quantitative research questions about technology

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54 Most Interesting Technology Research Topics for 2023

May 30, 2023

Scrambling to find technology research topics for the assignment that’s due sooner than you thought? Take a scroll down these 54 interesting technology essay topics in 10 different categories, including controversial technology topics, and some example research questions for each.

Social technology research topics

Whether you have active profiles on every social media platform, you’ve taken a social media break, or you generally try to limit your engagement as much as possible, you probably understand how pervasive social technologies have become in today’s culture. Social technology will especially appeal to those looking for widely discussed, mainstream technology essay topics.

• How do viewers respond to virtual influencers vs human influencers? Is one more effective or ethical over the other?
• Across social media platforms, when and where is mob mentality most prevalent? How do the nuances of mob mentality shift depending on the platform or topic?
• Portable devices like cell phones, laptops, and tablets have certainly made daily life easier in some ways. But how have they made daily life more difficult?
• How does access to social media affect developing brains? And what about mature brains?
• Can dating apps alter how users perceive and interact with people in real life?
• Studies have proven “doomscrolling” to negatively impact mental health—could there ever be any positive impacts?

Cryptocurrency and blockchain technology research topics

Following cryptocurrency and blockchain technology has been a rollercoaster the last few years. And since Bitcoin’s conception in 2009, cryptocurrency has consistently showed up on many lists of controversial technology topics.

• Is it ethical for celebrities or influential people to promote cryptocurrencies or cryptographic assets like NFTs ?
• What are the environmental impacts of mining cryptocurrencies? Could those impacts ever change?
• How does cryptocurrency impact financial security and financial health?
• Could the privacy cryptocurrency offers ever be worth the added security risks?
• How might cryptocurrency regulations and impacts continue to evolve?
• Created to enable cryptocurrency, blockchain has since proven useful in several other industries. What new uses could blockchain have?

Artificial intelligence technology research topics

We started 2023 with M3GAN’s box office success, and now we’re fascinated (or horrified) with ChatGPT , voice cloning , and deepfakes . While people have discussed artificial intelligence for ages, recent advances have really pushed this topic to the front of our minds. Those searching for controversial technology topics should pay close attention to this one.

• OpenAI –the company behind ChatGPT–has shown commitment to safe, moderated AI tools that they hope will provide positive benefits to society. Sam Altman, their CEO, recently testified before a US Senate He described what AI makes possible and called for more regulation in the industry. But even with companies like OpenAI displaying efforts to produce safe AI and advocating for regulations, can AI ever have a purely positive impact? Are certain pitfalls unavoidable?
• In a similar vein, can AI ever actually be ethically or safely produced? Will there always be certain risks?
• How might AI tools impact society across future generations?
• Countless movies and television shows explore the idea of AI going wrong, going back all the way to 1927’s Metropolis . What has a greater impact on public perception—representations in media or industry developments? And can public perception impact industry developments and their effectiveness?

Beauty and anti-aging technology 

Throughout human history, people in many cultures have gone to extreme lengths to capture and maintain a youthful beauty. But technology has taken the pursuit of beauty and youth to another level. For those seeking technology essay topics that are both timely and timeless, this one’s a gold mine.

• With augmented reality technology, companies like Perfect allow app users to virtually try on makeup, hair color, hair accessories, and hand or wrist accessories. Could virtual try-ons lead to a somewhat less wasteful beauty industry? What downsides should we consider?
• Users of the Perfect app can also receive virtual diagnoses for skin care issues and virtually “beautify” themselves with smoothed skin, erased blemishes, whitened teeth, brightened under-eye circles, and reshaped facial structures. How could advancements in beauty and anti-aging technology affect self-perception and mental health?
• What are the best alternatives to animal testing within the beauty and anti-aging industry?
• Is anti-aging purely a cosmetic pursuit? Could anti-aging technology provide other benefits?
• Could people actually find a “cure” to aging? And could a cure to aging lead to longer lifespans?
• How might longer human lifespans affect the Earth?

Geoengineering technology research topics

An umbrella term, geoengineering refers to large-scale technologies that can alter the earth and its climate. Typically, these types of technologies aim to combat climate change. Those searching for controversial technology topics should consider looking into this one.

• What benefits can solar geoengineering provide? Can they outweigh the severe risks?
• Compare solar geoengineering methods like mirrors in space, stratospheric aerosol injection, marine cloud brightening, and other proposed methods. How have these methods evolved? How might they continue to evolve?
• Which direct air capture methods are most sustainable?
• How can technology contribute to reforestation efforts?
• What are the best uses for biochar? And how can biochar help or harm the earth?
• Out of all the carbon geoengineering methods that exist or have been proposed, which should we focus on the most?

Creative and performing arts technology topics

While tensions often arise between artists and technology, they’ve also maintained a symbiotic relationship in many ways. It’s complicated. But of course, that’s what makes it interesting. Here’s another option for those searching for timely and timeless technology essay topics.

• How has the relationship between art and technology evolved over time?
• How has technology impacted the ways people create art? And how has technology impacted the ways people engage with art?
• Technology has made creating and viewing art widely accessible. Does this increased accessibility change the value of art? And do we value physical art more than digital art?
• Does technology complement storytelling in the performing arts? Or does technology hinder storytelling in the performing arts?
• Which current issues in the creative or performing arts could potentially be solved with technology?

Cellular agriculture technology research topics

And another route for those drawn to controversial technology topics: cellular agriculture. You’ve probably heard about popular plant-based meat options from brands like Impossible and Beyond Meat . While products made with cellular agriculture also don’t require the raising and slaughtering of livestock, they are not plant-based. Cellular agriculture allows for the production of animal-sourced foods and materials made from cultured animal cells.

• Many consumers have a proven bias against plant-based meats. Will that same bias extend to cultured meat, despite cultured meat coming from actual animal cells?
• Which issues can arise from patenting genes?
• Does the animal agriculture industry provide any benefits that cellular agriculture may have trouble replicating?
• How might products made with cellular agriculture become more affordable?
• Could cellular agriculture conflict with the notion of a “ circular bioeconomy ?” And should we strive for a circular bioeconomy? Can we create a sustainable relationship between technology, capitalism, and the environment, with or without cellular agriculture?

Transportation technology research topics

For decades, we’ve expected flying cars to carry us into a techno-utopia, where everything’s shiny, digital, and easy. We’ve heard promises of super fast trains that can zap us across the country or even across the world. We’ve imagined spring breaks on the moon, jet packs, and teleportation. Who wouldn’t love the option to go anywhere, anytime, super quickly? Transportation technology is another great option for those seeking widely discussed, mainstream technology essay topics.

• Once upon a time, Lady Gaga was set to perform in space as a promotion for Virgin Galactic . While Virgin Galactic never actually launched the iconic musician/actor, soon, they hope to launch their first commercial flight full of civilians–who paid $450,000 a pop–on a 90-minute trip into the stars. And if you think that’s pricey, SpaceX launched three businessmen into space for $55 million in April, 2022 (though with meals included, this is actually a total steal). So should we be launching people into space just for fun? What are the impacts of space tourism?
• Could technology improve the way hazardous materials get transported?
• How can the 5.9 GHz Safety Band affect drivers?
• Which might be safer: self-driving cars or self-flying airplanes?
• Compare hyperloop and maglev Which is better and why?
• Can technology improve safety for cyclists?

Gaming technology topics

A recent study involving over 2000 children found links between video game play and enhanced cognitive abilities. While many different studies have found the impacts of video games to be positive or neutral, we still don’t fully understand the impact of every type of video game on every type of brain. Regardless, most people have opinions on video gaming. So this one’s for those seeking widely discussed, mainstream, and controversial technology topics.

• Are different types or genres of video games more cognitively beneficial than others? Or are certain gaming consoles more cognitively beneficial than others?
• How do the impacts of video games differ from other types of games, such as board games or puzzles?
• What ethical challenges and safety risks come with virtual reality gaming?
• How does a player perceive reality during a virtual reality game compared to during other types of video games?
• Can neurodivergent brains benefit from video games in different ways than neurotypical brains?

Medical technology 

Advancements in healthcare have the power to change and save lives. In the last ten years, countless new medical technologies have been developed, and in the next ten years, countless more will likely emerge. Always relevant and often controversial, this final technology research topic could interest anyone.

• Which ethical issues might arise from editing genes using CRISPR-Cas9 technology? And should this technology continue to be illegal in the United States?
• How has telemedicine impacted patients and the healthcare they receive?
• Can neurotechnology devices potentially affect a user’s agency, identity, privacy, and/or cognitive liberty?
• How could the use of medical 3-D printing continue to evolve?
• Are patients more likely to skip digital therapeutics than in-person therapeutic methods? And can the increased screen-time required by digital therapeutics impact mental health

What do you do next?

Now that you’ve picked from this list of technology essay topics, you can do a deep dive and immerse yourself in new ideas, new information, and new perspectives. And of course, now that these topics have motivated you to change the world, look into the best computer science schools , the top feeders to tech and Silicon Valley , the best summer programs for STEM students , and the best biomedical engineering schools .

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Mariya holds a BFA in Creative Writing from the Pratt Institute and is currently pursuing an MFA in writing at the University of California Davis. Mariya serves as a teaching assistant in the English department at UC Davis. She previously served as an associate editor at Carve Magazine for two years, where she managed 60 fiction writers. She is the winner of the 2015 Stony Brook Fiction Prize, and her short stories have been published in Mid-American Review , Cutbank , Sonora Review , New Orleans Review , and The Collagist , among other magazines.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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Quantitative Research Questions Examples & Types

Quantitative research questions form the backbone of solid data analysis, a crucial step in understanding market trends and consumer behavior. As we delve into the art of crafting precise, measurable questions, remember: the clarity of your inquiry directly impacts the quality of your findings. It’s not just about asking; it’s about asking right. Here at Plerdy, where data-driven insights are paramount, we recognize the power of well-structured questions in revealing actionable truths. In this article, we’ll guide you through shaping questions that bring clear, objective results, ensuring your research strikes the perfect balance between depth and simplicity. Discover the keys of effective quantitative inquiry and transform your data analysis with Plerdy.

Understanding Quantitative Research Questions

Quantitative research questions are the gateway to unlocking a world of data-driven insights. Central to effective research, these questions help us quantify variables, compare groups, and establish relationships in a structured, objective manner.

Definition: At their core, quantitative research questions seek measurable, numeric answers. They are designed to collect data that can be statistically analyzed, ensuring precise, objective outcomes. This approach is ideal for studies that require definitive results rather than subjective interpretations.

Characteristics:

• Specificity: They are clear and focused, aiming at specific variables or groups.
• Measurability: These questions ensure that responses can be quantified in numerical terms.
• Objectivity: They maintain neutrality, avoiding any bias in phrasing.
• Identifying Trends: By quantifying responses, these questions help in spotting patterns and trends in data.
• Making Comparisons: They allow for the comparison of different groups or variables.
• Predicting Outcomes: They assist in forecasting future trends based on current data.

Quantitative research questions are a vital tool for researchers and analysts. They provide a structured path to gaining valuable insights, crucial for making informed decisions. Whether you’re exploring market dynamics or investigating social trends, crafting these questions with precision is key to obtaining reliable, actionable data. As we journey through the nuances of these questions, keep in mind their potential to transform your understanding of the world around us.

Types of Quantitative Research Questions

In the realm of data analysis, understanding the types of quantitative research questions is pivotal for conducting robust research. These questions are classified based on their objective, leading to distinct approaches in data collection and interpretation.

Descriptive Questions:

• Objective: These questions aim to describe characteristics or functions.
• Structure: Often begin with “What is” or “How many.”
• What is the average income of a family in a specific region?
• How many hours per week do teenagers spend on social media?

Comparative Questions:

• Objective: Designed to compare two or more groups or variables.
• Structure: Typically structured as “How does X compare to Y?”
• How does the customer satisfaction level differ between Brand A and Brand B?
• What is the difference in test scores between students who study online and those who attend traditional classes?

Relationship-based Questions:

• Objective: Explore the relationship between variables.
• Structure: Often phrased as “What is the relationship between X and Y?”
• What is the relationship between diet and heart health?
• How does exercise frequency relate to stress levels in working adults?

These types of questions are the bedrock of quantitative research, providing a clear path to analyze and interpret data. Descriptive questions lay the foundation by establishing basic facts. Comparative questions build on this by highlighting differences or similarities, while relationship-based questions delve deeper into how variables interact and influence each other.

To effectively employ these questions, researchers must be clear and precise in their phrasing, ensuring each question aligns with their specific research goals. By mastering these types, you can unlock a wealth of information and insights, critical for making informed decisions in any field. Remember that quantitative research may simplify complex data into usable knowledge.

Crafting Effective Quantitative Research Questions

Crafting effective quantitative research questions is a crucial step in any data-driven study, setting the stage for meaningful and reliable results. To ensure precision and clarity, following a structured approach is essential.

• Identifying Variables: Start by pinpointing the independent and dependent variables. The dependent variable is measured, while the independent variable is changed. For example, in a study on education, “teaching methods” could be your independent variable, and “student performance” could be the dependent variable. Understanding these variables helps in formulating a focused question.
• Question Structure: A well-structured question is clear and to the point. It directly addresses the relationship or comparison you’re investigating. Use phrases like “What impact does…,” “How does…,” or “What is the correlation between…” to structure your question. Keep it concise to avoid confusion.
• Ensuring Clarity and Precision: Avoid ambiguity. Your question should be understandable to someone outside your field. This means avoiding technical jargon and being as specific as possible about what you are investigating.

For instance:

• Unclear: How does technology affect learning?
• Clear: What is the impact of interactive digital textbooks on high school students’ math test scores?

Crafting effective quantitative research questions involves a balance of specificity, clarity, and structure. Begin by identifying your variables, then structure your question in a way that clearly conveys your investigative aim. Finally, ensure the wording is precise and free from ambiguity. This approach will not only refine your research focus but also enhance the comprehensibility and relevancy of your study, making it a valuable contribution to your field.

Real-world Examples of Quantitative Research Questions

Exploring quantitative research problems in real-world settings shows their practicality across fields. These examples not only demonstrate the diversity of these questions but also provide insight into how they drive specific, measurable outcomes.

• Education: In the educational sector, a common focus is on evaluating teaching methods and their effectiveness. An example question could be, “What is the impact of blended learning on the mathematics achievement of high school students compared to traditional teaching methods?” This question targets a specific teaching approach and measurable student performance.
• Healthcare: Healthcare research often revolves around patient outcomes and treatment efficacy. A question like, “How does a 6-week physical therapy program affect the recovery rate of post-operative knee surgery patients?” precisely addresses a treatment duration and a measurable patient outcome.
• Social Sciences: In social sciences, research questions might explore societal trends or behaviors. An example could be, “What is the correlation between social media usage and anxiety levels among young adults in urban areas?” This question is aimed at understanding the relationship between a widespread modern habit and a specific psychological condition.

Some real-world quantitative research questions on marketing strategy and social media monitoring:

Marketing Strategy Research Questions

• “How does varying the headline of an online advertisement influence its CTR?”
• “What impact does the use of different images in ads have on viewer engagement rates?”
• “Does incorporating video content in ads increase the conversion rate compared to static images?”
• “How does the integration of user testimonials in ad layouts affect viewer response rates?”
• “What effect does changing the color palette of an ad have on viewer attention span?”
• “Does the use of brighter colors in ads lead to an increased number of views and interactions?”
• “How does modifying the length and tone of ad copy influence the time users spend on the corresponding landing page?”
• “What is the effect of using direct vs. suggestive call-to-actions in ad texts on the user response rate?”
• “How does the positioning of an ad on a webpage influence the advertising cost per click?”
• “Does the placement of ads above the fold result in better engagement compared to below the fold?”
• “What is the effect of using demographic-based targeting on the total number of ad impressions?”
• “How does altering location targeting in digital ads influence the audience reach and diversity?”

Social Media Monitoring Research Questions

• “How frequently is our brand mentioned on social media platforms within a given time frame?”
• “What is the ratio of positive to negative brand mentions on social media during product launch periods?”
• “Which types of social media posts (images, videos, text) generate the highest engagement for our brand?”
• “What are the prevalent themes in user-generated content related to our brand on social platforms?”
• “How does the introduction of a new hashtag influence engagement and sharing rates on our social media channels?”
• “What impact do social media promotional campaigns have on follower growth and interaction rates?”

Advanced Ad Analysis Questions:

• “What is the click-through rate for ads with interactive elements like quizzes or polls compared to standard ads?”
• “How does the inclusion of interactive features in ads influence the time spent by users on the website?”
• “How do ad engagement rates vary during different seasons or major holidays?”
• “What impact does season-specific ad theming have on conversion rates?”
• “What is the optimal frequency for displaying retargeting ads to maximize conversions without causing ad fatigue?”
• “How does the timing of ad displays (time of day/week) affect user engagement and click rates?”

Deep Dive into Social Media Dynamics Questions:

• “What is the change in brand mentions and engagement rates after collaborating with social media influencers?”
• “How does influencer marketing affect the demographic profile of the brand’s social media followers?”
• “What is the average time spent by users on our social media pages before and after specific campaign launches?”
• “Which types of content (live videos, stories, posts) lead to the highest user interaction rates on our social media platforms?”
• “How does the engagement rate for our brand differ across various social media platforms?”
• “What are the differences in audience demographics and interaction patterns across different social media channels?”
• “What is the overall sentiment (positive, negative, neutral) expressed in user comments on our social media posts?”
• “How do product launches or service updates influence the sentiment of discussions around the brand on social media?”

Optimizing Digital Presence Questions:

• “How does social media traffic contribute to user behavior and conversion rates on the company’s website?”
• “What is the correlation between social media activity and lead generation on the company’s digital platforms?”
• “Which content strategies lead to the highest growth in followers and engagement on our social media channels?”
• “How does the frequency and type of content posted on social media influence brand perception and customer loyalty?”

These quantitative research questions are designed to provide concrete data that can help businesses refine their marketing strategies and social media presence for maximum effectiveness and engagement.

These real-world examples demonstrate the value of concise, targeted, and measurable quantitative research topics. By following this approach, researchers can effectively investigate and draw significant conclusions in their respective fields. Whether it’s understanding educational techniques, medical treatments, or societal behaviors, well-structured quantitative research questions are instrumental in uncovering valuable insights and contributing to informed decision-making.

Common Mistakes to Avoid

In the process of formulating quantitative research questions, certain common mistakes can significantly hinder the effectiveness of your study. Being aware of these pitfalls is essential for conducting meaningful research.

• Vague Wording: Ambiguity is the enemy of clarity. Questions like “How does social media influence behavior?” are too broad. Instead, specify the aspect of behavior, such as “How does social media use impact the attention span of teenagers?”
• Over-complicating Questions: Simplicity is key. Avoid convoluted questions that might confuse respondents. For instance, instead of asking “What are the various factors that affect the decision-making process of consumers purchasing technological gadgets?” simplify it to “What key factors influence consumer decisions when buying technological gadgets?”

Crafting clear, concise, and focused quantitative research questions is crucial. Avoid vague wording and over-complication. By steering clear of these common mistakes, you ensure that your research questions are robust and yield valuable, actionable data. This approach not only enhances the quality of your research but also increases its relevance and applicability to your target audience.

Quantitative research question writing is essential for gaining insights in any discipline. Through clarity, specificity, and focus, these questions become powerful tools in your analytical arsenal. Remember, the precision of your inquiry shapes the depth of your understanding. As we’ve explored various facets of quantitative questioning, the potential for data-driven decision-making becomes evident. For more insights and strategies to elevate your research, explore other articles on the Plerdy blog. Ready to dive deeper into data analytics? Plerdy offers an array of tools to enhance your digital strategy. Check out Plerdy’s solutions for your next project – a step towards transforming data into actionable insights.

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

Top 400 Information Technology Research Topics – Full Guide!

The field of IT is progressive and ever-changing due to the rapid development of hardware, software, and networking technologies. The demand for innovative research in IT has also continued to rise as businesses and organizations embrace digital systems and data-driven solutions. 

Understanding the salient areas of study in IT will help professionals keep up with changes that arise and enable organizations to leverage emerging technologies effectively. 

Cybersecurity, artificial intelligence, cloud computing , and big data analytics have emerged through IT research. These fundamental factors shape the modern technology landscape, giving rise to immense possibilities for boosting productivity, raising efficiency, and improving competitiveness across sectors. 

However, companies wanting to navigate the complexities of today’s digital age and exploit new technological advances must examine some of the latest IT research topics.

Understanding Information Technology Research

Table of Contents

In the world of technology, research is a compass that helps us navigate its convoluted evolutions. For instance, Information Technology (IT) research has been conducted in computer science, software engineering, data analytics, and cybersecurity.

IT research involves systematic inquiry to advance knowledge, problem-solving, and innovation. This includes conducting rigorous experiments and analyzing results to unveil new theories or approaches that improve technologies or bring breakthroughs.

Therefore, interdisciplinarity is at the core of IT research, with collaboration cutting across various disciplines. Whether using AI to reinforce cyber security or big data analytics in healthcare, collaboration leads to solutions to complex problems.

This is because IT research is changing rapidly due to technological advances. Thus, researchers need to be up-to-date to make meaningful contributions.

Ethics are involved so that technology can be responsibly deployed. The researchers grapple with privacy, security, bias, and equity issues to ensure technology benefits society.

As a result of this publication and conferences, which enable dissemination of findings, leading to further innovations, collaboration has supported progress, hence speeding it up.

Understanding IT research is vital for leveraging technology to address societal challenges and foster positive change.

Recommended Readings: “ Top 109+ Media Bias Research Topics | Full Guide! “.

Picking the Right Topic to Research: The Key to Finding New Things 

In the always-changing world of information technology, choosing the proper topic to research is like starting a smart path. It’s a big decision that sets where your hard work will go and how much your findings could mean.

Fitting with Industry Moves and Issues

Finding a research topic that fits current industry moves and big issues is important. By staying informed on the latest happenings and problems in the technology field, you can ensure your research stays useful and helps solve real-world troubles.

Growing Fresh Ideas and Practical Uses

Choosing a research topic that generates fresh ideas and practical applications is crucial. Your findings should not just add to school talks but also lead to real solutions that can be used in real situations, pushing technology forward and making work smoother.

Sparking Mind Curiosity and Excitement

Selecting a research topic that sparks your curiosity and excitement is essential. When you dive into an area that truly fascinates you, the research journey becomes more engaging, and your drive to uncover big insights is stronger.

Finding Gaps and Unexplored Areas

Finding gaps in existing knowledge or unexplored areas in the technology landscape can lead to big discoveries. Entering uncharted spaces can uncover fresh insights and meaningfully advance the field.

Considering Potential Wide Effect and Growth

Considering your research topic’s potential wide effect and growth is crucial. Will your findings have far-reaching effects across industries? Can your solutions grow and shift to address changing challenges? Evaluating these things can help you prioritize research areas with the greatest potential for big impact.

By carefully choosing the right research topic, you can open the door to discoveries, push technology forward, and contribute to the constant evolution of the technology information landscape.

Top 400 Information Technology Research Topics

The list of the top 400 information technology research topics is organized into different categories. Let’s examine it. 

Artificial Intelligence (AI) and Machine Learning (ML)

• Easy AI: Explaining and Using
• Group Learning: Getting Better Together
• AI in Health: Diagnosing and Helping
• Robots Learning on Their Own
• Being Fair with Computers
• Talking to Computers in Normal Language
• AI Fighting Bad Guys on the Internet
• AI Driving Cars: How Safe Is It?
• Sharing What We’ve Learned with Other Machines
• AI in Schools: Computers Learning About You

Cybersecurity and Encryption

• Trusting Computers: How to Stay Safe
• Keeping Secrets Safe with Fancy Math
• Secret Codes Computers Use: Safe or Not?
• Spy Games: Watching Out for Bad Stuff
• Keeping Secrets, Even from Friends
• Your Body as Your Password: Is It Safe?
• Fighting Against Computer Ransomers
• Keeping Your Secrets Secret, Even When Sharing
• Making Sure Your Smart Stuff Isn’t Spying on You
• Insuring Against Computer Bad Luck

Data Science and Big Data

• Sharing Secrets: How to Be Safe
• Watching the World in Real-Time
• Big Data: Big Computers Handling Big Jobs
• Making Data Pretty to Look At
• Cleaning Up Messy Data
• Predicting the Future with Numbers
• Finding Patterns in Connected Dots
• Keeping Your Secrets Safe in Big Data
• Sharing Our Secrets Without Telling Anyone
• Helping the Planet with Numbers

Cloud Computing

• Computers Without a Home: Where Do They Live?
• Keeping Computers Close to Home
• Moving Our Stuff to New Homes
• Juggling Many Clouds at Once
• Making Computers That Live in the Cloud
• Keeping Clouds Safe from Bad Guys
• Keeping Clouds Safe from Sneaky Spies
• Making Sure Clouds Do What They’re Supposed To
• Computers Need Energy Too!
• Making the Internet of Things Even Smarter

Internet of Things (IoT)

• Smart Stuff Everywhere: How Does It Work?
• Watching Out for Bad Stuff in Smart Things
• Smart Stuff: Is It Safe?
• Taking Care of Smart Toys
• Making Smart Things That Don’t Need Batteries
• Making Smart Factories Even Smarter
• Smart Cities: Making Cities Better Places to Live
• Your Clothes Can Be Smart, Too!
• Helping Farmers with Smart Farming
• Keeping Secrets Safe in Smart Stuff

Human-Computer Interaction (HCI)

• Magic Glasses: How Do They Work?
• Making Computers Easy to Use
• Making Computers for Everyone
• Talking to Computers with Your Hands
• Making Sure Computers Are Nice to People
• Talking to Computers with Your Voice
• Playing with Computers, You Can Touch
• Trusting Computers to Drive for Us
• Computers That Understand Different People
• Making Computers That Read Our Minds

Software Engineering

• Making Computers Work Together Smoothly
• Building Computers from Tiny Pieces
• Playing Games to Make Computers Better
• Making Sure Computers Work Right
• Making Old Computers New Again
• Making Computers Like to Exercise
• Making Computers Easier to Understand
• Building Computers with Blueprints
• Making Sure Computers Don’t Get Sick
• Sharing Computer Secrets with Everyone

Mobile Computing

• Keeping Phones Safe from Bad Guys
• Making Apps for Every Kind of Phone
• Keeping Phones Safe in the Cloud
• Finding Your Way with Your Phone
• Paying with Your Phone: Safe or Not?
• Checking Your Health with Your Phone
• Seeing the World Through Your Phone
• Wearing Your Phone on Your Wrist
• Learning on the Go with Your Phone
• Making Phones Even Smarter with Clouds

Networking and Communications

• Making Sure Computers Can Talk to Each Other
• Making Computers Work Together Without Wires
• Making the Internet Faster for Everyone
• Getting More Internet Addresses for More Computers
• Cutting the Internet into Pieces
• Making the Internet Even More Invisible
• Talking to Computers with Light
• Making Sure Tiny Computers Talk to Each Other
• Sending Messages Even When It’s Hard
• Making the Radio Smarter for Computers

Bioinformatics and Computational Biology

• Reading Your DNA with Computers
• Making Medicine Just for You
• Meeting the Microscopic World with Computers
• Building Computer Models of Living Things
• Finding New Medicine with Computers
• Building Computer Models of Tiny Machines
• Making Family Trees for Living Things
• Counting Germs with Computers
• Making Big Lists of Living Things
• Making Computers Think Like Brains

Quantum Computing

• Making Computers Better at Some Math Problems
• Keeping Computers Safe from Small Mistakes
• Making Computers Even Harder to Spy On
• Making Computers Learn Faster with Quantum Tricks
• Making Fake Worlds for Computers to Explore
• Building Computers from Super-Cold Stuff
• Making Computers Cold to Think Better
• Making Computers Think Like Chemists
• Making the Internet Even Safer with Computers
• Showing Off What Computers Can Do Best

Green Computing

• Saving Energy with Computers
• Using Wind and Sun to Power Computers
• Making Phones Last Longer Without Plugging In
• Making Computers Kinder to the Planet
• Recycling Old Computers to Save the Earth
• Computers That Care About Their Trash
• Saving Energy in Big Rooms Full of Computers
• Making Computers Save Energy and Work Faster
• Counting the Trash from Computers
• Making Computers Kinder to the Planet’s Air

Information Systems

• Making Computers Work Together in Big Companies
• Making Computers Remember Their Friends
• Making Computers Share What They Know
• Making Computers Smart About Money
• Making Computers Send Presents to Their Friends
• Helping Computers Make Big Decisions
• Making Government Computers Talk to Each Other
• Making Computers Count Likes and Shares
• Assisting computers to Find What You Asked For
• Assisting companies to Keep Their Friends Happy

Semantic Web and Linked Data

• Making Computers Understand Each Other Better
• Making Computers Talk About Themselves
• Making the Internet More Friendly for Computers
• Helping Computers Find What They Need
• Making Computers Smarter by Talking to Each Other
• Making Computers Friends with Different Languages
• Making Computers Understand Different Ideas
• Making Computers Think Like Us
• Making Computers Smarter About Old Stuff
• Making Computers Share Their Secrets Safely

Social Computing and Online Communities

• Making Friends on the Internet
• Getting Good Suggestions from the Internet
• Making Computers Work Together to Solve Problems
• Learning from Your Friends on the Internet
• Stopping Fake News on the Internet
• Knowing How People Feel on the Internet
• Helping Each Other on the Internet During Emergencies
• Making Sure Computers Are Nice to Everyone
• Keeping Secrets on the Internet
• Making the Internet a Better Place for Everyone

Game Development and Virtual Worlds

• Making Games That Play Fair
• Letting Computers Make Their Fun
• Making Fake Worlds for Fun
• Learning with Games
• Making the Rules for Fun
• Watching How People Play Together
• Seeing Things That Aren’t There
• Letting Lots of People Play Together
• Making the Engines for Fun
• Playing Games to Learn

E-Learning and Educational Technology

• Making Learning Easy for Everyone
• Taking Classes on the Internet
• Learning from Your Computer’s Teacher
• Learning from What Computers Know
• Learning Anywhere with Your Computer
• Making Learning Fun with Games
• Learning Without a Real Lab
• Learning with Free Stuff on the Internet
• Mixing School with Your Computer
• Making School More Fun with Your Computer

Digital Forensics and Incident Response

• Solving Computer Mysteries
• Looking for Clues in Computers
• Finding Bad Guys on the Internet
• Looking for Clues on Phones and Tablets
• Hiding Clues on Computers
• Helping When Computers Get Sick
• Solving Mysteries While the Computer Is On
• Finding Clues on Your Smart Watch
• Finding Tools for Finding Clues
• Following the Rules When Solving Mysteries

Wearable Technology and Smart Devices

• Keeping Healthy with Smart Watches
• Making Clothes That Talk to Computers
• Listening to the Earth with Your Shirt
• Wearing Glasses That Show Cool Stuff
• Making Your Home Smarter with Your Phone
• Using Your Body to Unlock Your Phone
• Helping People Move with Special Shoes
• Assisting people to See with Special Glasses
• Making Your Clothes Do More Than Keep You Warm
• Keeping Secrets Safe on Your Smart Stuff

Robotics and Automation

• Making Friends with Robots
• Letting Robots Do the Hard Work
• Robots That Work Together Like Ants
• Learning Tricks from People
• Robots That Feel Like Jelly
• Helping Doctors and Nurses with Robots
• Robots That Help Farmers Grow Food
• Making Cars Without People
• Teaching Robots to Recognize Things
• Robots That Learn from Animals

Health Informatics

• Computers That Help Doctors Keep Track of Patients
• Sharing Secrets About Your Health with Other Computers
• Seeing the Doctor on Your Computer
• Keeping Track of Your Health with Your Phone
• Making Medicine Better with Computers
• Keeping Your Health Secrets Safe with Computers
• Learning About Health with Computers
• Keeping Health Secrets Safe on the Internet
• Watching Out for Germs with Computers
• Making Sure the Doctor’s Computer Plays Nice

Geographic Information Systems (GIS)

• Watching the World Change with Computers
• Making Maps on the Internet
• Seeing the World from Very Far Away
• Finding Hidden Patterns with Computers
• Making Cities Better with Computers
• Keeping Track of the Earth with Computers
• Keeping Track of Wild Animals with Computers
• Making Maps with Everyone’s Help
• Seeing the World in 3D
• Finding Things on the Map with Your Phone

Knowledge Management

• Helping Computers Remember Things
• Making Computers Talk About What They Know
• Finding Secrets in Big Piles of Data
• Helping Companies Remember What They Know
• Sharing Secrets with Computers at Work
• Making Computers Learn from Each Other
• Making Computers Talk About Their Friends
• Making Companies Remember Their Secrets
• Keeping Track of What Companies Know

Computational Linguistics and Natural Language Processing (NLP)

• Finding Out How People Feel on the Internet
• Finding Names and Places in Stories
• Making Computers Talk to Each Other
• Making Computers Answer Questions
• Making Summaries for Busy People
• Making Computers Understand Stories
• Making Computers Understand Pictures and Sounds
• Making Computers Learn New Words
• Making Computers Remember What They Read
• Making Sure Computers Aren’t Mean to Anyone

Information Retrieval and Search Engines

• Finding Stuff on the Internet
• Getting Suggestions from the Internet
• Finding Stuff at Work
• Helping Computers Find Stuff Faster
• Making Computers Understand What You Want
• Finding Stuff on Your Phone
• Finding Stuff When You’re Moving
• Finding Stuff Near Where You Are
• Making Sure Computers Look Everywhere for What You Want

Computer Vision

• Finding Stuff in Pictures
• Cutting Up Pictures
• Watching Videos for Fun
• Learning from Lots of Pictures
• Making Pictures with Computers
• Finding Stuff That Looks Like Other Stuff
• Finding Secrets in Medical Pictures
• Finding Out If Pictures Are Real
• Looking at People’s Faces to Know Them

Quantum Information Science

• Making Computers Learn Faster with Tricks

Social Robotics

• Robots That Help People Who Have Trouble Talking
• Robots That Teach People New Things
• Making Robots Work with People
• Helping Kids Learn with Robots
• Making Sure Robots Aren’t Mean to Anyone
• Making Robots Understand How People Feel
• Making Friends with Robots from Different Places
• Making Sure Robots Respect Different Cultures
• Helping Robots Learn How to Be Nice

Cloud Robotics

• Making Robots Work Together from Far Away
• Making Robots Share Their Toys
• Making Robots Do Hard Jobs in Different Places
• Making Robots Save Energy
• Making Robots Play Together Nicely
• Making Robots Practice Being Together
• Making Sure Robots Play Fair
• Making Robots Follow the Rules

Cyber-Physical Systems (CPS)

• Making Robots Work Together with Other Things
• Keeping Robots Safe from Small Mistakes
• Keeping Factories Safe from Bad Guys
• Making Sure Robots Respect Different People
• Making Sure Robots Work Well with People
• Keeping Robots Safe from Bad Guys
• Making Sure Robots Follow the Rules

Biomedical Imaging

• Taking Pictures of Inside You with Computers
• Seeing Inside You with Computers
• Cutting Up Pictures of Inside You
• Finding Problems Inside You with Computers
• Cutting Up Pictures and Putting Them Together
• Counting Inside You with Pictures
• Making Pictures to Help Doctors
• Making Lists from Pictures Inside You
• Making Sure Pictures of You Are Safe

Remote Sensing

• Watching Earth from Far Away with Computers
• Making Pictures of Earth Change
• Taking Pictures from Very High Up
• Watching Crops Grow with Computers
• Watching Cities Grow with Computers
• Watching Earth Change with Computers
• Watching Earth from Far Away During Emergencies
• Making Computers Work Together to See Earth
• Putting Pictures of Earth Together
• Making Sure Pictures of Earth Are Safe

Cloud Gaming

• Playing Games from Far Away
• Making Games Work Faster from Far Away
• Keeping Games Safe from Bad Guys
• Making Sure Everyone Can Play Together
• Making Games Faster from Far Away
• Watching People Play Games from Far Away
• Making Sure Games Look Good from Far Away
• Watching Games Get More Popular

Augmented Reality (AR)

• Making Glasses That Show Cool Stuff
• Making Cool Stuff for Glasses to Show
• Watching Glasses Follow You
• Watching Phones Show Cool Stuff
• Making Cool Stuff to Show with Phones
• Making Places Even Better with Phones
• Making Factories Even Better with Glasses
• Making Places Even Better with Glasses
• Making Sure Glasses Don’t Scare Anyone

Virtual Reality (VR)

• Making Glasses That Show Different Worlds
• Making Glasses That Follow Your Hands
• Making Therapy Fun with Glasses
• Making Learning Fun with Glasses
• Making Glasses That Make Jobs Safer
• Making Glasses That Show Your Friends
• Making Sure Glasses Are Friendly
• Making Glasses That Make Buildings Better
• Making Sure Glasses Aren’t Scary

Digital Twins

• Making Computers That Copy the Real World
• Making People Better with Computers
• Making Flying Safer with Computers
• Making Cars Safer with Computers
• Making Energy Better with Computers
• Making Buildings Better with Computers
• Making Cities Safer with Computers
• Making Sure Computers Copy the Real World Safely
• Making Computers Follow the Rules

Edge Computing

• Making Computers Work Faster Near You
• Keeping Computers Safe Near You
• Making Computers Work with Far-Away Computers
• Making Computers Work Fast with You
• Making Computers Work Together Near You
• Making Phones Work Faster Near You
• Making Computers Work Near You
• Making Computers Work in Busy Places

Explainable AI (XAI)

• Making Computers Explain What They Do
• Making Medicine Safer with Computers
• Making Money Safer with Computers
• Making Computers Safe to Drive Cars
• Making Computers Fair to Everyone
• Making Computers Explain What They Think
• Making Computers Easy to Understand

Blockchain and Distributed Ledger Technology (DLT)

• Making Secret Codes Computers Use
• Making Contracts Computers Can Understand
• Making Computers Share Secrets Safely
• Making Money Safe with Computers
• Making Computers Work Together Nicely
• Making Computers Keep Secrets Safe
• Making Computers Work Together Fairly
• Making Stuff Move Safely with Computers

Quantum Communication

• Making Computers Talk to Each Other Safely
• Making Computers Talk to Each Other from Far Away
• Making Computers Talk to Each Other in Secret
• Making Money Move Safely with Computers

This list covers a broad spectrum of topics within Information Technology, ranging from foundational concepts to cutting-edge research areas. Feel free to choose any topic that aligns with your interests and expertise for further exploration and study!

Emerging Trends in Information Technology Research

In the rapidly changing world of Computer Studies, keeping up with the latest trends is indispensable. Technology keeps changing, and so does research in computer studies. From awesome things like clever robots to how we can safeguard our online information, computer studies research is always discovering new ways to improve our lives. Therefore, let us delve into some of the most exciting new trends shaping computer studies’ future.

• Smart Computers:

Right now, smart computers are a hot item. They can learn from experience, recognize patterns, and even understand language like humans do. This helps in many areas, such as healthcare or finance. So researchers are working on making smart computers smarter yet so that they can make decisions alone and be fair to everyone.

• Fast Computing:

As more devices connect to the Internet, we need ways to process information quickly. Fast computing helps bring processing power closer to where the information comes from, making things quicker and more efficient. Thus, researchers have been figuring out how to improve fast computing, especially for analyzing real-time data.

• Keeping Things Safe:

With all the cool tech around, keeping our information safe from bad guys is important. We must develop methods to safeguard our data and networks from cyber attackers. In addition, they have also been considering how to ensure the privacy of our personal information so that only authorized individuals can access it.

• Fancy Computers:

The next big thing in computing is quantum computers. They can do calculations at a high speed that ordinary ones cannot. Researchers are working hard to achieve quantum computing because it could be useful in cracking codes and creating new drugs.

• New Ways of Doing Things Together:

Blockchain is an exciting technology that allows us to collaborate without a central authority. Its use in cryptocurrencies is quite popular but it has other applications too. Blockchain can be applied for purposes such as helping us discover where products come from, proving who we are on the internet, and making contracts that cannot be changed later on.

• Virtual Reality Adventures:

Entering a completely different world is what Virtual Reality (VR) and Augmented Reality (AR) do. The feeling of being in reality is what these two technologies create, which is not real. These researchers are working hard on making VRs and ARs better so that they can be used for learning, training, and amusement in more innovative ways.

In summary, computer studies research keeps changing with new trends such as smart computers, rapid computing, cybersecurity issues, high-end computers, collaboration platforms and immersive games or virtual reality escapades. 

By exploring these trends and developing new ideas, researchers ensure that technology keeps improving and making our lives easier and more exciting.

How can I brainstorm research topics in information technology?

Start by identifying your areas of interest and exploring recent advancements in the field. Consider consulting with mentors or peers for suggestions and feedback.

What are some ethical considerations in AI research?

Ethical considerations in AI research include fairness, transparency, accountability, and privacy. Researchers should ensure their algorithms and models do not perpetuate bias or harm individuals.

How can I stay updated on emerging trends in IT research?

Follow reputable journals, conferences, and online forums dedicated to information technology. Engage with the academic community through discussions and networking events.

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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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• 33 Technology Survey Questions + [Template Examples]

To keep up with technology, you need to monitor trends and patterns among users in specific contexts constantly. Whether in the workplace or school, a technology survey is an effective way to determine what stakeholders think about using technology. 

The right technology survey questions help you discover insights and establish patterns around users’ behaviors and technology perceptions. With this data, you can accurately measure people’s skills, identify any technological knowledge gaps and take time to plan for improvements. 

What is a Technology Survey? 

A technological survey is a method of gathering insights on software, tools, and relevant tech skills in a particular context. Technology surveys have a broad range—from being used to find out how tech-savvy your target audience is to conducting market research and collecting product feedback. 

For instance, a school can conduct a technology survey to determine how much students know about the internet or how well students use the internet for learning. The data gathered through this process can help the students improve their knowledge as needed. 

Types of Technology Survey 

• Student Technology Survey

A student technology survey is a research-based method of discovering your students’ knowledge and perception of technology. It typically outlines relevant survey questions that allow the students to share their experiences with technology within and outside your school. With a student technology survey, you can understand how to use technology more effectively to benefit students.

Technology is a vital part of 21st-century learning. By conducting a student technology survey, the school’s administration and stakeholders can have more precise knowledge about students’ attitudes towards technology and how technology can effectively improve learning. 

For instance, before introducing 100% online learning to your school, you need to administer a student technology survey to know what the students think about this idea. The survey would help you to answer questions like: Do the students love online learning? Do they have prior experience with online learning? What challenges have they faced in the past with online learning? 

The responses to these survey questions will guide you on the best way to execute this idea. If your students think online learning is exciting, you can introduce it right away. However, if they have some reservations, you need to handle them first before introducing online learning. 

Student Technology Survey Question Samples

1. Do you have any previous experience with online learning?

2. What are your thoughts about online learning? 

3. On a scale of 1–5, how would you rate the school’s preparedness for online learning? 

4. Outline 3 things you like about online learning.

5. What three things do you dislike about online learning? 

4. Do you own an internet-enabled device? 

5. How comfortable are you with sourcing relevant information on the internet? 

• Very comfortable
• Somewhat comfortable
• Uncomfortable

6. Would you need any help with adapting to online learning? 

7. How can we make your online learning experience better? 

8. How often do you use a computer away from school? 

• I don’t use a computer after school hours.

9. Have you ever used the internet to complete a school task? 

• Parent Technology Survey 

Parent technology survey is a data-collection process that helps you determine what parents think about using technology in your school. Parent technology survey also allows the parents to assess submitting technology in their teaching and learning methods. 

Like other types of parent surveys, this research method aims to understand the parents’ opinions, points of view, and attitudes towards technology as it affects their children in the school. It plays an essential part in aligning your school’s goals with the expectations of the parents. 

Let’s go back to our online learning example. Even if the students are excited and ready for online learning, you also need their parents to understand and support your new initiative. After conducting the student technology survey, you need to find out what parents think about the subject. Administering a parent technology survey with open-ended and Likert questions will help you gather real-time insights from the parents for better decision-making. 

Parent Technology Survey Question Samples

1. What do you think about technology?

2. On a scale of 1–10, rate the importance of technology in learning?

3. I encourage my children to use technology for their school work.

• Strongly agree
• Neither agree nor disagree
• Strongly disagree

4. The school provides adequate access to technological tools for learning.

5. How would you rate your child’s computer skills?

• I prefer not to say

6. How often does your child use the internet for assignments? 

• Once in a while

7. Does your ward have any experience with online learning?

8. How can we improve the use of technology in the school? 

9. Do you support the use of gadgets during learning? (Phones, laptops) 

9. Please suggest three ways to improve the online learning experience.

10. Does your child have access to the internet at home? 

• Teacher Technology Survey

A teacher technology survey allows you to discover how proficient your teachers are with technology. Here, you can get first-hand information on your teachers’ technological skills and how well they use different tools and software. 

Teacher technology surveys are used for teacher assessments. From the responses given, you can identify any knowledge gaps and plan for training programs and workshops that address the challenges teachers face with technology. 

A teacher technology survey is also an effective way to gather feedback on your school’s existing tech-driven methods. You can ask your teachers to rate students’ performances before and after the teaching method was introduced and to also highlight areas needing improvement. 

Teacher Technology Survey Question Samples

1. How familiar are you with online teaching methods? 

• Very familiar
• Somewhat familiar
• Neither familiar nor unfamiliar
• Very unfamiliar

2. What online teaching tools are you familiar with? Choose all the options that apply. 

• Microsoft Teams
• Google Classroom

3. What tools do you use for educational assessment in the classroom? Choose all that applies.

• Google Docs
• Khan Academy

4. How would you rate your proficiency in technology? 

5. How would you rate our school’s use of technology in learning and teaching?

6. List three specific areas we need to improve in the use of technology for teaching.

7. Do you use any online tools to plan your lessons?

8. On a scale of 1–10, how integrated is your teaching process with technology?

9. List 3 challenges you have faced with the use of technology in the classroom. 

10. List three new tech skills you are looking to learn this year. 

11. How can we support your use of technology in the classroom? 

• Employee Technology Survey   

Also known as a staff technology survey, this method collects valuable data on employees’ perceptions and knowledge of different workplace software and tech tools. Employee technology survey also helps the organization’s management assess the staff’s proficiency levels and understand how they use technology to execute tasks faster. 

This survey method provides in-depth primary research insights into workplace technologies. It helps you to discover any knowledge gaps and define strategies to speed up the adoption of relevant industry technology in your organization. 

Employee Technology Survey Question Samples

1. To what extent has tech made your work more efficient? 

• To a large extent
• Tech has not made my work more efficient

2. Which tech tools do you use most frequently in your job? 

3. The organization uses technology to achieve its objectives.

4. What tools are complex for you to use? 

5. Please suggest three ways to improve the use of technology in the workplace.

6. Do you keep up with technological trends and changes related to your job? 

7. What are your top 3 tech skills? 

8. Please mention three ways we can use technology to improve our organization’s process. 

9. How often do you take courses on workplace technology?

• Somewhat often

10. How have you used technology to improve communication within your team?

11. How have you used technology to improve feedback within your team? 

Importance of Technology Survey

• A technology survey is a valid method of collecting first-hand feedback from different stakeholders on their technological skills and competencies as required.
• It provides verifiable data for decision-making. 
• It is a great way to discover the specific roles technology plays in your school or organization and how it helps you achieve specific outcomes. 
• Technology survey helps you learn about different perceptions and opinions about using technology for teaching and learning or in the workplace. 
• It is an affordable and time-efficient method of gathering feedback from stakeholders. For instance, instead of organizing a PTA meeting, you can administer a parent technology survey to determine what parents think about using technology for learning. 
• Technology survey is a proven method of getting everyone involved in relevant decision-making processes for changes that affect them. 
• Typically, it is easier to organize and analyze the data gathered from surveys than interpreting usage data.  

How to Conduct Technology Surveys with Formplus

You can create different types of technology surveys on Formplus in no time with our drag-and-drop form builder. Formplus has more than 30 features that you can access to create beautiful and functional forms for technology surveys. 

Follow these steps to create and administer your first Formplus technology survey. 

1. Visit the Formplus website to sign up for a new account if you don’t have one. Fill in the requested information and follow the prompts to create your free account easily. 

2. Sign into your account and click on the “create new form” button to access the form builder immediately.

3. On the far left of the builder, you will find the form fields section. Here, there are more than 30 field options that you can drag and drop into your survey. 

4. After adding your preferred fields, click on the pencil icon beside each field to edit it. You can add your questions and answer options to each field in the form edit tab. 

5. Click on the save icon to save all the changes you have made to the survey. This also takes you to the builder’s customization section automatically. 

6. In the form customization section, you can change your form’s look and feel without writing a single line of code. Choose your preferred form theme, choose another font and change the font size, among other things, as you want. 

7. Copy the form link and share it with respondents. You can also send out email invitations to respondents. 

8. Track the responses and other data in our form analytics dashboard. 

Conclusion  

In this article, we have examined the different types of technology surveys based on various contexts. Technology surveys help individuals and organizations to keep up with the changing trends and align with the best industry practices. 

To get valuable insights, you must ask good technology survey questions. A good technology survey question is easy to understand and asks the respondents for one piece of information at a time. You should avoid vague questions and other bad survey questions that can be misleading. 

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How to Write Quantitative Research Questions: Types With Examples

For research to be effective, it becomes crucial to properly formulate the quantitative research questions in a correct way. Otherwise, you will not get the answers you were looking for.

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

About the author

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

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100+ Top Technology Research Topics for Students

When pursuing their studies, learners are required to write papers and essays on technology research topics. This is a major academic task that influences the final grade that learners graduate with. But, the grades that students score are largely dependent on the technology topics that they opt to write about. Technology is generally a broad study field. As such, choosing research topics on technology is not always easy. If struggling to choose a good technology research topic for your academic paper or essay, here are some of the best ideas to consider.

Trendy Technology Research Topics

Perhaps, you need a prominent research topic about technology. In that case, you should consider prominent technology research paper topics. Here are some of the most trendy topics about technology to consider.

• Technology use in education (here is our list of 110 topics in education research )
• Space and technology studies (check out our top 30 space research topics )
• Current and stunning developments in technology
• Shocking inventions in modern technology that most people don’t know yet
• What technologies can be considered harmful and destructive?
• How does technology affect people’s values and health?
• Can humans be replaced by robots completely in the workplace?
• How have different countries contributed to modern technology developments
• Transport safety and technology
• Discuss the scope of the use of nanotechnologies
• Discuss the use of technology in medicine
• Which technologies can influence human mental health?
• Discuss how technology is changing human life
• What are the positive effects of technologies on personal safety?
• How does technology affect personal safety negatively?
• Discuss how modern technology facilitates the improvement of educational processes
• How do modern technologies influence users’ mental health?
• Why are robots likely to replace humans in the workplace?
• How has technology influenced space travel?
• Is food preservation technology safe?

This category also includes some of the most controversial technology topics. Nevertheless, each topic should be researched extensively before writing a paper or an essay.

Interesting Information Technology Topics

If pursuing a college or university program in information technology, this category has some of the best options for you. Here are some of the best information technology research topics to consider.

• How useful is unlimited data storage?
• How can humans manage large amounts of information?
• How blurred is the line between the human brain and a computer?
• Is entertainment technology something good or bad?
• Discuss the differences between digital reading and print reading
• How does Google impact the attention span of young people?
• How important are traditional research skills in the current era of advanced information technologies?
• How credible is the information provided by different platforms on the internet?
• Do blogs and books compare?
• Should schools and guardians encourage or discourage the use of media by children?
• Does Google provide the best information when it prefers its specific brands?
• Are humans losing the intelligence developed via conventional reading and research in the current digital age?
• How important is learning to how use social media, iPads, and Smart Boards?
• Should modern technologies be incorporated into teaching?
• How has Google search changed humans?
• How is intelligence gauged by humans?
• Is online information format making the readers skim rather than digest information?
• Is the ease of finding information on the internet something bad or good?
• Is technology changing how people read?
• Can using information technology make you smarter?

Students have many information technology research paper topics to choose from. However, select a topic that you find interesting to research and write about.

Interesting Science and Technology Topics

Are you looking for a science and technology-related topics? If yes, consider topics in this category. Here are some of the most interesting topic ideas in science and technology.

• Discuss the greatest technological and scientific breakthroughs of the 21st century
• How significant is number 0 in science and technology?
• How important is the first black hole image?
• Discuss the unlimited fractals’ perimeter despite their limited area
• How can a person perform mental calculations rapidly?
• Discuss the fourth dimension
• Discuss the math behind the Draft lottery by the NBA
• Differentiate non-parametric and parametric statistics
• Discuss the concept of something being random or impossible to prove mathematically
• Discuss some of the greatest modern age mathematicians
• How are the latest automobile technology improvements protecting the environment?
• Why are Smartphones resistant to viruses and bugs in comparison to computers?
• Discuss the Internet of Things story
• What made vector graphics mainstream and not pixels?
• Discuss the latest technology advances that relate to medicine
• Describe Molten Salt Nuclear Reactors
• Is it possible to power everything with solar energy?
• Explain why smart electronics get slower with time
• Differentiate closed and open systems in technology
• Discuss the process of converting old recordings into new formats

This category has amazing topics on technology and science. Select an idea that you find interesting to research and write a paper or essay about.

The Best Computer Technology Topics

If you’re pursuing a program on computer technologies, you will find educational technology topics in this category very interesting. Here are some of the best topics for technology and computers to consider.

• How can you describe the Machine Learning future?
• Discuss computer science that will be the most important in the future
• Discuss how big data and bioinformatics change biology
• What is the borderline for hardware and software in cloud computing?
• How moving everything to the cloud affects human life?
• Can robots become more intelligent and like people with reinforced learning?
• How can computer programmers enhance device protection with open-source getting trendier?
• Is Google becoming the first machine learning firm?
• Explain machine learning in detail
• Discuss the importance of machine learning
• Which sectors does machine learning affect the most?
• How will virtualization change the entertainment industry?
• Describe virtualization
• Can virtual reality be something bad or good?
• How will virtual reality change education?
• What can humans expect from the internet?
• What improvements can be made on the internet?
• How are robots changing the health sector?
• Are humans yet to invent any computer language?
• What will happen if most tasks that are currently done by humans are taken over by computers?

These are great technology essay topics to consider if pursuing a computer technology program in college or university. They can also be great technology debate topics. Nevertheless, extensive research is required when writing about any of these technology essay topics.

Controversial Topics in Technology for Research Papers and Essays

Are you looking for interesting technology topics that your audience will love to read about? If yes, consider one of these technology controversy topics to research and write about.

• Do law enforcement cameras invade privacy?
• Does the technology age turn humans into zombies?
• Has technology advancement led to a throw-away society?
• How has cloud technology changed data storage?
• How have Smartphones reduced live communication?
• Our modern technologies changing teaching?
• How does the use of IT by construction companies lead to under-spending and recession?
• Discuss the technologies used by NASA to explore Mars
• How dangerous are cell phones?
• How does media technology affect child development?
• Is the use of technology in planning lessons good or bad?
• How does technology influence the educational system?
• Discuss the application of green technologies in engineering, architecture, and construction
• Can modern technologies like cryptocurrencies help in identity theft prevention?
• How can technology be used to enhance energy efficiency?
• How are self-driving cars likely to change human life?
• How did Steve Jobs and Bill Gates change the world with technology
• What is the impact of drone warfare on humans?
• Can the actual reality be substituted by virtual reality?
• Discuss the use of technologies and smart materials in road building

If looking for hot topics in technology, consider some of the ideas in this category. Nevertheless, you can also find technology persuasive speech topics here. That’s because this category has some of the most debatable topics. If you still don’t find a great idea from this list, consider technology security topics or contact our thesis writers . Remember that extensive research is required to write a great paper or essay regardless of the topic that you opt to write about.

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What Are Quantitative Survey Questions? Types and Examples

Table of contents: 

• Types of quantitative survey questions - with examples 
• Quantitative question formats
• How to write quantitative survey questions 
• Examples of quantitative survey questions 

Leveraging quantilope for your quantitative survey 

In a quantitative research study brands will gather numeric data for most of their questions through formats like numerical scale questions or ranking questions. However, brands can also include some non-quantitative questions throughout their quantitative study - like open-ended questions, where respondents will type in their own feedback to a question prompt. Even so, open-ended answers can be numerically coded to sift through feedback easily (e.g. anyone who writes in 'Pepsi' in a soda study would be assigned the number '1', to look at Pepsi feedback as a whole).  One of the biggest benefits of using a quantitative research approach is that insights around a research topic can undergo statistical analysis; the same can’t be said for qualitative data like focus group feedback or interviews. Another major difference between quantitative and qualitative research methods is that quantitative surveys require respondents to choose from a limited number of choices in a close-ended question - generating clear, actionable takeaways. However, these distinct quantitative takeaways often pair well with freeform qualitative responses - making quant and qual a great team to use together.  The rest of this article focuses on quantitative research, taking a closer look at quantitative survey question types and question formats/layouts. 

Back to table of contents 

Types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions - with examples 

Quantitative questions come in many forms, each with different benefits depending on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139784">your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives. Below we’ll explore some of these dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139785">survey question dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139785" data-dropdown-placement-param="top" data-term-id="281139785"> types, which are commonly used together in a single survey to keep things interesting for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . The style of questioning used during dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139739">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139750">data dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139750" data-dropdown-placement-param="top" data-term-id="281139750"> collection is important, as a good mix of the right types of questions will deliver rich data, limit dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent fatigue, and optimize the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139757">response rate . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">Questionnaires should be enjoyable - and varying the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755">types of dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139755" data-dropdown-placement-param="top" data-term-id="281139755">quantitative research dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139755"> questions used throughout your survey will help achieve that. 

Descriptive survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139763">Descriptive research questions (also known as usage and attitude, or, U&A questions) seek a general indication or prediction about how a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139773">group of people behaves or will behave, how that group is characterized, or how a group thinks.

For example, a business might want to know what portion of adult men shave, and how often they do so. To find this out, they will survey men (the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience ) and ask descriptive questions about their frequency of shaving (e.g. daily, a few times a week, once per week, and so on.) Each of these frequencies get assigned a numerical ‘code’ so that it’s simple to chart and analyze the data later on; daily might be assigned ‘5’, a few times a week might be assigned ‘4’, and so on. That way, brands can create charts using the ‘top two’ and ‘bottom two’ values in a descriptive question to view these metrics side by side.

Another business might want to know how important local transit issues are to residents, so dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions will allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to indicate the degrees of opinion attached to various transit issues. Perhaps the transit business running this survey would use a sliding numeric scale to see how important a particular issue is.

Comparative survey questions

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139782">Comparative research questions are concerned with comparing individuals or groups of people based on one or more variables. These questions might be posed when a business wants to find out which segment of its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience might be more profitable, or which types of products might appeal to different sets of consumers.

For example, a business might want to know how the popularity of its chocolate bars is spread out across its entire customer base (i.e. do women prefer a certain flavor? Are children drawn to candy bars by certain packaging attributes? etc.). Questions in this case will be designed to profile and ‘compare’ segments of the market.

Other businesses might be looking to compare coffee consumption among older and younger consumers (i.e. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic segments), the difference in smartphone usage between younger men and women, or how women from different regions differ in their approach to skincare.

Relationship-based survey questions

As the name suggests, relationship-based survey questions are concerned with the relationship between two or more variables within one or more dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic groups. This might be a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link between one thing and the other - for example, the consumption of caffeine and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents ’ reported energy levels throughout the day. In this case, a coffee or energy drink brand might be interested in how energy levels differ between those who drink their caffeinated line of beverages and those who drink decaf/non-caffeinated beverages.

Alternatively, it might be a case of two or more factors co-existing, without there necessarily being a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139759">causal link - for example, a particular type of air freshener being more popular amongst a certain dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139741">demographic (maybe one that is controlled wirelessly via Bluetooth is more popular among younger homeowners than one that’s plugged into the wall with no controls). Knowing that millennials favor air fresheners which have options for swapping out scents and setting up schedules would be valuable information for new product development.

Advanced method survey questions

Aside from descriptive, comparative, and relationship-based survey questions, brands can opt to include advanced methodologies in their quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire for richer depth. Though advanced methods are more complex in terms of the insights output, quantilope’s Consumer Intelligence Platform automates the setup and analysis of these methods so that researchers of any background or skillset can leverage them with ease.

With quantilope’s pre-programmed suite of 12 advanced methodologies , including MaxDiff , TURF , Implicit , and more, users can drag and drop any of these into a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire and customize for their own dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research objectives.

For example, consider a beverage company that’s looking to expand its flavor profiles. This brand would benefit from a MaxDiff which forces dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to make tradeoff decisions between a set of flavors. A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent might say that coconut is their most-preferred flavor, and lime their least (when in a consideration set with strawberry), yet later on in the MaxDiff that same dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent may say Strawberry is their most-preferred flavor (over black cherry and kiwi). While this is just one example of an advanced method, instantly you can see how much richer and more actionable these quantitative metrics become compared to a standard usage and attitude question .

Advanced methods can be used alongside descriptive, comparison, or relationship questions to add a new layer of context wherever a business sees fit. Back to table of contents 

Quantitative question formats  

So we’ve covered the kinds of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative research questions you might want to answer using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research , but how do these translate into the actual format of questions that you might include on your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire ?

Thinking ahead to your reporting process during your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139742">questionnaire setup is actually quite important, as the available chart types differ among the types of questions asked; some question data is compatible with bar chart displays, others pie charts, others in trended line graphs, etc. Also consider how well the questions you’re asking will translate onto different devices that your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents might be using to complete the survey (mobile, PC, or tablet).

Single Select questions

Single select questions are the simplest form of quantitative questioning, as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents are asked to choose just one answer from a list of items, which tend to be ‘either/or’, ‘yes/no’, or ‘true/false’ questions. These questions are useful when you need to get a clear answer without any qualifying nuances.

Multi-select questions

Multi-select questions (aka, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139767">multiple choice ) offer more flexibility for responses, allowing for a number of responses on a single question. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents can be asked to ‘check all that apply’ or a cap can be applied (e.g. ‘select up to 3 choices’).

For example:

Aside from asking text-based questions like the above examples, a brand could also use a single or multi-select question to ask respondents to select the image they prefer more (like different iterations of a logo design, packaging options, branding colors, etc.). 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139766">scale dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139766" data-dropdown-placement-param="top" data-term-id="281139766"> questions

A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139749">Likert scale   is widely used as a convenient and easy-to-interpret rating method. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents find it easy to indicate their degree of feelings by selecting the response they most identify with.

Slider scales

Slider scales are another good interactive way of formatting questions. They allow dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to customize their level of feeling about a question, with a bit more variance and nuance allowed than a numeric scale:

One particularly common use of a slider scale in a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139770">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139770" data-dropdown-placement-param="top" data-term-id="281139770"> study is known as a NPS (Net Promoter Score) - a way to measure dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139775">customer experience and loyalty . A 0-10 scale is used to ask customers how likely they are to recommend a brand’s product or services to others. The NPS score is calculated by subtracting the percentage of ‘detractors’ (those who respond with a 0-6) from the percentage of promoters (those who respond with a 9-10). dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">Respondents who select 7-8 are known as ‘passives’.

For example: 

Drag and drop questions

Drag-and-drop question formats are a more ‘gamified’ approach to survey capture as they ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to do more than simply check boxes or slide a scale. Drag-and-drop question formats are great for ranking exercises - asking dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents to place answer options in a certain order by dragging with their mouse. For example, you could ask survey takers to put pizza toppings in order of preference by dragging options from a list of possible answers to a box displaying their personal preferences:

Matrix questions

Matrix   questions are a great way to consolidate a number of questions that ask for the same type of response (e.g. single select yes/no, true/false, or multi-select lists). They are mutually beneficial - making a survey look less daunting for the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondent , and easier for a brand to set up than asking multiple separate questions.

Items in a matrix question are presented one by one, as respondents cycle through the pages selecting one answer for each coffee flavor shown. 

While the above example shows a single-matrix question - meaning a respondent can only select one answer per element (in this case, coffee flavors), a matrix setup can also be used for multiple-choice questions - allowing respondents to choose multiple answers per element shown, or for rating questions - allowing respondents to assign a rating (e.g. 1-5) for a list of elements at once.  Back to table of contents 

How to write dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

We’ve reviewed the types of questions you might ask in a quantitative survey, and how you might format those questions, but now for the actual crafting of the content.

When considering which questions to include in your survey, you’ll first want to establish what your research goals are and how these relate to your business goals. For example, thinking about the three types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions explained above - descriptive, comparative, and relationship-based - which type (or which combination) will best meet your research needs? The questions you ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents may be phrased in similar ways no matter what kind of layout you leverage, but you should have a good idea of how you’ll want to analyze the results as that will make it much easier to correctly set up your survey.

Quantitative questions tend to start with words like ‘how much,’ ‘how often,’ ‘to what degree,’ ‘what do you think of,’ ‘which of the following’ - anything that establishes what consumers do or think and that can be assigned a numerical code or value. Be sure to also include ‘other’ or ‘none of the above’ options in your quant questions, accommodating those who don’t feel the pre-set answers reflect their true opinion. As mentioned earlier, you can always include a small number of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139748">open-ended questions in your quant survey to account for any ideas or expanded feedback that the pre-coded questions don’t (or can’t) cover. Back to table of contents 

Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">quantitative survey questions  

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139745">Quantitative survey questions impose limits on the answers that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents can choose from, and this is a good thing when it comes to measuring consumer opinions on a large scale and comparing across dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents . A large volume of freeform, open-ended answers is interesting when looking for themes from qualitative studies, but impractical to wade through when dealing with a large dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139756">sample size , and impossible to subject to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139774">statistical analysis .

For example, a quantitative survey might aim to establish consumers' smartphone habits. This could include their frequency of buying a new smartphone, the considerations that drive purchase, which features they use their phone for, and how much they like their smartphone.

Some examples of quantitative survey questions relating to these habits would be:

Q. How often do you buy a new smartphone?

[single select question]

More than once per year

Every 1-2 years

Every 3-5 years

Every 6+ years

Q. Thinking about when you buy a smartphone, please rank the following factors in order of importance:

[drag and drop ranking question]

screen size

storage capacity

Q. How often do you use the following features on your smartphone?

[matrix question]

Q. How do you feel about your current smartphone?

[sliding scale]

I love it <-------> I hate it

Answers from these above questions, and others within the survey, would be analyzed to paint a picture of smartphone usage and attitude trends across a population and its sub-groups. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">Qualitative research might then be carried out to explore those findings further - for example, people’s detailed attitudes towards their smartphones, how they feel about the amount of time they spend on it, and how features could be improved. Back to table of contents 

quantilope’s Consumer Intelligence Platform specializes in automated, advanced survey insights so that researchers of any skill level can benefit from quick, high-quality consumer insights. With 12 advanced methods to choose from and a wide variety of quantitative question formats, quantilope is your one-stop-shop for all things dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139740">market research (including its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139776">in-depth dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139738">qualitative research solution - inColor ).

When it comes to building your survey, you decide how you want to go about it. You can start with a blank slate and drop questions into your survey from a pre-programmed list, or you can get a head start with a survey dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139765">template for a particular business use case (like concept testing ) and customize from there. Once your survey is ready to launch, simply specify your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139743">target audience , connect any panel (quantilope is panel agnostic), and watch as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139737">respondents dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139783">answer questions in your survey in real-time by monitoring the fieldwork section of your project. AI-driven dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139764">data analysis takes the raw data and converts it into actionable findings so you never have to worry about manual calculations or statistical testing.

Whether you want to run your quantitative study entirely on your own or with the help of a classically trained research team member, the choice is yours on quantilope’s platform. For more information on how quantilope can help with your next dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139736">quantitative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281139768">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281139768" data-dropdown-placement-param="top" data-term-id="281139768"> project , get in touch below!

Get in touch to learn more about quantitative research with quantilope!

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Artificial Intelligence vs. Human Coaches: A Mixed Methods Randomized Controlled Experiment on Client Experiences and Outcomes

Barger, Amber

The rise of artificial intelligence (AI) challenges us to explore whether human-to-human relationships can extend to AI, potentially reshaping the future of coaching. The purpose of this study was to examine client perceptions of being coached by a simulated AI coach, who was embodied as a vocally conversational live-motion avatar, compared to client perceptions of a human coach. It explored if and how client ratings of coaching process measures and outcome measures aligned between the two coach treatments. In this mixed methods randomized controlled trial (RCT), 81 graduate students enrolled in the study and identified a personally relevant goal to pursue. The study deployed an alternative-treatments between-subjects design, with one-third of participants receiving coaching from simulated AI coaches, another third engaging with seasoned human coaches, and the rest forming the control group. Both treatment groups had one 60-minute session guided by the CLEAR (contract, listen, explore, action, review) coaching model to support each person to gain clarity about their goal and identify specific behaviors that could help each make progress towards their goal. Quantitative data were captured through three surveys and qualitative input was captured through open-ended survey questions and 27 debrief interviews. The study utilized a Wizard of Oz technique from human-computer interaction research, ingeniously designed to sidestep the rapid obsolescence of technology by simulating an advanced AI coaching experience where participants unknowingly interacted with professional human coaches, enabling the assessment of responses to AI coaching in the absence of fully developed autonomous AI systems. The aim was to glean insights into client reactions to a future, fully autonomous AI with the expert capabilities of a human coach. Contrary to expectations from previous literature, participants did not rate professional human coaches higher than simulated AI coaches in terms of working alliance, session value, or outcomes, which included self-rated competence and goal achievement. In fact, both coached groups made significant progress compared to the control group, with participants convincingly engaging with their respective coaches, as confirmed by a novel believability index. The findings challenge prevailing assumptions about human uniqueness in relation to technology. The rapid advancement of AI suggests a revolutionary shift in coaching, where AI could take on a central and surprisingly effective role, redefining what we thought only human coaches could do and reshaping their role in the age of AI.

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This item is currently under embargo. It will be available starting 2029-05-14.

More About This Work

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Five key questions to get a tech transformation right

Why do some technology transformations 1 In the survey and in this article, we define technology transformations as large-scale change efforts that are more comprehensive than short-term improvement programs. succeed while others fail? This is a perennial question that has frustrated IT departments for years, presenting both a clear-cut opportunity for growth and the risk of falling further behind. Our latest McKinsey Global Survey  of technology and business leaders 2 The online survey was in the field from April 11 to April 22, 2022, and garnered responses from 468 participants, including responses from mostly C-suite and senior managers in a variety of industries. The participants represent the full range of regions, industries, company sizes, and tenures. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. set out to better understand the keys to transformation success. We identified respondents working for the most financially successful organizations (whom we refer to as “top performers” 3 Top performers are defined as organizations that, according to respondents, have seen an average growth rate of at least 10 percent in both revenue and EBIT over the past three years. Of the 468 respondents in the survey sample, 84 qualified as top performers. ), whose results suggest that they reap greater benefits than others from their tech transformations.

According to the survey, these top performers pursue transformations differently from their peers in a few critical ways: they are more likely than others to anchor their technology and digital strategies in their overall business strategies, they report greater effectiveness at building environments that foster organizational change, they are more likely to invest in foundational technologies, and they are better at retaining their tech talent. What’s more, the top performers are more likely than others to combine their initiatives—a set of eight “plays” that comprise our Tech: Forward approach to technology transformations —in ways that maximize business value.

All told, we see their methods as a foundation for successful tech transformations—and the source of five critical questions that every organization should ask itself in order to increase its chances of achieving similar success.

Top financial performers are more likely than others to anchor their tech transformations in strategic business priorities, to build environments that foster change, to invest in foundational technologies, and to retain tech talent.

1. What value is IT delivering?

Most respondents with experience in tech transformations continue to report meaningful benefits from their efforts, as we have seen in previous years . For example, at least 60 percent of all respondents credit their transformations with improving performance on a range of metrics.

Yet the top performers seem to have positioned themselves even better than others in terms of maximizing a transformation’s impact, especially to spur business growth (Exhibit 1). These respondents report a stronger financial return from their investment in tech transformations (87 percent of top performers say they have seen a positive impact on generating new revenue streams, compared with 58 percent of everyone else), as well as greater impact on all other metrics we tested.

How did they do it? One reason for their success appears to be that top performers tend to anchor their technology and digital strategy in their overall business strategy. In our survey, respondents from these companies are 14 percent more likely than others to say they develop their business and technology and digital strategies in tandem, which can enhance the business value they reap from tech transformations.

Axel Schell biography

Earned a PhD in business administration and economics, an MBA, and a bachelor’s degree in information systems from the University of Augsburg

Career highlights

Allianz Technology (2021–present) Chief technology and transformation officer

Allianz Deutschland (2018–21) Chief technology officer

Allianz Technology of America (2015–19) CEO and president

Board member at Deutsche Sporthilfe (German national sports funding organization) since 2020

One financial-services firm, Allianz, has taken a business-led approach in its recent tech transformation. Chief technology and transformation officer for Austria, Germany, and Switzerland Axel Schell says the company has shifted its mindset during this transformation: from putting the technology first to cracking business problems with technology. This means not only optimizing purely technical KPIs (such as the number of app features and releases) but also favoring specific business KPIs (such as customer interactions and purchases in the app).

Yexi Liu biography

Earned an MBA from Clemson University; received a bachelor’s degree in international finance from Southwestern University of Finance and Economics

Rich Products Corporation (2019–present) Executive vice president and chief information officer

Westinghouse Electric Company (2017–19) Chief information officer

Has served in IT leadership roles in Asia, Europe, Latin America, and North America

In the recent modernization of its IT organization, B2B and consumer player Rich Products Corporation has made digital technology a critical part of creating value for the company’s customers and enabling its long-term growth and productivity. According to executive vice president and global chief information officer Yexi Liu, the company deployed new digital systems and tools at scale, so technology touches every corner of the company: from the sales and culinary teams, which now have access to data and analytics that help them deliver solutions to customers in real time, to supply chain teams, which enjoy greater transparency and access to information at a speed the company has never experienced before.

2. How holistic is your approach to transformation?

In general, the top performers appear to be taking a more comprehensive and strategic approach to their tech transformations than others. Relative to their peers, they have pursued a higher number of tech initiatives (an average of 3.5 initiatives, out of 8.0 we asked about, compared with 2.7) in their transformations of the past two years. But their distinctiveness is about more than just the number of initiatives in play. In line with our other research, 4 “ The Tech: Forward recipe for a successful technology transformation ,” McKinsey, December 15, 2022. the survey results confirm that deploying certain plays in combination with others can unleash even greater impact than if they were deployed in isolation, as many of these plays have effects on one another.

Exploring the combinations that most top performers have pursued offers insights into the relationships between the initiatives that have underpinned their success and that other companies can also follow (Exhibit 2). For example, at least 50 percent of top performers who say their companies reshaped their business strategy to be more technology based say they also redesigned their tech organization and operating model, scaled their data-related capabilities, and transformed talent management and sourcing.

Fittingly, Rich Products has taken a holistic approach to its recent technology transformation. Liu says the company is implementing multiple company-wide initiatives that focus on future proofing its technology foundation and enabling future scaling and growth—for example, shifting to a global software as a service (SaaS) enterprise resource planning solution and embedding new customer relationship management, supply chain, and procurement platforms in more than 60 countries. At the same time, Rich Products is also changing how its technology function operates, from a decentralized and regionally managed approach to a global model with teams organized around enterprise products and platforms.

At Allianz, Schell says the company is taking a similarly comprehensive route. Allianz is approaching the modernization of its IT function holistically, setting minimum standards on architecture across applications and infrastructure platforms, data, and cybersecurity. The company is also adjusting governance processes in its operating model to enforce this in daily work and investing in engineering capabilities to foster good technology craftmanship over time. These shifts, along with ongoing efforts to scale cloud solutions, are enabling Allianz to move more quickly and autonomously when following standards but also making it deliberately more difficult in the case of exceptions.

3. What is holding you back from developing and deploying new technology at the rate and quality of top performers?

The survey suggests that top performers are also more likely than others to have invested in modernizing and securing their IT foundation—that is, their infrastructure, data, and cybersecurity—so they can deploy technology rapidly and effectively (Exhibit 3). They are three times more likely than others to report building next-generation data platforms and enabling AI applications throughout their organizations. They are also more likely to say they are strengthening their cybersecurity defenses and modernizing their infrastructure, through initiatives such as automation and cloud migration and management.

Top performers are also less likely than others to say their existing technology foundation is an impediment to their tech aspirations. Twenty-two percent of them cite the slow speed of their legacy platforms as a top three challenge during their transformations, compared with 38 percent of their peers. What’s more, the top-performing organizations are more effective overall at managing their data architecture and infrastructure.

In our experience, there are multiple reasons that could prevent companies from leveraging their tech foundations to the fullest. One is a lack of investment, as technology organizations often meet ad hoc demand from multiple stakeholders on the business side and aren’t always able to focus enough time or resources on making foundational improvements, which are difficult and often time-consuming to execute. Second is that products are often built in silos without communication or alignment between technology and business stakeholders. Third, it can be hard to get buy-in for modernizing legacy technology without a strong business case or strategic alignment on the need for these changes.

4. How can you make the most of the talent you already have?

Finding and retaining tech talent is a perennial challenge  and tops the agenda for nearly every technology and business leader. But the competition for tech talent will only intensify as organizations seek to attract and retain an increasingly mobile, ambitious, and purpose-driven workforce.

Yet, even when it comes to broad corporate concerns such as talent, the top performers are significantly ahead of their peers. According to the survey, top performers are more likely than others to say they were effective at retaining top talent, recruiting new talent, training existing talent, and having a healthy IT work culture (Exhibit 4).

At Allianz, Schell says people are the most important factor for their transformation activities’ success. The company fosters global collaboration through teams that are staffed with colleagues in different continents and across communities of practices, where engineers regularly exchange their ideas. The combination of individual strengths and diversified expertise is a powerful asset and enables these teams to perform in an even more customer-centric way. In its transformation, Allianz has also increased internal awareness of what individuals, teams, and the entire organization are capable of—and communicated that self-awareness externally as part of its employer brand.

5. Does your operating model reflect the changes you are making to your technology foundation?

While transforming the IT organization and operating model is a critical way for companies to get the most out of their technology, that change alone will not necessarily lead to better performance. In fact, the proportion of respondents who say they have a mature operating model is nearly the same among top performers as it is among everyone else. 5 We define a mature operating model as one that is either digitally integrated (that is, both conventional and digital technologies are delivered at scale through a unified operating model, which is often organized by tech products or platforms) or fully digital (that is, cross-functional, full-stack, product-based teams are responsible for technology delivery and operate in a digital manner). What sets the top performers apart is that they are twice as likely as their peers to rely on operating models that are product- and platform-centric .

Our experience suggests that these types of operating models, 6 We define a product- or platform-centric operating model as one where teams and their work are cross-functional, led by product managers, and organized around a user-facing product or platform rather than a project. which focus on the end-user experience, present tremendous business benefits. To name a few, product- and platform-centric operating models can help streamline product development, reduce time to market, help cut costs, promote accountability within the organization, and ensure that the company is creating the highest-quality products.

At Rich Products, Liu says the company has continued to emphasize that the transformation is not only an implementation of new systems but also a fundamental transformation of its work, workforce, and workplace—all enabled by technology that is simple, standard, and global. Indeed, the company has organized work around product- and platform-based teams, which has enabled it to benefit from business and technology partnerships across the organization while also maintaining the agility to account for local nuances.

In its tech transformation, Allianz also made meaningful operating-model changes to get the most out of its foundational improvements. According to Schell, agile tribes are assuming more end-to-end ownership of their products, including innovation and operations. Agile teams are staffed with experts from the business and technology sides of the organization, and on the best teams there is no distinction between people who came from the business and who came from IT. This setup enables more autonomous decision making at the team level rather than by steering committees that don’t fully understand a decision or have to deal with its consequences.

Allianz’s agile journey also involves reducing complexity: that is, moving from large, long-term programs and monolithic platforms to a minimum viable product approach and microsystems. One recent example of where the transformation’s efforts are already paying off: the migration of the Allianz Business System from the mainframe to Linux, where the business case is clearly positive.

The survey content and analysis were developed by Anusha Dhasarathy , Pranav Himatsingka, and Naufal Khan , a partner, associate partner, and senior partner, respectively, in McKinsey’s Chicago office; and Thomas Elsner , a partner in the Munich office.

They wish to thank Vilde Haslund and Mary Morris for their contributions to this work.

This article was edited by Daniella Seiler, an executive editor in the Washington, DC, office.

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May 23, 2024

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High-frequency quantitative ultrasound imaging for cement paste can measure carbonation depth nondestructively

by JooHyeon Heo, Ulsan National Institute of Science and Technology

A research team has introduced a new quantitative ultrasound (QUS) technology. This technology is capable of nondestructively measuring carbonation depth using high-frequency ultrasound signals (2.5 MHz). According to the research team, this marks the first time that the existing QUS method, generally used in a wide variety of clinical settings, has been successfully applied to the construction industry.

Their findings have been published in Cement and Concrete Research .

Concrete carbonation involves the capture of atmospheric CO 2 and trapping it in concrete structures , making it a crucial technology for achieving carbon neutrality in the construction industry. Until now, measuring the microstructure changes associated with carbonation has relied on destructive methods due to the difficulty of assessing it using the existing nondestructive technique.

By analyzing ultrasonic scattering and attenuation characteristics of materials from collected ultrasonic signals and converting them into images, the team, led by Professor Gun Kim in the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, succeeded in detecting microstructural changes, specifically the carbonation depth, with an error margin of only about 1 mm compared to results obtained through the phenolphthalein indicator method, a traditional destructive testing approach.

This breakthrough demonstrates the feasibility of non-destructively measuring carbonation depth with pinpoint accuracy.

Typically, universal ultrasound imaging examinations require expert interpretation due to inherent resolution limitations. However, the developed technology utilizes quantitative indicators specific to the material to construct image pixels, enabling even inexperienced individuals to easily identify structural changes in materials.

Professor Kim Gun commented, "This study is groundbreaking in demonstrating the applicability of quantitative ultrasound imaging technology from the biomedical field to the construction sector. The potential applications of this technology are vast, ranging from predicting the lifespan of automobile batteries to precise visualization of cancer tissues in future research endeavors."

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