how to write a questionnaire for a dissertation

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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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Find out how to use a dissertation questionnaire for your masters.

Prof Martyn Denscombe, author of " The Good Research Guide, 6th edition ", gives expert advice on using a questionnaire survey for your postgraduate dissertation.

Questionnaire surveys are a well-established way of collecting data. They work with relatively small-scale research projects so design and deliver research questionnaires quickly and cheaply. When it comes to conducting research for a master’s dissertation, questionnaire surveys feature prominently as the method of choice.

Using the post for bulky and lengthy surveys is normal. Sometimes questionnaires go by hand. The popularity of questionnaire surveys is principally due to the benefits of using online web-based questionnaires. There are two main aspects to this.

Designing questionnaires

First, the software for producing and delivering web questionnaires. Simple to use features such as drop-down menus and tick-box answers, is user-friendly and inexpensive.

Second, online surveys make it possible to contact people across the globe without travelling anywhere. Given the time and resource constraints faced when producing a dissertation, makes online surveys all the more enticing. Social media such as Facebook, Instagram and WhatsApp is great for contacting people to participate in the survey.

In the context of a master’s dissertation, however, the quality of the survey data is a vital issue. The grade for the dissertation will depend on being able to defend the use of the data from the survey. This is the basis for advanced, master’s level academic enquiry.

Pro's and con's

It is not good enough to simply rely on getting 100 or so people to complete your questionnaire. Be aware of the pros and cons of questionnaire surveys. You need to justify the value of the data you have collected in the face of probing questions, such as:

• Who are the respondents and how they were selected?
• How representative are the respondents of the whole group being studied?
• What response rate was achieved by the survey?
• Are the questions suitable in relation to the topic and the particular respondents?
• What likelihood is there that respondents gave honest answers to the questions?

This is where The Good Research Guide, 6th edition becomes so valuable.

It identifies the key points that need to be addressed in order to conduct a competent questionnaire survey. It gets right to the heart of the matter, with plenty of practical guidance on how to deal with issues.

In a straightforward style, using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects and outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe's advice on using a Case Study for your postgraduate dissertation.

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Frequently asked questions.

There are a few key steps in creating a dissertation for use in your thesis. Firstly, you should think about the topic you are studying and who you need to respond to your questionnaire. You then need to think about how you can deliver the questions, this can be in the form of in-person interviews or emails. You can begin to formulate your questions and format.

Visit the Studying for a PhD section for more information and advice.

Prof Martyn Denscombe, author of “ The Good Research Guide, 6th edition ”, gives expert advice on using a questionnaire survey for your postgraduate dissertation.

Questionnaire surveys are a well-established way of collecting data. They can be used with relatively small-scale research projects, and research questionnaires can be designed and delivered quite quickly and cheaply. It is not surprising, therefore, that when it comes to conducting research for a master’s dissertation, questionnaire surveys feature prominently as the research method of choice.

Occasionally such thesis surveys will be sent out by post, and sometimes the questionnaires will be distributed by hand. But the popularity of questionnaire surveys in the context of master’s dissertations is principally due to the benefits of using online web-based questionnaires. There are two main aspects to this.

First, the software for producing and delivering web questionnaires, with their features such as drop-down menus and tick-box answers, is user-friendly and inexpensive.

Second, online surveys make it possible to contact people across the globe without travelling anywhere which, given the time and resource constraints faced when producing a dissertation, makes online surveys all the more enticing. (And, for the more adventurous students, there are also developing possibilities for the use of social media such as Facebook and SMS texts for contacting people to participate in the survey.)

In the context of a master’s dissertation, however, the quality of the survey data is a vital issue. The grade for the dissertation will depend on being able to defend the use of the data from the survey as the basis for advanced, master’s level academic enquiry. Which means it is not good enough to simply rely on getting 100 or so people to complete your questionnaire. Students are expected to be aware of the pros and cons of questionnaire surveys and to be able to justify the value of the data they have collected in the face of probing questions such as:

• Who are the respondents and how they were selected?
• How representative are the respondents of the whole group being studied?
• What response rate was achieved by the survey?
• Are the questions suitable in relation to the topic and the particular respondents?
• What likelihood is there that respondents gave honest answers to the questions?

This is where The Good Research Guide, 6th edition becomes so valuable.

It not only identifies the key points that need to be addressed in order to conduct a competent questionnaire survey, it gets right to the heart of the matter with plenty of practical guidance on how to deal with the issues. In a straightforward style, using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects and outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe’s advice on using a Case Study for your postgraduate dissertation

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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!

Sign up now

Free unlimited surveys, questions and responses.

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How to Develop a Questionnaire for Research

Last Updated: December 4, 2022 Fact Checked

This article was co-authored by Alexander Ruiz, M.Ed. . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. There are 13 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 589,357 times.

A questionnaire is a technique for collecting data in which a respondent provides answers to a series of questions. [1] X Research source To develop a questionnaire that will collect the data you want takes effort and time. However, by taking a step-by-step approach to questionnaire development, you can come up with an effective means to collect data that will answer your unique research question.

Designing Your Questionnaire

• Come up with a research question. It can be one question or several, but this should be the focal point of your questionnaire.
• Develop one or several hypotheses that you want to test. The questions that you include on your questionnaire should be aimed at systematically testing these hypotheses.

• Dichotomous question: this is a question that will generally be a “yes/no” question, but may also be an “agree/disagree” question. It is the quickest and simplest question to analyze, but is not a highly sensitive measure.
• Open-ended questions: these questions allow the respondent to respond in their own words. They can be useful for gaining insight into the feelings of the respondent, but can be a challenge when it comes to analysis of data. It is recommended to use open-ended questions to address the issue of “why.” [2] X Research source
• Multiple choice questions: these questions consist of three or more mutually-exclusive categories and ask for a single answer or several answers. [3] X Research source Multiple choice questions allow for easy analysis of results, but may not give the respondent the answer they want.
• Rank-order (or ordinal) scale questions: this type of question asks your respondent to rank items or choose items in a particular order from a set. For example, it might ask your respondents to order five things from least to most important. These types of questions forces discrimination among alternatives, but does not address the issue of why the respondent made these discriminations. [4] X Research source
• Rating scale questions: these questions allow the respondent to assess a particular issue based on a given dimension. You can provide a scale that gives an equal number of positive and negative choices, for example, ranging from “strongly agree” to “strongly disagree.” [5] X Research source These questions are very flexible, but also do not answer the question “why.”

• Write questions that are succinct and simple. You should not be writing complex statements or using technical jargon, as it will only confuse your respondents and lead to incorrect responses.
• Ask only one question at a time. This will help avoid confusion
• Asking questions such as these usually require you to anonymize or encrypt the demographic data you collect.
• Determine if you will include an answer such as “I don’t know” or “Not applicable to me.” While these can give your respondents a way of not answering certain questions, providing these options can also lead to missing data, which can be problematic during data analysis.
• Put the most important questions at the beginning of your questionnaire. [7] X Research source This can help you gather important data even if you sense that your respondents may be becoming distracted by the end of the questionnaire.

• Only include questions that are directly useful to your research question. [9] X Trustworthy Source Food and Agricultural Organization of the United Nations Specialized agency of the United Nations responsible for leading international efforts to end world hunger and improve nutrition Go to source A questionnaire is not an opportunity to collect all kinds of information about your respondents.
• Avoid asking redundant questions. This will frustrate those who are taking your questionnaire.

• Consider if you want your questionnaire to collect information from both men and women. Some studies will only survey one sex.
• Consider including a range of ages in your target demographic. For example, you can consider young adult to be 18-29 years old, adults to be 30-54 years old, and mature adults to be 55+. Providing the an age range will help you get more respondents than limiting yourself to a specific age.
• Consider what else would make a person a target for your questionnaire. Do they need to drive a car? Do they need to have health insurance? Do they need to have a child under 3? Make sure you are very clear about this before you distribute your questionnaire.

• Consider an anonymous questionnaire. You may not want to ask for names on your questionnaire. This is one step you can take to prevent privacy, however it is often possible to figure out a respondent’s identity using other demographic information (such as age, physical features, or zipcode).
• Consider de-identifying the identity of your respondents. Give each questionnaire (and thus, each respondent) a unique number or word, and only refer to them using that new identifier. Shred any personal information that can be used to determine identity.
• Remember that you do not need to collect much demographic information to be able to identify someone. People may be wary to provide this information, so you may get more respondents by asking less demographic questions (if it is possible for your questionnaire).
• Make sure you destroy all identifying information after your study is complete.

Writing your questionnaire

• My name is Jack Smith and I am one of the creators of this questionnaire. I am part of the Department of Psychology at the University of Michigan, where I am focusing in developing cognition in infants.
• I’m Kelly Smith, a 3rd year undergraduate student at the University of New Mexico. This questionnaire is part of my final exam in statistics.
• My name is Steve Johnson, and I’m a marketing analyst for The Best Company. I’ve been working on questionnaire development to determine attitudes surrounding drug use in Canada for several years.

• I am collecting data regarding the attitudes surrounding gun control. This information is being collected for my Anthropology 101 class at the University of Maryland.
• This questionnaire will ask you 15 questions about your eating and exercise habits. We are attempting to make a correlation between healthy eating, frequency of exercise, and incidence of cancer in mature adults.
• This questionnaire will ask you about your recent experiences with international air travel. There will be three sections of questions that will ask you to recount your recent trips and your feelings surrounding these trips, as well as your travel plans for the future. We are looking to understand how a person’s feelings surrounding air travel impact their future plans.

• Beware that if you are collecting information for a university or for publication, you may need to check in with your institution’s Institutional Review Board (IRB) for permission before beginning. Most research universities have a dedicated IRB staff, and their information can usually be found on the school’s website.
• Remember that transparency is best. It is important to be honest about what will happen with the data you collect.
• Include an informed consent for if necessary. Note that you cannot guarantee confidentiality, but you will make all reasonable attempts to ensure that you protect their information. [12] X Research source

• Time yourself taking the survey. Then consider that it will take some people longer than you, and some people less time than you.
• Provide a time range instead of a specific time. For example, it’s better to say that a survey will take between 15 and 30 minutes than to say it will take 15 minutes and have some respondents quit halfway through.
• Use this as a reason to keep your survey concise! You will feel much better asking people to take a 20 minute survey than you will asking them to take a 3 hour one.

• Incentives can attract the wrong kind of respondent. You don’t want to incorporate responses from people who rush through your questionnaire just to get the reward at the end. This is a danger of offering an incentive. [13] X Research source
• Incentives can encourage people to respond to your survey who might not have responded without a reward. This is a situation in which incentives can help you reach your target number of respondents. [14] X Research source
• Consider the strategy used by SurveyMonkey. Instead of directly paying respondents to take their surveys, they offer 50 cents to the charity of their choice when a respondent fills out a survey. They feel that this lessens the chances that a respondent will fill out a questionnaire out of pure self-interest. [15] X Research source
• Consider entering each respondent in to a drawing for a prize if they complete the questionnaire. You can offer a 25$ gift card to a restaurant, or a new iPod, or a ticket to a movie. This makes it less tempting just to respond to your questionnaire for the incentive alone, but still offers the chance of a pleasant reward.

• Always proof read. Check for spelling, grammar, and punctuation errors.
• Include a title. This is a good way for your respondents to understand the focus of the survey as quickly as possible.
• Thank your respondents. Thank them for taking the time and effort to complete your survey.

Distributing Your Questionnaire

• Was the questionnaire easy to understand? Were there any questions that confused you?
• Was the questionnaire easy to access? (Especially important if your questionnaire is online).
• Do you feel the questionnaire was worth your time?
• Were you comfortable answering the questions asked?
• Are there any improvements you would make to the questionnaire?

• Use an online site, such as SurveyMonkey.com. This site allows you to write your own questionnaire with their survey builder, and provides additional options such as the option to buy a target audience and use their analytics to analyze your data. [19] X Research source
• Consider using the mail. If you mail your survey, always make sure you include a self-addressed stamped envelope so that the respondent can easily mail their responses back. Make sure that your questionnaire will fit inside a standard business envelope.
• Conduct face-to-face interviews. This can be a good way to ensure that you are reaching your target demographic and can reduce missing information in your questionnaires, as it is more difficult for a respondent to avoid answering a question when you ask it directly.
• Try using the telephone. While this can be a more time-effective way to collect your data, it can be difficult to get people to respond to telephone questionnaires.

• Make your deadline reasonable. Giving respondents up to 2 weeks to answer should be more than sufficient. Anything longer and you risk your respondents forgetting about your questionnaire.
• Consider providing a reminder. A week before the deadline is a good time to provide a gentle reminder about returning the questionnaire. Include a replacement of the questionnaire in case it has been misplaced by your respondent. [20] X Research source

Community Q&A

You Might Also Like

• ↑ https://www.questionpro.com/blog/what-is-a-questionnaire/
• ↑ https://www.hotjar.com/blog/open-ended-questions/
• ↑ https://www.questionpro.com/a/showArticle.do?articleID=survey-questions
• ↑ https://surveysparrow.com/blog/ranking-questions-examples/
• ↑ https://www.lumoa.me/blog/rating-scale/
• ↑ http://www.sciencebuddies.org/science-fair-projects/project_ideas/Soc_survey.shtml
• ↑ http://www.monash.edu.au/lls/hdr/design/2.4.3.html
• ↑ http://www.fao.org/docrep/W3241E/w3241e05.htm
• ↑ http://managementhelp.org/businessresearch/questionaires.htm
• ↑ https://www.surveymonkey.com/mp/survey-rewards/
• ↑ http://www.ideafit.com/fitness-library/how-to-develop-a-questionnaire
• ↑ https://www.surveymonkey.com/mp/take-a-tour/?ut_source=header

About This Article

To develop a questionnaire for research, identify the main objective of your research to act as the focal point for the questionnaire. Then, choose the type of questions that you want to include, and come up with succinct, straightforward questions to gather the information that you need to answer your questions. Keep your questionnaire as short as possible, and identify a target demographic who you would like to answer the questions. Remember to make the questionnaires as anonymous as possible to protect the integrity of the person answering the questions! For tips on writing out your questions and distributing the questionnaire, keep reading! Did this summary help you? Yes No

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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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Thank you. I will try my best to write my results.

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A strong analytical question

• speaks to a genuine dilemma presented by your sources . In other words, the question focuses on a real confusion, problem, ambiguity, or gray area, about which readers will conceivably have different reactions, opinions, or ideas.  
• yields an answer that is not obvious . If you ask, "What did this author say about this topic?” there’s nothing to explore because any reader of that text would answer that question in the same way. But if you ask, “how can we reconcile point A and point B in this text,” readers will want to see how you solve that inconsistency in your essay.  
• suggests an answer complex enough to require a whole essay's worth of discussion. If the question is too vague, it won't suggest a line of argument. The question should elicit reflection and argument rather than summary or description.  
• can be explored using the sources you have available for the assignment , rather than by generalizations or by research beyond the scope of your assignment.  

How to come up with an analytical question  

One useful starting point when you’re trying to identify an analytical question is to look for points of tension in your sources, either within one source or among sources. It can be helpful to think of those points of tension as the moments where you need to stop and think before you can move forward. Here are some examples of where you may find points of tension:

• You may read a published view that doesn’t seem convincing to you, and you may want to ask a question about what’s missing or about how the evidence might be reconsidered.  
• You may notice an inconsistency, gap, or ambiguity in the evidence, and you may want to explore how that changes your understanding of something.  
• You may identify an unexpected wrinkle that you think deserves more attention, and you may want to ask a question about it.  
• You may notice an unexpected conclusion that you think doesn’t quite add up, and you may want to ask how the authors of a source reached that conclusion.  
• You may identify a controversy that you think needs to be addressed, and you may want to ask a question about how it might be resolved.  
• You may notice a problem that you think has been ignored, and you may want to try to solve it or consider why it has been ignored.  
• You may encounter a piece of evidence that you think warrants a closer look, and you may raise questions about it.  

Once you’ve identified a point of tension and raised a question about it, you will try to answer that question in your essay. Your main idea or claim in answer to that question will be your thesis.

• "How" and "why" questions generally require more analysis than "who/ what/when/where” questions.  
• Good analytical questions can highlight patterns/connections, or contradictions/dilemmas/problems.  
• Good analytical questions establish the scope of an argument, allowing you to focus on a manageable part of a broad topic or a collection of sources.  
• Good analytical questions can also address implications or consequences of your analysis.
• picture_as_pdf Asking Analytical Questions

Dissertation

Ai generator.

Dissertations are structured documents that present findings, arguments, and conclusions in a formal manner. They demonstrate a student’s ability to conduct independent research, critically evaluate literature, and communicate complex ideas effectively.

What is a Dissertation?

A dissertation is a comprehensive research project often pursued at the postgraduate level. It involves extensive study, analysis, and original contributions to a specific field. Dissertations showcase a student’s ability to conduct independent research, critically evaluate literature, and communicate complex ideas effectively.

Pronunciation of Dissertation

• American English Pronunciation : In American English, “dissertation” is pronounced as /ˌdɪs.ərˈteɪ.ʃən/.
• British English Pronunciation : In British English, the pronunciation is /ˌdɪs.əˈteɪ.ʃən/.
• Dis- : This syllable is pronounced with a short “i” sound, like in the word “this”.
• -ser- : The middle syllable is pronounced with a short “e” sound, similar to “set”.
• -tay- : This part is pronounced with a long “a” sound, as in “day”.
• -shun : The final syllable has the “shun” sound, like in “mission”.
• The stress is typically on the second syllable in both American and British English.
• It’s important to enunciate each syllable clearly for correct pronunciation.

Types of Dissertations

• Conducts original research using empirical methods.
• Involves data collection, analysis, and interpretation.
• Common in scientific and social science disciplines.
• Focuses on analyzing and synthesizing existing literature.
• Examines theories, concepts, or debates within a field.
• May involve a systematic review or meta-analysis.
• Integrates theoretical knowledge with practical application.
• Often found in professional fields like education , business , or healthcare.
• Includes a reflective component on real-world experiences.
• Explores and develops new theories or conceptual frameworks.
• Emphasizes conceptual analysis and argumentation.
• Common in philosophy , theoretical physics , and humanities.
• Combines qualitative and quantitative research approaches.
• Offers a comprehensive understanding of complex phenomena.
• Utilizes both data collection methods for triangulation.
• Focuses on in-depth examination of a specific case or phenomenon.
• Provides detailed insights into real-life contexts .
• Often used in psychology , sociology, and business research.

Dissertation Format

Here’s an overview of the typical format for a dissertation:

• Includes the title of the dissertation, author’s name, institution, department, degree program, date, and possibly the supervisor’s name.
• Provides a concise summary of the dissertation’s purpose, methodology, key findings, and conclusions.
• Usually limited to a certain word count or character limit.
• Lists the main sections and subsections of the dissertation with corresponding page numbers.
• Enumerates all figures and tables included in the dissertation, along with their respective page numbers.
• Sets the stage for the research by introducing the topic, context, significance, objectives, and research questions.
• Provides an overview of the structure of the dissertation.
• Surveys relevant literature and theoretical frameworks related to the research topic.
• Analyzes and synthesizes existing research to establish a theoretical foundation for the study.
• Describes the research design, methods, data collection procedures, and analysis techniques used in the study.
• Justifies the chosen methodology and explains how it aligns with the research objectives.
• Presents the findings of the research in a clear and organized manner.
• Includes tables, figures, and descriptive statistics to illustrate the data.
• Interprets the results in relation to the research questions, hypotheses, and theoretical framework.
• Analyzes the implications of the findings and discusses their significance in the broader context of the field.
• Summarizes the main findings and their implications for theory, practice, or policy.
• Reflects on the limitations of the study and suggests directions for future research.
• Lists all the sources cited in the dissertation in a consistent citation style (e.g., APA, MLA, Chicago).
• Includes supplementary materials such as questionnaires, interview transcripts, or raw data.
• Provides additional details that support the main text but are not essential for understanding the dissertation.

Dissertation Topics

Here are some broad categories of dissertation topics, along with examples within each category:

• The impact of technology on student learning outcomes.
• Strategies for improving student engagement in online education.
• The effectiveness of inclusive education programs for students with disabilities.
• Assessing the role of parental involvement in children’s academic achievement.
• Investigating the relationship between teacher motivation and student performance.
• The influence of corporate social responsibility on consumer behavior.
• Strategies for managing workplace diversity and inclusion.
• Analyzing the factors affecting employee job satisfaction and retention.
• The role of leadership styles in organizational change management.
• Exploring the impact of digital marketing on consumer purchase decisions.
• Assessing the effectiveness of telemedicine in improving patient access to healthcare services.
• Investigating the psychological effects of long-term illness on patients and their families.
• Analyzing the factors influencing healthcare professionals’ adoption of electronic health records.
• Exploring the role of preventive healthcare interventions in reducing the prevalence of chronic diseases.
• Assessing the impact of healthcare policies on healthcare equity and access.
• Understanding the relationship between social media use and mental health outcomes among adolescents.
• Investigating the factors influencing public perceptions of climate change and environmental policies.
• Exploring the impact of immigration policies on immigrant integration and social cohesion.
• Analyzing the effects of income inequality on social mobility and economic development.
• Assessing the effectiveness of community-based interventions in reducing crime rates.
• Investigating the adoption and diffusion of renewable energy technologies in developing countries.
• Analyzing the ethical implications of artificial intelligence and machine learning algorithms.
• Exploring the role of blockchain technology in revolutionizing supply chain management.
• Assessing the impact of smart city initiatives on urban sustainability and quality of life.
• Investigating the factors influencing consumers’ acceptance of autonomous vehicles.

Synonym & Antonyms For Dissertation

How to write a dissertation.

Writing a dissertation is a comprehensive process that requires careful planning and execution. Here’s a step-by-step guide on how to write a dissertation:

• Select a topic that aligns with your interests, expertise, and the requirements of your academic program.
• Ensure the topic is researchable, relevant, and contributes to the existing body of knowledge in your field.
• Conduct a thorough literature review to familiarize yourself with existing research on your topic.
• Identify gaps, controversies, or unanswered questions that your dissertation can address.
• Develop research questions or hypotheses to guide your study.
• Outline the purpose, scope, objectives, and methodology of your dissertation in a research proposal.
• Seek feedback from your advisor or committee members and revise the proposal accordingly.
• Develop a detailed timeline or schedule for completing each stage of the dissertation writing process.
• Break down tasks into manageable chunks and set deadlines for completing each chapter or section.
• Start with the introduction, which provides background information, states the research objectives, and outlines the structure of the dissertation.
• Proceed to the literature review, methodology, results, discussion, and conclusion chapters, following the structure outlined in your proposal.
• Write each chapter systematically, using clear and concise language, and supporting your arguments with evidence from research.
• Review each draft of your dissertation carefully, focusing on clarity, coherence, and logical flow of ideas.
• Edit for grammar, punctuation, spelling, and formatting errors.
• Seek feedback from your advisor, peers, or academic writing support services, and incorporate suggested revisions.
• Compile all chapters, appendices, tables, figures, and references into a cohesive document.
• Ensure consistency in formatting and citation style throughout the dissertation.
• Proofread the final version to ensure accuracy and completeness.
• Submit the finalized dissertation to your advisor or committee for review and approval.
• Prepare for a dissertation defense, where you’ll present your research findings and answer questions from your committee.
• Address any feedback or revisions requested by your committee and finalize the dissertation for submission.

Dissertation vs. Thesis

Examples of dissertation in education.

• Investigating the effectiveness of flipped classroom approaches in enhancing student engagement and academic performance across various subjects.
• Analyzing the correlation between emotional intelligence levels among teachers and their ability to create supportive learning environments and facilitate student success.
• Examining the benefits of parental involvement in early childhood education and identifying effective strategies for promoting collaboration between families and schools.
• Investigating disparities in access to technology resources among students from different socio-economic backgrounds and exploring interventions to bridge the digital divide.
• Assessing the effectiveness of multicultural education programs in fostering cultural competence, diversity awareness, and inclusivity among students in diverse learning environments.
• Evaluating the effectiveness of social-emotional learning (SEL) interventions in promoting mental health, resilience, and well-being among students, teachers, and school staff.
• Identifying barriers to gender equity in STEM (science, technology, engineering, and mathematics) education and exploring strategies to encourage girls’ participation and success in STEM fields.
• Investigating the knowledge, attitudes, and practices of teachers related to assessment and exploring professional development initiatives to improve assessment literacy and enhance student learning outcomes.
• Examining the implementation of inclusive education policies and practices for students with disabilities, including challenges faced, effective strategies, and policy implications for inclusive schooling.
• Analyzing the impact of school leadership practices on teacher professional development, instructional quality, and overall school improvement efforts.

Examples of Dissertation in Psychology

• Investigating the relationship between social media usage patterns and the prevalence of depression, anxiety, and other mental health issues among adolescents.
• Examining the efficacy of cognitive-behavioral therapy (CBT) in treating anxiety disorders and exploring the underlying mechanisms of therapeutic change.
• Assessing the long-term effects of different parenting styles (e.g., authoritarian, authoritative, permissive) on children’s emotional regulation, social skills, and overall development.
• Investigating the neurobiological mechanisms underlying addiction and exploring implications for developing more effective treatment and prevention strategies.
• Examining the factors that contribute to psychological resilience in individuals who have experienced trauma, such as childhood abuse, natural disasters, or combat exposure.
• Investigating the bidirectional relationship between sleep quality and mental health outcomes, including the impact of sleep disturbances on emotional regulation and psychological well-being.
• Comparing cultural differences in the conceptualization and assessment of personality traits, such as individualism-collectivism, and exploring implications for cross-cultural psychology.
• Evaluating the effectiveness of mindfulness-based interventions (e.g., mindfulness-based stress reduction, mindfulness-based cognitive therapy) in reducing stress, improving well-being, and enhancing psychological resilience.
• Examining the role of sociocultural factors (e.g., media influence, peer pressure) in shaping body image ideals and exploring interventions to prevent and treat eating disorders.
• Investigating the psychological processes underlying procrastination behavior, including motivational factors, self-regulation strategies, and interventions to promote task completion and productivity.

Examples of Dissertation in literature

• Understanding how stories from countries once colonized talk about who they are and where they fit in the world.
• Checking out how women are shown and what roles they have in old stories from the Victorian times.
• Finding out why scary stories from the past are still popular now and what they’re all about.
• Seeing how heroes are the same in stories from different places and why they’re important to us.
• Learning from stories that care about nature and how writers make us think about protecting the environment.
• Finding out why some new stories are tricky and playful with how they’re written, and what makes them special.
• Understanding how people who lived through terrible events tell their stories, and why it’s important.
• Seeing how Black artists in Harlem made cool stuff and changed how people think about Black culture.
• Exploring stories about moving to new places and how they mix different cultures together.
• Checking out how Shakespeare’s stories get turned into movies and other fun things we like today.

Examples of Dissertation in Politics

• Investigating how different types of political systems, such as democracies and autocracies, influence economic development and growth.
• Analyzing government policies and international agreements aimed at addressing climate change and their effectiveness in mitigating environmental degradation.
• Exploring the rise of populist movements and their impact on political polarization, democratic norms, and institutions.
• Examining the ethical and legal considerations surrounding humanitarian intervention and the protection of human rights in conflict zones.
• Investigating the role of nationalism and identity politics in shaping public attitudes towards immigration, multiculturalism, and social integration.
• Analyzing the effects of globalization on state sovereignty, economic policies, and the balance of power between states and multinational corporations.
• Comparing different electoral systems and their impact on political representation, party competition, and the functioning of democratic institutions.
• Examining the tension between national security concerns and civil liberties, particularly in the context of counterterrorism policies and surveillance practices.
• Assessing the barriers to women’s political participation and representation in decision-making roles, and exploring strategies for achieving gender equality in politics.
• Analyzing the role of the United Nations and other international organizations in addressing global challenges, such as conflict resolution, humanitarian crises, and sustainable development.

What exactly is a Dissertation?

A dissertation is a scholarly document that presents original research on a specific topic, typically completed as part of a doctoral program. It demonstrates the candidate’s ability to conduct independent research and contribute to their field of study.

How long is a Dissertation?

The length of a dissertation varies widely depending on the academic discipline, program requirements, and research topic. On average, it ranges from 80 to 200 pages, but some dissertations can be shorter or longer based on the depth and scope of the research.

What Do You Write a Dissertation For?

A dissertation is written as a culmination of doctoral studies to demonstrate a candidate’s ability to conduct independent research, contribute new knowledge to their field, and obtain a doctoral degree. It showcases expertise, critical thinking, and scholarly communication skills.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

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