There is great concern that scholars are unsure of the differences between research methodology and research methods. This blog post will help you differentiate between the two and offer easy-to-use sources, so your research paper, thesis, or dissertation stands out from the crowd as one that has both a well-written research methodology section and a well-written research methods section.

What is Research Methodology?

Research methodology is a broad view of the different strategies of inquiry available (qualitative, quantitative, or mixed-methods). The scholar will define each with textbook descriptions and narrow down the optimal choice with a rationale of why they chose a particular research methodology. 

A good research methodology section in your paper will document each possible option, define them from various textbooks, and explain why one was selected and why the others were not selected for your particular study. 

The Creswell book includes great content about various methodologies and methods. If your school does not use the Creswell book, keep reading. I will have more recommendations here in this blog post. Cozby and Bates is also another recommendation.

Research Methodology: What is Quantitative Research?

The quantitative inquiry will include a discussion of experimental and non-experimental designs. When talking about these different types of designs, also include a look at comparative, quasi-experimental, exploratory, explanatory, causal, descriptive, and correlational studies. As with the previous paragraph, define these different designs and explain why you selected the one you did for your study.

Research Methodology: What is Qualitative Research?

Qualitative Research will include a discussion of broad qualitative research methodologies such as ethnography, phenomenological, grounded theory, narrative, and case study. Please keep in mind that some will say phenomenological is a methodology and case study is a method. 

It will depend on how you are using this in your own study. See the Yin book on Case Study Research and the Moustakas book on Phenomenological Research Methods for more information if you are considering case study and/or phenomenological.

Research Methodology: What are Mixed Methods?

Mixed methods will include content about concurrent, sequential, or transformative. Some use phrases such as combined, convergence, integrated, and multi-method. In short, the mixed methods study includes both qualitative and quantitative. Therefore, review the above two paragraphs and include information for both in your paper.

When talking about research methodology, I also usually include conversations about cross-sectional versus longitudinal. Field-based versus lab-based is also a good discussion to have in the methodology section. Again, use a textbook definition of each and explain why you are doing the type of study you are doing.

What are Research Methods?

Research methods, techniques, or procedures include questions, data collection, data analysis, interpretation, write-up, validation, and more. I call this the recipe for the study. While the methodology is broad, the methods section is at the micro-level, explaining how you will do the study you plan to do.

Research Methods: What are Quantitative Methods?

Quantitative methods are predetermined. They will need to be discussed in the research proposal. They may include instrument-based (survey, questionnaire, index, etc.) questions. They can also include performance data such as key performance indicators (KPIs). 

Quantitative data can be primary or secondary data. Primary is where the researcher collects the data themselves. Secondary (also called archival) is where the researcher collects data from an established secondary source. Using a quantitative book such as Hair et al., Pallant, or Tabachnick and Fidell will offer guidance in the step-by-step approach for the quantitative methods section.

Research Methods: What is Qualitative Data?

Qualitative data includes a variety of options such as interviews, observations, documents, and audio-visual files. Mooh et al. (2016) conducted a study to see what methods and methodology were included in 146 projects. The top qualitative methods were semi-structured interviews, focus groups, open interviews, participant observations, questionnaires, workshops, structured interviews, document analysis, ethnographic interviews, group interviews, life history, meetings, narrative elicitation, surveys, field studies, and open interviews, and scoping. 

Once the data collection method is described, the methods section should also include a discussion about reliability and validity. Using a qualitative book such as Patton, Yin, or Saldana will help with the step-by-step approach needed for qualitative inquiry. If you are doing a hermeneutical or exegetical study, please see Henson, Robbins, or Osborne for more details.

In summary, this blog helps you to know the difference between research methodology and research methods. They should both be included in your paper. The Methods section, or Chapter 3, as many refer to this should be textbook-heavy. It should include a well-written definition for each keyword and then an explanation of why you chose what you chose. Your manuscript will stand out from the crowd because you are including accurate language in both sections. This is also a good chance for you to use these two sections as teachable moments. Writing them in such a way that you are learning enough to teach other people is the way you become the expert. Keep up the good work!

Cronbach’s Alpha, Coefficient Alpha, Internal Consistency, and Reliability Scores

When I first started hearing about Cronbach’s alpha, coefficient alpha, internal consistency, and reliability scores, I became more confused with each empirical article using different names and wondering if they were talking about the same thing.

In a study to get to the bottom of the situation, I found that they were talking about the same test. Cronbach’s test for internal consistency is the most popular test for scale reliability; however, the naming conventions are all over the place. In this blog post, I urge that we agree to use one name going forward to reduce confusion and demonstrate our understanding of this concept. Coefficient α seems to be the best option.

By the way, alpha should be abbreviated with “α” instead of writing out the word.

What’s the origin of the Coefficient Alpha?

Cronbach introduced the reliability test in 1951. He never intended his name to be used with the test results, as it has been done for decades. In a paper written by Cronbach and Shavelson, he explained that it was an embarrassment that the formula adopted his name, becoming known as Cronbach’s alpha (2004, p. 397).

What is the Coefficient Alpha?

The test itself, whatever it is called, is the most common reliability test for internal consistency; however, there are important factors to know about this test. First, it is a unidimensional test. This means that if you are testing the reliability of an instrument that has four subscales (dimensions), the alpha should be computed for each of the dimensions separately, not for the entire survey.

What are some examples of the Coefficient Alpha?

An example is the Leader-Member Exchange (LMX) instrument by Liden and Maslyn (1998). The overall survey measures LMX. However, the questions that make up each dimension are used to compute the coefficient α. The LMX has the following four dimensions: affect, loyalty, professional respect, and contribution. In their original 1998 study, Liden and Maslyn reported the coefficient alpha as .92 for effect, .90 for loyalty, .78 for professional respect, and .60 for contribution.

They retested with another sample of participants and found the reliability of the data to be .90 for effect, .89 for loyalty, 74 for professional respect, and .57 for contribution. They performed one more test with a different group of participants and found the coefficient alphas as .83 for effect, .79 for professional respect, .66 for loyalty, and .56 for contribution.

Another important factor for the coefficient α is the generally accepted threshold. This, too, has been confusing over the years and likely misinterpreted. We seem to all agree that the range is .00 to 1.0. However, many textbooks say the minimum threshold is .70, meaning if the coefficient α is at or above .70, the data is reliable.

In the example above by Liden and Maslan, many of the scores were below .70. However, the original text by Nunnally actually implies that .70 is “miserably low,” and depending on the purpose of the data, .90 may not be high enough (1975, p. 10).

In my quest to find the answers, I wrote a book chapter titled Clearly Communicating Conceptions of Validity and Reliability (Dean, 2021). I found that a satisfactory level of reliability depends on how it is being used. Nunnally wrote that for modest reliability, “.70 or higher will suffice,” yet for the purpose of applied settings, a score of “.80 is not nearly high enough;” however, “a reliability of .90 is the minimum that should be tolerated, and a reliability of .95 should be considered the desirable standard” (Nunnally, 1978, pp. 245-246). In other words, it is time to raise the standards for reliability.

The Data is Reliable vs The Instrument is Reliable

The final misconception of coefficient alpha is the common phrase where someone says the instrument is valid and reliable. The coefficient alpha is not measuring the reliability of the instrument itself. It measures the data gathered from the instrument. It is more accurate to say the data is reliable instead of saying the instrument is reliable.

References

Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.

Cronbach, L. J., & Shavelson, R. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64(3), 391-418. https://doi.org/10.1177/0013164404266386

Dean, D. (2021). Clearly Communicating Conceptions of Validity and Reliability (M. Bocarnea, B. Winston, & D. Dean, Eds.). In Advancements in organizational data collection and measurements: Strategies for addressing attitudes, beliefs, and behaviors. Hershey, PA: Business Science Reference.

Liden, R. C., & Maslyn, J. M. (1998). Multidimensionality of Leader-Member Exchange: An empirical assessment through scale development. Journal of Management, 24(1), 43-72.

Nunnally, J. (1975). Psychometric theory. 25 years ago and now. Educational Researcher, 4(10), 7-21. https://doi.org/10.2307/1175619

Nunnally J. (1978) An Overview of Psychological Measurement. In: Wolman B.B. (eds) Clinical Diagnosis of Mental Disorders. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-2490-4_4

Can APA be in first person vs. second person vs. third person? The style and tone of your dissertation will depend greatly on your point of view. You may want to make this decision with your dissertation chair as some prefer the first person while others prefer the third person. Also, deciding on this direction early in the dissertation writing process will help to avoid rewriting sections of the paper later. It is much easier to start with the correct point of view rather than going back to try and change all of the occurrences of the third person to the first person later.

Can APA Be In First Person?

Writing in the first person is recommended by APA to avoid ambiguity. First-person writing includes words such as “I,” “me,” “we,” and “us.” When writing a paper by yourself, it may be appropriate to use words such as “I,” whereas writing a paper with more than one author would enable you to write with words such as “we.” Be careful not to use “we” if you are the only author. This causes me to question if you have a mouse in your pocket. APA 7 prefers for authors to not refer to themselves in the third person. Instead of saying “the author did research,” you will simply say, “I did research.”

Can APA Be In Second Person?

Second-person writing includes words such as “you” and “your.” It is best to avoid the second-person point of view in scholarly writing entirely. This may go back to the psychological teaching of using “I statements” instead of “you statements” for best effect. When a person uses “you” instead of “I,” it seems to provoke a defensive stance from the recipient as if they are being accused of something or they are having a finger pointed at them. Therefore, it is best to avoid writing and speaking with “you” statements.

Can APA be In Third Person?

Third-person writing uses words such as the researcher, the scholar, or the writer (instead of I). Historically, many of us were taught to use the third person for formal writing. However, a shift in this decision was made at some point in time, and APA 7 clearly encourages writers to use the first-person point of view instead of the third person. Using third-person writing can get complicated and confusing in a dissertation because there are so many mentions of other authors that the reader becomes unsure if the text is talking about the dissertation student or the empirical author. Again, please speak with your dissertation Chair to see which direction they want you to head in.

If you want expert advice on how to write your dissertation, schedule a free strategy call today!

Years ago, I was on a dissertation committee where the student spent 1-year working on their dissertation. Prior to the dissertation defense, I was sent a copy of their final dissertation to read. Frankly, I was disappointed. Not for me, but for the student. My gut reaction was that he had spent a year doing the research and found no significant findings. Did this mean they did something wrong or that it was a waste of time? NO – Absolutely not. Instead, I had to reframe my thought. The value of reporting insignificant or no significant findings is huge.

Recently, I chaired another dissertation and the student used a well-known survey. He had plenty of data that proved to be reliable using the coefficient alpha. However, when we looked at the statistical significance of his data, we found it to be greater than .05. That same feeling appeared in my gut. I thought to myself, “Oh no. Poor guy. He did all this work and has nothing to show for it.” Then I reconsidered the value of his project and the importance of publishing.

When we do a research project and find the results are insignificant, the findings are hugely valuable because they are helping to define the theory. The main goal of testing theory is to see if it works with specific conditions. When we find that it does not work with a specific set of conditions, we should shout it from the mountaintop to show we have found something that is drawing the line in the sand, something that has put up a boundary or a guardrail to the theory.

Instead of considering insignificant as a negative term, we should view it in a positive light. Having an insignificant finding means we have found something that is defining the theory in such a way that a, traditionally thought of, statistically significant finding would not.

I encourage you to publish your research; all of your research. Instead of viewing it as a failed project, which it certainly was not, please reflect on the positive benefits of publishing such a paper.

When papers are published about insignificant results, we see the boundaries of the theory. For many researchers, this helps us to know what not to test in the future and gives us new ideas of what to test. It also helps us to see that the theory is not immortal. Personally, I find the insignificant findings quite attractive to me as a researcher because we are pushing the limits to see what will and will not be included in the theory.

Publish your work, all of your work. Each research project that is done with high-quality standards demonstrating robust methodology is valuable, regardless of the findings.

The last thing we want to do is program the Variable View in SPSS wrong. Especially after spending months or even years on writing the research proposal, defending the proposal, applying and getting human subjects research approval, and setting up the survey. The Variable View in SPSS is not intuitive. This blog post will give you easy instruction to complete this seemingly menial, but highly important task.

Once the data is imported into SPSS, the Variable View tab needs to be fixed. This tab should show each name of your dataset, the type of data, the width of data, decimals, etc. At first glance, many may assume it is correct as is; however, there is a 99.9% chance that the Variable View needs your attention.

Name

First, make sure each row of data has been transferred into SPSS. I call this the gut check. Just make sure everything looks the way you would expect. The name of each column should be listed as a row in Variable View. Feel free to revise the names in the Name column so they are logically named and not too long as these names will show up in the SPSS output.

Type

The type of data is important. The options include numeric, comma, dot, etc. Typically, we need the data to be numeric, although there may be string data if you asked an open-ended question. String data will not show up as an option in SPSS analysis commands because SPSS needs to compute numbers, not alpha characters.

To fix this, a numerical code will need to be created. Dummy codes, as some call this, basically assign a numerical value to the category. For example, instead of listing male and female, code the two options as 0 and 1. To clean up SPSS, I will sometimes remove the data that I will not use during the analysis. However, if in doubt of knowing if it should stay there or not, just leave it.

Width

The width column is basically how long the data will be. It is similar to the length in Microsoft Excel. If your data is 01-99, the default width should be 2. It does not hurt to have this number bigger than needed, but you won’t want it smaller. I don’t usually need to touch this field in the Variable View.

Decimal

The decimal column is for aesthetics. As a general rule, two numbers after the decimal is usually sufficient. If you want to round to the nearest whole number, place a zero in this field. As an FYI, the width of data for APA tables may follow this rule of thumb: whole numbers (no decimal), average/mean (1 decimal), p-values (3-decimals).

Label

The label column is more of a notes field. I don’t usually use this, but for some researchers, it may be helpful. This is completely up to you. It will not show on your SPSS output, so feel free to use whatever you want in the label field or to ignore it entirely.

Values

Values and measure are the last two columns that I want to include in this blog post. Values will need to be revised to match your survey. If you used 1 for male and 2 for female in your survey, this would need to match in SPSS. If you used a Likert scale, add the value labels as indicated on the original survey. For example, 1 = greatly inhibits, 2 = somewhat inhibits, 3 = slightly inhibits, etc. It is important to make sure this information matches how the survey was set up.

Measure

The last column that could throw a wrench in your plans is measure. The following three options are available in SPSS: Nominal, Ordinal, and Scale.

Notice how certain datasets could be scale, ordinal, or nominal depending on how it is presented. For example, age from 0 – 120 would be scale, but if I categorize the ages into groups of 0-10, 11-20, 21-30, 31-40, etc. then it would be considered nominal. One way to think of these options is in a hierarchy. Scale is the highest level of the hierarchy.

It is possible to change scale data to ordinal or nominal, but not possible to change ordinal or nominal data into scale. The second level in the hierarchy is ordinal. It is possible to change ordinal data into nominal, but it is not possible to move ordinal data into scale.

Finally, nominal data is just that. It cannot be changed to ordinal or scale. Think about the measure options as a hierarchy with one being Scale, two being Ordinal, and three being nominal. It is mathematically possible to move the scale to ordinal or nominal, but it is not possible to move nominal to ordinal or scale.

Scale

Is your data continuous such as age or income? Scale might be the best option if your data could be placed on a measurement stick such as a ruler, thermometer, or a scale to measure weight.

One might assume that scale is used for Likert-type scales, but that is not correct. Scale is used for a measurement of 0-1000, as an example. This may also include test scores for ACT, GED, GRE, or SAT. The possible option may only be 1 – 36, but it would still be treated the same as if the possible outcome was 1-100 or an infinite number beyond 1000.

Ordinal

When your data is categorized with some type of natural order or rank order, choose ordinal. This may include ranking data as mild, medium, or hot. It could also include Likert-type scales such as measuring levels of satisfaction from 1 = highly dissatisfied to 5 = highly satisfied or measuring cultural differences with 1 = greatly inhibits, 2 = somewhat inhibits, 3 = slightly inhibits, etc. Ordinal data could also include a grading system such as A, B, C, etc.

Nominal

Categorical variables cannot be added, subtracted, divided, or multiplied. When your data is categorized but has no specific rank order, select nominal. This could include the department a person works in, gender, mode of transportation, region, or religious affiliation.

Conclusion

In closing, the Variable View in SPSS is something that deserves attention. This seemingly boring task, especially defining the measures, could have an unintended impact on your results. If in doubt, reach out to your methodologist for help or reach out to us and schedule a strategy call!

To abbreviate or not to abbreviate – that is the question!

Have you found yourself writing your dissertation and you question if you should use a country name or some variation of an abbreviation? This blog post will help.

As an editor and professor, I read lots of dissertations and I find that it is common for there to be inconsistency with how students write out country names or use abbreviations in general. This blog post will focus on the abbreviation of countries, when to use periods with an abbreviation, and when to use abbreviations in general.

As a general rule, most abbreviations need to be introduced with the full spelling of each word first. For example, the American Psychological Association (APA) is the writing style of choice. Notice how APA is spelled out first. This should be the process with most abbreviations.

Commonly used abbreviations may not need to be written out. If the abbreviation is listed as a word in Merriam-Webster’s Collegiate Dictionary then it will not need to be written out prior to using it as an abbreviation. Examples of such abbreviations include APA, FBI, HTML, NASA, NBA, NFL, and PDF.

When writing your dissertation in APA 7, the rule of thumb is to spell out the full name of the country if it is used as a noun. For example, the United Kingdom or the United States of America. Below is an example:

Smith (2022) conducted a study of 331 healthcare workers in the United States of America (noun). She acquired their names and contact information from the U.S. (adjective) Database of Doctors.

An author can abbreviate the country name if it is used as an adjective. For example, the U.S. stock report or the U.K. education system. In the example above, I use the U.S. Database of Doctors.

Notice that when abbreviating a country, the abbreviation includes periods unless it is the heading or on the title page. It is best to avoid using abbreviations if at all possible in a heading or on the title page.

Using periods in the abbreviation of a country is counterintuitive as many other abbreviations do not use periods with APA style. If the abbreviation is in all capital letters, such as CIA, NYT, or TOC, the periods are not needed.

If the abbreviation is for measurements, the periods are not needed unless it is for inches. For example measurements of length would be ft or mi for feet or mile. The abbreviation for inch actually uses one period to avoid confusion (in.). Measurements of time would be abbreviated hr or min for hour or minute.

Other abbreviations should be used only if they assist the reader with clarity or if the abbreviation is easily recognized. It is usually easier to read a chapter if the words are spelled out. For example, transformational leadership theory includes the following four domains: idealized influence, inspirational motivation, intellectual stimulation, and individual consideration. Many authors will abbreviate each domain as II, IM, IS, and IC. While this is convenient on one hand, it can also cause challenges with readability on the other hand.

The problem usually enters the paper as the abbreviations are not used consistently. As a general rule, if an abbreviation is used, it should be used at least three times. Otherwise, it is not really adding any value to the paper. Another rule of thumb is that if an abbreviation is used, it should be reintroduced periodically, yet consistently. In the example of transformational leadership domains, I would advise against using the abbreviations; however, if an author wants to use them I suggest introducing the abbreviations the first time the words are used and then using the abbreviations for the rest of the section (think Microsoft style 2^nd heading). When the next section is presented, I would spell out the abbreviation again the first time in that heading and then use the abbreviations consistently throughout that section. This reminds the reader periodically of the full name without them having to search for it.

Overall, my advice is to avoid abbreviations unless they are absolutely necessary or they increase the readability of the document. If an abbreviation is used, make sure they are used properly and consistently. One final thought on readability is to use the Microsoft readability feature. To do this with your Word document open, select Home > Editor > Document Stats > OK. The readability scores will include Flesh Reading Ease and Flesch-Kincaid Grade Level. I have seen recommendations for dissertations to be at or below the 8/9 grade level. Check with your school to see if they have such a requirement.

Please contact us if have any questions about writing your dissertation!

Writing the Interview Questions

In my previous post, I showed you how to draft the research questions that are an extension of your problem and purpose statements. Now that I have my research questions, I need to write my interview questions for research. These are questions that I will ask my participants and will use personal pronouns such as “you” and “I” because we will be using this in a conversation with our participants.

Importantly, interview questions for research should be aligned to my research questions and reflect the themes from the literature. Because of this, it’s best to hold off on completely writing your interview guide until you’ve completed your literature review, or at least until you’ve completed your annotated bibliography for your literature review.

If you organize it well, you can draft some interview questions for research that you might like to ask your participants as you read research studies for your literature review. These can be questions you enter into your research journal that gets revised at the end. But this is a great way to have a large collection of questions from which to design your interview guide.

In addition to aligning the interview questions for research to my literature review, I also want to align them to my research questions. If I look at my three research questions, I might write three or so interview questions for each, knowing that I may add probing questions as I go to get more insight from my participants. In doing this, I will, by default, be aligning my interview questions to my theoretical framework, as two of my research questions contained the tenets of my theoretical framework.

See the image below:

Other considerations in writing your interview questions

Just like the research questions, there are considerations for your interview questions. The first is that in most cases – especially in a one-on-one interview – the interview questions should be open-ended. This allows the participant to answer in detail rather than answering yes or no. It’s easy to slip into writing closed-ended questions, so be sure to check your questions before you submit them to your chair for review. If you can answer yes or no to the questions, change them to open-ended “how” or “why” questions.

In addition to the open-ended nature of the questions, the questions should be conversational in a way that enables the participant to understand what you’re asking. Remember, even though your participants are living the phenomenon you’re asking about, they’re not schooled in the terms and jargon of your theoretical framework or research literature.

Check the readability score of your interview questions using online readability checkers like the one on plagarismsearch.com. Practice delivering interview questions and see how easily you can ask them and how they sound. Practice your questions with a friend or colleague, not for the purposes of collecting data but just to see if they think your questions make sense. We can get into a researcher ‘blind spot’ when we write interview questions, forgetting that these questions need to be for people not ensconced in scholarly literature and writing.

Finalizing the interview guide

As you pull together the various interview questions you have collected throughout your writing process, you will want to sculpt your draft questions so that they flow well and are not redundant. You will want to make sure that your interview questions are not focused too heavily on one aspect; they should cover the depth and breadth of the themes in your research. Depending on the number of interviews you will conduct with each participant, you also need to ensure that you consider how long it will take to get through the interview questions you have designed.

The final interview guide is for you to use during your interviews with participants. In many styles of qualitative research, the questions are just there for you to refer to, but not necessarily used in the precise order you have them listed because you are having a conversational encounter. Your participants, if given the time and space to answer your questions, may answer the question you have asked and address a later question, too. Be prepared to move seamlessly among your interview questions, pulling up the relevant question based on where the interview takes you.

Learning how to develop research questions is a big step in designing your research study and forces you to think about what you want to know as you gather data from your participants. No longer is the topic you’ve selected just a hypothetical issue you’re interested in addressing; once you have written the research questions, you realize that you must explain to the reader exactly what it is that you want to know at the end of your research. However, I often talk with dissertation writers who are confused with the difference between research questions and interview questions. Or, I will be reviewing draft interview questions for a client and come across questions that are much more suited for interview questions.

What, then, is the difference between research questions and interview questions? In short, research questions are questions you want to answer by completing your study. Interview questions are questions you ask of your participants to help you address your research questions. Sounds like circular logic, doesn’t it? Let’s dig a little deeper.

The need for interview questions is self-evident: you have participants and so you need to ask them questions in a qualitative study in order to collect data. So, why do you need research questions? Research questions are the pillars of your study: those key concepts you want to explore so that you can address the gap you have identified. See the image below:

Image description: Identification of the problem or gap leads to the creation of the purpose of your study. You’ve identified the purpose – the statement that explains what your study will do. The purpose statement is then further refined by the use of research questions.

Examples of research questions

What can we do to learn how to develop research questions? Writing the research questions is dependent upon several things: (1) the problem or gap you have identified, (2) the purpose of your study, (3) the qualitative approach you are using, and, sometimes (4) your theoretical framework. Let’s look at an example:

My problem statement is:

Instructional barriers exist for nursing educators in teaching with full body patient simulators remotely when instruction on campus is limited due to the global pandemic.

The gap I’ve identified is:

While there is extensive literature on the use of full body patient simulators in on-campus nursing instruction(citation of several sources would be listed here in a real example), there is a lack of research on how nursing educators can modify this instruction when learning is remote.

The purpose statement is:

The purpose of this qualitative descriptive study is to explore how nursing educators describe their strategies in teaching with full body patient simulators in remote delivery for ASN nursing students at a community college.

The theoretical framework I’ve chosen is:

Self-efficacy theory by Albert Bandur

In the above, I have addressed all four elements that I’ll need for creating my research questions, including #3, the qualitative approach. I’ve sneaked that into my purpose statement by writing, “this qualitative descriptive study.” Given that we know these four things, now we can begin writing our research questions.

How many research questions should you create?

I’m a fan of “less is more” when it comes to research questions, but again, the four elements above will dictate how many you need. Another factor – possibly a fifth one to consider – is how many does your dissertation chair want you to have? That will always be a consideration.

However, remember this: the number of research questions you have will determine how you divide up your data and report on it in chapter four. Thus, if you have four research questions, then you will have to divide up the reporting of your data into four discrete parts with each research question as a heading. If you have two research questions, you will divide the reporting of your data into two discrete parts.

Further warning, though: I’ve worked with many dissertation writers who believe more research questions are better, and then end up having to figure out how to split their data in a way that the answers to each research question is unique from the rest. Making it not sound redundant is challenging.

Pulling it together

Given the pieces of the puzzle I’ve listed above, let’s draft some research questions.

How do nursing educators describe their experiences in teaching ASN nursing students using full body patient simulators in a remote environment?

Note my setup of the question – how do they describe their experiences? That addresses my descriptive approach. This question is almost a rephrasing of the purpose statement as a question.

How do nursing educators describe the mastery experiences that influenced their strategies used in teaching with full-body patient simulators in a remote environment?

Here I’ve used describe again, and now I’m bringing in one of the tenets of self-efficacy theory that I will be using in my study – mastery experiences.

How do nursing educators describe the social models that influenced their strategies used in teaching with full-body patient simulators in a remote environment?

This question is similar to the one above it, but uses a different tenet of self-efficacy theory.

Here I will stop with my questions. In this made-up research study, I am only going to use two of the tenets of self-efficacy theory and I’ve created research questions around them.

Looking over my three questions, I see that two are different enough from each other – addressing mastery experiences and another addressing social models – to gather unique data to result in clearly discrete parts in my write-up in chapter 4. The first question is broader and gives room for me to create interview questions that perhaps align with the themes in the literature.

In the next post, I will show you how to develop your interview questions for your research to ensure that they are an outgrowth of your research questions and written in a way that will engage your participants, generating good data for your study. So, did you learn how to develop research questions? Let us know where you are in developing your research questions in the comments below.