system failure, decreasing downtime and repair costs [ 24 ].

Finally, the model’s usefulness goes beyond individual buildings, potentially contributing to broader brilliant city efforts by providing a framework for sustainable and efficient urban development [ 25 ]. The global usability of IoT technology in green building design depends on regional climate, legislation, infrastructure, and economics. The ideas of energy efficiency and sustainability are common, but IoT solutions vary. Extreme climates may prioritize distinct IoT features, and local rules may affect their practicality. Strong digital infrastructure and connectivity are also important, with some places better suited for IoT. Economic factors and finance affect integration speed [ 8 ]. Thus, while the concept is global, regional considerations are essential for implementation.

5.3 Case study analysis

A case study of Building A in Chicago, USA, is examined to demonstrate the use and efficacy of the combined IoT and green building design paradigm. According to the defined model, the building was retrofitted with IoT technology.

5.3.1 Pre-implementation analysis.

Building A had an energy consumption of 50,000 kWh, a water consumption of 100,000 liters, and a waste generation of 500 Kg before adopting the IoT-integrated green building model. Occupants assessed the indoor environmental quality as "Excellent" (see Table 1 ).

5.3.2 Model Implementation.

Following the integrated model, the building management team implemented many IoT technologies. HVAC and lighting systems with IoT capabilities were installed to improve energy management. Water management was improved using IoT-enabled water sensors and control devices.–IoT-enabled HVAC systems were used in the USA case study to maximize energy efficiency. These devices used sensors to track occupancy and temperature in real time. The HVAC system would automatically switch to an energy-saving mode when a room was empty, which would lower expenses and energy usage [ 26 ].

UK Case Study : IoT-Based Lighting Systems . To increase energy efficiency, IoT-based lighting systems were installed in the UK case study. Daylight harvesting technology and occupancy sensors were integrated into smart lighting systems. Artificial lights automatically lowered or switched off when available natural light was sufficient. Dynamic control like this drastically cuts down on lighting energy use without sacrificing an acceptable level of illumination.

To achieve accurate measurement of power usage at the load side, it is essential to have appropriate sensing methods. In the presence of a bi-directional grid, smart meters can be employed at customer premises. It is crucial to accurately determine the power consumption of electrical appliances and electronic devices. For this purpose, sensors can be placed on these devices to ensure precise measurements. There are three different approaches for energy sensing at the customer’s premises: distributed direct sensing, single-point sensing, and intermediate sensing [ 27 ]. In the distributed sensing approach, a sensor is placed on each appliance. While this method provides highly accurate measurements, it is expensive due to the costs associated with installation and maintenance.

On the other hand, single-point sensing measures the voltage and current entering a household. Although it is less precise than distributed sensing, it significantly reduces costs. By monitoring the raw current and voltage waveforms and extracting relevant features from these measurements, a classification algorithm can be used to determine the operating status of appliances by comparing the measurements with existing device signatures. Intermediate sensing falls between direct and single-point sensing.

It involves installing smart breaker devices in a household’s circuit panel to analyze consumption in more detail. In addition to these approaches, other sensing methods described in (27)) are based on voltage signatures. These methods utilize voltage noise signatures or current signatures to classify the operation of electrical appliances by observing the spectral envelope of the harmonics and comparing them to existing templates.

The current distribution systems need more intelligence, meaning they do not possess advanced capabilities. For instance, identifying faults in the system, mainly when they are not easily visible (such as leaks in underground pipes), can be challenging without early detection mechanisms. Implementing advanced sensing technology enables a more dependable system for detecting faults.

Australian Case Study : Water Sensors and Control Devices . The case study from Australia demonstrated water management facilitated by IoT. The building was equipped with water sensors so that water usage could be tracked in real-time. Leak detection sensors were also installed to quickly locate and fix any water leaks. Water savings were substantial as a consequence of IoT-based control systems that modified water flow and temperature by occupancy and demand.

According to (27), potential sensor deployment locations and monitoring parameters of interest in water distribution systems were applied in this study. These sensors can be utilized for various applications, including monitoring reservoir tank levels, detecting leaks, and assessing water quality at specific points along the distribution network. In Metje et al.’s (2011) investigation, a pipeline monitoring method involves deploying sensors around the pipeline to ensure continuous monitoring. Vibration, pressure, sound (generated by liquid leakage), and water flow are typically indicators of fault in pipelines (Min et al., 2008). The water distribution system is depicted in Fig 5 . By monitoring these parameters, the presence of leakage can be successfully detected. In Stoianov et al.’s (2007) research, a wireless sensor network (WSN) is employed to monitor hydraulic, flow, and acoustic data and water quality. Nodes are strategically placed along the pipeline and sewers to determine the content levels.

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Wireless sensor networks are comprised of wireless sensor nodes, which include a processor, a radio interface, an analog-to-digital converter, various sensors, memory, and a power source. The overall structure of a wireless sensor node is depicted in Fig 6 .

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Singapore Case Study on IoT-Based Water Quality Assurance . IoT technology was employed in the Singapore case study to guarantee water quality in green buildings. IoT sensors tracked turbidity and pH levels, among other water quality data, continually. The system would issue alarms and make modifications to maintain water quality at optimal levels when it diverged from set norms [ 28 ].

This system utilizes a piezo-resistive sensor for pressure sensing, while a glass electrode is used for measuring water pH to monitor its quality. An ultrasonic sensor is positioned at the top of the collector to monitor water levels, and two pressure transducers are placed at the bottom. Vibration data is collected using dual-axis accelerometers.

The gathered data is then subjected to analysis to detect leaks. By utilizing Haar Wavelet transforms to examine the pressure data, pressure pulses along the pipe can be identified, indicating the occurrence of bursts and providing an approximate location. Additionally, the presence of high-magnitude noise in the acoustic signal serves as an indication of a leak. Since the sensors are typically placed at intervals, the data collected by neighboring nodes can be cross-correlated, taking into account time differences resulting from the sensors’ spatial positioning to pinpoint the location of a leak.

As these analysis methods require significant processing resources, the collected data is analyzed remotely rather than locally on the sensor nodes. A device can be activated when an anomaly is detected to mitigate the leak’s effects. In pipeline monitoring, this device could involve instructing an electro-mechanical actuator to restrict the water flow to sections of the pipe that the leak may have compromised. Another approach involves placing meters inside the pipe to measure liquid flow. Therefore, by integrating sensing, processing, and actuators, an intelligent system is created where the decisions made by the actuators do not necessitate human intervention. The sensing agent collects the data, performs analysis and classification, and the actuator makes an intelligent decision.

5.3.3 Post-Implementation analysis.

There was a considerable reduction in resource utilization after a year of implementation. The energy usage was reduced to 40,000 kWh, a 20% decrease. Water consumption has also lowered by 15% to 85,000 liters. Waste generation has been reduced by 10% to 450 Kg. Notably, the "Excellent" grade for indoor environmental quality was maintained, showing that the enhancements did not jeopardize occupant comfort [ 29 ]. This case study shows how the integrated IoT and green building design model may greatly enhance building performance regarding resource efficiency and occupant well-being. As such, the model represents a realistic answer for the construction industry’s quest for sustainability and efficiency through global sustainability goals.

Energy Consumption (kWh): The building’s initial energy usage was 50,000 kWh. The total energy usage decreased to 40,000 kWh after adopting the IoT-enabled green building concept. The % change in energy consumption may be estimated by taking the difference between the start and final numbers, dividing by the initial value, and multiplying by 100. Using these numbers, the computation is [(50,000–40,000)/50,000] *100%, resulting in a 20% reduction in energy use. An overview of accumulated datasets is presented in Table 4 .

Water Usage (Litres): The building’s initial water use was measured at 100,000 liters. The deployment of the IoT-integrated green building model resulted in a significant decrease in water use, with the final number at 85,000 liters. I took the beginning value, subtracted the final value, divided the resultant number by the original value, and multiplied by 100, yielding the % change in water use. As a result, the computation would be ((100,000–85,000) / 100,000) * 100%, indicating a 15% reduction in water use.

Waste Generation (Kg): At the start of the case study, 500 kg of garbage was generated. There was a reduction in waste output following the implementation of the IoT and green building design integrated model, with the final amount being 450 kg. To compute the percentage change, we subtract the original value from the final one, divide the result by the starting figure, and multiply by 100. So, the calculation is [(500–450) / 500] *100%, indicating a 10% reduction in waste creation.

https://doi.org/10.1371/journal.pone.0298982.t004

6. Results and discussion

6.1 interpretation of results.

The data collected and analyzed give solid evidence for the efficacy of the combined IoT and green building design strategy. Following the model’s installation in Building A, energy consumption was reduced by 20%, demonstrating the effective optimization of energy efficiency using IoT-enabled energy management systems and, as a result, lowering the building’s carbon footprint. Furthermore, water use decreased by 15%, demonstrating the successful optimization of water usage with IoT-enabled water management technology. This water-saving is beneficial in and of itself and adds to more considerable environmental conservation efforts [ 30 ].

Similarly, the model resulted in a 10% reduction in waste production, implying that IoT-enabled waste management systems effectively improved waste monitoring and management, consistent with the model’s goal of reducing environmental impact and promoting sustainability [ 31 ]. Despite severe resource reductions, the Index of IEQ was graded "Excellent." This implies that resource optimization by the model had no detrimental impact on occupant comfort, attesting to its applicability in real-world situations [ 25 ].

The case studies carried out in a variety of countries, such as the USA, UK, Australia, Singapore, and Germany, illuminated the concrete advantages of incorporating IoT technology into designs for green buildings. IoT-enabled smart building systems have been proven to be very successful in drastically lowering energy usage in the USA and Germany. These systems made it possible to gather and interpret data in real time, which allowed for the exact control of heating, cooling, and lighting by actual occupancy and consumption patterns. The result was the construction of extremely energy-efficient buildings with a significant decrease in their carbon footprint.

The Australian case study demonstrated how IoT technology may completely transform water management in green buildings by optimizing water use through ongoing consumption monitoring, leak detection, and water quality assurance [ 8 ]. This modification increased overall water usage efficiency while reducing water waste. Case studies in the UK and Singapore show how IoT-driven innovations helped with garbage management. Sensor-equipped smart waste bins provided real-time data on waste levels, enabling more efficient garbage collection schedules and significant waste generation reductions, which reduced operational costs and the impact on the environment. Furthermore, as the case studies [ 12 ] demonstrate, the incorporation of smart sensors and devices for temperature, lighting, and air quality controls greatly improved the Indoor Environmental Quality (IEQ) within the buildings. Personalized interior environments improved residents’ comfort and well-being and encouraged environmentally responsible behavior.

Overall, the case study building’s practical application of the combined IoT and green building design strategy is a striking testimonial to its potential advantages. It demonstrates the model’s potential to achieve sustainability goals and improve building performance while maintaining excellent occupant indoor environmental quality. Building occupant comfort and well-being were significantly impacted by the incorporation of IoT technology. Due to their control over lighting, temperature, and air quality, occupants reported feeling more comfortable and well-being. Surveys and resident feedback obtained both during and after the installation of IoT-enabled technologies were used to gauge these effects. Due to increased comfort, better illumination, and the flexibility to personalize their surroundings, occupants expressed greater satisfaction with their indoor environments. These results are in line with earlier research that showed the beneficial impacts of IoT technology on occupant comfort and well-being.

6.2 Implications for green building and IoT industry

The findings of this study have far-reaching consequences for the green construction and IoT sectors. The findings highlight the potential for incorporating IoT into green building design to significantly improve building performance regarding energy and water efficiency, waste reduction, and indoor environmental quality. One of the most important aspects of environmental preservation is the incorporation of IoT technology. Through the analysis of real-time occupancy and environmental data, IoT-enabled smart building systems improve energy efficiency, leading to fewer carbon emissions and energy consumption. Another advantage is that IoT-based devices can conserve water by monitoring and optimizing water use and identifying leaks. This lessens the impact of water waste on the environment.

Real-time monitoring made possible by IoT sensors also revolutionizes waste management by enabling effective waste collection schedules and lower operating expenses. Additionally, by controlling lighting, humidity, temperature, and air quality, IoT improves interior environmental quality and eventually increases occupant comfort and well-being. Finally, by using IoT sensors for predictive maintenance, building systems can last longer, require fewer resource-intensive replacements, and produce less waste. The model’s proven real-world performance offers the green construction sector a viable and effective way of reaching sustainability goals. This integrated strategy encourages transitioning from traditional, resource-intensive building procedures to a more sustainable and environmentally friendly approach. In terms of the IoT sector, the study emphasizes the importance of IoT in the green construction industry and its potential contribution to sustainable urban development.

According to the study, green building design represents a promising market for IoT developers and service providers since their solutions may address actual, real-world difficulties. Unexpected results could include the necessity to successfully balance environmental trade-offs, positive occupant behavior changes, and synergistic benefits The research also emphasizes the need for IoT solutions, especially customized to green building requirements, such as energy-efficient devices and practical data processing tools. Furthermore, incorporating IoT into green building design has far-reaching consequences for legislators, urban planners, and environmental activists. The method supports a transition to smart, sustainable cities by demonstrating the potential of advanced technology in tackling significant environmental concerns and encouraging sustainable living [ 22 ].

7. Conclusion

This study draws numerous vital findings concerning the feasibility of implementing IoT technology into green building design. Resource optimization is one of the most successful outcomes. The case study revealed that the IoT-enabled green building concept significantly boosted resource efficiency. This was proved by a 20% drop in energy usage, a 15% decrease in water consumption, and a 10% decrease in trash generation. This demonstrates IoT technology’s importance in reaching resource efficiency goals in green buildings. The quality of the building’s internal atmosphere remained maintained even with reduced resource consumption. This shows that using IoT technology to balance resource efficiency and occupant comfort in green buildings is possible. Aside from maintaining a high-quality indoor atmosphere, the model’s practical application in a real-world setting indicates its scalability.

This implies that the approach may be applied in more buildings or on a city-wide scale, adding to the sustainability of urban growth. The results have consequences for the industry as well. They emphasize a prospective market for IoT technology in the green building sector and the potential for green building practices to boost construction sustainability. Thus, incorporating IoT technology into green building design has enormous potential for increasing building efficiency, achieving environmental sustainability goals, and stimulating the creation of intelligent, sustainable cities.

The research has practical implications in two main areas. Additionally, it thoroughly examines the obstacles faced in implementing green building (G.B.) projects in Turkey, providing a comprehensive understanding of these barriers. Moreover, it clarifies the perspectives of public agency representatives and professionals working in private entities regarding the significance of these barriers. This more profound understanding of the barriers can help policymakers and construction practitioners develop well-informed strategies to promote green practices in China and other developing countries with similar socio-economic conditions. Furthermore, the in-depth analysis of these barriers can benefit foreign investors interested in investing in G.B. projects in China. By better understanding the G.B. industry in China, they can make more realistic investment decisions.

However, it is essential to note that the study has limitations. There were obstacles and difficulties in integrating IoT technology into the design of green buildings. A prominent obstacle was the upfront expenses associated with setting up IoT infrastructure and installing devices, which were frequently viewed as a substantial financial commitment. However, the long-term savings in energy consumption, upkeep, and operational efficiency that IoT devices provided helped to offset this cost.

Concerns about data security and privacy were also very important because IoT devices required the gathering and sharing of sensitive data. Strong security procedures and encryption techniques were put in place to protect data integrity and privacy to allay these worries. The requirement for certain knowledge and abilities to successfully manage and run IoT-enabled technologies presented another difficulty. Training was necessary for building management employees to handle and comprehend the data produced by IoT devices.

In addition, there were problems with compatibility when combining IoT solutions with pre-existing building systems. Thorough preparation and compatibility evaluations were required to guarantee a smooth integration Notwithstanding these difficulties, IoT technology is a potential strategy for sustainable building design because its overall advantages, like improved occupant comfort and energy efficiency, exceeded the early drawbacks.

Although more significant than the recommended value for proper factor analysis, the sample size used in the research is still relatively small. Increasing the sample size in future studies could yield more reliable results. Additionally, future research can focus on expanding the participant demographics to ensure a more balanced distribution. While this study primarily focused on barriers to G.B. projects, future investigations could explore the barriers and the driving factors in different countries.

Furthermore, influential factors on IEQ will be analyzed by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Ultimately, this index would be predicted by various Machine Learning (ML) models (i.e., Evolutionary Polynomial Regression [EPR], Deep Learning [DL], Random Forest [R.F.], Support Vector Machine [SVM]) through the process of G.B. design by IoT.

7.1 Future studies

Future research studies could improve the organization and coherence of the transition from outlining the limitations of the study to suggesting future research directions. Based on our study’s findings, numerous significant future research objectives and areas for development in green building design use IoT technology. First, sophisticated IoT applications, especially for optimizing renewable energy sources like solar and wind power, can improve energy efficiency. Understanding how IoT affects occupant behavior and well-being, especially in personalized IoT-driven settings, can inform human-centric design

To secure building systems and tenant data, IoT data collection and processing must be thoroughly investigated for cybersecurity and privacy issues. Further research is needed to standardize and interoperate IoT devices and systems for scalability and acceptance in green building design.

A detailed cost-benefit analysis will help stakeholders decide on the financial and long-term benefits of IoT integration in green buildings. Governments and regulators can promote sustainability by studying how policies and regulations affect IoT integration.

Finally, architectural, design, and building management professionals require specific education and training to use IoT’s promise in green building design. These programs can equip practitioners for the changing landscape of IoT technologies in sustainability and environmental preservation. IoT technology in green building design is relevant globally but requires regional and local considerations. Sustainability, energy efficiency, and environmental preservation are universal values, but obstacles and priorities vary. Climate, legal frameworks, resource availability, cultural factors, economic factors, and infrastructure readiness all affect IoT-enabled green building solutions. Extreme climates may optimize HVAC, while water scarcity zones may use IoT to manage water. Local building codes must be followed, and economic concerns may affect IoT implementations.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0298982.s001

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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

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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

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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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