writing a questionnaire for dissertation

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13.1 Writing effective survey questions and questionnaires

Learning objectives.

Learners will be able to…

• Describe some of the ways that survey questions might confuse respondents and how to word questions and responses clearly
• Create mutually exclusive, exhaustive, and balanced response options
• Define fence-sitting and floating
• Describe the considerations involved in constructing a well-designed questionnaire
• Discuss why pilot testing is important

In the previous chapter, we reviewed how researchers collect data using surveys. Guided by their sampling approach and research context, researchers should choose the survey approach that provides the most favorable tradeoffs in strengths and challenges. With this information in hand, researchers need to write their questionnaire and revise it before beginning data collection. Each method of delivery requires a questionnaire, but they vary a bit based on how they will be used by the researcher. Since phone surveys are read aloud, researchers will pay more attention to how the questionnaire sounds than how it looks. Online surveys can use advanced tools to require the completion of certain questions, present interactive questions and answers, and otherwise afford greater flexibility in how questionnaires are designed. As you read this chapter, consider how your method of delivery impacts the type of questionnaire you will design.

Start with operationalization

The first thing you need to do to write effective survey questions is identify what exactly you wish to know. As silly as it sounds to state what seems so completely obvious, we can’t stress enough how easy it is to forget to include important questions when designing a survey. Begin by looking at your research question and refreshing your memory of the operational definitions you developed for those variables from Chapter 11. You should have a pretty firm grasp of your operational definitions before starting the process of questionnaire design. You may have taken those operational definitions from other researchers’ methods, found established scales and indices for your measures, or created your own questions and answer options.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

STOP! Make sure you have a complete operational definition for the dependent and independent variables in your research question. A complete operational definition contains the variable being measured, the measure used, and how the researcher interprets the measure. Let’s make sure you have what you need from Chapter 11 to begin writing your questionnaire.

List all of the dependent and independent variables in your research question.

• It’s normal to have one dependent or independent variable. It’s also normal to have more than one of either.
• Make sure that your research question (and this list) contain all of the variables in your hypothesis. Your hypothesis should only include variables from you research question.

For each variable in your list:

• If you don’t have questions and answers finalized yet, write a first draft and revise it based on what you read in this section.
• If you are using a measure from another researcher, you should be able to write out all of the questions and answers associated with that measure. If you only have the name of a scale or a few questions, you need to access to the full text and some documentation on how to administer and interpret it before you can finish your questionnaire.
• For example, an interpretation might be “there are five 7-point Likert scale questions…point values are added across all five items for each participant…and scores below 10 indicate the participant has low self-esteem”
• Don’t introduce other variables into the mix here. All we are concerned with is how you will measure each variable by itself. The connection between variables is done using statistical tests, not operational definitions.
• Detail any validity or reliability issues uncovered by previous researchers using the same measures. If you have concerns about validity and reliability, note them, as well.

TRACK 2 (IF YOU  AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are interested in researching the decision-making processes of parents of elementary-aged children during the beginning of the COVID-19 pandemic in 2020. Specifically, you want to if and how parents’ socioeconomic class impacted their decisions about whether to send their children to school in-person or instead opt for online classes or homeschooling.

• Create a working research question for this topic.
• What is the dependent variable in this research question? The independent variable? What other variables might you want to control?

For the independent variable, dependent variable, and at least one control variable from your list:

• What measure (the specific question and answers) might you use for each one? Write out a first draft based on what you read in this section.

If you completed the exercise above and listed out all of the questions and answer choices you will use to measure the variables in your research question, you have already produced a pretty solid first draft of your questionnaire! Congrats! In essence, questionnaires are all of the self-report measures in your operational definitions for the independent, dependent, and control variables in your study arranged into one document and administered to participants. There are a few questions on a questionnaire (like name or ID#) that are not associated with the measurement of variables. These are the exception, and it’s useful to think of a questionnaire as a list of measures for variables. Of course, researchers often use more than one measure of a variable (i.e., triangulation ) so they can more confidently assert that their findings are true. A questionnaire should contain all of the measures researchers plan to collect about their variables by asking participants to self-report.

Sticking close to your operational definitions is important because it helps you avoid an everything-but-the-kitchen-sink approach that includes every possible question that occurs to you. Doing so puts an unnecessary burden on your survey respondents. Remember that you have asked your participants to give you their time and attention and to take care in responding to your questions; show them your respect by only asking questions that you actually plan to use in your analysis. For each question in your questionnaire, ask yourself how this question measures a variable in your study. An operational definition should contain the questions, response options, and how the researcher will draw conclusions about the variable based on participants’ responses.

Writing questions

So, almost all of the questions on a questionnaire are measuring some variable. For many variables, researchers will create their own questions rather than using one from another researcher. This section will provide some tips on how to create good questions to accurately measure variables in your study. First, questions should be as clear and to the point as possible. This is not the time to show off your creative writing skills; a survey is a technical instrument and should be written in a way that is as direct and concise as possible. As I’ve mentioned earlier, your survey respondents have agreed to give their time and attention to your survey. The best way to show your appreciation for their time is to not waste it. Ensuring that your questions are clear and concise will go a long way toward showing your respondents the gratitude they deserve. Pilot testing the questionnaire with friends or colleagues can help identify these issues. This process is commonly called pretesting, but to avoid any confusion with pretesting in experimental design, we refer to it as pilot testing.

Related to the point about not wasting respondents’ time, make sure that every question you pose will be relevant to every person you ask to complete it. This means two things: first, that respondents have knowledge about whatever topic you are asking them about, and second, that respondents have experienced the events, behaviors, or feelings you are asking them to report. If you are asking participants for second-hand knowledge—asking clinicians about clients’ feelings, asking teachers about students’ feelings, and so forth—you may want to clarify that the variable you are asking about is the key informant’s perception of what is happening in the target population. A well-planned sampling approach ensures that participants are the most knowledgeable population to complete your survey.

If you decide that you do wish to include questions about matters with which only a portion of respondents will have had experience, make sure you know why you are doing so. For example, if you are asking about MSW student study patterns, and you decide to include a question on studying for the social work licensing exam, you may only have a small subset of participants who have begun studying for the graduate exam or took the bachelor’s-level exam. If you decide to include this question that speaks to a minority of participants’ experiences, think about why you are including it. Are you interested in how studying for class and studying for licensure differ? Are you trying to triangulate study skills measures? Researchers should carefully consider whether questions relevant to only a subset of participants is likely to produce enough valid responses for quantitative analysis.

Many times, questions that are relevant to a subsample of participants are conditional on an answer to a previous question. A participant might select that they rent their home, and as a result, you might ask whether they carry renter’s insurance. That question is not relevant to homeowners, so it would be wise not to ask them to respond to it. In that case, the question of whether someone rents or owns their home is a filter question , designed to identify some subset of survey respondents who are asked additional questions that are not relevant to the entire sample. Figure 13.1 presents an example of how to accomplish this on a paper survey by adding instructions to the participant that indicate what question to proceed to next based on their response to the first one. Using online survey tools, researchers can use filter questions to only present relevant questions to participants.

Researchers should eliminate questions that ask about things participants don’t know to minimize confusion. Assuming the question is relevant to the participant, other sources of confusion come from how the question is worded. The use of negative wording can be a source of potential confusion. Taking the question from Figure 13.1 about drinking as our example, what if we had instead asked, “Did you not abstain from drinking during your first semester of college?” This is a double negative, and it’s not clear how to answer the question accurately. It is a good idea to avoid negative phrasing, when possible. For example, “did you not drink alcohol during your first semester of college?” is less clear than “did you drink alcohol your first semester of college?”

Another 877777771`issue arises when you use jargon, or technical language, that people do not commonly know. For example, if you asked adolescents how they experience imaginary audience , they would find it difficult to link those words to the concepts from David Elkind’s theory. The words you use in your questions must be understandable to your participants. If you find yourself using jargon or slang, break it down into terms that are more universal and easier to understand.

Asking multiple questions as though they are a single question can also confuse survey respondents. There’s a specific term for this sort of question; it is called a double-barreled question . Figure 13.2 shows a double-barreled question. Do you see what makes the question double-barreled? How would someone respond if they felt their college classes were more demanding but also more boring than their high school classes? Or less demanding but more interesting? Because the question combines “demanding” and “interesting,” there is no way to respond yes to one criterion but no to the other.

Another thing to avoid when constructing survey questions is the problem of social desirability . We all want to look good, right? And we all probably know the politically correct response to a variety of questions whether we agree with the politically correct response or not. In survey research, social desirability refers to the idea that respondents will try to answer questions in a way that will present them in a favorable light. (You may recall we covered social desirability bias in Chapter 11. )

Perhaps we decide that to understand the transition to college, we need to know whether respondents ever cheated on an exam in high school or college for our research project. We all know that cheating on exams is generally frowned upon (at least I hope we all know this). So, it may be difficult to get people to admit to cheating on a survey. But if you can guarantee respondents’ confidentiality, or even better, their anonymity, chances are much better that they will be honest about having engaged in this socially undesirable behavior. Another way to avoid problems of social desirability is to try to phrase difficult questions in the most benign way possible. Earl Babbie (2010) [1] offers a useful suggestion for helping you do this—simply imagine how you would feel responding to your survey questions. If you would be uncomfortable, chances are others would as well.

Try to step outside your role as researcher for a second, and imagine you were one of your participants. Evaluate the following:

• Is the question too general? Sometimes, questions that are too general may not accurately convey respondents’ perceptions. If you asked someone how they liked a certain book and provide a response scale ranging from “not at all” to “extremely well”, and if that person selected “extremely well,” what do they mean? Instead, ask more specific behavioral questions, such as “Will you recommend this book to others?” or “Do you plan to read other books by the same author?” 
• Is the question too detailed? Avoid unnecessarily detailed questions that serve no specific research purpose. For instance, do you need the age of each child in a household or is just the number of children in the household acceptable? However, if unsure, it is better to err on the side of details than generality.
• Is the question presumptuous? Does your question make assumptions? For instance, if you ask, “what do you think the benefits of a tax cut would be?” you are presuming that the participant sees the tax cut as beneficial. But many people may not view tax cuts as beneficial. Some might see tax cuts as a precursor to less funding for public schools and fewer public services such as police, ambulance, and fire department. Avoid questions with built-in presumptions.
• Does the question ask the participant to imagine something? Is the question imaginary? A popular question on many television game shows is “if you won a million dollars on this show, how will you plan to spend it?” Most participants have never been faced with this large amount of money and have never thought about this scenario. In fact, most don’t even know that after taxes, the value of the million dollars will be greatly reduced. In addition, some game shows spread the amount over a 20-year period. Without understanding this “imaginary” situation, participants may not have the background information necessary to provide a meaningful response.

Try to step outside your role as researcher for a second, and imagine you were one of your participants. Use the following prompts to evaluate your draft questions from the previous exercise:

Cultural considerations

When researchers write items for questionnaires, they must be conscientious to avoid culturally biased questions that may be inappropriate or difficult for certain populations.

[insert information related to asking about demographics and how this might make some people uncomfortable based on their identity(ies) and how to potentially address]

You should also avoid using terms or phrases that may be regionally or culturally specific (unless you are absolutely certain all your respondents come from the region or culture whose terms you are using). When I first moved to southwest Virginia, I didn’t know what a holler was. Where I grew up in New Jersey, to holler means to yell. Even then, in New Jersey, we shouted and screamed, but we didn’t holler much. In southwest Virginia, my home at the time, a holler also means a small valley in between the mountains. If I used holler in that way on my survey, people who live near me may understand, but almost everyone else would be totally confused.

Testing questionnaires before using them

Finally, it is important to get feedback on your survey questions from as many people as possible, especially people who are like those in your sample. Now is not the time to be shy. Ask your friends for help, ask your mentors for feedback, ask your family to take a look at your survey as well. The more feedback you can get on your survey questions, the better the chances that you will come up with a set of questions that are understandable to a wide variety of people and, most importantly, to those in your sample.

In sum, in order to pose effective survey questions, researchers should do the following:

• Identify how each question measures an independent, dependent, or control variable in their study.
• Keep questions clear and succinct.
• Make sure respondents have relevant lived experience to provide informed answers to your questions.
• Use filter questions to avoid getting answers from uninformed participants.
• Avoid questions that are likely to confuse respondents—including those that use double negatives, use culturally specific terms or jargon, and pose more than one question at a time.
• Imagine how respondents would feel responding to questions.
• Get feedback, especially from people who resemble those in the researcher’s sample.

Table 13.1 offers one model for writing effective questionnaire items.

Let’s complete a first draft of your questions.

• In the first exercise, you wrote out the questions and answers for each measure of your independent and dependent variables. Evaluate each question using the criteria listed above on effective survey questions.
• Type out questions for your control variables and evaluate them, as well. Consider what response options you want to offer participants.

Now, let’s revise any questions that do not meet your standards!

•  Use the BRUSO model in Table 13.1 for an illustration of how to address deficits in question wording. Keep in mind that you are writing a first draft in this exercise, and it will take a few drafts and revisions before your questions are ready to distribute to participants.
• In the first exercise, you wrote out the question and answers for your independent, dependent, and at least one control variable. Evaluate each question using the criteria listed above on effective survey questions.
•  Use the BRUSO model in Table 13.1 for an illustration of how to address deficits in question wording. In real research, it will take a few drafts and revisions before your questions are ready to distribute to participants.

Writing response options

While posing clear and understandable questions in your survey is certainly important, so too is providing respondents with unambiguous response options. Response options are the answers that you provide to the people completing your questionnaire. Generally, respondents will be asked to choose a single (or best) response to each question you pose. We call questions in which the researcher provides all of the response options closed-ended questions . Keep in mind, closed-ended questions can also instruct respondents to choose multiple response options, rank response options against one another, or assign a percentage to each response option. But be cautious when experimenting with different response options! Accepting multiple responses to a single question may add complexity when it comes to quantitatively analyzing and interpreting your data.

Surveys need not be limited to closed-ended questions. Sometimes survey researchers include open-ended questions in their survey instruments as a way to gather additional details from respondents. An open-ended question does not include response options; instead, respondents are asked to reply to the question in their own way, using their own words. These questions are generally used to find out more about a survey participant’s experiences or feelings about whatever they are being asked to report in the survey. If, for example, a survey includes closed-ended questions asking respondents to report on their involvement in extracurricular activities during college, an open-ended question could ask respondents why they participated in those activities or what they gained from their participation. While responses to such questions may also be captured using a closed-ended format, allowing participants to share some of their responses in their own words can make the experience of completing the survey more satisfying to respondents and can also reveal new motivations or explanations that had not occurred to the researcher. This is particularly important for mixed-methods research. It is possible to analyze open-ended response options quantitatively using content analysis (i.e., counting how often a theme is represented in a transcript looking for statistical patterns). However, for most researchers, qualitative data analysis will be needed to analyze open-ended questions, and researchers need to think through how they will analyze any open-ended questions as part of their data analysis plan. Open-ended questions cannot be operationally defined because you don’t know what responses you will get. We will address qualitative data analysis in greater detail in Chapter 19.

To write an effective response options for closed-ended questions, there are a couple of guidelines worth following. First, be sure that your response options are mutually exclusive . Look back at Figure 13.1, which contains questions about how often and how many drinks respondents consumed. Do you notice that there are no overlapping categories in the response options for these questions? This is another one of those points about question construction that seems fairly obvious but that can be easily overlooked. Response options should also be exhaustive . In other words, every possible response should be covered in the set of response options that you provide. For example, note that in question 10a in Figure 13.1, we have covered all possibilities—those who drank, say, an average of once per month can choose the first response option (“less than one time per week”) while those who drank multiple times a day each day of the week can choose the last response option (“7+”). All the possibilities in between these two extremes are covered by the middle three response options, and every respondent fits into one of the response options we provided.

Earlier in this section, we discussed double-barreled questions. Response options can also be double barreled, and this should be avoided. Figure 13.3 is an example of a question that uses double-barreled response options. Other tips about questions are also relevant to response options, including that participants should be knowledgeable enough to select or decline a response option as well as avoiding jargon and cultural idioms.

Even if you phrase questions and response options clearly, participants are influenced by how many response options are presented on the questionnaire. For Likert scales, five or seven response options generally allow about as much precision as respondents are capable of. However, numerical scales with more options can sometimes be appropriate. For dimensions such as attractiveness, pain, and likelihood, a 0-to-10 scale will be familiar to many respondents and easy for them to use. Regardless of the number of response options, the most extreme ones should generally be “balanced” around a neutral or modal midpoint. An example of an unbalanced rating scale measuring perceived likelihood might look like this:

Unlikely  |  Somewhat Likely  |  Likely  |  Very Likely  |  Extremely Likely

Because we have four rankings of likely and only one ranking of unlikely, the scale is unbalanced and most responses will be biased toward “likely” rather than “unlikely.” A balanced version might look like this:

Extremely Unlikely  |  Somewhat Unlikely  |  As Likely as Not  |  Somewhat Likely  | Extremely Likely

In this example, the midpoint is halfway between likely and unlikely. Of course, a middle or neutral response option does not have to be included. Researchers sometimes choose to leave it out because they want to encourage respondents to think more deeply about their response and not simply choose the middle option by default. Fence-sitters are respondents who choose neutral response options, even if they have an opinion. Some people will be drawn to respond, “no opinion” even if they have an opinion, particularly if their true opinion is the not a socially desirable opinion. Floaters , on the other hand, are those that choose a substantive answer to a question when really, they don’t understand the question or don’t have an opinion. 

As you can see, floating is the flip side of fence-sitting. Thus, the solution to one problem is often the cause of the other. How you decide which approach to take depends on the goals of your research. Sometimes researchers specifically want to learn something about people who claim to have no opinion. In this case, allowing for fence-sitting would be necessary. Other times researchers feel confident their respondents will all be familiar with every topic in their survey. In this case, perhaps it is okay to force respondents to choose one side or another (e.g., agree or disagree) without a middle option (e.g., neither agree nor disagree) or to not include an option like “don’t know enough to say” or “not applicable.” There is no always-correct solution to either problem. But in general, including middle option in a response set provides a more exhaustive set of response options than one that excludes one. 

==This came from 10.3 under “Measuring unidimensional concepts” but it seems more appropriate in the chapter about writing survey questions. We need to make sure this section flows well. Maybe there should be a better organized subsection on rating scales?  Where does this go? Does it need any revision?===

The number of response options on a typical rating scale is usually five or seven, though it can range from three to 11. Five-point scales are best for unipolar scales where only one construct is tested, such as frequency (Never, Rarely, Sometimes, Often, Always). Seven-point scales are best for bipolar scales where there is a dichotomous spectrum, such as liking (Like very much, Like somewhat, Like slightly, Neither like nor dislike, Dislike slightly, Dislike somewhat, Dislike very much). For bipolar questions, it is useful to offer an earlier question that branches them into an area of the scale; if asking about liking ice cream, first ask “Do you generally like or dislike ice cream?” Once the respondent chooses like or dislike, refine it by offering them relevant choices from the seven-point scale. Branching improves both reliability and validity (Krosnick & Berent, 1993). [2] Although you often see scales with numerical labels, it is best to only present verbal labels to the respondents but convert them to numerical values in the analyses. Avoid partial labels or length or overly specific labels. In some cases, the verbal labels can be supplemented with (or even replaced by) meaningful graphics. The last rating scale shown in Figure 10.1 is a visual-analog scale, on which participants make a mark somewhere along the horizontal line to indicate the magnitude of their response.

Finalizing Response Options

The most important check before your finalize your response options is to align them with your operational definitions. As we’ve discussed before, your operational definitions include your measures (questions and responses options) as well as how to interpret those measures in terms of the variable being measured. In particular, you should be able to interpret all response options to a question based on your operational definition of the variable it measures. If you wanted to measure the variable “social class,” you might ask one question about a participant’s annual income and another about family size. Your operational definition would need to provide clear instructions on how to interpret response options. Your operational definition is basically like this social class calculator from Pew Research , though they include a few more questions in their definition.

To drill down a bit more, as Pew specifies in the section titled “how the income calculator works,” the interval/ratio data respondents enter is interpreted using a formula combining a participant’s four responses to the questions posed by Pew categorizing their household into three categories—upper, middle, or lower class. So, the operational definition includes the four questions comprising the measure and the formula or interpretation which converts responses into the three final categories that we are familiar with: lower, middle, and upper class.

It’s perfectly normal for operational definitions to change levels of measurement, and it’s also perfectly normal for the level of measurement to stay the same. The important thing is that each response option a participant can provide is accounted for by the operational definition. Throw any combination of family size, location, or income at the Pew calculator, and it will define you into one of those three social class categories.

Unlike Pew’s definition, the operational definitions in your study may not need their own webpage to define and describe. For many questions and answers, interpreting response options is easy. If you were measuring “income” instead of “social class,” you could simply operationalize the term by asking people to list their total household income before taxes are taken out. Higher values indicate higher income, and lower values indicate lower income. Easy. Regardless of whether your operational definitions are simple or more complex, every response option to every question on your survey (with a few exceptions) should be interpretable using an operational definition of a variable. Just like we want to avoid an everything-but-the-kitchen-sink approach to questions on our questionnaire, you want to make sure your final questionnaire only contains response options that you will use in your study.

One note of caution on interpretation (sorry for repeating this). We want to remind you again that an operational definition should not mention more than one variable. In our example above, your operational definition could not say “a family of three making under $50,000 is lower class; therefore, they are more likely to experience food insecurity.” That last clause about food insecurity may well be true, but it’s not a part of the operational definition for social class. Each variable (food insecurity and class) should have its own operational definition. If you are talking about how to interpret the relationship between two variables, you are talking about your data analysis plan . We will discuss how to create your data analysis plan beginning in Chapter 14 . For now, one consideration is that depending on the statistical test you use to test relationships between variables, you may need nominal, ordinal, or interval/ratio data. Your questions and response options should match the level of measurement you need with the requirements of the specific statistical tests in your data analysis plan. Once you finalize your data analysis plan, return to your questionnaire to confirm the level of measurement matches with the statistical test you’ve chosen.

In summary, to write effective response options researchers should do the following:

• Avoid wording that is likely to confuse respondents—including double negatives, use culturally specific terms or jargon, and double-barreled response options.
• Ensure response options are relevant to participants’ knowledge and experience so they can make an informed and accurate choice.
• Present mutually exclusive and exhaustive response options.
• Consider fence-sitters and floaters, and the use of neutral or “not applicable” response options.
• Define how response options are interpreted as part of an operational definition of a variable.
• Check level of measurement matches operational definitions and the statistical tests in the data analysis plan (once you develop one in the future)

Look back at the response options you drafted in the previous exercise. Make sure you have a first draft of response options for each closed-ended question on your questionnaire.

• Using the criteria above, evaluate the wording of the response options for each question on your questionnaire.
• Revise your questions and response options until you have a complete first draft.
• Do your first read-through and provide a dummy answer to each question. Make sure you can link each response option and each question to an operational definition.

Look back at the response options you drafted in the previous exercise.

From this discussion, we hope it is clear why researchers using quantitative methods spell out all of their plans ahead of time. Ultimately, there should be a straight line from operational definition through measures on your questionnaire to the data analysis plan. If your questionnaire includes response options that are not aligned with operational definitions or not included in the data analysis plan, the responses you receive back from participants won’t fit with your conceptualization of the key variables in your study. If you do not fix these errors and proceed with collecting unstructured data, you will lose out on many of the benefits of survey research and face overwhelming challenges in answering your research question.

Designing questionnaires

Based on your work in the previous section, you should have a first draft of the questions and response options for the key variables in your study. Now, you’ll also need to think about how to present your written questions and response options to survey respondents. It’s time to write a final draft of your questionnaire and make it look nice. Designing questionnaires takes some thought. First, consider the route of administration for your survey. What we cover in this section will apply equally to paper and online surveys, but if you are planning to use online survey software, you should watch tutorial videos and explore the features of of the survey software you will use.

Informed consent & instructions

Writing effective items is only one part of constructing a survey. For one thing, every survey should have a written or spoken introduction that serves two basic functions (Peterson, 2000) . [3] One is to encourage respondents to participate in the survey. In many types of research, such encouragement is not necessary either because participants do not know they are in a study (as in naturalistic observation) or because they are part of a subject pool and have already shown their willingness to participate by signing up and showing up for the study. Survey research usually catches respondents by surprise when they answer their phone, go to their mailbox, or check their e-mail—and the researcher must make a good case for why they should agree to participate. Thus, the introduction should briefly explain the purpose of the survey and its importance, provide information about the sponsor of the survey (university-based surveys tend to generate higher response rates), acknowledge the importance of the respondent’s participation, and describe any incentives for participating.

The second function of the introduction is to establish informed consent . Remember that this involves describing to respondents everything that might affect their decision to participate. This includes the topics covered by the survey, the amount of time it is likely to take, the respondent’s option to withdraw at any time, confidentiality issues, and other ethical considerations we covered in Chapter 6. Written consent forms are not always used in survey research (when the research is of minimal risk and completion of the survey instrument is often accepted by the IRB as evidence of consent to participate), so it is important that this part of the introduction be well documented and presented clearly and in its entirety to every respondent.

Organizing items to be easy and intuitive to follow

The introduction should be followed by the substantive questionnaire items. But first, it is important to present clear instructions for completing the questionnaire, including examples of how to use any unusual response scales. Remember that the introduction is the point at which respondents are usually most interested and least fatigued, so it is good practice to start with the most important items for purposes of the research and proceed to less important items. Items should also be grouped by topic or by type. For example, items using the same rating scale (e.g., a 5-point agreement scale) should be grouped together if possible to make things faster and easier for respondents. Demographic items are often presented last. This can be because they are easy to answer in the event respondents have become tired or bored, because they are least interesting to participants, or because they can raise concerns for respondents from marginalized groups who may see questions about their identities as a potential red flag. Of course, any survey should end with an expression of appreciation to the respondent.

Questions are often organized thematically. If our survey were measuring social class, perhaps we’d have a few questions asking about employment, others focused on education, and still others on housing and community resources. Those may be the themes around which we organize our questions. Or perhaps it would make more sense to present any questions we had about parents’ income and then present a series of questions about estimated future income. Grouping by theme is one way to be deliberate about how you present your questions. Keep in mind that you are surveying people, and these people will be trying to follow the logic in your questionnaire. Jumping from topic to topic can give people a bit of whiplash and may make participants less likely to complete it.

Using a matrix is a nice way of streamlining response options for similar questions. A matrix is a question type that lists a set of questions for which the answer categories are all the same. If you have a set of questions for which the response options are the same, it may make sense to create a matrix rather than posing each question and its response options individually. Not only will this save you some space in your survey but it will also help respondents progress through your survey more easily. A sample matrix can be seen in Figure 13.4.

Once you have grouped similar questions together, you’ll need to think about the order in which to present those question groups. Most survey researchers agree that it is best to begin a survey with questions that will want to make respondents continue (Babbie, 2010; Dillman, 2000; Neuman, 2003). [4] In other words, don’t bore respondents, but don’t scare them away either. There’s some disagreement over where on a survey to place demographic questions, such as those about a person’s age, gender, and race. On the one hand, placing them at the beginning of the questionnaire may lead respondents to think the survey is boring, unimportant, and not something they want to bother completing. On the other hand, if your survey deals with some very sensitive topic, such as child sexual abuse or criminal convictions, you don’t want to scare respondents away or shock them by beginning with your most intrusive questions.

Your participants are human. They will react emotionally to questionnaire items, and they will also try to uncover your research questions and hypotheses. In truth, the order in which you present questions on a survey is best determined by the unique characteristics of your research. When feasible, you should consult with key informants from your target population determine how best to order your questions. If it is not feasible to do so, think about the unique characteristics of your topic, your questions, and most importantly, your sample. Keeping in mind the characteristics and needs of the people you will ask to complete your survey should help guide you as you determine the most appropriate order in which to present your questions. None of your decisions will be perfect, and all studies have limitations.

Questionnaire length

You’ll also need to consider the time it will take respondents to complete your questionnaire. Surveys vary in length, from just a page or two to a dozen or more pages, which means they also vary in the time it takes to complete them. How long to make your survey depends on several factors. First, what is it that you wish to know? Wanting to understand how grades vary by gender and year in school certainly requires fewer questions than wanting to know how people’s experiences in college are shaped by demographic characteristics, college attended, housing situation, family background, college major, friendship networks, and extracurricular activities. Keep in mind that even if your research question requires a sizable number of questions be included in your questionnaire, do your best to keep the questionnaire as brief as possible. Any hint that you’ve thrown in a bunch of useless questions just for the sake of it will turn off respondents and may make them not want to complete your survey.

Second, and perhaps more important, how long are respondents likely to be willing to spend completing your questionnaire? If you are studying college students, asking them to use their very limited time to complete your survey may mean they won’t want to spend more than a few minutes on it. But if you ask them to complete your survey during down-time between classes and there is little work to be done, students may be willing to give you a bit more of their time. Think about places and times that your sampling frame naturally gathers and whether you would be able to either recruit participants or distribute a survey in that context. Estimate how long your participants would reasonably have to complete a survey presented to them during this time. The more you know about your population (such as what weeks have less work and more free time), the better you can target questionnaire length.

The time that survey researchers ask respondents to spend on questionnaires varies greatly. Some researchers advise that surveys should not take longer than about 15 minutes to complete (as cited in Babbie 2010), [5] whereas others suggest that up to 20 minutes is acceptable (Hopper, 2010). [6] As with question order, there is no clear-cut, always-correct answer about questionnaire length. The unique characteristics of your study and your sample should be considered to determine how long to make your questionnaire. For example, if you planned to distribute your questionnaire to students in between classes, you will need to make sure it is short enough to complete before the next class begins.

When designing a questionnaire, a researcher should consider:

• Weighing strengths and limitations of the method of delivery, including the advanced tools in online survey software or the simplicity of paper questionnaires.
• Grouping together items that ask about the same thing.
• Moving any questions about sensitive items to the end of the questionnaire, so as not to scare respondents off.
• Moving any questions that engage the respondent to answer the questionnaire at the beginning, so as not to bore them.
• Timing the length of the questionnaire with a reasonable length of time you can ask of your participants.
• Dedicating time to visual design and ensure the questionnaire looks professional.

Type out a final draft of your questionnaire in a word processor or online survey tool.

• Evaluate your questionnaire using the guidelines above, revise it, and get it ready to share with other student researchers.
• Take a look at the question drafts you have completed and decide on an order for your questions. E valuate your draft questionnaire using the guidelines above, and revise as needed.

Pilot testing and revising questionnaires

A good way to estimate the time it will take respondents to complete your questionnaire (and other potential challenges) is through pilot testing . Pilot testing allows you to get feedback on your questionnaire so you can improve it before you actually administer it. It can be quite expensive and time consuming if you wish to pilot test your questionnaire on a large sample of people who very much resemble the sample to whom you will eventually administer the finalized version of your questionnaire. But you can learn a lot and make great improvements to your questionnaire simply by pilot testing with a small number of people to whom you have easy access (perhaps you have a few friends who owe you a favor). By pilot testing your questionnaire, you can find out how understandable your questions are, get feedback on question wording and order, find out whether any of your questions are boring or offensive, and learn whether there are places where you should have included filter questions. You can also time pilot testers as they take your survey. This will give you a good idea about the estimate to provide respondents when you administer your survey and whether you have some wiggle room to add additional items or need to cut a few items.

Perhaps this goes without saying, but your questionnaire should also have an attractive design. A messy presentation style can confuse respondents or, at the very least, annoy them. Be brief, to the point, and as clear as possible. Avoid cramming too much into a single page. Make your font size readable (at least 12 point or larger, depending on the characteristics of your sample), leave a reasonable amount of space between items, and make sure all instructions are exceptionally clear. If you are using an online survey, ensure that participants can complete it via mobile, computer, and tablet devices. Think about books, documents, articles, or web pages that you have read yourself—which were relatively easy to read and easy on the eyes and why? Try to mimic those features in the presentation of your survey questions. While online survey tools automate much of visual design, word processors are designed for writing all kinds of documents and may need more manual adjustment as part of visual design.

Realistically, your questionnaire will continue to evolve as you develop your data analysis plan over the next few chapters. By now, you should have a complete draft of your questionnaire grounded in an underlying logic that ties together each question and response option to a variable in your study. Once your questionnaire is finalized, you will need to submit it for ethical approval from your IRB. If your study requires IRB approval, it may be worthwhile to submit your proposal before your questionnaire is completely done. Revisions to IRB protocols are common and it takes less time to review a few changes to questions and answers than it does to review the entire study, so give them the whole study as soon as you can. Once the IRB approves your questionnaire, you cannot change it without their okay.

Key Takeaways

• A questionnaire is comprised of self-report measures of variables in a research study.
• Make sure your survey questions will be relevant to all respondents and that you use filter questions when necessary.
• Effective survey questions and responses take careful construction by researchers, as participants may be confused or otherwise influenced by how items are phrased.
• The questionnaire should start with informed consent and instructions, flow logically from one topic to the next, engage but not shock participants, and thank participants at the end.
• Pilot testing can help identify any issues in a questionnaire before distributing it to participants, including language or length issues.

It’s a myth that researchers work alone! Get together with a few of your fellow students and swap questionnaires for pilot testing.

• Use the criteria in each section above (questions, response options, questionnaires) and provide your peers with the strengths and weaknesses of their questionnaires.
• See if you can guess their research question and hypothesis based on the questionnaire alone.

It’s a myth that researchers work alone! Get together with a few of your fellow students and compare draft questionnaires.

• What are the strengths and limitations of your questionnaire as compared to those of your peers?
• Is there anything you would like to use from your peers’ questionnaires in your own?
• Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. ↵
• Krosnick, J.A. & Berent, M.K. (1993). Comparisons of party identification and policy preferences: The impact of survey question format. American Journal of Political Science, 27(3), 941-964. ↵
• Peterson, R. A. (2000).  Constructing effective questionnaires . Thousand Oaks, CA: Sage. ↵
• Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth; Dillman, D. A. (2000). Mail and Internet surveys: The tailored design method (2nd ed.). New York, NY: Wiley; Neuman, W. L. (2003). Social research methods: Qualitative and quantitative approaches (5th ed.). Boston, MA: Pearson. ↵
• Babbie, E. (2010). The practice of social research  (12th ed.). Belmont, CA: Wadsworth. ↵
• Hopper, J. (2010). How long should a survey be? Retrieved from  http://www.verstaresearch.com/blog/how-long-should-a-survey-be ↵

According to the APA Dictionary of Psychology, an operational definition is "a description of something in terms of the operations (procedures, actions, or processes) by which it could be observed and measured. For example, the operational definition of anxiety could be in terms of a test score, withdrawal from a situation, or activation of the sympathetic nervous system. The process of creating an operational definition is known as operationalization."

Triangulation of data refers to the use of multiple types, measures or sources of data in a research project to increase the confidence that we have in our findings.

Testing out your research materials in advance on people who are not included as participants in your study.

items on a questionnaire designed to identify some subset of survey respondents who are asked additional questions that are not relevant to the entire sample

a question that asks more than one thing at a time, making it difficult to respond accurately

When a participant answers in a way that they believe is socially the most acceptable answer.

the answers researchers provide to participants to choose from when completing a questionnaire

questions in which the researcher provides all of the response options

Questions for which the researcher does not include response options, allowing for respondents to answer the question in their own words

respondents to a survey who choose neutral response options, even if they have an opinion

respondents to a survey who choose a substantive answer to a question when really, they don’t understand the question or don’t have an opinion

An ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact analyses, step-by-step, that you plan to run to answer your research question.

A process through which the researcher explains the research process, procedures, risks and benefits to a potential participant, usually through a written document, which the participant than signs, as evidence of their agreement to participate.

a type of survey question that lists a set of questions for which the response options are all the same in a grid layout

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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Dissertation surveys: Questions, examples, and best practices

Collect data for your dissertation with little effort and great results.

Dissertation surveys are one of the most powerful tools to get valuable insights and data for the culmination of your research. However, it’s one of the most stressful and time-consuming tasks you need to do. You want useful data from a representative sample that you can analyze and present as part of your dissertation. At SurveyPlanet, we’re committed to making it as easy and stress-free as possible to get the most out of your study.

With an intuitive and user-friendly design, our templates and premade questions can be your allies while creating a survey for your dissertation. Explore all the options we offer by simply signing up for an account—and leave the stress behind.

How to write dissertation survey questions

The first thing to do is to figure out which group of people is relevant for your study. When you know that, you’ll also be able to adjust the survey and write questions that will get the best results.

The next step is to write down the goal of your research and define it properly. Online surveys are one of the best and most inexpensive ways to reach respondents and achieve your goal.

Before writing any questions, think about how you’ll analyze the results. You don’t want to write and distribute a survey without keeping how to report your findings in mind. When your thesis questionnaire is out in the real world, it’s too late to conclude that the data you’re collecting might not be any good for assessment. Because of that, you need to create questions with analysis in mind.

You may find our five survey analysis tips for better insights helpful. We recommend reading it before analyzing your results.

Once you understand the parameters of your representative sample, goals, and analysis methodology, then it’s time to think about distribution. Survey distribution may feel like a headache, but you’ll find that many people will gladly participate.

Find communities where your targeted group hangs out and share the link to your survey with them. If you’re not sure how large your research sample should be, gauge it easily with the survey sample size calculator.

Need help with writing survey questions? Read our guide on well-written examples of good survey questions .

Dissertation survey examples

Whatever field you’re studying, we’re sure the following questions will prove useful when crafting your own.

At the beginning of every questionnaire, inform respondents of your topic and provide a consent form. After that, start with questions like:

• Please select your gender:
• What is the highest educational level you’ve completed?
• High school
• Bachelor degree
• Master’s degree
• On a scale of 1-7, how satisfied are you with your current job?
• Please rate the following statements:
• I always wait for people to text me first.
• Strongly Disagree
• Neither agree nor disagree
• Strongly agree
• My friends always complain that I never invite them anywhere.
• I prefer spending time alone.
• Rank which personality traits are most important when choosing a partner. Rank 1 - 7, where 1 is the most and 7 is the least important.
• Flexibility
• Independence
• How openly do you share feelings with your partner?
• Almost never
• Almost always
• In the last two weeks, how often did you experience headaches?

Dissertation survey best practices

There are a lot of DOs and DON’Ts you should keep in mind when conducting any survey, especially for your dissertation. To get valuable data from your targeted sample, follow these best practices:

Use the consent form.

The consent form is a must when distributing a research questionnaire. A respondent has to know how you’ll use their answers and that the survey is anonymous.

Avoid leading and double-barreled questions

Leading and double-barreled questions will produce inconclusive results—and you don’t want that. A question such as: “Do you like to watch TV and play video games?” is double-barreled because it has two variables.

On the other hand, leading questions such as “On a scale from 1-10 how would you rate the amazing experience with our customer support?” influence respondents to answer in a certain way, which produces biased results.

Use easy and straightforward language and questions

Don’t use terms and professional jargon that respondents won’t understand. Take into consideration their educational level and demographic traits and use easy-to-understand language when writing questions.

Mix close-ended and open-ended questions

Too many open-ended questions will annoy respondents. Also, analyzing the responses is harder. Use more close-ended questions for the best results and only a few open-ended ones.

Strategically use different types of responses

Likert scale, multiple-choice, and ranking are all types of responses you can use to collect data. But some response types suit some questions better. Make sure to strategically fit questions with response types.

Ensure that data privacy is a priority

Make sure to use an online survey tool that has SSL encryption and secure data processing. You don’t want to risk all your hard work going to waste because of poorly managed data security. Ensure that you only collect data that’s relevant to your dissertation survey and leave out any questions (such as name) that can identify the respondents.

Create dissertation questionnaires with SurveyPlanet

Overall, survey methodology is a great way to find research participants for your research study. You have all the tools required for creating a survey for a dissertation with SurveyPlanet—you only need to sign up . With powerful features like question branching, custom formatting, multiple languages, image choice questions, and easy export you will find everything needed to create, distribute, and analyze a dissertation survey.

Happy data gathering!

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• How to Frame and Explain the Survey Data Used in a Thesis

Surveys are a special research tool with strengths, weaknesses, and a language all of their own. There are many different steps to designing and conducting a survey, and survey researchers have specific ways of describing what they do.

This handout, based on an annual workshop offered by the Program on Survey Research at Harvard, is geared toward undergraduate honors thesis writers using survey data.

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Home » Dissertation – Format, Example and Template

Dissertation – Format, Example and Template

Table of Contents

Dissertation

Definition:

Dissertation is a lengthy and detailed academic document that presents the results of original research on a specific topic or question. It is usually required as a final project for a doctoral degree or a master’s degree.

Dissertation Meaning in Research

In Research , a dissertation refers to a substantial research project that students undertake in order to obtain an advanced degree such as a Ph.D. or a Master’s degree.

Dissertation typically involves the exploration of a particular research question or topic in-depth, and it requires students to conduct original research, analyze data, and present their findings in a scholarly manner. It is often the culmination of years of study and represents a significant contribution to the academic field.

Types of Dissertation

Types of Dissertation are as follows:

Empirical Dissertation

An empirical dissertation is a research study that uses primary data collected through surveys, experiments, or observations. It typically follows a quantitative research approach and uses statistical methods to analyze the data.

Non-Empirical Dissertation

A non-empirical dissertation is based on secondary sources, such as books, articles, and online resources. It typically follows a qualitative research approach and uses methods such as content analysis or discourse analysis.

Narrative Dissertation

A narrative dissertation is a personal account of the researcher’s experience or journey. It typically follows a qualitative research approach and uses methods such as interviews, focus groups, or ethnography.

Systematic Literature Review

A systematic literature review is a comprehensive analysis of existing research on a specific topic. It typically follows a qualitative research approach and uses methods such as meta-analysis or thematic analysis.

Case Study Dissertation

A case study dissertation is an in-depth analysis of a specific individual, group, or organization. It typically follows a qualitative research approach and uses methods such as interviews, observations, or document analysis.

Mixed-Methods Dissertation

A mixed-methods dissertation combines both quantitative and qualitative research approaches to gather and analyze data. It typically uses methods such as surveys, interviews, and focus groups, as well as statistical analysis.

How to Write a Dissertation

Here are some general steps to help guide you through the process of writing a dissertation:

• Choose a topic : Select a topic that you are passionate about and that is relevant to your field of study. It should be specific enough to allow for in-depth research but broad enough to be interesting and engaging.
• Conduct research : Conduct thorough research on your chosen topic, utilizing a variety of sources, including books, academic journals, and online databases. Take detailed notes and organize your information in a way that makes sense to you.
• Create an outline : Develop an outline that will serve as a roadmap for your dissertation. The outline should include the introduction, literature review, methodology, results, discussion, and conclusion.
• Write the introduction: The introduction should provide a brief overview of your topic, the research questions, and the significance of the study. It should also include a clear thesis statement that states your main argument.
• Write the literature review: The literature review should provide a comprehensive analysis of existing research on your topic. It should identify gaps in the research and explain how your study will fill those gaps.
• Write the methodology: The methodology section should explain the research methods you used to collect and analyze data. It should also include a discussion of any limitations or weaknesses in your approach.
• Write the results: The results section should present the findings of your research in a clear and organized manner. Use charts, graphs, and tables to help illustrate your data.
• Write the discussion: The discussion section should interpret your results and explain their significance. It should also address any limitations of the study and suggest areas for future research.
• Write the conclusion: The conclusion should summarize your main findings and restate your thesis statement. It should also provide recommendations for future research.
• Edit and revise: Once you have completed a draft of your dissertation, review it carefully to ensure that it is well-organized, clear, and free of errors. Make any necessary revisions and edits before submitting it to your advisor for review.

Dissertation Format

The format of a dissertation may vary depending on the institution and field of study, but generally, it follows a similar structure:

• Title Page: This includes the title of the dissertation, the author’s name, and the date of submission.
• Abstract : A brief summary of the dissertation’s purpose, methods, and findings.
• Table of Contents: A list of the main sections and subsections of the dissertation, along with their page numbers.
• Introduction : A statement of the problem or research question, a brief overview of the literature, and an explanation of the significance of the study.
• Literature Review : A comprehensive review of the literature relevant to the research question or problem.
• Methodology : A description of the methods used to conduct the research, including data collection and analysis procedures.
• Results : A presentation of the findings of the research, including tables, charts, and graphs.
• Discussion : A discussion of the implications of the findings, their significance in the context of the literature, and limitations of the study.
• Conclusion : A summary of the main points of the study and their implications for future research.
• References : A list of all sources cited in the dissertation.
• Appendices : Additional materials that support the research, such as data tables, charts, or transcripts.

Dissertation Outline

Dissertation Outline is as follows:

Title Page:

• Title of dissertation
• Author name
• Institutional affiliation
• Date of submission
• Brief summary of the dissertation’s research problem, objectives, methods, findings, and implications
• Usually around 250-300 words

Table of Contents:

• List of chapters and sections in the dissertation, with page numbers for each

I. Introduction

• Background and context of the research
• Research problem and objectives
• Significance of the research

II. Literature Review

• Overview of existing literature on the research topic
• Identification of gaps in the literature
• Theoretical framework and concepts

III. Methodology

• Research design and methods used
• Data collection and analysis techniques
• Ethical considerations

IV. Results

• Presentation and analysis of data collected
• Findings and outcomes of the research
• Interpretation of the results

V. Discussion

• Discussion of the results in relation to the research problem and objectives
• Evaluation of the research outcomes and implications
• Suggestions for future research

VI. Conclusion

• Summary of the research findings and outcomes
• Implications for the research topic and field
• Limitations and recommendations for future research

VII. References

• List of sources cited in the dissertation

VIII. Appendices

• Additional materials that support the research, such as tables, figures, or questionnaires.

Example of Dissertation

Here is an example Dissertation for students:

Title : Exploring the Effects of Mindfulness Meditation on Academic Achievement and Well-being among College Students

This dissertation aims to investigate the impact of mindfulness meditation on the academic achievement and well-being of college students. Mindfulness meditation has gained popularity as a technique for reducing stress and enhancing mental health, but its effects on academic performance have not been extensively studied. Using a randomized controlled trial design, the study will compare the academic performance and well-being of college students who practice mindfulness meditation with those who do not. The study will also examine the moderating role of personality traits and demographic factors on the effects of mindfulness meditation.

Chapter Outline:

Chapter 1: Introduction

• Background and rationale for the study
• Research questions and objectives
• Significance of the study
• Overview of the dissertation structure

Chapter 2: Literature Review

• Definition and conceptualization of mindfulness meditation
• Theoretical framework of mindfulness meditation
• Empirical research on mindfulness meditation and academic achievement
• Empirical research on mindfulness meditation and well-being
• The role of personality and demographic factors in the effects of mindfulness meditation

Chapter 3: Methodology

• Research design and hypothesis
• Participants and sampling method
• Intervention and procedure
• Measures and instruments
• Data analysis method

Chapter 4: Results

• Descriptive statistics and data screening
• Analysis of main effects
• Analysis of moderating effects
• Post-hoc analyses and sensitivity tests

Chapter 5: Discussion

• Summary of findings
• Implications for theory and practice
• Limitations and directions for future research
• Conclusion and contribution to the literature

Chapter 6: Conclusion

• Recap of the research questions and objectives
• Summary of the key findings
• Contribution to the literature and practice
• Implications for policy and practice
• Final thoughts and recommendations.

References :

List of all the sources cited in the dissertation

Appendices :

Additional materials such as the survey questionnaire, interview guide, and consent forms.

Note : This is just an example and the structure of a dissertation may vary depending on the specific requirements and guidelines provided by the institution or the supervisor.

How Long is a Dissertation

The length of a dissertation can vary depending on the field of study, the level of degree being pursued, and the specific requirements of the institution. Generally, a dissertation for a doctoral degree can range from 80,000 to 100,000 words, while a dissertation for a master’s degree may be shorter, typically ranging from 20,000 to 50,000 words. However, it is important to note that these are general guidelines and the actual length of a dissertation can vary widely depending on the specific requirements of the program and the research topic being studied. It is always best to consult with your academic advisor or the guidelines provided by your institution for more specific information on dissertation length.

Applications of Dissertation

Here are some applications of a dissertation:

• Advancing the Field: Dissertations often include new research or a new perspective on existing research, which can help to advance the field. The results of a dissertation can be used by other researchers to build upon or challenge existing knowledge, leading to further advancements in the field.
• Career Advancement: Completing a dissertation demonstrates a high level of expertise in a particular field, which can lead to career advancement opportunities. For example, having a PhD can open doors to higher-paying jobs in academia, research institutions, or the private sector.
• Publishing Opportunities: Dissertations can be published as books or journal articles, which can help to increase the visibility and credibility of the author’s research.
• Personal Growth: The process of writing a dissertation involves a significant amount of research, analysis, and critical thinking. This can help students to develop important skills, such as time management, problem-solving, and communication, which can be valuable in both their personal and professional lives.
• Policy Implications: The findings of a dissertation can have policy implications, particularly in fields such as public health, education, and social sciences. Policymakers can use the research to inform decision-making and improve outcomes for the population.

When to Write a Dissertation

Here are some situations where writing a dissertation may be necessary:

• Pursuing a Doctoral Degree: Writing a dissertation is usually a requirement for earning a doctoral degree, so if you are interested in pursuing a doctorate, you will likely need to write a dissertation.
• Conducting Original Research : Dissertations require students to conduct original research on a specific topic. If you are interested in conducting original research on a topic, writing a dissertation may be the best way to do so.
• Advancing Your Career: Some professions, such as academia and research, may require individuals to have a doctoral degree. Writing a dissertation can help you advance your career by demonstrating your expertise in a particular area.
• Contributing to Knowledge: Dissertations are often based on original research that can contribute to the knowledge base of a field. If you are passionate about advancing knowledge in a particular area, writing a dissertation can help you achieve that goal.
• Meeting Academic Requirements : If you are a graduate student, writing a dissertation may be a requirement for completing your program. Be sure to check with your academic advisor to determine if this is the case for you.

Purpose of Dissertation

some common purposes of a dissertation include:

• To contribute to the knowledge in a particular field : A dissertation is often the culmination of years of research and study, and it should make a significant contribution to the existing body of knowledge in a particular field.
• To demonstrate mastery of a subject: A dissertation requires extensive research, analysis, and writing, and completing one demonstrates a student’s mastery of their subject area.
• To develop critical thinking and research skills : A dissertation requires students to think critically about their research question, analyze data, and draw conclusions based on evidence. These skills are valuable not only in academia but also in many professional fields.
• To demonstrate academic integrity: A dissertation must be conducted and written in accordance with rigorous academic standards, including ethical considerations such as obtaining informed consent, protecting the privacy of participants, and avoiding plagiarism.
• To prepare for an academic career: Completing a dissertation is often a requirement for obtaining a PhD and pursuing a career in academia. It can demonstrate to potential employers that the student has the necessary skills and experience to conduct original research and make meaningful contributions to their field.
• To develop writing and communication skills: A dissertation requires a significant amount of writing and communication skills to convey complex ideas and research findings in a clear and concise manner. This skill set can be valuable in various professional fields.
• To demonstrate independence and initiative: A dissertation requires students to work independently and take initiative in developing their research question, designing their study, collecting and analyzing data, and drawing conclusions. This demonstrates to potential employers or academic institutions that the student is capable of independent research and taking initiative in their work.
• To contribute to policy or practice: Some dissertations may have a practical application, such as informing policy decisions or improving practices in a particular field. These dissertations can have a significant impact on society, and their findings may be used to improve the lives of individuals or communities.
• To pursue personal interests: Some students may choose to pursue a dissertation topic that aligns with their personal interests or passions, providing them with the opportunity to delve deeper into a topic that they find personally meaningful.

Advantage of Dissertation

Some advantages of writing a dissertation include:

• Developing research and analytical skills: The process of writing a dissertation involves conducting extensive research, analyzing data, and presenting findings in a clear and coherent manner. This process can help students develop important research and analytical skills that can be useful in their future careers.
• Demonstrating expertise in a subject: Writing a dissertation allows students to demonstrate their expertise in a particular subject area. It can help establish their credibility as a knowledgeable and competent professional in their field.
• Contributing to the academic community: A well-written dissertation can contribute new knowledge to the academic community and potentially inform future research in the field.
• Improving writing and communication skills : Writing a dissertation requires students to write and present their research in a clear and concise manner. This can help improve their writing and communication skills, which are essential for success in many professions.
• Increasing job opportunities: Completing a dissertation can increase job opportunities in certain fields, particularly in academia and research-based positions.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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• Knowledge Base
• Dissertation

How to Write a Dissertation | A Guide to Structure & Content

A dissertation or thesis is a long piece of academic writing based on original research, submitted as part of an undergraduate or postgraduate degree.

The structure of a dissertation depends on your field, but it is usually divided into at least four or five chapters (including an introduction and conclusion chapter).

The most common dissertation structure in the sciences and social sciences includes:

• An introduction to your topic
• A literature review that surveys relevant sources
• An explanation of your methodology
• An overview of the results of your research
• A discussion of the results and their implications
• A conclusion that shows what your research has contributed

Dissertations in the humanities are often structured more like a long essay , building an argument by analysing primary and secondary sources . Instead of the standard structure outlined here, you might organise your chapters around different themes or case studies.

Other important elements of the dissertation include the title page , abstract , and reference list . If in doubt about how your dissertation should be structured, always check your department’s guidelines and consult with your supervisor.

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Table of contents

Acknowledgements, table of contents, list of figures and tables, list of abbreviations, introduction, literature review / theoretical framework, methodology, reference list.

The very first page of your document contains your dissertation’s title, your name, department, institution, degree program, and submission date. Sometimes it also includes your student number, your supervisor’s name, and the university’s logo. Many programs have strict requirements for formatting the dissertation title page .

The title page is often used as cover when printing and binding your dissertation .

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The acknowledgements section is usually optional, and gives space for you to thank everyone who helped you in writing your dissertation. This might include your supervisors, participants in your research, and friends or family who supported you.

The abstract is a short summary of your dissertation, usually about 150-300 words long. You should write it at the very end, when you’ve completed the rest of the dissertation. In the abstract, make sure to:

• State the main topic and aims of your research
• Describe the methods you used
• Summarise the main results
• State your conclusions

Although the abstract is very short, it’s the first part (and sometimes the only part) of your dissertation that people will read, so it’s important that you get it right. If you’re struggling to write a strong abstract, read our guide on how to write an abstract .

In the table of contents, list all of your chapters and subheadings and their page numbers. The dissertation contents page gives the reader an overview of your structure and helps easily navigate the document.

All parts of your dissertation should be included in the table of contents, including the appendices. You can generate a table of contents automatically in Word.

If you have used a lot of tables and figures in your dissertation, you should itemise them in a numbered list . You can automatically generate this list using the Insert Caption feature in Word.

If you have used a lot of abbreviations in your dissertation, you can include them in an alphabetised list of abbreviations so that the reader can easily look up their meanings.

If you have used a lot of highly specialised terms that will not be familiar to your reader, it might be a good idea to include a glossary . List the terms alphabetically and explain each term with a brief description or definition.

In the introduction, you set up your dissertation’s topic, purpose, and relevance, and tell the reader what to expect in the rest of the dissertation. The introduction should:

• Establish your research topic , giving necessary background information to contextualise your work
• Narrow down the focus and define the scope of the research
• Discuss the state of existing research on the topic, showing your work’s relevance to a broader problem or debate
• Clearly state your objectives and research questions , and indicate how you will answer them
• Give an overview of your dissertation’s structure

Everything in the introduction should be clear, engaging, and relevant to your research. By the end, the reader should understand the what , why and how of your research. Not sure how? Read our guide on how to write a dissertation introduction .

Before you start on your research, you should have conducted a literature review to gain a thorough understanding of the academic work that already exists on your topic. This means:

• Collecting sources (e.g. books and journal articles) and selecting the most relevant ones
• Critically evaluating and analysing each source
• Drawing connections between them (e.g. themes, patterns, conflicts, gaps) to make an overall point

In the dissertation literature review chapter or section, you shouldn’t just summarise existing studies, but develop a coherent structure and argument that leads to a clear basis or justification for your own research. For example, it might aim to show how your research:

• Addresses a gap in the literature
• Takes a new theoretical or methodological approach to the topic
• Proposes a solution to an unresolved problem
• Advances a theoretical debate
• Builds on and strengthens existing knowledge with new data

The literature review often becomes the basis for a theoretical framework , in which you define and analyse the key theories, concepts and models that frame your research. In this section you can answer descriptive research questions about the relationship between concepts or variables.

The methodology chapter or section describes how you conducted your research, allowing your reader to assess its validity. You should generally include:

• The overall approach and type of research (e.g. qualitative, quantitative, experimental, ethnographic)
• Your methods of collecting data (e.g. interviews, surveys, archives)
• Details of where, when, and with whom the research took place
• Your methods of analysing data (e.g. statistical analysis, discourse analysis)
• Tools and materials you used (e.g. computer programs, lab equipment)
• A discussion of any obstacles you faced in conducting the research and how you overcame them
• An evaluation or justification of your methods

Your aim in the methodology is to accurately report what you did, as well as convincing the reader that this was the best approach to answering your research questions or objectives.

Next, you report the results of your research . You can structure this section around sub-questions, hypotheses, or topics. Only report results that are relevant to your objectives and research questions. In some disciplines, the results section is strictly separated from the discussion, while in others the two are combined.

For example, for qualitative methods like in-depth interviews, the presentation of the data will often be woven together with discussion and analysis, while in quantitative and experimental research, the results should be presented separately before you discuss their meaning. If you’re unsure, consult with your supervisor and look at sample dissertations to find out the best structure for your research.

In the results section it can often be helpful to include tables, graphs and charts. Think carefully about how best to present your data, and don’t include tables or figures that just repeat what you have written  –  they should provide extra information or usefully visualise the results in a way that adds value to your text.

Full versions of your data (such as interview transcripts) can be included as an appendix .

The discussion  is where you explore the meaning and implications of your results in relation to your research questions. Here you should interpret the results in detail, discussing whether they met your expectations and how well they fit with the framework that you built in earlier chapters. If any of the results were unexpected, offer explanations for why this might be. It’s a good idea to consider alternative interpretations of your data and discuss any limitations that might have influenced the results.

The discussion should reference other scholarly work to show how your results fit with existing knowledge. You can also make recommendations for future research or practical action.

The dissertation conclusion should concisely answer the main research question, leaving the reader with a clear understanding of your central argument. Wrap up your dissertation with a final reflection on what you did and how you did it. The conclusion often also includes recommendations for research or practice.

In this section, it’s important to show how your findings contribute to knowledge in the field and why your research matters. What have you added to what was already known?

You must include full details of all sources that you have cited in a reference list (sometimes also called a works cited list or bibliography). It’s important to follow a consistent reference style . Each style has strict and specific requirements for how to format your sources in the reference list.

The most common styles used in UK universities are Harvard referencing and Vancouver referencing . Your department will often specify which referencing style you should use – for example, psychology students tend to use APA style , humanities students often use MHRA , and law students always use OSCOLA . M ake sure to check the requirements, and ask your supervisor if you’re unsure.

To save time creating the reference list and make sure your citations are correctly and consistently formatted, you can use our free APA Citation Generator .

Your dissertation itself should contain only essential information that directly contributes to answering your research question. Documents you have used that do not fit into the main body of your dissertation (such as interview transcripts, survey questions or tables with full figures) can be added as appendices .

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Dissertation Questionnaire

Questionnaire generator.

A dissertation is a document usually a requirement for a doctoral degree especially in the field of philosophy. This long essay discusses a particular subject matter uses questionnaires   and other sources of data and is used to validate its content. The  questionnaire’s importance is evident in the processes of data gathering as it can make the dissertation factual, effective and usable.

Having a well-curated and formatted document to follow when making a dissertation can be very beneficial to an individual who is currently immersed in the data gathering stage of the specific research study. We have gathered downloadable samples and templates of questionnaires so it will be easier for you to curate your own.

Dissertation Timeline Gantt Chart Template

Size: 55 KB

Dissertation Research Gantt Chart Template

Size: 43 KB

Dissertation Project Gantt Chart Template

Size: 41 KB

Dissertation Plan Gantt Chart Template

Size: 51 KB

Dissertation Research Questionnaire

Size: 18 KB

Dissertation Proposal Questionnaire

Size: 131 KB

Sample Dissertation Questionnaire

Size: 10 KB

What Is a Dissertation Questionnaire?

A dissertation questionnaire can be defined as follows:

• It is a document used in the processes of data gathering.
• Questionnaires in PDF used for a dissertation contain questions that can help assess the current condition of the community which is the subject of study within the dissertation.
• It specifies the questions that are needed to be answered to assure that there is a basis in terms of the results that will be presented in a dissertation.

How to Write a Dissertation Questionnaire

Writing an efficient and comprehensive dissertation questionnaire can greatly affect the entire dissertation. You can make one by following these steps:

• Be specific with the kind of dissertation that you are creating and align the purposes of the dissertation questionnaire that you need to make to your study.
• List down the information needed from the community who will provide the answers to your questions.
• Open a software where you can create a questionnaire template. You may also download  survey questionnaire examples   and templates to have a faster time in formatting the document.
• The purpose of the dissertation questionnaire.
• The guidelines and instructions in answering the dissertation questions.
• The name of the person to who will use the questionnaire results to his/her dissertation.
• The institution to whom the dissertation will be passed.
• List down the questions based on your needs.

Undergraduate Dissertation Questionnaire

Size: 12 KB

Project Management Dissertation

Size: 54 KB

Guidelines for Writing a Dissertation Questionnaire

There are no strict rules in writing a dissertation questionnaire. However, there are some tips that can help you to create a dissertation questionnaire that is relevant to the study that you are currently doing. Some guidelines:

• Make sure that you are well aware of the data that is needed in your dissertation so you can properly curate questions that can supply your information needs.
• It will be best to use a dissertation questionnaire format that is organized, easy to understand, and properly structured. This will help the people who will answer the dissertation questionnaire quickly know how they can provide the items that you would like to know.
• Always make sure that your instructions in answering the questions are precise and directly stated.
• You may look at  questionnaires in Word   for comparisons. Doing this will help you assess whether there are still areas of improvement that you may tap with the content and format of the dissertation questionnaire that you have created.

Keeping this guidelines in mind and implementing them accordingly will allow you to create a dissertation questionnaire that is beneficial to the processes that you need to have an outstanding dissertation.

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Israel-Hamas war

Hunter Biden's gun trial

D-Day 80th anniversary

Attorney General Garland testifies at House Judiciary Committee

By Devan Cole, Hannah Rabinowitz and Annie Grayer

House Judiciary Committee hearing concludes

The House Judiciary Committee has concluded its Justice Department oversight hearing following hours of testimony from Attorney General Merrick Garland.

Members from both sides of the aisle quizzed Garland on a range of topics for about five hours on Tuesday, with GOP lawmakers occasionally engaging in testy exchanges with him over former President Donald Trump’s criminal cases and other DOJ matters.

Democrats spent much of their time heaping praise on the department and defending its actions amid attacks on the DOJ from their Republican colleagues.

The hearing has resumed

The committee hearing has started back up again after members took a short break.

The committee is taking a short break

The hearing has paused as committee members take a brief break. It is expected to resume shortly.

Garland: There's been "a terrible explosion" of anti-Semitic and anti-Arab threats in recent months

Attorney General Merrick Garland said on Tuesday that there has been a “terrible explosion” of anti-Semitic, anti-Arab and anti-Muslim threats since Hamas’ October 7 attack on Israel.

“Since October 7, there has been a terrible explosion of anti-Semitic threats. Also anti-Arab, anti-Muslim threats in this country that make all of these communities afraid,” he told lawmakers. “And we regard it as an important element of our civil rights work to deter and to investigate and to prosecute and to stop these threats.”

Earlier Tuesday, Garland said he’s “worried” about a possible terrorist attack following the events of October 7, saying the threat level for US law enforcement “has gone up enormously.”

Garland says he has never spoken to Hunter Biden

Attorney General Merrick Garland said he has never spoken to Hunter Biden, as a Republican lawmaker grilled him over the Department of Justice’s handling of the criminal investigation into the president’s son.

When GOP Rep. Ben Cline asked Garland if he spoke with the president’s son when they were both in attendance at a state dinner at the White House in May, Garland said, “I have never spoken to Hunter Biden in my life as far as I know.”

Garland also denied various accusations that Cline brought forward about DOJ’s handling of the case into the president’s son and maintained that he cannot discuss ongoing legal cases.

When asked if he attempted to obstruct the criminal investigation into Hunter Biden, Garland said, “absolutely not.”

Cline presented various claims brought forward by IRS whistleblowers who have alleged DOJ deviated from standard investigative procedure on the case, to which Garland said he did not know anything about those individuals and affirmed that special counsel David Weiss will be able to testify about the specifics.

On the accusation that his decision to appoint Weiss to be special counsel was connected to certain statue of limitations in the criminal case lapsing, Garland said, “there is no connection between whatever happened with the statute of limitations, which I don’t know about, and the appointment of Mr. Weiss.”

Garland continues testimony as hearing resumes

Attorney General Merrick Garland is answering more questions from lawmakers as the GOP-led House Judiciary Committee's resumes following a break.

The hearing is on a short break

The hearing is paused as members take a brief break. It is expected to resume shortly.

What the DOJ has said about keeping the audio tape of Biden's interview in classified documents probe secret

From CNN's Katelyn Polantz

Attorney General Merrick at least once in today's hearing has pointed to the Justice Department's statements in court to keep the audio tape of President Joe Biden's interview in his classified documents investigation secret.

CNN is suing for access, through the federal Freedom of Information Act, and a federal judge in Washington, DC, is looking at whether the DOJ is appropriately withholding the audio tape from public access. The case isn't resolved, and it's always possible the courts could order the tape's release before Congress might obtain it.

In court, the DOJ reasons echo why Garland says Congress shouldn't have the tape either: because it could harm future investigations by chilling other witnesses who may be willing to testify.

But in court, DOJ also says there are privacy interests at play. The audio wouldn't give enough more information about the investigation, on top of a transcript of Biden's interview that's already public, to justify its release, the Department said in court.

"The President’s status as a public figure does not eliminate these significant privacy interests," DOJ lawyers wrote on Friday.

The Justice Department also argued in court "the threat of malicious manipulation of audio files" also factors into their reasoning to keep the audio tape private.

Democrat highlights Trump allies prosecuted by his own DOJ as Republicans allege politicization

As House Republicans repeatedly accuse the Justice Department of improperly targeting allies of the former president, Democratic Rep. Ted Lieu listed off people around Trump who were prosecuted under the previous Republican administration.

Those individuals included Trump’s former campaign chairman Paul Manafort, former deputy campaign chairman Rick Gates, former campaign foreign policy adviser George Papadopoulos, former lawyer Michael Cohen, former advisor Roger Stone, fundraiser Elliott Broidy -- all of whom were prosecuted under attorneys general nominated by Trump himself.

“It is not the fault of the department of justice that Donald Trump has surrounded himself with criminals,” Lieu said. “Trump brought that upon himself.”

Lieu also noted that President Biden's son Hunter is being prosecuted “in federal court right now” by Garland’s Justice Department.

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