variable discussion in research example

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
• How to Edit Your Work

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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable.

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

Adam Goulston, PsyD, MS, MBA, MISD, ELS

Science Marketing Consultant

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Ensure your structure and ideas are consistent and clearly communicated

Pair your Premium Editing with our add-on service Presubmission Review for an overall assessment of your manuscript.

What are Examples of Variables in Research?

Table of contents, introduction.

In writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing excellent research. What are variables, and how do you use variables in your research?

I explain this key research concept below with lots of examples of variables commonly used in a study.

You may find it challenging to understand just what variables are in research, especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this vital concept of research, as well as statistics.

Understanding what variables mean is crucial in writing your thesis proposal because you will need these in constructing your conceptual framework  and in analyzing the data that you have gathered.

Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will use your data for statistical analysis.

I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.

Definition of Variable

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary,” meaning, changing in amount, volume, number, form, nature, or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

The next section provides examples of variables related to climate change , academic performance, crime, fish kill, crop growth, and how content goes viral. Note that the variables in these phenomena can be measured, except the last one, where a bit more work is required.

Examples of Variables in Research: 6 Phenomena

The following are examples of phenomena from a global to a local perspective. The corresponding list of variables is given to illustrate how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.

Phenomenon 1: Climate change

Examples of variables related to climate change :

• temperature
• the amount of carbon emission
• the amount of rainfall

Phenomenon 2: Crime and violence in the streets

Examples of variables related to crime and violence :

• number of robberies
• number of attempted murders
• number of prisoners
• number of crime victims
• number of laws enforcers
• number of convictions
• number of carnapping incidents

Phenomenon 3: Poor performance of students in college entrance exams

Examples of variables related to poor academic performance :

• entrance exam score
• number of hours devoted to studying
• student-teacher ratio
• number of students in the class
• educational attainment of teachers
• teaching style
• the distance of school from home
• number of hours devoted by parents in providing tutorial support

Phenomenon 4: Fish kill

Examples of variables related to fish kill :

• dissolved oxygen
• water salinity
• age of fish
• presence or absence of parasites
• presence or absence of heavy metal
• stocking density

Phenomenon 5: Poor crop growth

Examples of variables related to poor crop growth :

• the amount of nitrogen in the soil
• the amount of phosphorous in the soil
• the amount of potassium in the ground
• frequency of weeding
• type of soil

Phenomenon 6:  How Content Goes Viral

• interesting,
• surprising, and
• causing physiological arousal.

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

But researchers devised ways to measure those variables by grouping the respondents’ answers on whether content is positive, interesting, prominent, among others (see the  full description here ).

Thus, the variables in the last phenomenon represent the  nominal scale of measuring variables .

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis . Variable descriptions, correlations, or differences are then determined.

Difference Between Independent and Dependent Variables

Which of the above examples of variables are the independent and the dependent variables?

Independent Variables

The independent variables are those variables that may influence or affect the other variable, i.e., the dependent variable.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

• number of robberies,
• number of attempted murders,
• number of prisoners, 
• number of crime victims, and
• the number of carnapping incidents.

The five variables listed under crime and violence in the streets as the theme of a study are all dependent variables.

Dependent Variables

The dependent variable, as previously mentioned, is the variable affected or influenced by the independent variable.

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

I will leave the classification of the other variables to you. Find out whether those are independent or dependent variables. Note, however, that some variables can be both independent or dependent variables, as the context of the study dictates.

Finding the relationship between variables

How will you know that one variable may cause the other to behave in a certain way?

Finding the relationship between variables requires a thorough  review of the literature . Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information with your peers, who may have difficulty in understanding what the variables are in research.

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How to write the conceptual framework in a research proposal, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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Your question is unclear to me Biyaminu. What do you mean? If you want to cite this, see the citation box after the article.

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Dear Calvin, when you state your research objectives that’s where you will know if you need to use variables or not.

Great work. I’d just like to know in which situations are variables not used in scientific research please. thank you.

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I salute your work, before I was have no enough knowledge about variable I think I was claimed from my lecturers, but the real meaning I was in the mid night. thanks

Thank you very much for your nice NOTE! I have a question: Can you please give me any examples of variables in students’ indiscipline?

A well articulated exposition! Pls, I need a simple guide on the variables of the following topic : IMPACT OF TAX REFORMS ON REVENUE GENERATION IN NIGERIA: A CASE STUDY OF KOGI STATE. THANKS A LOT.

thanks for the explanation a bout variables. keep on posting information a bout reseach on my email.

This was extremely helpful and easy to digest

Dear Hamse, That depends on what variables you are studying. Are you doing a study on cause and effect?

Dear Sophia and Hamse,

As I mentioned earlier, please read the last part of the above article on how to determine the dependent and independent variables.

CHALLENGES FACING DEVELOPMENT OF COOPERATIVE MOVEMENT IN TANA RIVER COUNTY

What is the IV and DV of this Research topic?

You can see in the last part of the above article an explanation about dependent and independent variables.

Dear Maur, what you just want to do is to describe the challenges. No need for a conceptual framework.

Hey, I really appreciate your explanation however I’m having a hard time figuring out the IV and DV on the topic about fish kill, can you help me?

I am requested to write 50 variables in my research as per my topic which is about street vending. I am really clueless.

Hi Regoniel…your articles are much more guiding….pls am writing my thesis on impact of insurgency on Baga Road fish market Maiduguri.

How will my conceptual framework looks like What do I need to talk on

Dear Alhaji, just be clear about what you want to do. Your research question must be clearly stated before you build your conceptual framework.

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Thanks so much ! This article is so much simple to my understanding. A friend of my referred me to this site and I am so greatful. Please Sir, when writing the dependent and independent variables should it be in a table form ?

Dear Grace, Good day. I don’t understand what you mean. But if your school requires that the independent and dependent variables be written in table form, I see no problem with that. It’s just a way for you to clearly show what variables you are analyzing. And you need to justify that.

Can you please give me what are the possible variables in terms of installation of street lights along barangay roads of calauan, laguna: an assessment?

Hello sir, sorry to bother you but what are the guidelines for writing a good report

Guidelines for writing a good research report?

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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The Discussion section is where the author(s) explain the results. They will talk about how the research answered, or failed to answer, the research question. They will address how the outcomes filled gaps in the research, how they might be applied more broadly, how the study's results may have limitations, and discuss new questions that came up in the course of the research. 

What Criteria to Look For

The Discussion is where the authors will most likely discuss how to  generalize  from their research to other samples and situations. 

Finding the Criteria

The Discussion section will have a "Discussion" heading fairly consistently. However, many articles may also have additional headings or subheadings in the Discussion section. Where the information on generalization is located may vary based on which headings the author(s) decide to use. 

What Headings to Look Under

• General heading for the section. If this is the only heading, there should be discussion of generalization here. 
• If there are additional headings, the discussion of generalization may be in those headings or spread across multiple headings including the general discussion heading.
• Final overview of the research. 
• Likely some mention of how to generalize from the results. 
• Where the author(s) will discuss how the research results affects the understanding of the topic or how it could be used in practice.
• If this section exists, there is likely discussion of generalization here.
• Will explain any weaknesses in the study's design or sample that would make the research results less usable. 
• The author(s) will likely discuss how to  not  generalize the results in this section.
• The author(s) will identify possible research projects to follow up with this research.
• May discuss how to address issues from Limitations that would improve generalization. 
• Has a single heading for "Discussion" beginning on page 93.
• Final paragraph on on page 95 discusses how the results of the study might be generalized and applied at a larger community level after noting possible limitations in the study. 
• Discussion section begins on page 545, which include the headings "Discussion," "Policy and Practice Implications," "Limitations and Future Directions," and "Summary and Conclusions."
• Each section addresses what evidence the study does, or does not, provide for larger debates on the subject. 
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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.

Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

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Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

• Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
• Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
• Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
• Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
• Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
• Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
• Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

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2.2: Concepts, Constructs, and Variables

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• Anol Bhattacherjee
• University of South Florida via Global Text Project

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We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

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Qualitative Variable – Types and Examples

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Qualitative Variable

Definition:

Qualitative variable, also known as a categorical variable, is a type of variable in statistics that describes an attribute or characteristic of a data point, rather than a numerical value.

Qualitative variables are typically represented by labels or categories, such as “male” or “female,” and are often used in surveys and polls to gather information about a population’s characteristics.

Types Qualitative Variable

There are two main types of qualitative variables:

Nominal Variables

A nominal variable is a Qualitative Variable where the categories are not ordered in any particular way. For example, gender (male or female), race (Asian, Black, Hispanic, etc.), or religion (Christian, Muslim, Hindu, etc.). Nominal variables can be represented using numbers, but the numbers do not have any quantitative meaning. For example, a researcher might assign the number “1” to male and “2” to female, but these numbers do not represent a quantitative difference between the categories.

Ordinal Variables

An ordinal variable is a Qualitative Variable where the categories are ordered in some way. For example, educational level (high school, college, graduate school), income level (low, medium, high), or level of agreement (strongly agree, somewhat agree, neutral, somewhat disagree, strongly disagree). Ordinal variables can be represented using numbers, and the numbers have a quantitative meaning, but the distance between the categories is not necessarily equal. For example, the difference between “high school” and “college” may not be the same as the difference between “college” and “graduate school.”

Examples of Qualitative Variables

Here are some examples of qualitative variables:

• Gender : Male or female
• Marital status: Married, single, divorced, widowed
• Race : Asian, Black, Hispanic, White, etc.
• Religious affiliation: Christian, Muslim, Hindu, Buddhist, etc.
• Political affiliation : Democrat, Republican, Independent, etc.
• Educational level : High school, college, graduate school
• Type of employment : Full-time, part-time, self-employed, unemployed
• Type of housing: Apartment, house, condo, etc.
• Method of transportation : Car, bus, train, bike, etc.
• Language spoken: English, Spanish, French, etc.

Applications of Qualitative Variable

Qualitative variables are used in many applications in different fields, including:

• Market research : Qualitative variables are often used in market research to understand consumer behavior and preferences. For example, a company might use qualitative variables such as age, gender, and income to segment their target market and create customized marketing campaigns.
• Public opinion polling : Qualitative variables are used in public opinion polling to gather information about people’s attitudes, beliefs, and opinions. Pollsters may ask questions about political affiliation, religious affiliation, or social issues to understand public opinion on a particular topic.
• Social sciences research: Qualitative variables are commonly used in social sciences research to study human behavior, culture, and society. Researchers may use qualitative variables to categorize people based on their demographic information or cultural background, and to analyze patterns and trends in behavior or attitudes.
• Healthcare research: Qualitative variables are used in healthcare research to identify risk factors and to understand the impact of treatments on patients. Researchers may use qualitative variables such as age, gender, or medical history to identify populations at risk for certain diseases, and to evaluate the effectiveness of different treatment options.
• Education research: Qualitative variables are used in education research to study the effectiveness of different teaching methods and to identify factors that influence student learning. Researchers may use qualitative variables such as socio-economic status, educational level, or learning style to analyze patterns and trends in student performance.

When to use Qualitative Variable

Qualitative variables should be used in research when the variable being studied is categorical and does not involve numerical values. Here are some situations where qualitative variables are appropriate:

• When studying demographic characteristics: Qualitative variables are useful for studying demographic characteristics such as age, gender, ethnicity, and religion. These variables can be used to segment a population into groups and to compare differences between groups.
• When studying attitudes and beliefs : Qualitative variables can be used to study people’s attitudes and beliefs about various topics, such as politics, social issues, or religion. Researchers can use surveys or interviews to gather data on these variables.
• When studying cultural differences: Qualitative variables are often used in cross-cultural research to study differences between cultures. Researchers may use qualitative variables such as language spoken, nationality, or cultural background to identify groups for comparison.
• When studying consumer behavior : Qualitative variables can be used in market research to study consumer behavior and preferences. Researchers can use qualitative variables such as brand loyalty, product preference, or buying habits to understand consumer behavior.
• When studying patient outcomes: Qualitative variables can be used in healthcare research to study patient outcomes, such as quality of life, satisfaction with treatment, or adherence to medication. Researchers can use qualitative variables to identify factors that influence patient outcomes and to develop interventions to improve patient care.

Purpose of Qualitative Variable

The purpose of a qualitative variable is to categorize data into distinct groups based on non-numerical characteristics or attributes. The use of qualitative variables allows researchers to describe and analyze non-quantifiable phenomena, such as attitudes, beliefs, behaviors, and demographic characteristics, and to identify patterns and trends in the data. The main purposes of qualitative variables are:

• To describe and categorize : Qualitative variables are used to describe and categorize data into meaningful groups based on characteristics or attributes that are not numerical.
• To compare and contrast: Qualitative variables allow researchers to compare and contrast different groups or categories of data, such as different demographic groups or cultural backgrounds.
• To identify patterns and trends: Qualitative variables allow researchers to identify patterns and trends in data that may not be apparent with numerical data. For example, a researcher may use qualitative variables to identify cultural differences in attitudes toward healthcare.
• To develop hypotheses: Qualitative variables can be used to develop hypotheses or research questions for further study. For example, a researcher may use qualitative variables to identify risk factors for a particular disease, which can then be further studied using quantitative methods.
• To inform decision-making: Qualitative variables can provide important information to inform decision-making in fields such as healthcare, education, and business. For example, healthcare providers may use qualitative variables to identify patient preferences and needs, which can inform treatment decisions.

Characteristics of Qualitative Variable

Here are some of the characteristics of qualitative variables:

• Categorical : Qualitative variables are categorical in nature, meaning that they describe characteristics or attributes that are not numerical. They can be nominal, ordinal or binary.
• Non-numeric : Qualitative variables do not involve numerical values, but rather descriptive or categorical data such as colors, shapes, types, or names.
• Limited number of categories: Qualitative variables are often limited to a small number of categories, such as male/female, married/single/divorced, or white/black/Asian.
• Mutually exclusive categories : Categories in a qualitative variable must be mutually exclusive, meaning that each observation can only belong to one category.
• No numerical order : Unlike quantitative variables, qualitative variables do not have a numerical order or ranking. Categories are assigned based on non-numerical criteria.
• Can be used for comparison : Qualitative variables are often used for comparison purposes, such as comparing the frequency of certain behaviors or attitudes across different demographic groups.
• Can be used for classification: Qualitative variables can be used to classify data into distinct groups based on common characteristics or attributes. For example, people can be classified into different racial or ethnic groups based on their ancestry.
• Can be used for hypothesis testing : Qualitative variables can be used to test hypotheses about differences between groups or categories of data. For example, a researcher may hypothesize that men and women have different attitudes toward a particular social issue, and use a qualitative variable to test this hypothesis.

Advantages of Qualitative Variable

There are several advantages of using qualitative variables.

• Rich data: Qualitative variables can provide rich data about complex phenomena such as attitudes, behaviors, and cultural differences. This data can be useful for gaining a deep understanding of a particular issue or topic.
• Flexibility : Qualitative variables are flexible and can be used in a variety of research methods, such as interviews, focus groups, and observations. This allows researchers to choose the method that best suits their research question and participants.
• Participant perspective : Qualitative variables allow researchers to capture the participant’s perspective and experience. By using open-ended questions or prompts, researchers can gain insight into how participants perceive and interpret a particular issue.
• Depth of understanding: Qualitative variables allow for a depth of understanding that may not be possible with quantitative variables alone. Qualitative data can provide details and context that quantitative data may miss.
• Contextualization : Qualitative variables can provide contextualization, allowing researchers to understand the cultural, social, and historical factors that shape attitudes and behaviors.
• Theory development: Qualitative variables can be useful for developing new theories or refining existing ones. By gathering rich data and analyzing it using qualitative methods, researchers can identify patterns and relationships that can inform the development of new theories.
• Researcher reflexivity : Qualitative variables require the researcher to be reflexive and acknowledge their own biases and assumptions. This can help to ensure that the research is ethical and inclusive, and that the data collected is valid and reliable.

Limitations of Qualitative Variable

Some Limitations of Qualitative Variable are as follows:

• Subjectivity : Qualitative data is often collected through open-ended questions or prompts, which can lead to subjective responses that are difficult to quantify or compare. This can make it challenging to establish inter-rater reliability and can limit the generalizability of the findings.
• Limited sample size : Qualitative research often involves small sample sizes, which can limit the generalizability of the findings. While qualitative research is typically focused on gaining a deep understanding of a particular issue, the findings may not be representative of the broader population.
• Time-consuming: Qualitative research can be time-consuming, particularly when collecting and analyzing data. Researchers must spend significant amounts of time in the field, conducting interviews or focus groups, and then transcribing and analyzing the data.
• Limited control: Qualitative research often involves limited control over the research environment and the participants. This can make it challenging to ensure that the data collected is valid and reliable.
• Limited generalizability: Qualitative research is typically focused on gaining a deep understanding of a particular issue, rather than testing hypotheses or making generalizations about the broader population. As a result, the findings may be less generalizable than those obtained through quantitative research methods.
• Ethical concerns: Qualitative research often involves collecting sensitive or personal information from participants. Researchers must take care to ensure that participants are fully informed about the research, that their privacy is protected, and that they are not harmed in any way by their participation.
• Bias : Qualitative research can be subject to bias, particularly if the researcher has a vested interest in the outcome of the research. Researchers must take care to acknowledge their own biases and assumptions, and to use multiple sources of data to ensure the validity and reliability of the findings.

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This article investigates the potential of belonging as a marketing argument, focusing on customer behaviors driven by this sense of connection with brands. This variable is explored using six robust indicators to define the sense of belonging and its relationship with customer behavior. The research was carried out in the context of Higher Education, highlighting the transformation of this area to offer continuous training and innovative skills. The article highlights the importance of incorporating belonging into marketing strategies, especially for educational institutions seeking to optimize student engagement, especially in those institutions that are the result of ventures and that have given rise to new institutions. This study reveals the sense of belonging of graduates to their institutions and highlights its importance in various sectors. Companies must strive to cultivate a sense of belonging among their customers, using marketing strategies and policies to build lasting relationships and consolidate their ventures. This latent variable has the potential to influence customer behavior and therefore deserves further study.

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Introduction

Today, the potential of using belonging as a marketing argument lies in the ability to build meaningful relationships with consumers. When brands make consumers feel part of something bigger, they are not just selling products, they are creating experiences and emotional connections that can last over time. Belonging thus becomes an essential element to build and maintain strong relationships with customers.

In entrepreneurship, building a strong brand identity is essential. Marketing plays a crucial role in communicating this identity effectively. When entrepreneurs manage to convey an authentic and engaging narrative (Cegarra-Navarro et al., 2024 ) through marketing, the foundation is laid for the consumer's emotional connection and sense of belonging to the brand. In both entrepreneurship and marketing, creating communities around the brand is an effective strategy. Communities not only create a sense of belonging, but also facilitate direct feedback, customer loyalty, and word-of-mouth promotion (Surej & Rouxelle, 2022 ). Successful entrepreneurs use marketing strategies to encourage engagement and build strong communities.

To address these concerns in the context of entrepreneurship, it was decided to study the field of Higher Education (HE), as universities and other educational institutions seek strategies to guarantee their sustainability. In the last two decades, universities have faced globalization, which implies worldwide competition. Innovation, through new programs and methodologies, is key to standing out among institutions. To respond in a more entrepreneurial way to the growing demand for higher education, it is suggested that universities be more entrepreneurial and seek external resources, being more financially independent from states.

Large international investors intensify the purchase of national educational groups who seek high returns in a sector in full transformation. Footnote 1 Virtual schools or online courses as new forms of education stimulate entrepreneurs to invest in education and are known as “ edupreneurs ” (Lacatus & Staicuslescu, 2016 ) whose entrepreneurial motivation is passion driven towards emotional experiences and creative process (Galindo-Martín et al., 2023 ).

True financial independence for educational institutions will only be achieved if they generate the revenue necessary to maintain their activities, such as programs and degrees. Attracting students becomes a challenge, comparable to more traditional business contexts, highlighting the importance of considering pre-admission as a business process in the management of Higher Education institutions (HEi) and their rapid development of public and private together with competence of competition, high registration fees and limited government financing exert pressure on universities and institutions of higher education to embrace market-oriented relationship management strategies (Gordan et al., 2012 ). Universities are aware that strong relationships with all interested parties, including students, will help them face market challenges, face the environment in constant change and capitalize on the opportunities that these changes present. To achieve this goal, universities also use different online communication channels, including websites, email and social media channels to interact with their students (John & De Villiers, 2022 ), moreover the connection of alumni with their HEi is a multidimensional variable that can capture the complexity of student´s relationship. The connection consists of relatability, dependence and a sense of community, which combine the experiences of the students with the university services (Maulana et al., 2023 ).

Brand branding, understood as the ability of companies to generate recognition and admiration in their customers, raises questions about how it can go beyond simply being known and influence attitudes and feelings. Despite the controversies that may arise when addressing this variable as an explanatory argument for behavior, we propose future research along this line to deepen the understanding of this dynamic.

Understanding customer behavior is crucial in marketing research, and advances in technology and data analytics open new opportunities for deep insights. In this article, it is explored the inclusion of sense of belonging as a significant variable in marketing science and examine its potential impact on customer behavior toward brands and companies. Traditionally, marketing research has focused on factors such as price, quality, convenience, and promotion to influence customer decisions. However, recent advances highlight the importance of emotional and psychological drivers in customer actions. Sense of belonging, encompassing connection, identity and affiliation, emerges as a key factor in understanding customer behavior. This article seeks to integrate the sense of belonging into existing marketing models, presenting it as a valuable argument and, the sense of belonging is proposed as a useful marketing tool in HEi. To this scope, a survey was applied to these organizations, in order to integrate the sense of belonging into marketing models, given the potential to transform customer relationships with brands and companies.

In this way, the main question addressed is whether the sense of belonging can influence repurchasing decisions. The article aims to shed light on the potential use of the variable as an effective marketing tool. In marketing, continuous efforts are made to improve customer-related decision-making processes and measure variables such as perceived quality, value and satisfaction, which have become key arguments for managers. But, also the membership idea has great potential as a complement to the exhaustive list of variables that marketers, both in academia and the field, need to monitor and control as a component of the belonging. Although belonging has often been associated with customer loyalty, it goes beyond mere attitudes and represents a distinct form of behavior exhibited by highly engaged customers deeply connected to a particular company. In the context of HEi, strategies focused on promoting student identification are gaining importance, with the aim of improving student progression and engagement (Mahoney et al., 2022 ).

In summary, the current article provides information to address the following research question: how does the sense of belonging influence the repurchase intention of the alumni to return to Higher Education institutions?

However, before asking this question, it will be necessary to configure the sense of belonging variable, proposing several indicators that allow this pride to be measured. That is, the article has been developed by proposing six indicators as components of the sense of belonging and whose levels can be measured through the responses given by clients, or in this case, alumni of the HEIs. These components are: positive references, participation in events, membership in an association, following on social networks, donations to institutions and recommendation for recruitment, with the community of alumni of the institution being the one that carries out these actions in relation to your Institution.

Therefore, these considerations allow us to propose several hypotheses about the components of the sense of belonging such as:

H 1 : Positive reference is an indicator of the sense of belonging variable.

H 2 : Participation in events is an indicator of the sense of belonging variable.

H 3 : Membership to an association is an indicator of the sense of belonging variable.

H 4 : Follow up on social media is an indicator of the sense of belonging variable.

H 5 : Donation to the institution is an indicator of the sense of belonging variable.

H 6 : Recommendation for a recruiting is an indicator of the sense of belonging variable.

Theoretical framework

Today, marketing refers to customer communities and uses the concept of affiliation to create links between companies and their customers (McAlexander et al., 2002 ). Affiliation, based on pride in belonging to a brand, can be used as a strategy to retain customers and change repurchase behavior. Creating a marketing strategy based on pride, or the sense of belonging, can be one possibility among others to increase the profitability of companies and brands.

Sentiment-based marketing proposes that what customers feel about a product or brand can be used as an argument for loyalty marketing. The variable pride or sense of belonging has been studied from points of view based on psychology or sociology, but to date there are no relevant references that include the sense of belonging as a useful tool for marketing studies. On the other hand, in the education context some researches begin to point out that a strong social presence should be created in online learning communities (Swan,  2003 ; Swan et al., 2009 ), in which a sense of belonging and connection develops and strengthens the motivation and involvement of students (Lee, 2021 ). Online cooperation with other students gives a feeling of belonging to a group” and this element is valued higher, incredibly, by students with a low level of academic performance than by those who obtain better results (Arsenijević et al., 2023 ).

Communities tend to identify themselves based on similarity or identification among their members (neighbourhood, occupation, taste, or devotion to a brand) (McAlexander et al., 2002 ). Assuming that a sense of belonging develops in a market community, what effects would the sense of belonging have on customer behavior? Would a sense of belonging influence repurchase behavior?

Repurchase as a consequence of a sense of belonging is a fascinating phenomenon in consumer behavior. When customers feel emotionally connected, valued, and part of a community associated with a brand, they are more likely to choose to make a repeat purchase. The sense of belonging creates deep-rooted loyalty, where identification with the brand goes beyond the commercial transaction. This loyalty based on emotional and social connection can translate into repeat purchasing decisions, contributing significantly to the sustained success of a company. Repurchase, in this context, becomes a tangible indicator of the strength and depth of the relationship between the brand and the consumer, highlighting the strategic importance of cultivating a sense of belonging in marketing strategies.

Belonging is a concept that has not been widely discussed or researched. The literature on the concept is scarce and much of it is narrative rather than empirical (Hagerty et al., 1992 ). Membership involves the recognition and acceptance of one member by another member of a group (Anant, 1966 ).

Although academic literature has explored belonging as an influential variable in human behavior from the perspectives of psychology and sociology (Bachrach & Zautra, 1985 ; Doolittle & MacDonald, 1978 ; Hillery, 1955 ; Kasarda & Janowitz, 1974 ; McMillan & Chavis, 1986 :9), has rarely been approached from an economic perspective. The connection between the sense of belonging and repeat purchase, evidenced by brands that generate loyalty through a pride of belonging, has been scarcely explored in economic terms. Academic literature cites the need to identify as many uses of the concept as possible; an initial stage of concept analysis (Walker & Avant, 2005 ). Belonging can be considered from psychological, sociological, physical, or even spiritual perspectives. Anant ( 1966 ) pointed out that belonging, from a psychological point of view, is an internal affective or evaluative feeling or perception. It is the feeling of belonging when a person perceives themselves valued by an external reference, experiencing an adjustment between themselves and that reference. Sociologically, it implies affiliation to significant groups or systems, observable through behaviors such as participation in a group or social networks.

This component of belonging, closely linked to educational institutions, connects students and alumni with them. In addition to the sociological aspect, belonging encompasses physical possession, including objects, people, or places (Hagerty et al., 1992 ). This concept aligns with the corporate brand approach (Curtis et al., 2009 ), where brands, in addition to connecting with users, must foster belonging through interaction and engagement. This engagement can be achieved through content tailored to specific groups (Lasorsa et al., 2012 ), such as future students (Rutter et al., 2016 ). Thus, pride, in terms of the emotional connection that some brands generate in consumers, is essential. Brands like Harley-Davidson exemplify this pride, evident in owners' strong emotional connection to these recognizable motorcycles. This sense of belonging and pride extends to other brands in different sectors, such as Ferrari, Lamborghini, and Porsche, being used strategically in customer relationship marketing.

Pride of ownership marketing goes beyond relationship marketing which, when it first appeared, was a business idea that earned the favour and loyalty of customers by satisfying their wants and needs (Berry, 1995 ).

Therefore, in order to frame the concept of belonging, there are also references for this works as Strayhorn ( 2018 ) defined: "In university terms, sense of belonging refers to the social support perceived by students on campus, a feeling or sense of connection, and the experience of caring or feeling cared for, accepted, respected, valued, and important." For the campus community or others on campus, such as faculty, staff, and peers" and other definitions have emerged from the reflections of scholars, as a feeling validated by students through program design and interactions with peers and faculty (Santangelo et al., 2022 ; Santa-Ramirez, 2022 ).

Pride of belonging: the theory of community feeling

In Hillery ( 1955 ) the first definitions of community and group cohesion appeared. From 1955 to the second decade of the twenty-first century, the idea of community has changed a lot. McMillan and Chavis ( 1986 :9) defined belonging as a feeling of community: "a feeling that members care about each other and the group, and a shared belief that members' needs will be met by their commitment to being together."

Previously, other authors had developed Sense of Community Scales (SCS) such as:

Doolittle and MacDonald ( 1978 ) designed a 40-item scale to measure communicative behaviors and attitudes at the community or neighbourhoods’ level of social organization.

Glynn ( 1981 ) designed a questionnaire distributed to randomly selected members of the Division of Community Psychology of the American Psychological Association.

Bachrach and Zautra ( 1985 ) and Kasarda and Janowitz ( 1974 ) developed a measure with seven items: feeling at home in the community, agreement with the values and beliefs of the community, satisfaction with the community, feeling of belonging to the community, interest in what happens. in the community, feeling an important part of the community and attachment to the community. The authors found this scale to be internally consistent.

A sense of belonging, although intrinsically powerful in its ability to forge emotional and social connections, can be a temporary and highly context-sensitive phenomenon (Dost & Mazzoli, 2023 ). The ephemeral nature of this feeling manifests itself in specific situations or moments in which people experience a momentary connection with a brand, community, or group. Factors such as events, marketing campaigns, or changes in the environment can influence the intensity and duration of the sense of belonging. Furthermore, sensitivity to context means that what creates belonging at one time may not be equally effective at another. This dynamic nuance highlights the importance for companies to understand the temporality of belonging and adapt marketing and engagement strategies based on changes in the environment and the changing needs of consumers.

To develop the sense of belonging applied to marketing we will take as a reference the pride of belonging of the graduates, a good starting point to build a model based on the sense of belonging in a HEi, which could be in professional schools. The authors Gruen et al. ( 2000 ) conceptualized and empirically examined the relationship-building efforts of professional associations, which are the provision of basic services, rewards for contributions, dissemination of organizational knowledge, member interdependence-enhancing activities, and dependence on external requirements of membership.

In line with this theme, the commitment relationship between alumni and IES reinforces the importance of strengthening aspects related to a weak alumni culture (Pedro et al., 2021 ). HE providers must adopt customer-centric, service-oriented tactics and actions and must earn the trust and loyalty of customers from their marketing practices (John & De Villiers, 2022 ), the result of which can also be the alumni membership development.

It is also theorized that three components of commitment: affective, permanence, and normative differentially mediate the correlation between associations' relationship-building efforts and their members' relational behaviors (member retention, exchange-based participation, and exchange-based co-production). cooperation) (Gruen et al., 2000 ).

In the HE context, there are multiple forms of membership and membership organizations; professional associations provide an interesting and important context for developing and testing theories about membership relations, some of them very similar to an alumni organization, for example. These relationships are characterized by a formalized agreement that includes regular payment of dues and annual membership renewal. Members choose their level of participation and consumption of the benefits offered by the association (Gruen et al., 2000 ).

Also, if a former student is a follower of a social network (X-before Twitter, Facebook, Instagram, or Tik-Tok), it can be assumed that there is a link and interest in the institution: a feeling of belonging, without a doubt. The importance of social networks as a platform for social interaction, communication and marketing is evident today. More and more companies in various sectors have already integrated or plan to integrate social media applications into their marketing programs. Institutions are showing growing interest in the potential of social networks as a marketing tool (Constantinides & Stagno, 2011 ).

On the other hand, some authors have studied how social networks influence teacher-student relationships within educational institutions (Arteaga-Sánchez et al., 2014 ; Roblyer et al., 2010 ).

Aaker et al. ( 2004 ) have made significant contributions to the understanding of brand-consumer relationships and sense of belonging. Their research has explored how people develop emotional connections with brands and how these connections affect consumer behavior. Similarly, Muniz and O’Guinn ( 2001 ) have addressed the construction of brand communities and how these communities contribute to the consumer's sense of belonging. They have highlighted the importance of consumer interactions and participation in forming online communities that reinforce brand loyalty.

Cornwell ( 2020 ) focuses on the connection between sense of belonging and sports marketing. His studies have shown how identification with sports teams and events can generate a strong sense of belonging and loyalty among consumers. Finally, Nardini et al. ( 2022 ) have explored how community-based marketing strategies can influence consumer sense of belonging. Their work highlights the importance of creating shared brand experiences that strengthen emotional ties to the brand.

Our research develops the pride of belonging to an institution, considering that it can be built on six components: WOM, presence at events, membership in an association of the institution, recommendation for recruitment, financial support and following on social networks of some platforms. of the institution.

Components of the sense of belonging

With the advent of social media, customer relationship management strategies have changed, and companies have been forced to consider new ways of interacting with their consumers and customers. Although sense of belonging and other behaviours related to human pride have been described to a large extent by some disciplines (psychology, sociology, politics), this concept has been little addressed by companies and even less by marketing.

By developing the sense of belonging in this research, we aim to demonstrate that, through pride of belonging to styles, fashions and brands, companies can encourage repurchase in their customers. This is in line with our objective: to model repurchase behaviour through pride of ownership in HE institutions. The review literature and the state of the art show some ideas that we will apply to define the sense of belonging variable and model it: among them, we can highlight word of mouth, membership in associations, following institutions on social media, collaboration with non-profit causes, recommendation of grantees to hire, etc.

Thus, following the first study on belonging-based marketing, the construct of sense of belonging based on the following components, which can be measured in the survey, has been used and studied in different researches. Matarranz ( 2021 ) points out the components of the sense of belonging variable of alumni of HEi and the references related to the components.

Moreover, the authors, Hsu et al. ( 2015 ) highlighted the importance of satisfied alumni in providing financial support to their educational institutions, creating employment opportunities for subsequent graduates, and engaging in positive word-of-mouth communication.

The components identified by Matarranz ( 2021 ) are discussed in more detail below:

Word of mouth (WOM)

Word of mouth plays a crucial role in the relationship between students, graduates and potential applicants to a university or college (Alves & Raposo, 2007 ; Athiyaman, 1997 ; Bean & Bradley, 1986 ; Hsu et al., 2015 ; McAlexander & Koenig, 2001 ). Like employees in labour organisations, university students share membership criteria and interact frequently with other members of the institution (Bean & Bradley, 1986 ).

Alves and Raposo ( 2007 ) suggest that high levels of student satisfaction lead to favourable word-of-mouth communication, such as recommending programmes or returning as graduate students. These behaviours align with a graduate's pride or sense of belonging requirements. They also argued that student satisfaction influences loyalty and positive word-of-mouth actions. Satisfied students tend to show loyalty to their institution and engage in positive word-of-mouth actions (Athiyaman, 1997 ). It is important to note that satisfaction directly influences word-of-mouth actions and not only indirectly (Alves & Raposo, 2007 ).

Social media network (SNS) monitoring

The use of online marketing, in particular social media marketing has gained significant attention in HE marketing (Brech et al., 2017 ; Constantinides & Stagno, 2011 ,  2012 ; Kuzma & Wright, 2013 ; Leng, 2012 ; Palmer, 2013 ; Rekhter, 2012 ; Rutter et al., 2016 ; Sandlin & Peña, 2014 ). Most universities have a presence on at least one social media platform, with Facebook and Twitter being the most widely used platforms (Brech et al., 2017 ). Universities usually have a main Facebook page to target various interest groups, such as potential students, current students, and alumni. In addition, specific brand pages are also established (Brech et al., 2017 ).

Universities use social media platforms to increase the authenticity of their recruitment marketing materials. For example, student-written blogs are used on admissions websites to provide authentic experiences and views about campus life, thereby fostering connection and identification with university students by prospective students (Sandlin & Peña, 2014 ). The authenticity of blogs is perceived when student bloggers share personal details and feelings about campus life, even when the main topic is admissions and university-related activities.

Sandlin and Peña ( 2014 ) highlighted the role of connection and identification with university students in shaping prospective students' expectations and feelings of belonging during the university search process. Several studies have explored the use of social media channels, including Twitter, by universities for purposes such as marketing, student recruitment, student support and communication with alumni (Kimmons et al., 2017 ; Lackovic et al., 2017 ; Palmer, 2013 ; Ricoy & Feliz, 2016 ; Sarwar et al., 2019 ; Tur & Marn, 2015 ). However, in our research, we focus specifically on the role of social media as a marketing and communication tool with alumni according to Table  1 .

If interaction with social media networks prior to student recruitment fosters an early sense of pride in belonging to the university (Rutter et al., 2016 ), it is reasonable to assume that the feeling of belonging should persist as students complete their studies and become alumni, while maintaining interaction with the university's social media network.

These components of word of mouth and social media network monitoring contribute to our understanding of the feeling of belonging and its relationship to repurchase intention in the context of higher education institutions.

Event participants and social life

Social life emerges as a dimension to measure the satisfaction of university students and for most students, social life is an important and satisfying activity at university (Betz et al., 1970 ) and, therefore, students who view their social life positively are expected to be more satisfied with their university experience (Bean & Bradley, 1986 ). Betz et al. ( 1970 ) included as social life opportunities to achieve socially relevant goals, such as dating, meeting compatible or interesting people, making friends, participating in campus events and informal social activities. The role that a sense of belonging plays in educational contexts and living environments is essential. Involvement experiences reveal how students' sense of belonging can be inspired or diminished, such as when they run for office in student government or pledge a sorority (Strayhorn, 2018 ), and in general a sense of belonging should be considered to be present when students participate in social events and engage in the social life of their universities (Astin, 1984; Bean & Bradley, 1986 ; Mael & Asforth, 1992 ; Strayhorn, 2018 ; Wolf-Wendel et al., 2009 ).

Student engagement refers to both academic and social (e.g., extracurricular) activities: the investment of physical and psychological energy in different objects or activities, occurring along a continuum (Strayhorn, 2018 ). Activities such as "working on campus, living on campus, interacting with peers, being a member of clubs, and socialising with faculty members are types of engagement that are measured according to Engagement Theory" (Wolf-Wendel et al., 2009 ).

In relation to the feeling of belonging, engagement is conceptually distinguished in at least two ways according to Strayhorn ( 2018 ):

Engagement refers to the amount of time and effort students devote to their academic responsibilities, such as studies, and to other activities, such as sports and clubs "that lead to the experience and outcomes that constitute student success" (Wolf-Wendel et al., 2009 ).

Engagement refers to how institutions invest resources and structure learning opportunities to "encourage students to participate in and benefit from such activities" (Wolf-Wendel, et al., 2009 ).

Involvement in the social life of the university is positively associated with students' sense of belonging to the university, according to the positive correlations found between students' participation in campus activity (e.g. working on a committee/organisation) and their perception of campus support and belonging (Astin, 1984 ; Strayhorn, 2018 ). All of this development is based on students' participation and engagement during their time at the institution and as undergraduates. However, we will try to apply this idea to alumni and how they could also get involved and participate in some university activities for them, as a sign of belonging.

Membership of alumni associations

There are many forms of membership and membership organizations (Bhattarcharya et al., 1995 ; Gruen et al., 2000 ; Mael & Asforth, 1992 ; Newman & Petrosko, 2011 ; Strayhorn, 2018 ; Stuart, 2009 ). These relationships are characterised by a formalised agreement that includes the payment of regular dues and an annual renewal of membership (Gruen et al., 2000 ).

Identification is defined as the "perception of belonging to an organisation" of which the person is a member. Alumni, as future customers, in their role as members, identify with organisations (Bhattarcharya et al., 1995 ). The latter authors pointed out that organisations can resort to more direct strategies to ensure identification by consumers: for example, non-profit organisations, such as museums, try to create identification by attracting consumers "to themselves" by making them members. The phenomenon of identification also occurs in the case of an organisation's employees as in the case of alumni of an educational institution according to some organisational researchers such as (Dutton et al., 1994 ; Dutton & Dukerich, 1991 ; Mael & Asforth, 1992 ; O'Reilly & Chatman, 1986 ).

Mael and Asforth ( 1992 ) stated that when a person identifies with an organisation, they perceive a sense of connection to it and define themselves in terms of the organisation. The existence of both formal and informal alumni organisations in American educational institutions dates back to the early nineteenth century (Brubacher, 2017 ). For the alumnus, continued affiliation with the alma mater often provides intellectual stimulation, prestige, identity stability and a vehicle for altruistic or tax-motivated giving (Pickett, 1986 ), while alumni provide various types of support: financial donations, recruitment, career counselling or job placement for graduates, participation in alumni events, and volunteer support for funding applications and organisational events (Mael & Asforth, 1992 ).

Authors Mael and Asforth ( 1992 ), together with the other authors cited above, explained that affiliation entails several affiliation behaviours included in our variable, such as: financial support, recruitment, career counselling or job placement for graduates, which are described below.

Grants for institutional development

In recent decades, HEi increasingly consider their alumni as valuable sources of both information and financial support (Cabrera et al., 2005 ; Gaier, 2005 ; Hsu et al., 2015 ; Mael & Asforth, 1992 ; Volkwein, 2010 ; Weerts & Ronca, 2008 ). Since the 1980s, more and more campuses have been using alumni surveys to assess the impact of the university experience on students' cognitive and non-cognitive development (Cabrera et al., 2005 ). It has been said that alumni are the financial backbone of educational organisations (Bakal, 1979 ) and that "few constituents are more important to an institution than its alumni" (Ransdell, 1985 ).

However, positive experiences with institutions are not enough to drive alumni financial support; the most important indicators are: age, family, income, vocational and educational background, current job duties and responsibilities, board membership in profit and non-profit organisations, honours, achievements, publications, creative works, leisure activities and hobbies, spouse's vocational and educational background, board membership, activities, achievements and awards, as well as age and schooling of children and grandchildren (Cabrera et al., 2005 ).

Consistent with this argument, Weerts and Ronca ( 2008 ) also suggested that ability variables related to gender, residence and general civic engagement tend to predict alumni donors. Donor alumni often play important roles as volunteers and political advocates.

In recent years, HE institutions increasingly treat their alumni as sources of valuable information and financial support (Volkwein, 2010 ). Hsu et al. ( 2015 ) referred to alumni satisfaction as a factor related to financial support. Satisfied alumni can help educational institutions financially (Gaier, 2005 ).

Recommending the recruitment of scholarship holders

A sign of belonging is the belief that the best professionals a recruiter can hire as employees for a company have studied at the same university or college as the recruiter (Hsu et al., 2015 ; McAlexander & Koenig, 2001 ; Volkwein, 2010 ; Weerts & Ronca, 2008 ). Alumni can offer good prospects for academic asset programmes and are often used as mentors in student recruitment (Volkwein, 2010 ).

McAlexander and Koenig ( 2001 ) noted that satisfied alumni generate a positive word-of-mouth effect and provide jobs for subsequent graduates. Alumni volunteers also provide other important services, such as mentors, recruiters and leaders of alumni clubs that raise the profile of the institution in their areas (Weerts & Ronca, 2008 ). If the recruiter had a good experience during the stay at your university and considers the level of knowledge acquired to be high, he/she will select a graduate from your institution. This criterion would be related to the feeling of belonging that we want to investigate in this paper.

The importance of alumni is great in this role, as they can provide a more objective view and better assess the appropriateness of curricula to job requirements (Hsu et al., 2015 ).

The literature shows that sense of belonging can be used in two circumstances. On the one hand, Strayhorn's ( 2018 ) sense of belonging theory refers to the student's academic success (Cisneros et al., 2019 ) and, on the other hand, the sense of belonging is prolonged after the stay at the university if alumni satisfaction is high (Hsu et al., 2015 ).

And so, after leaving the university, alumni may continue to maintain a relationship with the institution through cooperation by offering internships to students, offering employment to recent graduates, or cooperating in research projects (Dlacic et al., 2014 ), beyond WOM as a cause of loyalty (Alves & Raposo, 2007 ; Hsu et al., 2015 ).

To address this question, in this study we suggest breaking down the sense of belonging into six parts. We suggest that these parts would represent alumni behaviours and attitudes that can be applied to marketing of HEi.

In short, we hope that a sense of belonging will become an important issue in a sector such as HE, where links between alumni themselves and between alumni and their institutions can grow and be maintained over time. We believe that this topic will be very important for entrepreneurial projects for new higher education institutions that will try to link alumni so that they want to return to the institutions.

Methodology

To design the belonging variable and its explanation for this paper, the HE context has been used. Research conducted in three HEi sought to link the repurchase intention variable through alumni's sense of belonging. The introduction of pride in belonging or sense of belonging as a model variable for the research is because we believe it can explain some behaviours related to repurchase intention.

The consolidation of all the data allowed us to obtain a sample of 359 participants. Three universities participated in this research by organising a survey of their graduates (one Spanish, one American and one Colombian higher education institution).

The fieldwork was carried out in three universities by means of a survey sent to alumni of the institutions: in Spain, the United States and Colombia. A total of 359 responses were received, enough responses for the number of relationships that have been proposed in the model.

The questionnaire consists of three parts. The first one is divided in four blocks, one block for each of the independent latent variables that are integrated into the model: quality service perception (20 questions), perceived value (6 questions), satisfaction (8 questions) and sense of belonging (6 questions). To answer these questions, a Likert scale from 1 to 7 is used, considering that 1 represents total disagreement or that the probability that it occurs is 0% and 7 represents a total agreement or that the probability that it happen is 100%.

Method of analysis

The tool for this study is PLS-SEM for a multivariant analysis, and SmartPLS was the software used. The sense of belonging variable was constructed with six indicators, as explained above: WOM (SOB1), participations in events of the institution (SOB2), membership in institutional associations (SOB3), following in social networks (SOB4), donations for the institution (SOB5) and recruitment of grantees for the companies themselves (SOB6).

The first step in this research was to check the external loading factors of the reflective indicators of the sense of belonging variable. The indicator will remain part of the variable if its loading factor is higher than 0.7 (Carmines & Zeller, 1979 ), but a loading factor higher than 0.6 can also be considered part of the variable measure (Bagozzi & Yi, 1988 ).

For the research, the tool based on PLS analysis was used to determine which indicators can be included in the sense of belonging variable. The conditions of the PLS technique require a prior analysis of the measurement of the model and its variables, showing the relationships between the latent variables and their measurements (indicators).

This work has its origin in a model that includes other variables also linked to repurchase intention. This model included perceived quality, perceived value, student satisfaction and sense of belonging to study how they influence students' intention to repurchase their educational institutions.

The research used as a reference for this article was a reflective analysis of the measurement model that included: the calculation of internal consistency reliability, convergent validity (examining the external landings of the indicators to determine the average variance extracted (AVE) of each construct) and discriminant validity (Fornell-Larcker criterion) (Hair et al., 2017 ) (Fig.  1 ). 

Source: Matarranz ( 2021 )

General study model.

However, the scope of this article will only be the calculation of the factor loadings for the sense of belonging variable. This first step will be a validation of the quality of the measure using confirmatory factor analysis (CFA). The variable to be assessed is valid when its indicators measure what it is really intended to measure and involves considering two types of validations: content and convergent, the former depending on the researcher's criteria and the latter related to the shared variance of the indicators for the latent variable (Aldás & Uriel, 2017 ).

To test whether the convergent validity of a model measuring pride of belonging is good, the mean of the standardised factor loadings should be around 0.7 or higher (Hair et al., 2017 ) and each one separately should be above 0.6 (Bagozzi & Yi, 1988 ). In the PLS, item reliability is assessed by examining the loadings and all of them exceeded the recommended threshold of 0.7 (Carmines & Zeller, 1979 ). On the other hand, the average variance extracted (AVE) of the indicators for the latent variable should be greater than 0.5 (Fornell & Larcker, 1981 ).

Another aspect to take into account in these indicators is their internal consistency (as part of the reliability of the tool), which can be assessed using Cronbachs' alpha and the composite reliability coefficient (CR), as defined (Aldás & Uriel, 2017 ).

Figure  2 and Tables 2 and 3 show the results of the external load indicators and the CA, RC and AVE that have been obtained for the case studied.

Source: Own elaboration

Reflective indicators of the sense of belonging variable.

These results obtained through the PLS-SEM model, using SmartPLS, in a first evaluation yielded the convergent validity of the proposed model: since all the indicators that are part of the variable and the mean of the extracted variable (AVE) were values above 0.7 or very close to it (Carmines & Zeller, 1979 ; Hair et al., 2017 ), which would allow explaining more than 50% of the variance of the variable. Cronbach's Alpha, as a measure of internal consistency, and the composite reliability obtained will also serve to confirm the validity of the model for the measurement of this construct (Hair et al., 2017 ; Martínez Avila & Fierro Moreno, 2018 ). Therefore, these results allow us not to reject the hypotheses proposed above. Initially, for this work, the six planned components can be considered as possible parts of the sense of belonging that can be used to predict repurchase in the context of HE.

Conclusions and discussion

This study focuses on measuring alumni's sense of belonging to their institutions and highlights the importance of this variable in various sectors. Sense of belonging has been related to Psychology and Sociology over time, but we intend that the sense of belonging can be associated with products, services or brands that have meaning for customers, providing them with status, differentiation, emotional ties, and loyalty to the client.

The indicators used to construct the sense of belonging variable may vary depending on the circumstances and nature of the customer-company relationship. This study aims that sense of belonging can be hold by six indicators: WOM, social media network (SNS) monitoring, event participants and social life, membership of alumni associations, grants for institutional development and recommending the recruitment of scholarship holders.

While different indicators may be required for specific relationships, some aspects should be universally represented when measuring ownership. These include following the brand or company on social media, participating in brand-sponsored events or being a member of brand clubs. Therefore, incorporating some of these indicators into the measurement of belonging sense is often advisable.

Sense of belonging can be a valuable approach to marketing as it aligns with the objectives of building customer loyalty and encouraging repeat purchases. As a driver, belonging can contribute to customer profitability. Therefore, companies should focus on fostering a sense of belonging among their customers. Managers can drive this objective through their marketing strategies and policies, thereby building long-lasting customer relationships.

Different industries and sectors can benefit from measuring sense of belonging, as higher levels of ownership are often associated with higher customer repurchase and retention. However, specific considerations will arise depending on the sector and the benefits that customers derive. For example, the benefits offered by banks or insurance companies may differ from those associated with brands in the automotive industry. Sense of belonging may also be influenced by factors such as the origin of the company and how customers can identify with it. These points highlight the need for further studies and research to explore the various aspects of belonging over time.

Equally, entrepreneurial projects or new business can gain more profitable if they get that customers are loyalty, and they have developed certain sense of belonging to the companies. Different factors can influence the sense of belonging, and investigating these factors can provide valuable information.

In conclusion, this research was focused on understanding alumni behaviour and proposes the sense of belonging as a predictive variable in marketing. The study establishes the internal consistency and validity of the variable using multivariate analysis techniques. Sense of belonging is defined by six indicators, five of which exceed the recommended threshold of 0.7 in the analysis of a sample of 359 respondents.

Sense of belonging is a latent variable that holds potential for studying customer behaviour. As marketing science continues to explore customer-company interactions, understanding the role of belonging can offer insights into loyalty and repurchase. While this study focuses on the HE sector, and the unique relationship between students and institutions, similar attitudes, preferences, and communication tools can be applied in other sectors, particularly where there is a long-standing customer-company relationship, high perceived value, and a sense of differentiation.

Therefore, it is recommended to incorporate these indicators in the evaluation of the variable for other studies in which a certain sense of belonging can be measured with all or, one of the six indicators shown in this article.

Limitations and future lines of research

The fieldwork that serves as the basis for this article, although it has an international dimension, was limited to three institutions, which is a limitation as a basis for the study. This would give rise to the application of similar models to those used in other educational institutions. Furthermore, it raises the possibility of further exploring new dimensions of this sense, or pride of belonging and how it can be used in other areas. Brands such as Ferrari or Harley-Davison have managed to develop customer pride in the brand over time and the management of this effect on the customer could be positive. The development of this concept we believe can be useful, above all, to take initiatives that seek to foster the links and bonds of belonging of customers towards companies or service provider brands, mainly as an economically profitable decision, which helps the financial sustainability of these.

In this research, the variable sense of belonging has been modelled with six indicators or components that allow us to understand it. The components are a set of behaviors related to Alumni and we admit that others could be used for the model of this variable. However, given that some of our components are closely linked to new Marketing trends, such as the monitoring of social networks or WOM and, on the other hand, others are classic arguments of human behavior (membership, donation, recommendation…), we argue that using these components in future research will be interesting.

And finally, we hope that some entrepreneurial projects can obtain advantages since of a first moment if they can detect the following of customers in social media or membership for customer clubs.

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Matarranz, J.L., García-Madariaga, J. & Carvajal, M. Approach to the sense of belonging: construct for the marketing of entrepreneurships in higher education. Int Entrep Manag J (2024). https://doi.org/10.1007/s11365-024-00974-6

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• “What are the emerging trends in college students’ use of social media for academic purposes?”
• “Why do some college students engage in academic dishonesty despite awareness of the consequences?”
• “How effective are university mental health services in addressing students’ mental health issues?”
• “How do different learning styles affect the academic success of college students in online courses?”
• “What strategies can be employed to improve retention rates among first-year college students?”
• “How does participation in extracurricular activities influence leadership skills development in college students?”

Research Question Examples in Statistics

• “What are the most common statistical methods used in medical research?”
• “How does the accuracy of machine learning models compare to traditional statistical methods in predicting housing prices?”
• “What is the relationship between sample size and the power of a statistical test in clinical trials?”
• “How does the use of random sampling affect the validity of survey results in social science research?”
• “What are the emerging trends in the application of Bayesian statistics in data science?”
• “Why do some datasets require transformation before applying linear regression models?”
• “How effective are bootstrapping techniques in estimating the confidence intervals of small sample data?”
• “How do different imputation methods impact the results of analyses with missing data?”
• “What strategies can improve the interpretation of interaction effects in multiple regression analysis?”
• “How does the choice of statistical software affect the efficiency of data analysis in academic research?”

Research Question Examples in Socialogy

• “What are the primary social factors contributing to urban poverty in major cities?”
• “How does the level of social integration differ between immigrants and native-born citizens in urban areas?”
• “What is the relationship between educational attainment and social mobility in different socioeconomic classes?”
• “How does exposure to social media influence political participation among young adults?”
• “What are the emerging trends in family structures and their impact on child development?”
• “Why do certain communities exhibit higher levels of civic engagement than others?”
• “How effective are community policing strategies in reducing crime rates in diverse neighborhoods?”
• “How do socialization processes differ in single-parent households compared to two-parent households?”
• “What strategies can be implemented to reduce racial disparities in higher education enrollment?”
• “How does the implementation of public housing policies affect the quality of life for low-income families?”

Research Question Examples in Biology

• “What are the primary characteristics of the various stages of mitosis in eukaryotic cells?”
• “How do the reproductive strategies of amphibians compare to those of reptiles?”
• “What is the relationship between genetic diversity and the resilience of plant species to climate change?”
• “How does the presence of pollutants in freshwater ecosystems impact the growth and development of aquatic organisms?”
• “What are the emerging trends in the use of CRISPR technology for gene editing in agricultural crops?”
• “Why do certain bacteria develop antibiotic resistance more rapidly than others?”
• “How effective are different conservation strategies in protecting endangered species?”
• “How do various environmental factors influence the process of photosynthesis in marine algae?”
• “What strategies can enhance the effectiveness of reforestation programs in tropical rainforests?”
• “How does the method of seed dispersal affect the spatial distribution and genetic diversity of plant populations?”

Research Question Examples in History

• “What were the key social and economic factors that led to the Industrial Revolution in Britain?”
• “How did the political systems of ancient Athens and ancient Sparta differ in terms of governance and citizen participation?”
• “What is the relationship between the Renaissance and the subsequent scientific revolution in Europe?”
• “How did the Treaty of Versailles contribute to the rise of Adolf Hitler and the onset of World War II?”
• “What are the emerging perspectives on the causes and impacts of the American Civil Rights Movement?”
• “Why did the Roman Empire decline and eventually fall despite its extensive power and reach?”
• “How effective were the New Deal programs in alleviating the effects of the Great Depression in the United States?”
• “How did the processes of colonization and decolonization affect the political landscape of Africa in the 20th century?”
• “What strategies did the suffragette movement use to secure voting rights for women in the early 20th century?”
• “How did the logistics and strategies of the D-Day invasion contribute to the Allied victory in World War II?”

Importance of Research Questions

Research questions are fundamental to the success and integrity of any study. Their importance can be highlighted through several key aspects:

• Research questions provide a clear focus and direction for the study, ensuring that the researcher remains on track.
• Example: “How does online learning impact student engagement in higher education?”
• They establish the boundaries of the research, determining what will be included or excluded.
• Example: “What are the effects of air pollution on respiratory health in urban areas?”
• Research questions dictate the choice of research design, methodology, and data collection techniques.
• Example: “What is the relationship between physical activity and mental health in adolescents?”
• They make the objectives of the research explicit, providing clarity and precision to the study’s goals.
• Example: “Why do some startups succeed in securing venture capital while others fail?”
• Well-crafted research questions emphasize the significance and relevance of the study, justifying its importance.
• Example: “How effective are public health campaigns in increasing vaccination rates among young adults?”
• They enable a systematic approach to inquiry, ensuring that the study is coherent and logically structured.
• Example: “What are the social and economic impacts of remote work on urban communities?”
• Research questions offer a framework for analyzing and interpreting data, guiding the researcher in making sense of the findings.
• Example: “How does social media usage affect self-esteem among teenagers?”
• By addressing specific gaps or exploring new areas, research questions ensure that the study contributes meaningfully to the existing body of knowledge.
• Example: “What are the emerging trends in the use of artificial intelligence in healthcare?”
• Clear and precise research questions increase the credibility and reliability of the research by providing a focused approach.
• Example: “How do educational interventions impact literacy rates in low-income communities?”
• They help in clearly communicating the purpose and findings of the research to others, including stakeholders, peers, and the broader academic community.
• Example: “What strategies are most effective in reducing youth unemployment in developing countries?”

Research Question vs. Hypothesis

Chracteristics of research questions.

Research questions are fundamental to the research process as they guide the direction and focus of a study. Here are the key characteristics of effective research questions:

1. Clear and Specific

• The question should be clearly articulated and specific enough to be understood without ambiguity.
• Example: “What are the effects of social media on teenagers’ mental health?” rather than “How does social media affect people?”

2. Focused and Researchable

• The question should be narrow enough to be answerable through research and data collection.
• Example: “How does participation in extracurricular activities impact academic performance in high school students?” rather than “How do activities affect school performance?”

3. Complex and Analytical

• The question should require more than a simple yes or no answer and should invite analysis and discussion.
• Example: “What factors contribute to the success of renewable energy initiatives in urban areas?” rather than “Is renewable energy successful?”

4. Relevant and Significant

• The question should address an important issue or problem in the field of study and contribute to knowledge or practice.
• Example: “How does climate change affect agricultural productivity in developing countries?” rather than “What is climate change?”

5. Feasible and Practical

• The question should be feasible to answer within the constraints of time, resources, and access to information.
• Example: “What are the challenges faced by remote workers in the tech industry during the COVID-19 pandemic?” rather than “What are the challenges of remote work?”

6. Original and Novel

• The question should offer a new perspective or explore an area that has not been extensively studied.
• Example: “How do virtual reality technologies influence empathy in healthcare training?” rather than “What is virtual reality?”
• The question should be framed in a way that ensures the research can be conducted ethically.
• Example: “What are the impacts of privacy laws on consumer data protection in the digital age?” rather than “How can we collect personal data more effectively?”

8. Open-Ended

• The question should encourage detailed responses and exploration, rather than limiting answers to a simple yes or no.
• Example: “In what ways do cultural differences affect communication styles in multinational companies?” rather than “Do cultural differences affect communication?”

9. Aligned with Research Goals

• The question should align with the overall objectives of the research project or study.
• Example: “How do early childhood education programs influence long-term academic achievement?” if the goal is to understand educational impacts.

10. Based on Prior Research

• The question should build on existing literature and research, identifying gaps or new angles to explore.
• Example: “What strategies have proven effective in reducing urban air pollution in European cities?” after reviewing current studies on air pollution strategies.

Benefits of Research Question

Research questions are fundamental to the research process and offer numerous benefits, which include the following:

1. Guides the Research Process

A well-defined research question provides a clear focus and direction for your study. It helps in determining what data to collect, how to collect it, and how to analyze it.

Benefit: Ensures that the research stays on track and addresses the specific issue at hand.

2. Clarifies the Purpose of the Study

Research questions help to articulate the purpose and objectives of the study. They make it clear what the researcher intends to explore, describe, compare, or test.

Benefit: Helps in communicating the goals and significance of the research to others, including stakeholders and funding bodies.

3. Determines the Research Design

The type of research question informs the research design, including the choice of methodology, data collection methods, and analysis techniques.

Benefit: Ensures that the chosen research design is appropriate for answering the specific research question, enhancing the validity and reliability of the results.

4. Enhances Literature Review

A well-crafted research question provides a framework for conducting a thorough literature review. It helps in identifying relevant studies, theories, and gaps in existing knowledge.

Benefit: Facilitates a comprehensive understanding of the topic and ensures that the research is grounded in existing literature.

5. Focuses Data Collection

Research questions help in identifying the specific data needed to answer them. This focus prevents the collection of unnecessary data and ensures that all collected data is relevant to the study.

Benefit: Increases the efficiency of data collection and analysis, saving time and resources.

6. Improves Data Analysis

Having a clear research question aids in the selection of appropriate data analysis methods. It helps in determining how the data will be analyzed to draw meaningful conclusions.

Benefit: Enhances the accuracy and relevance of the findings, making them more impactful.

7. Facilitates Hypothesis Formation

In quantitative research, research questions often lead to the development of hypotheses that can be tested statistically.

Benefit: Provides a basis for hypothesis testing, which is essential for establishing cause-and-effect relationships.

8. Supports Result Interpretation

Research questions provide a lens through which the results of the study can be interpreted. They help in understanding what the findings mean in the context of the research objectives.

Benefit: Ensures that the conclusions drawn from the research are aligned with the original aims and objectives.

9. Enhances Reporting and Presentation

A clear research question makes it easier to organize and present the research findings. It helps in structuring the research report or presentation logically.

Benefit: Improves the clarity and coherence of the research report, making it more accessible and understandable to the audience.

10. Encourages Critical Thinking

Formulating research questions requires critical thinking and a deep understanding of the subject matter. It encourages researchers to think deeply about what they want to investigate and why.

Benefit: Promotes a more thoughtful and analytical approach to research, leading to more robust and meaningful findings.

How to Write a Research Question

Crafting a strong research question is crucial for guiding your study effectively. Follow these steps to write a clear and focused research question:

Identify a Broad Topic:

Start with a general area of interest that you are passionate about or that is relevant to your field. Example: “Climate change”

Conduct Preliminary Research:

Explore existing literature and studies to understand the current state of knowledge and identify gaps. Example: “Impact of climate change on agriculture”

Narrow Down the Topic:

Focus on a specific aspect or issue within the broad topic to make the research question more manageable. Example: “Effect of climate change on crop yields”

Consider the Scope:

Ensure the question is neither too broad nor too narrow. It should be specific enough to be answerable but broad enough to allow for thorough exploration. Example: “How does climate change affect corn crop yields in the Midwest United States?”

Determine the Research Type:

Decide whether your research will be descriptive, comparative, relational, or causal, as this will shape your question. Example: “How does climate change affect corn crop yields in the Midwest United States over the past decade?”

Formulate the Question:

Write a clear, concise question that specifies the variables, population, and context. Example: “What is the impact of increasing temperatures and changing precipitation patterns on corn crop yields in the Midwest United States from 2010 to 2020?”

Ensure Feasibility:

Make sure the question can be answered within the constraints of your resources, time, and data availability. Example: “How have corn crop yields in the Midwest United States been affected by climate change-related temperature increases and precipitation changes between 2010 and 2020?”

Review and Refine:

Evaluate the question for clarity, focus, and relevance. Revise as necessary to ensure it is well-defined and researchable. Example: “What are the specific impacts of temperature increases and changes in precipitation patterns on corn crop yields in the Midwest United States from 2010 to 2020?”

What is a research question?

A research question is a specific query guiding a study’s focus and objectives, shaping its methodology and analysis.

Why is a research question important?

It provides direction, defines scope, ensures relevance, and guides the methodology of the research.

How do you formulate a research question?

Identify a topic, narrow it down, conduct preliminary research, and ensure it is clear, focused, and researchable.

What makes a good research question?

Clarity, specificity, feasibility, relevance, and the ability to guide the research effectively.

Can a research question change?

Yes, it can evolve based on initial findings, further literature review, and the research process.

What is the difference between a research question and a hypothesis?

A research question guides the study; a hypothesis is a testable prediction about the relationship between variables.

How specific should a research question be?

It should be specific enough to provide clear direction but broad enough to allow for comprehensive investigation.

What are examples of good research questions?

Examples include: “How does social media affect academic performance?” and “What are the impacts of climate change on agriculture?”

Can a research question be too broad?

Yes, a too broad question can make the research unfocused and challenging to address comprehensively.

What role does a research question play in literature reviews?

It helps identify relevant studies, guides the search for literature, and frames the review’s focus.

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