conclusion questionnaire

Get science-backed answers as you write with Paperpal's Research feature

How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

Align your conclusion’s tone with the rest of your research paper. Start Writing with Paperpal Now!  

The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

Write your research paper conclusion 2x faster with Paperpal. Try it now!

Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• 5 Reasons for Rejection After Peer Review
• Ethical Research Practices For Research with Human Subjects

7 Ways to Improve Your Academic Writing Process

• Paraphrasing in Academic Writing: Answering Top Author Queries

Preflight For Editorial Desk: The Perfect Hybrid (AI + Human) Assistance Against Compromised Manuscripts

You may also like, how to write a high-quality conference paper, academic editing: how to self-edit academic text with..., measuring academic success: definition & strategies for excellence, phd qualifying exam: tips for success , ai in education: it’s time to change the..., is it ethical to use ai-generated abstracts without..., what are journal guidelines on using generative ai..., quillbot review: features, pricing, and free alternatives, what is an academic paper types and elements , should you use ai tools like chatgpt for....

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Writing Survey Questions

Perhaps the most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the public. Accurate random sampling will be wasted if the information gathered is built on a shaky foundation of ambiguous or biased questions. Creating good measures involves both writing good questions and organizing them to form the questionnaire.

Questionnaire design is a multistage process that requires attention to many details at once. Designing the questionnaire is complicated because surveys can ask about topics in varying degrees of detail, questions can be asked in different ways, and questions asked earlier in a survey may influence how people respond to later questions. Researchers are also often interested in measuring change over time and therefore must be attentive to how opinions or behaviors have been measured in prior surveys.

Surveyors may conduct pilot tests or focus groups in the early stages of questionnaire development in order to better understand how people think about an issue or comprehend a question. Pretesting a survey is an essential step in the questionnaire design process to evaluate how people respond to the overall questionnaire and specific questions, especially when questions are being introduced for the first time.

For many years, surveyors approached questionnaire design as an art, but substantial research over the past forty years has demonstrated that there is a lot of science involved in crafting a good survey questionnaire. Here, we discuss the pitfalls and best practices of designing questionnaires.

Question development

There are several steps involved in developing a survey questionnaire. The first is identifying what topics will be covered in the survey. For Pew Research Center surveys, this involves thinking about what is happening in our nation and the world and what will be relevant to the public, policymakers and the media. We also track opinion on a variety of issues over time so we often ensure that we update these trends on a regular basis to better understand whether people’s opinions are changing.

At Pew Research Center, questionnaire development is a collaborative and iterative process where staff meet to discuss drafts of the questionnaire several times over the course of its development. We frequently test new survey questions ahead of time through qualitative research methods such as  focus groups , cognitive interviews, pretesting (often using an  online, opt-in sample ), or a combination of these approaches. Researchers use insights from this testing to refine questions before they are asked in a production survey, such as on the ATP.

Measuring change over time

Many surveyors want to track changes over time in people’s attitudes, opinions and behaviors. To measure change, questions are asked at two or more points in time. A cross-sectional design surveys different people in the same population at multiple points in time. A panel, such as the ATP, surveys the same people over time. However, it is common for the set of people in survey panels to change over time as new panelists are added and some prior panelists drop out. Many of the questions in Pew Research Center surveys have been asked in prior polls. Asking the same questions at different points in time allows us to report on changes in the overall views of the general public (or a subset of the public, such as registered voters, men or Black Americans), or what we call “trending the data”.

When measuring change over time, it is important to use the same question wording and to be sensitive to where the question is asked in the questionnaire to maintain a similar context as when the question was asked previously (see  question wording  and  question order  for further information). All of our survey reports include a topline questionnaire that provides the exact question wording and sequencing, along with results from the current survey and previous surveys in which we asked the question.

The Center’s transition from conducting U.S. surveys by live telephone interviewing to an online panel (around 2014 to 2020) complicated some opinion trends, but not others. Opinion trends that ask about sensitive topics (e.g., personal finances or attending religious services ) or that elicited volunteered answers (e.g., “neither” or “don’t know”) over the phone tended to show larger differences than other trends when shifting from phone polls to the online ATP. The Center adopted several strategies for coping with changes to data trends that may be related to this change in methodology. If there is evidence suggesting that a change in a trend stems from switching from phone to online measurement, Center reports flag that possibility for readers to try to head off confusion or erroneous conclusions.

Open- and closed-ended questions

One of the most significant decisions that can affect how people answer questions is whether the question is posed as an open-ended question, where respondents provide a response in their own words, or a closed-ended question, where they are asked to choose from a list of answer choices.

For example, in a poll conducted after the 2008 presidential election, people responded very differently to two versions of the question: “What one issue mattered most to you in deciding how you voted for president?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.

When explicitly offered the economy as a response, more than half of respondents (58%) chose this answer; only 35% of those who responded to the open-ended version volunteered the economy. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8%) provided a response other than the five they were read. By contrast, fully 43% of those asked the open-ended version provided a response not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. (Also see  “High Marks for the Campaign, a High Bar for Obama”  for more information.)

Researchers will sometimes conduct a pilot study using open-ended questions to discover which answers are most common. They will then develop closed-ended questions based off that pilot study that include the most common responses as answer choices. In this way, the questions may better reflect what the public is thinking, how they view a particular issue, or bring certain issues to light that the researchers may not have been aware of.

When asking closed-ended questions, the choice of options provided, how each option is described, the number of response options offered, and the order in which options are read can all influence how people respond. One example of the impact of how categories are defined can be found in a Pew Research Center poll conducted in January 2002. When half of the sample was asked whether it was “more important for President Bush to focus on domestic policy or foreign policy,” 52% chose domestic policy while only 34% said foreign policy. When the category “foreign policy” was narrowed to a specific aspect – “the war on terrorism” – far more people chose it; only 33% chose domestic policy while 52% chose the war on terrorism.

In most circumstances, the number of answer choices should be kept to a relatively small number – just four or perhaps five at most – especially in telephone surveys. Psychological research indicates that people have a hard time keeping more than this number of choices in mind at one time. When the question is asking about an objective fact and/or demographics, such as the religious affiliation of the respondent, more categories can be used. In fact, they are encouraged to ensure inclusivity. For example, Pew Research Center’s standard religion questions include more than 12 different categories, beginning with the most common affiliations (Protestant and Catholic). Most respondents have no trouble with this question because they can expect to see their religious group within that list in a self-administered survey.

In addition to the number and choice of response options offered, the order of answer categories can influence how people respond to closed-ended questions. Research suggests that in telephone surveys respondents more frequently choose items heard later in a list (a “recency effect”), and in self-administered surveys, they tend to choose items at the top of the list (a “primacy” effect).

Because of concerns about the effects of category order on responses to closed-ended questions, many sets of response options in Pew Research Center’s surveys are programmed to be randomized to ensure that the options are not asked in the same order for each respondent. Rotating or randomizing means that questions or items in a list are not asked in the same order to each respondent. Answers to questions are sometimes affected by questions that precede them. By presenting questions in a different order to each respondent, we ensure that each question gets asked in the same context as every other question the same number of times (e.g., first, last or any position in between). This does not eliminate the potential impact of previous questions on the current question, but it does ensure that this bias is spread randomly across all of the questions or items in the list. For instance, in the example discussed above about what issue mattered most in people’s vote, the order of the five issues in the closed-ended version of the question was randomized so that no one issue appeared early or late in the list for all respondents. Randomization of response items does not eliminate order effects, but it does ensure that this type of bias is spread randomly.

Questions with ordinal response categories – those with an underlying order (e.g., excellent, good, only fair, poor OR very favorable, mostly favorable, mostly unfavorable, very unfavorable) – are generally not randomized because the order of the categories conveys important information to help respondents answer the question. Generally, these types of scales should be presented in order so respondents can easily place their responses along the continuum, but the order can be reversed for some respondents. For example, in one of Pew Research Center’s questions about abortion, half of the sample is asked whether abortion should be “legal in all cases, legal in most cases, illegal in most cases, illegal in all cases,” while the other half of the sample is asked the same question with the response categories read in reverse order, starting with “illegal in all cases.” Again, reversing the order does not eliminate the recency effect but distributes it randomly across the population.

Question wording

The choice of words and phrases in a question is critical in expressing the meaning and intent of the question to the respondent and ensuring that all respondents interpret the question the same way. Even small wording differences can substantially affect the answers people provide.

[View more Methods 101 Videos ]

An example of a wording difference that had a significant impact on responses comes from a January 2003 Pew Research Center survey. When people were asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule,” 68% said they favored military action while 25% said they opposed military action. However, when asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule  even if it meant that U.S. forces might suffer thousands of casualties, ” responses were dramatically different; only 43% said they favored military action, while 48% said they opposed it. The introduction of U.S. casualties altered the context of the question and influenced whether people favored or opposed military action in Iraq.

There has been a substantial amount of research to gauge the impact of different ways of asking questions and how to minimize differences in the way respondents interpret what is being asked. The issues related to question wording are more numerous than can be treated adequately in this short space, but below are a few of the important things to consider:

First, it is important to ask questions that are clear and specific and that each respondent will be able to answer. If a question is open-ended, it should be evident to respondents that they can answer in their own words and what type of response they should provide (an issue or problem, a month, number of days, etc.). Closed-ended questions should include all reasonable responses (i.e., the list of options is exhaustive) and the response categories should not overlap (i.e., response options should be mutually exclusive). Further, it is important to discern when it is best to use forced-choice close-ended questions (often denoted with a radio button in online surveys) versus “select-all-that-apply” lists (or check-all boxes). A 2019 Center study found that forced-choice questions tend to yield more accurate responses, especially for sensitive questions.  Based on that research, the Center generally avoids using select-all-that-apply questions.

It is also important to ask only one question at a time. Questions that ask respondents to evaluate more than one concept (known as double-barreled questions) – such as “How much confidence do you have in President Obama to handle domestic and foreign policy?” – are difficult for respondents to answer and often lead to responses that are difficult to interpret. In this example, it would be more effective to ask two separate questions, one about domestic policy and another about foreign policy.

In general, questions that use simple and concrete language are more easily understood by respondents. It is especially important to consider the education level of the survey population when thinking about how easy it will be for respondents to interpret and answer a question. Double negatives (e.g., do you favor or oppose  not  allowing gays and lesbians to legally marry) or unfamiliar abbreviations or jargon (e.g., ANWR instead of Arctic National Wildlife Refuge) can result in respondent confusion and should be avoided.

Similarly, it is important to consider whether certain words may be viewed as biased or potentially offensive to some respondents, as well as the emotional reaction that some words may provoke. For example, in a 2005 Pew Research Center survey, 51% of respondents said they favored “making it legal for doctors to give terminally ill patients the means to end their lives,” but only 44% said they favored “making it legal for doctors to assist terminally ill patients in committing suicide.” Although both versions of the question are asking about the same thing, the reaction of respondents was different. In another example, respondents have reacted differently to questions using the word “welfare” as opposed to the more generic “assistance to the poor.” Several experiments have shown that there is much greater public support for expanding “assistance to the poor” than for expanding “welfare.”

We often write two versions of a question and ask half of the survey sample one version of the question and the other half the second version. Thus, we say we have two  forms  of the questionnaire. Respondents are assigned randomly to receive either form, so we can assume that the two groups of respondents are essentially identical. On questions where two versions are used, significant differences in the answers between the two forms tell us that the difference is a result of the way we worded the two versions.

One of the most common formats used in survey questions is the “agree-disagree” format. In this type of question, respondents are asked whether they agree or disagree with a particular statement. Research has shown that, compared with the better educated and better informed, less educated and less informed respondents have a greater tendency to agree with such statements. This is sometimes called an “acquiescence bias” (since some kinds of respondents are more likely to acquiesce to the assertion than are others). This behavior is even more pronounced when there’s an interviewer present, rather than when the survey is self-administered. A better practice is to offer respondents a choice between alternative statements. A Pew Research Center experiment with one of its routinely asked values questions illustrates the difference that question format can make. Not only does the forced choice format yield a very different result overall from the agree-disagree format, but the pattern of answers between respondents with more or less formal education also tends to be very different.

One other challenge in developing questionnaires is what is called “social desirability bias.” People have a natural tendency to want to be accepted and liked, and this may lead people to provide inaccurate answers to questions that deal with sensitive subjects. Research has shown that respondents understate alcohol and drug use, tax evasion and racial bias. They also may overstate church attendance, charitable contributions and the likelihood that they will vote in an election. Researchers attempt to account for this potential bias in crafting questions about these topics. For instance, when Pew Research Center surveys ask about past voting behavior, it is important to note that circumstances may have prevented the respondent from voting: “In the 2012 presidential election between Barack Obama and Mitt Romney, did things come up that kept you from voting, or did you happen to vote?” The choice of response options can also make it easier for people to be honest. For example, a question about church attendance might include three of six response options that indicate infrequent attendance. Research has also shown that social desirability bias can be greater when an interviewer is present (e.g., telephone and face-to-face surveys) than when respondents complete the survey themselves (e.g., paper and web surveys).

Lastly, because slight modifications in question wording can affect responses, identical question wording should be used when the intention is to compare results to those from earlier surveys. Similarly, because question wording and responses can vary based on the mode used to survey respondents, researchers should carefully evaluate the likely effects on trend measurements if a different survey mode will be used to assess change in opinion over time.

Question order

Once the survey questions are developed, particular attention should be paid to how they are ordered in the questionnaire. Surveyors must be attentive to how questions early in a questionnaire may have unintended effects on how respondents answer subsequent questions. Researchers have demonstrated that the order in which questions are asked can influence how people respond; earlier questions can unintentionally provide context for the questions that follow (these effects are called “order effects”).

One kind of order effect can be seen in responses to open-ended questions. Pew Research Center surveys generally ask open-ended questions about national problems, opinions about leaders and similar topics near the beginning of the questionnaire. If closed-ended questions that relate to the topic are placed before the open-ended question, respondents are much more likely to mention concepts or considerations raised in those earlier questions when responding to the open-ended question.

For closed-ended opinion questions, there are two main types of order effects: contrast effects ( where the order results in greater differences in responses), and assimilation effects (where responses are more similar as a result of their order).

An example of a contrast effect can be seen in a Pew Research Center poll conducted in October 2003, a dozen years before same-sex marriage was legalized in the U.S. That poll found that people were more likely to favor allowing gays and lesbians to enter into legal agreements that give them the same rights as married couples when this question was asked after one about whether they favored or opposed allowing gays and lesbians to marry (45% favored legal agreements when asked after the marriage question, but 37% favored legal agreements without the immediate preceding context of a question about same-sex marriage). Responses to the question about same-sex marriage, meanwhile, were not significantly affected by its placement before or after the legal agreements question.

Another experiment embedded in a December 2008 Pew Research Center poll also resulted in a contrast effect. When people were asked “All in all, are you satisfied or dissatisfied with the way things are going in this country today?” immediately after having been asked “Do you approve or disapprove of the way George W. Bush is handling his job as president?”; 88% said they were dissatisfied, compared with only 78% without the context of the prior question.

Responses to presidential approval remained relatively unchanged whether national satisfaction was asked before or after it. A similar finding occurred in December 2004 when both satisfaction and presidential approval were much higher (57% were dissatisfied when Bush approval was asked first vs. 51% when general satisfaction was asked first).

Several studies also have shown that asking a more specific question before a more general question (e.g., asking about happiness with one’s marriage before asking about one’s overall happiness) can result in a contrast effect. Although some exceptions have been found, people tend to avoid redundancy by excluding the more specific question from the general rating.

Assimilation effects occur when responses to two questions are more consistent or closer together because of their placement in the questionnaire. We found an example of an assimilation effect in a Pew Research Center poll conducted in November 2008 when we asked whether Republican leaders should work with Obama or stand up to him on important issues and whether Democratic leaders should work with Republican leaders or stand up to them on important issues. People were more likely to say that Republican leaders should work with Obama when the question was preceded by the one asking what Democratic leaders should do in working with Republican leaders (81% vs. 66%). However, when people were first asked about Republican leaders working with Obama, fewer said that Democratic leaders should work with Republican leaders (71% vs. 82%).

The order questions are asked is of particular importance when tracking trends over time. As a result, care should be taken to ensure that the context is similar each time a question is asked. Modifying the context of the question could call into question any observed changes over time (see  measuring change over time  for more information).

A questionnaire, like a conversation, should be grouped by topic and unfold in a logical order. It is often helpful to begin the survey with simple questions that respondents will find interesting and engaging. Throughout the survey, an effort should be made to keep the survey interesting and not overburden respondents with several difficult questions right after one another. Demographic questions such as income, education or age should not be asked near the beginning of a survey unless they are needed to determine eligibility for the survey or for routing respondents through particular sections of the questionnaire. Even then, it is best to precede such items with more interesting and engaging questions. One virtue of survey panels like the ATP is that demographic questions usually only need to be asked once a year, not in each survey.

U.S. Surveys

Other research methods.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Market Research

Your ultimate guide to questionnaires and how to design a good one

The written questionnaire is the heart and soul of any survey research project. Whether you conduct your survey using an online questionnaire, in person, by email or over the phone, the way you design your questionnaire plays a critical role in shaping the quality of the data and insights that you’ll get from your target audience. Keep reading to get actionable tips.

What is a questionnaire?

A questionnaire is a research tool consisting of a set of questions or other ‘prompts’ to collect data from a set of respondents.

When used in most research, a questionnaire will consist of a number of types of questions (primarily open-ended and closed) in order to gain both quantitative data that can be analyzed to draw conclusions, and qualitative data to provide longer, more specific explanations.

A research questionnaire is often mistaken for a survey - and many people use the term questionnaire and survey, interchangeably.

But that’s incorrect.

Which is what we talk about next.

Get started with our free survey maker with 50+ templates

Survey vs. questionnaire – what’s the difference?

Before we go too much further, let’s consider the differences between surveys and questionnaires.

These two terms are often used interchangeably, but there is an important difference between them.

Survey definition

A survey is the process of collecting data from a set of respondents and using it to gather insights.

Survey research can be conducted using a questionnaire, but won’t always involve one.

Questionnaire definition

A questionnaire is the list of questions you circulate to your target audience.

In other words, the survey is the task you’re carrying out, and the questionnaire is the instrument you’re using to do it.

By itself, a questionnaire doesn’t achieve much.

It’s when you put it into action as part of a survey that you start to get results.

Advantages vs disadvantages of using a questionnaire

While a questionnaire is a popular method to gather data for market research or other studies, there are a few disadvantages to using this method (although there are plenty of advantages to using a questionnaire too).

Let’s have a look at some of the advantages and disadvantages of using a questionnaire for collecting data.

Advantages of using a questionnaire

1. questionnaires are relatively cheap.

Depending on the complexity of your study, using a questionnaire can be cost effective compared to other methods.

You simply need to write your survey questionnaire, and send it out and then process the responses.

You can set up an online questionnaire relatively easily, or simply carry out market research on the street if that’s the best method.

2. You can get and analyze results quickly

Again depending on the size of your survey you can get results back from a questionnaire quickly, often within 24 hours of putting the questionnaire live.

It also means you can start to analyze responses quickly too.

3. They’re easily scalable

You can easily send an online questionnaire to anyone in the world and with the right software you can quickly identify your target audience and your questionnaire to them.

4. Questionnaires are easy to analyze

If your questionnaire design has been done properly, it’s quick and easy to analyze results from questionnaires once responses start to come back.

This is particularly useful with large scale market research projects.

Because all respondents are answering the same questions, it’s simple to identify trends.

5. You can use the results to make accurate decisions

As a research instrument, a questionnaire is ideal for commercial research because the data you get back is from your target audience (or ideal customers) and the information you get back on their thoughts, preferences or behaviors allows you to make business decisions.

6. A questionnaire can cover any topic

One of the biggest advantages of using questionnaires when conducting research is (because you can adapt them using different types and styles of open ended questions and closed ended questions) they can be used to gather data on almost any topic.

There are many types of questionnaires you can design to gather both quantitative data and qualitative data - so they’re a useful tool for all kinds of data analysis.

Disadvantages of using a questionnaire

1. respondents could lie.

This is by far the biggest risk with a questionnaire, especially when dealing with sensitive topics.

Rather than give their actual opinion, a respondent might feel pressured to give the answer they deem more socially acceptable, which doesn’t give you accurate results.

2. Respondents might not answer every question

There are all kinds of reasons respondents might not answer every question, from questionnaire length, they might not understand what’s being asked, or they simply might not want to answer it.

If you get questionnaires back without complete responses it could negatively affect your research data and provide an inaccurate picture.

3. They might interpret what’s being asked incorrectly

This is a particular problem when running a survey across geographical boundaries and often comes down to the design of the survey questionnaire.

If your questions aren’t written in a very clear way, the respondent might misunderstand what’s being asked and provide an answer that doesn’t reflect what they actually think.

Again this can negatively affect your research data.

4. You could introduce bias

The whole point of producing a questionnaire is to gather accurate data from which decisions can be made or conclusions drawn.

But the data collected can be heavily impacted if the researchers accidentally introduce bias into the questions.

This can be easily done if the researcher is trying to prove a certain hypothesis with their questionnaire, and unwittingly write questions that push people towards giving a certain answer.

In these cases respondents’ answers won’t accurately reflect what is really happening and stop you gathering more accurate data.

5. Respondents could get survey fatigue

One issue you can run into when sending out a questionnaire, particularly if you send them out regularly to the same survey sample, is that your respondents could start to suffer from survey fatigue.

In these circumstances, rather than thinking about the response options in the questionnaire and providing accurate answers, respondents could start to just tick boxes to get through the questionnaire quickly.

Again, this won’t give you an accurate data set.

Questionnaire design: How to do it

It’s essential to carefully craft a questionnaire to reduce survey error and optimize your data . The best way to think about the questionnaire is with the end result in mind.

How do you do that?

Start with questions, like:

• What is my research purpose ?
• What data do I need?
• How am I going to analyze that data?
• What questions are needed to best suit these variables?

Once you have a clear idea of the purpose of your survey, you’ll be in a better position to create an effective questionnaire.

Here are a few steps to help you get into the right mindset.

1. Keep the respondent front and center

A survey is the process of collecting information from people, so it needs to be designed around human beings first and foremost.

In his post about survey design theory, David Vannette, PhD, from the Qualtrics Methodology Lab explains the correlation between the way a survey is designed and the quality of data that is extracted.

“To begin designing an effective survey, take a step back and try to understand what goes on in your respondents’ heads when they are taking your survey.

This step is critical to making sure that your questionnaire makes it as likely as possible that the response process follows that expected path.”

From writing the questions to designing the survey flow, the respondent’s point of view should always be front and center in your mind during a questionnaire design.

2. How to write survey questions

Your questionnaire should only be as long as it needs to be, and every question needs to deliver value.

That means your questions must each have an individual purpose and produce the best possible data for that purpose, all while supporting the overall goal of the survey.

A question must also must be phrased in a way that is easy for all your respondents to understand, and does not produce false results.

To do this, remember the following principles:

Get into the respondent's head

The process for a respondent answering a survey question looks like this:

• The respondent reads the question and determines what information they need to answer it.
• They search their memory for that information.
• They make judgments about that information.
• They translate that judgment into one of the answer options you’ve provided. This is the process of taking the data they have and matching that information with the question that’s asked.

When wording questions, make sure the question means the same thing to all respondents. Words should have one meaning, few syllables, and the sentences should have few words.

Only use the words needed to ask your question and not a word more .

Note that it’s important that the respondent understands the intent behind your question.

If they don’t, they may answer a different question and the data can be skewed.

Some contextual help text, either in the introduction to the questionnaire or before the question itself, can help make sure the respondent understands your goals and the scope of your research.

Use mutually exclusive responses

Be sure to make your response categories mutually exclusive.

Consider the question:

What is your age?

Respondents that are 31 years old have two options, as do respondents that are 40 and 55. As a result, it is impossible to predict which category they will choose.

This can distort results and frustrate respondents. It can be easily avoided by making responses mutually exclusive.

The following question is much better:

This question is clear and will give us better results.

Ask specific questions

Nonspecific questions can confuse respondents and influence results.

Do you like orange juice?

• Like very much
• Neither like nor dislike
• Dislike very much

This question is very unclear. Is it asking about taste, texture, price, or the nutritional content? Different respondents will read this question differently.

A specific question will get more specific answers that are actionable.

How much do you like the current price of orange juice?

This question is more specific and will get better results.

If you need to collect responses about more than one aspect of a subject, you can include multiple questions on it. (Do you like the taste of orange juice? Do you like the nutritional content of orange juice? etc.)

Use a variety of question types

If all of your questionnaire, survey or poll questions are structured the same way (e.g. yes/no or multiple choice) the respondents are likely to become bored and tune out. That could mean they pay less attention to how they’re answering or even give up altogether.

Instead, mix up the question types to keep the experience interesting and varied. It’s a good idea to include questions that yield both qualitative and quantitative data.

For example, an open-ended questionnaire item such as “describe your attitude to life” will provide qualitative data – a form of information that’s rich, unstructured and unpredictable. The respondent will tell you in their own words what they think and feel.

A quantitative / close-ended questionnaire item, such as “Which word describes your attitude to life? a) practical b) philosophical” gives you a much more structured answer, but the answers will be less rich and detailed.

Open-ended questions take more thought and effort to answer, so use them sparingly. They also require a different kind of treatment once your survey is in the analysis stage.

3. Pre-test your questionnaire

Always pre-test a questionnaire before sending it out to respondents. This will help catch any errors you might have missed. You could ask a colleague, friend, or an expert to take the survey and give feedback. If possible, ask a few cognitive questions like, “how did you get to that response?” and “what were you thinking about when you answered that question?” Figure out what was easy for the responder and where there is potential for confusion. You can then re-word where necessary to make the experience as frictionless as possible.

If your resources allow, you could also consider using a focus group to test out your survey. Having multiple respondents road-test the questionnaire will give you a better understanding of its strengths and weaknesses. Match the focus group to your target respondents as closely as possible, for example in terms of age, background, gender, and level of education.

Note: Don't forget to make your survey as accessible as possible for increased response rates.

Questionnaire examples and templates

There are free questionnaire templates and example questions available for all kinds of surveys and market research, many of them online. But they’re not all created equal and you should use critical judgement when selecting one. After all, the questionnaire examples may be free but the time and energy you’ll spend carrying out a survey are not.

If you’re using online questionnaire templates as the basis for your own, make sure it has been developed by professionals and is specific to the type of research you’re doing to ensure higher completion rates. As we’ve explored here, using the wrong kinds of questions can result in skewed or messy data, and could even prompt respondents to abandon the questionnaire without finishing or give thoughtless answers.

You’ll find a full library of downloadable survey templates in the Qualtrics Marketplace , covering many different types of research from employee engagement to post-event feedback . All are fully customizable and have been developed by Qualtrics experts.

Qualtrics // Experience Management

Qualtrics is the technology platform that organizations use to collect, manage, and act on experience data, also called X-data™. The Qualtrics XM Platform™ is a system of action, used by teams, departments, and entire organizations to manage the four core experiences of business—customer, product, employee, and brand—on one platform.

Over 12,000 enterprises worldwide, including more than 75 percent of the Fortune 100 and 99 of the top 100 U.S. business schools, rely on Qualtrics to consistently build products that people love, create more loyal customers, develop a phenomenal employee culture, and build iconic brands.

Related Articles

June 27, 2023

The fresh insights people: Scaling research at Woolworths Group

June 20, 2023

Bank less, delight more: How Bankwest built an engine room for customer obsession

June 16, 2023

How Qualtrics Helps Three Local Governments Drive Better Outcomes Through Data Insights

April 1, 2023

Academic Experience

Great survey questions: How to write them & avoid common mistakes

March 21, 2023

Sample size calculator

March 9, 2023

Experience Management

X4 2023: See the XM innovations unveiled for customer research, marketing, and insights teams

February 22, 2023

Achieving better insights and better product delivery through in-house research

December 6, 2022

Improved Topic Sentiment Analysis using Discourse Segmentation

Stay up to date with the latest xm thought leadership, tips and news., request demo.

Ready to learn more about Qualtrics?

How to Design Effective Research Questionnaires for Robust Findings

As a staple in data collection, questionnaires help uncover robust and reliable findings that can transform industries, shape policies, and revolutionize understanding. Whether you are exploring societal trends or delving into scientific phenomena, the effectiveness of your research questionnaire can make or break your findings.

In this article, we aim to understand the core purpose of questionnaires, exploring how they serve as essential tools for gathering systematic data, both qualitative and quantitative, from diverse respondents. Read on as we explore the key elements that make up a winning questionnaire, the art of framing questions which are both compelling and rigorous, and the careful balance between simplicity and depth.

Table of Contents

The Role of Questionnaires in Research

So, what is a questionnaire? A questionnaire is a structured set of questions designed to collect information, opinions, attitudes, or behaviors from respondents. It is one of the most commonly used data collection methods in research. Moreover, questionnaires can be used in various research fields, including social sciences, market research, healthcare, education, and psychology. Their adaptability makes them suitable for investigating diverse research questions.

Questionnaire and survey  are two terms often used interchangeably, but they have distinct meanings in the context of research. A survey refers to the broader process of data collection that may involve various methods. A survey can encompass different data collection techniques, such as interviews , focus groups, observations, and yes, questionnaires.

Pros and Cons of Using Questionnaires in Research:

While questionnaires offer numerous advantages in research, they also come with some disadvantages that researchers must be aware of and address appropriately. Careful questionnaire design, validation, and consideration of potential biases can help mitigate these disadvantages and enhance the effectiveness of using questionnaires as a data collection method.

Structured vs Unstructured Questionnaires

Structured questionnaire:.

A structured questionnaire consists of questions with predefined response options. Respondents are presented with a fixed set of choices and are required to select from those options. The questions in a structured questionnaire are designed to elicit specific and quantifiable responses. Structured questionnaires are particularly useful for collecting quantitative data and are often employed in surveys and studies where standardized and comparable data are necessary.

Advantages of Structured Questionnaires:

• Easy to analyze and interpret: The fixed response options facilitate straightforward data analysis and comparison across respondents.
• Efficient for large-scale data collection: Structured questionnaires are time-efficient, allowing researchers to collect data from a large number of respondents.
• Reduces response bias: The predefined response options minimize potential response bias and maintain consistency in data collection.

Limitations of Structured Questionnaires:

• Lack of depth: Structured questionnaires may not capture in-depth insights or nuances as respondents are limited to pre-defined response choices. Hence, they may not reveal the reasons behind respondents’ choices, limiting the understanding of their perspectives.
• Limited flexibility: The fixed response options may not cover all potential responses, therefore, potentially restricting respondents’ answers.

Unstructured Questionnaire:

An unstructured questionnaire consists of questions that allow respondents to provide detailed and unrestricted responses. Unlike structured questionnaires, there are no predefined response options, giving respondents the freedom to express their thoughts in their own words. Furthermore, unstructured questionnaires are valuable for collecting qualitative data and obtaining in-depth insights into respondents’ experiences, opinions, or feelings.

Advantages of Unstructured Questionnaires:

• Rich qualitative data: Unstructured questionnaires yield detailed and comprehensive qualitative data, providing valuable and novel insights into respondents’ perspectives.
• Flexibility in responses: Respondents have the freedom to express themselves in their own words. Hence, allowing for a wide range of responses.

Limitations of Unstructured Questionnaires:

• Time-consuming analysis: Analyzing open-ended responses can be time-consuming, since, each response requires careful reading and interpretation.
• Subjectivity in interpretation: The analysis of open-ended responses may be subjective, as researchers interpret and categorize responses based on their judgment.
• May require smaller sample size: Due to the depth of responses, researchers may need a smaller sample size for comprehensive analysis, making generalizations more challenging.

Types of Questions in a Questionnaire

In a questionnaire, researchers typically use the following most common types of questions to gather a variety of information from respondents:

1. Open-Ended Questions:

These questions allow respondents to provide detailed and unrestricted responses in their own words. Open-ended questions are valuable for gathering qualitative data and in-depth insights.

Example: What suggestions do you have for improving our product?

2. Multiple-Choice Questions

Respondents choose one answer from a list of provided options. This type of question is suitable for gathering categorical data or preferences.

Example: Which of the following social media/academic networking platforms do you use to promote your research?

• ResearchGate
• Academia.edu

3. Dichotomous Questions

Respondents choose between two options, typically “yes” or “no”, “true” or “false”, or “agree” or “disagree”.

Example: Have you ever published in open access journals before?

4. Scaling Questions

These questions, also known as rating scale questions, use a predefined scale that allows respondents to rate or rank their level of agreement, satisfaction, importance, or other subjective assessments. These scales help researchers quantify subjective data and make comparisons across respondents.

There are several types of scaling techniques used in scaling questions:

i. Likert Scale:

The Likert scale is one of the most common scaling techniques. It presents respondents with a series of statements and asks them to rate their level of agreement or disagreement using a range of options, typically from “strongly agree” to “strongly disagree”.For example: Please indicate your level of agreement with the statement: “The content presented in the webinar was relevant and aligned with the advertised topic.”

• Strongly Agree
• Strongly Disagree

ii. Semantic Differential Scale:

The semantic differential scale measures respondents’ perceptions or attitudes towards an item using opposite adjectives or bipolar words. Respondents rate the item on a scale between the two opposites. For example:

• Easy —— Difficult
• Satisfied —— Unsatisfied
• Very likely —— Very unlikely

iii. Numerical Rating Scale:

This scale requires respondents to provide a numerical rating on a predefined scale. It can be a simple 1 to 5 or 1 to 10 scale, where higher numbers indicate higher agreement, satisfaction, or importance.

iv. Ranking Questions:

Respondents rank items in order of preference or importance. Ranking questions help identify preferences or priorities.

Example: Please rank the following features of our app in order of importance (1 = Most Important, 5 = Least Important):

• User Interface
• Functionality
• Customer Support

By using a mix of question types, researchers can gather both quantitative and qualitative data, providing a comprehensive understanding of the research topic and enabling meaningful analysis and interpretation of the results. The choice of question types depends on the research objectives , the desired depth of information, and the data analysis requirements.

Methods of Administering Questionnaires

There are several methods for administering questionnaires, and the choice of method depends on factors such as the target population, research objectives , convenience, and resources available. Here are some common methods of administering questionnaires:

Each method has its advantages and limitations. Online surveys offer convenience and a large reach, but they may be limited to individuals with internet access. Face-to-face interviews allow for in-depth responses but can be time-consuming and costly. Telephone surveys have broad reach but may be limited by declining response rates. Researchers should choose the method that best suits their research objectives, target population, and available resources to ensure successful data collection.

How to Design a Questionnaire

Designing a good questionnaire is crucial for gathering accurate and meaningful data that aligns with your research objectives. Here are essential steps and tips to create a well-designed questionnaire:

1. Define Your Research Objectives : Clearly outline the purpose and specific information you aim to gather through the questionnaire.

2. Identify Your Target Audience : Understand respondents’ characteristics and tailor the questionnaire accordingly.

3. Develop the Questions :

• Write Clear and Concise Questions
• Avoid Leading or Biasing Questions
• Sequence Questions Logically
• Group Related Questions
• Include Demographic Questions

4. Provide Well-defined Response Options : Offer exhaustive response choices for closed-ended questions.

5. Consider Skip Logic and Branching : Customize the questionnaire based on previous answers.

6. Pilot Test the Questionnaire : Identify and address issues through a pilot study .

7. Seek Expert Feedback : Validate the questionnaire with subject matter experts.

8. Obtain Ethical Approval : Comply with ethical guidelines , obtain consent, and ensure confidentiality before administering the questionnaire.

9. Administer the Questionnaire : Choose the right mode and provide clear instructions.

10. Test the Survey Platform : Ensure compatibility and usability for online surveys.

By following these steps and paying attention to questionnaire design principles, you can create a well-structured and effective questionnaire that gathers reliable data and helps you achieve your research objectives.

Characteristics of a Good Questionnaire

A good questionnaire possesses several essential elements that contribute to its effectiveness. Furthermore, these characteristics ensure that the questionnaire is well-designed, easy to understand, and capable of providing valuable insights. Here are some key characteristics of a good questionnaire:

1. Clarity and Simplicity : Questions should be clear, concise, and unambiguous. Avoid using complex language or technical terms that may confuse respondents. Simple and straightforward questions ensure that respondents interpret them consistently.

2. Relevance and Focus : Each question should directly relate to the research objectives and contribute to answering the research questions. Consequently, avoid including extraneous or irrelevant questions that could lead to data clutter.

3. Mix of Question Types : Utilize a mix of question types, including open-ended, Likert scale, and multiple-choice questions. This variety allows for both qualitative and quantitative data collections .

4. Validity and Reliability : Ensure the questionnaire measures what it intends to measure (validity) and produces consistent results upon repeated administration (reliability). Validation should be conducted through expert review and previous research.

5. Appropriate Length : Keep the questionnaire’s length appropriate and manageable to avoid respondent fatigue or dropouts. Long questionnaires may result in incomplete or rushed responses.

6. Clear Instructions : Include clear instructions at the beginning of the questionnaire to guide respondents on how to complete it. Explain any technical terms, formats, or concepts if necessary.

7. User-Friendly Format : Design the questionnaire to be visually appealing and user-friendly. Use consistent formatting, adequate spacing, and a logical page layout.

8. Data Validation and Cleaning : Incorporate validation checks to ensure data accuracy and reliability. Consider mechanisms to detect and correct inconsistent or missing responses during data cleaning.

By incorporating these characteristics, researchers can create a questionnaire that maximizes data quality, minimizes response bias, and provides valuable insights for their research.

In the pursuit of advancing research and gaining meaningful insights, investing time and effort into designing effective questionnaires is a crucial step. A well-designed questionnaire is more than a mere set of questions; it is a masterpiece of precision and ingenuity. Each question plays a vital role in shaping the narrative of our research, guiding us through the labyrinth of data to meaningful conclusions. Indeed, a well-designed questionnaire serves as a powerful tool for unlocking valuable insights and generating robust findings that impact society positively.

Have you ever designed a research questionnaire? Reflect on your experience and share your insights with researchers globally through Enago Academy’s Open Blogging Platform . Join our diverse community of 1000K+ researchers and authors to exchange ideas, strategies, and best practices, and together, let’s shape the future of data collection and maximize the impact of questionnaires in the ever-evolving landscape of research.

Frequently Asked Questions

A research questionnaire is a structured tool used to gather data from participants in a systematic manner. It consists of a series of carefully crafted questions designed to collect specific information related to a research study.

Questionnaires play a pivotal role in both quantitative and qualitative research, enabling researchers to collect insights, opinions, attitudes, or behaviors from respondents. This aids in hypothesis testing, understanding, and informed decision-making, ensuring consistency, efficiency, and facilitating comparisons.

Questionnaires are a versatile tool employed in various research designs to gather data efficiently and comprehensively. They find extensive use in both quantitative and qualitative research methodologies, making them a fundamental component of research across disciplines. Some research designs that commonly utilize questionnaires include: a) Cross-Sectional Studies b) Longitudinal Studies c) Descriptive Research d) Correlational Studies e) Causal-Comparative Studies f) Experimental Research g) Survey Research h) Case Studies i) Exploratory Research

A survey is a comprehensive data collection method that can include various techniques like interviews and observations. A questionnaire is a specific set of structured questions within a survey designed to gather standardized responses. While a survey is a broader approach, a questionnaire is a focused tool for collecting specific data.

The choice of questionnaire type depends on the research objectives, the type of data required, and the preferences of respondents. Some common types include: • Structured Questionnaires: These questionnaires consist of predefined, closed-ended questions with fixed response options. They are easy to analyze and suitable for quantitative research. • Semi-Structured Questionnaires: These questionnaires combine closed-ended questions with open-ended ones. They offer more flexibility for respondents to provide detailed explanations. • Unstructured Questionnaires: These questionnaires contain open-ended questions only, allowing respondents to express their thoughts and opinions freely. They are commonly used in qualitative research.

Following these steps ensures effective questionnaire administration for reliable data collection: • Choose a Method: Decide on online, face-to-face, mail, or phone administration. • Online Surveys: Use platforms like SurveyMonkey • Pilot Test: Test on a small group before full deployment • Clear Instructions: Provide concise guidelines • Follow-Up: Send reminders if needed

Thank you, Riya. This is quite helpful. As discussed, response bias is one of the disadvantages in the use of questionnaires. One way to help limit this can be to use scenario based questions. These type of questions may help the respondents to be more reflective and active in the process.

Thank you, Dear Riya. This is quite helpful.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Publishing Research
• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Industry News
• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Achieving Research Excellence: Checklist for good research practices

Academia is built on the foundation of trustworthy and high-quality research, supported by the pillars…

• Promoting Research

Plain Language Summary — Communicating your research to bridge the academic-lay gap

Science can be complex, but does that mean it should not be accessible to the…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

As a researcher, what do you consider most when choosing an image manipulation detector?

• Foundations
• Write Paper

Search form

• Experiments
• Anthropology
• Self-Esteem
• Social Anxiety

• Research Paper >

Writing a Conclusion

Writing a conclusion is the final part of the research paper, drawing everything together and tying it into your initial research.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Write a Hypothesis
• Introduction

Browse Full Outline

• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

If you remember, a research paper starts with a broad look at the research and narrows down to the results , before the discussion opens it out again.

At the beginning of the research paper, you looked at all of the previous research and boiled it down into a research question .

In the discussion , you assess how the results answer to this question and discuss its relevance to the existing knowledge in the field.

When writing a conclusion, you should try to answer a few questions, as succinctly as possible.

You will have already answered some of these in your discussion, but the key is to leave some questions that another researcher can expand upon for their research project.

If you are planning a long career as a scientist, it is something that you can return to in the future. A good research project, whatever the results , will generate leads for others to follow.

What Has Your Research Shown?

This is a very quick synopsis of the results and discussion.

Writing a conclusion involves summing up the paper and giving a very brief description of the results, although you should not go into too much detail about this.

Anybody reading the conclusion has read the entire paper, so the conclusion merely acts as an aid to memory.

How Has It Added to What is Known About the Subject?

This is where you tie it in to the body of research highlighted in the introduction ; during the course of your literature review .

You should then point out the importance of the study and point out how it relates to the field.

You can also point out how your findings can be used by readers, pointing out the benefits. Even if you did not manage to reject the null , there is always a reason for this, and something has been learned.

What Were the Shortcomings?

Whilst writing the conclusion, you should highlight any deficiencies in your methods , explaining how they may have affected your results.

This will allow the next researcher to refine the methodology and learn from your mistakes, one of the foundations of the scientific process .

Has Your Research Left Some Unanswered Questions?

Do your findings open up any suggestions for future research?

For a shorter paper, this is not always essential, but you can highlight any possible areas of interest and give some ideas for those following.

Are My Results of Any Use in the Real World?

Again, this is not always applicable, but you can suggest any practical uses for your findings.

For example, if you uncovered a link between diet and the speed at which children learn, you could suggest a short plan for ensuring that children receive good nutrition.

With writing the conclusion finished, you are almost at the end of your research project.

All that remains is to perform the proof-reading and formatting , a little bit dull, but a sign that you are in the final stages.

• Psychology 101
• Flags and Countries
• Capitals and Countries

Martyn Shuttleworth (Sep 18, 2009). Writing a Conclusion. Retrieved May 17, 2024 from Explorable.com: https://explorable.com/writing-a-conclusion

You Are Allowed To Copy The Text

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0) .

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give appropriate credit and provide a link/reference to this page.

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Want to stay up to date? Follow us!

Check out the official book.

Learn how to construct, style and format an Academic paper and take your skills to the next level.

(also available as ebook )

Save this course for later

Don't have time for it all now? No problem, save it as a course and come back to it later.

Footer bottom

• Privacy Policy

• Subscribe to our RSS Feed
• Like us on Facebook
• Follow us on Twitter

How To Write The Conclusion Chapter

A Simple Explainer With Examples + Free Template

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021

So, you’ve wrapped up your results and discussion chapters, and you’re finally on the home stretch – the conclusion chapter . In this post, we’ll discuss everything you need to know to craft a high-quality conclusion chapter for your dissertation or thesis project.

Overview: The Conclusion Chapter

• What the thesis/dissertation conclusion chapter is
• What to include in your conclusion
• How to structure and write up your conclusion
• A few tips  to help you ace the chapter
• FREE conclusion template

What is the conclusion chapter?

The conclusion chapter is typically the final major chapter of a dissertation or thesis. As such, it serves as a concluding summary of your research findings and wraps up the document. While some publications such as journal articles and research reports combine the discussion and conclusion sections, these are typically separate chapters in a dissertation or thesis. As always, be sure to check what your university’s structural preference is before you start writing up these chapters.

So, what’s the difference between the discussion and the conclusion chapter?

Well, the two chapters are quite similar , as they both discuss the key findings of the study. However, the conclusion chapter is typically more general and high-level in nature. In your discussion chapter, you’ll typically discuss the intricate details of your study, but in your conclusion chapter, you’ll take a   broader perspective, reporting on the main research outcomes and how these addressed your research aim (or aims) .

A core function of the conclusion chapter is to synthesise all major points covered in your study and to tell the reader what they should take away from your work. Basically, you need to tell them what you found , why it’s valuable , how it can be applied , and what further research can be done.

Whatever you do, don’t just copy and paste what you’ve written in your discussion chapter! The conclusion chapter should not be a simple rehash of the discussion chapter. While the two chapters are similar, they have distinctly different functions.  

What should I include in the conclusion chapter?

To understand what needs to go into your conclusion chapter, it’s useful to understand what the chapter needs to achieve. In general, a good dissertation conclusion chapter should achieve the following:

• Summarise the key findings of the study
• Explicitly answer the research question(s) and address the research aims
• Inform the reader of the study’s main contributions
• Discuss any limitations or weaknesses of the study
• Present recommendations for future research

Therefore, your conclusion chapter needs to cover these core components. Importantly, you need to be careful not to include any new findings or data points. Your conclusion chapter should be based purely on data and analysis findings that you’ve already presented in the earlier chapters. If there’s a new point you want to introduce, you’ll need to go back to your results and discussion chapters to weave the foundation in there.

In many cases, readers will jump from the introduction chapter directly to the conclusions chapter to get a quick overview of the study’s purpose and key findings. Therefore, when you write up your conclusion chapter, it’s useful to assume that the reader hasn’t consumed the inner chapters of your dissertation or thesis. In other words, craft your conclusion chapter such that there’s a strong connection and smooth flow between the introduction and conclusion chapters, even though they’re on opposite ends of your document.

Need a helping hand?

How to write the conclusion chapter

Now that you have a clearer view of what the conclusion chapter is about, let’s break down the structure of this chapter so that you can get writing. Keep in mind that this is merely a typical structure – it’s not set in stone or universal. Some universities will prefer that you cover some of these points in the discussion chapter , or that you cover the points at different levels in different chapters.

Step 1: Craft a brief introduction section

As with all chapters in your dissertation or thesis, the conclusions chapter needs to start with a brief introduction. In this introductory section, you’ll want to tell the reader what they can expect to find in the chapter, and in what order . Here’s an example of what this might look like:

This chapter will conclude the study by summarising the key research findings in relation to the research aims and questions and discussing the value and contribution thereof. It will also review the limitations of the study and propose opportunities for future research.

Importantly, the objective here is just to give the reader a taste of what’s to come (a roadmap of sorts), not a summary of the chapter. So, keep it short and sweet – a paragraph or two should be ample.

Step 2: Discuss the overall findings in relation to the research aims

The next step in writing your conclusions chapter is to discuss the overall findings of your study , as they relate to the research aims and research questions . You would have likely covered similar ground in the discussion chapter, so it’s important to zoom out a little bit here and focus on the broader findings – specifically, how these help address the research aims .

In practical terms, it’s useful to start this section by reminding your reader of your research aims and research questions, so that the findings are well contextualised. In this section, phrases such as, “This study aimed to…” and “the results indicate that…” will likely come in handy. For example, you could say something like the following:

This study aimed to investigate the feeding habits of the naked mole-rat. The results indicate that naked mole rats feed on underground roots and tubers. Further findings show that these creatures eat only a part of the plant, leaving essential parts to ensure long-term food stability.

Be careful not to make overly bold claims here. Avoid claims such as “this study proves that” or “the findings disprove existing the existing theory”. It’s seldom the case that a single study can prove or disprove something. Typically, this is achieved by a broader body of research, not a single study – especially not a dissertation or thesis which will inherently have significant  limitations . We’ll discuss those limitations a little later.

Step 3: Discuss how your study contributes to the field

Next, you’ll need to discuss how your research has contributed to the field – both in terms of theory and practice . This involves talking about what you achieved in your study, highlighting why this is important and valuable, and how it can be used or applied.

In this section you’ll want to:

• Mention any research outputs created as a result of your study (e.g., articles, publications, etc.)
• Inform the reader on just how your research solves your research problem , and why that matters
• Reflect on gaps in the existing research and discuss how your study contributes towards addressing these gaps
• Discuss your study in relation to relevant theories . For example, does it confirm these theories or constructively challenge them?
• Discuss how your research findings can be applied in the real world . For example, what specific actions can practitioners take, based on your findings?

Be careful to strike a careful balance between being firm but humble in your arguments here. It’s unlikely that your one study will fundamentally change paradigms or shake up the discipline, so making claims to this effect will be frowned upon . At the same time though, you need to present your arguments with confidence, firmly asserting the contribution your research has made, however small that contribution may be. Simply put, you need to keep it balanced .

Step 4: Reflect on the limitations of your study

Now that you’ve pumped your research up, the next step is to critically reflect on the limitations and potential shortcomings of your study. You may have already covered this in the discussion chapter, depending on your university’s structural preferences, so be careful not to repeat yourself unnecessarily.

There are many potential limitations that can apply to any given study. Some common ones include:

• Sampling issues that reduce the generalisability of the findings (e.g., non-probability sampling )
• Insufficient sample size (e.g., not getting enough survey responses ) or limited data access
• Low-resolution data collection or analysis techniques
• Researcher bias or lack of experience
• Lack of access to research equipment
• Time constraints that limit the methodology (e.g. cross-sectional vs longitudinal time horizon)
• Budget constraints that limit various aspects of the study

Discussing the limitations of your research may feel self-defeating (no one wants to highlight their weaknesses, right), but it’s a critical component of high-quality research. It’s important to appreciate that all studies have limitations (even well-funded studies by expert researchers) – therefore acknowledging these limitations adds credibility to your research by showing that you understand the limitations of your research design .

That being said, keep an eye on your wording and make sure that you don’t undermine your research . It’s important to strike a balance between recognising the limitations, but also highlighting the value of your research despite those limitations. Show the reader that you understand the limitations, that these were justified given your constraints, and that you know how they can be improved upon – this will get you marks.

Next, you’ll need to make recommendations for future studies. This will largely be built on the limitations you just discussed. For example, if one of your study’s weaknesses was related to a specific data collection or analysis method, you can make a recommendation that future researchers undertake similar research using a more sophisticated method.

Another potential source of future research recommendations is any data points or analysis findings that were interesting or surprising , but not directly related to your study’s research aims and research questions. So, if you observed anything that “stood out” in your analysis, but you didn’t explore it in your discussion (due to a lack of relevance to your research aims), you can earmark that for further exploration in this section.

Essentially, this section is an opportunity to outline how other researchers can build on your study to take the research further and help develop the body of knowledge. So, think carefully about the new questions that your study has raised, and clearly outline these for future researchers to pick up on.

Step 6: Wrap up with a closing summary

Tips for a top-notch conclusion chapter

Now that we’ve covered the what , why and how of the conclusion chapter, here are some quick tips and suggestions to help you craft a rock-solid conclusion.

• Don’t ramble . The conclusion chapter usually consumes 5-7% of the total word count (although this will vary between universities), so you need to be concise. Edit this chapter thoroughly with a focus on brevity and clarity.
• Be very careful about the claims you make in terms of your study’s contribution. Nothing will make the marker’s eyes roll back faster than exaggerated or unfounded claims. Be humble but firm in your claim-making.
• Use clear and simple language that can be easily understood by an intelligent layman. Remember that not every reader will be an expert in your field, so it’s important to make your writing accessible. Bear in mind that no one knows your research better than you do, so it’s important to spell things out clearly for readers.

Hopefully, this post has given you some direction and confidence to take on the conclusion chapter of your dissertation or thesis with confidence. If you’re still feeling a little shaky and need a helping hand, consider booking a free initial consultation with a friendly Grad Coach to discuss how we can help you with hands-on, private coaching.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

17 Comments

Really you team are doing great!

Your guide on writing the concluding chapter of a research is really informative especially to the beginners who really do not know where to start. Im now ready to start. Keep it up guys

Really your team are doing great!

Very helpful guidelines, timely saved. Thanks so much for the tips.

This post was very helpful and informative. Thank you team.

A very enjoyable, understandable and crisp presentation on how to write a conclusion chapter. I thoroughly enjoyed it. Thanks Jenna.

This was a very helpful article which really gave me practical pointers for my concluding chapter. Keep doing what you are doing! It meant a lot to me to be able to have this guide. Thank you so much.

Nice content dealing with the conclusion chapter, it’s a relief after the streneous task of completing discussion part.Thanks for valuable guidance

Thanks for your guidance

I get all my doubts clarified regarding the conclusion chapter. It’s really amazing. Many thanks.

Very helpful tips. Thanks so much for the guidance

Thank you very much for this piece. It offers a very helpful starting point in writing the conclusion chapter of my thesis.

It’s awesome! Most useful and timely too. Thanks a million times

Bundle of thanks for your guidance. It was greatly helpful.

Wonderful, clear, practical guidance. So grateful to read this as I conclude my research. Thank you.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Conclusion and Checklist

The previous four chapters have provided a summary of questionnaire theory. Hopefully, they have also made a strong case for basing questionnaire design and processing on scientific principles rather than merely on the researcher's common sense. As emphasized in the Introduction, this book has been intended to serve practical purposes and therefore in this concluding section I will draw up a checklist of what I consider the most important points and recommendations for every phase of the questionnaire survey. Good luck with your future questionnaires!

Constructing the questionnaire

1. Only in exceptional cases should a questionnaire be more than 4 pages long and take more than 30 minutes to complete; if access to the participants is restricted to a certain amount of time, set the maximum length of the questionnaire with the slowest readers in mind so that everybody can finish within the given period.

2. When deciding on the questionnaire content, start by generating a theoretically driven list of the main areas to be covered.

3. Avoid the use of single-item variables; instead, include minimum 3-4 items addressing every content area.

4. Avoid truly open-ended questions that require lengthy answers.

5. Keep the number of items that are seeking confidential information to the minimum.

6. Be careful about how you formulate sensitive items (for specific guidelines, see Section 2.6.3).

7. Try and make the starter questions particularly involving.

8. Make sure that the questionnaire has a clear, logical, and well-marked structure.

9. Personal/factual questions about the respondent should go to the end.

10. Open-ended questions are the least intrusive if they are toward the end.

11. When writing items, observe the following:

• The best items are often the ones that sound as if they had been said by someone.

• Short items written in simple and natural language are good items.

• Avoid ambiguous, loaded, or difficult words; technical terms; negative constructions; and double-barreled questions.

• Avoid items that are likely to be answered the same way by most people.

• Include items that concern both positive and negative aspects of the target.

12. Strive for an attractive and professional design for the questionnaire; this typically involves:

• economical use of space with full but not overcrowded pages,

• an orderly layout that utilizes various typefaces and highlighting options, and appropriate sequence marking (of sections and items),

• good paper quality.

13. In the initial (general) instructions cover the following points:

• the topic and importance of the study,

• the sponsoring organization,

• point out that there are no right or wrong answers and request honest responses,

• promise confidentiality,

• thank the participants.

14. In the specific instructions to the tasks exemplify (rather than merely explain) how to answer the questions.

15. Thank the participants again at the end of the questionnaire.

16. Always pilot your questionnaire in a systematic manner and submit the items to item analysis (cf. Section 2.9).

Administering the questionnaire

17. Make the participant sample as representative of the total population you are investigating as possible (cf. Section 3.1.1).

18. Make the sample size large enough to allow for statistically significant results (cf. Section 3.1.2).

19. Beware of participant self-selection (cf. Section 3.1.3).

20. With postal administration:

• Formulate the cover letter very carefully (for a list of points to be covered, see Section 3.2.1).

• Print the return address on the questionnaire as well.

• About 2Vi weeks after the original mailing send a follow-up letter, and in another 10 days' time send another one.

• Apply various strategies to increase the return rate (for a list, see Section 3.2.1).

21. With one-to-one administration, make sure that you brief the questionnaire administrator well and consider giving him/her a cue card with the main points to cover when handing out the questionnaires.

22. To increase the quality and quantity of questionnaire response, apply the following strategies:

• Provide advance notice.

• Win the support of the various authority figures.

• Try to arrange some respectable institutional sponsorship for your survey.

• The administrator's overall conduct should be friendly and professional, and he/she should exhibit keen involvement and an obvious interest in the project.

• 'Sell' the survey by communicating well its purpose and significance.

• Emphasize confidentiality.

• Promise feedback on the results for those who are interested (and then remember to provide it...).

23. Observe the various ethical principles and regulations very closely

(cf. Section 3.4.1) and obtain the required 'human subjects' approval.

Processing questionnaire data

24. As soon as you have received the completed questionnaires, mark each with a unique identification code.

25. Record every important step you take during the processing of the data in a 'Research Logbook.'

26. Prepare a coding frame for each item and record these in a codebook.

27. Always prepare a backup of the data files. Do it now!

28. Submit your data to 'data cleaning procedures' before starting the analyses (cf. Section 4.3.1).

29. Consider the way you handle missing data very carefully.

30. Reverse the scoring of negatively worded items before starting the analyses (cf. Section 4.3.2).

31. Consider standardizing the data before starting the analyses (cf. Section 4.3.2).

32. Start the analyses of your questionnaire data by reducing the number of variables through computing multi-item scales.

33. Compute internal consistency reliability coefficients (Cronbach Alphas) for each multi-item scale.

34. Numerical questionnaire data are typically processed by means of statistical procedures; for most purposes you will need inferential statistics accompanied by indices of statistical significance (cf. Section 4.3.6).

35. Process open-ended questions by means of some systematic content analysis.

36. Exercise great caution when generalizing your results.

37. Make sure that you include all the necessary technical information about your survey in your research report (for a checklist, see Section 4.6.2).

38. Make use of charts/diagrams, schematic representations, and tables as much as possible when reporting your results.

39. Consider complementing your questionnaire data with information coming from other sources.

Continue reading here: References On Questionnaire

Was this article helpful?

Related Posts

• Processing Questionnaire Data
• Questionnaire items - Questionnaires
• Sample Instructions for semantic differential scales
• Main Types Of Questionnaire Administration
• Drawing up an item pool - Questionnaires

Readers' Questions

How to write a conclusion after reviewing a questionnaire sample?

When writing a conclusion after reviewing a questionnaire sample, it is essential to summarize the main findings and insights gleaned from the responses. This section provides a final opportunity to emphasize the significance of the study and propose further actions or recommendations based on the results. Here are some steps to follow when crafting a conclusion for a questionnaire review: Briefly summarize the purpose of the questionnaire and the research objectives. Remind the reader of the main research questions or objectives that guided the study. Present a concise overview of the participants' demographics and sample characteristics. Highlight any notable patterns or variations observed in the responses that are relevant to the research objectives. Summarize the main findings and key insights obtained from the questionnaire sample. Highlight the most prominent themes, trends, or patterns emerging from the responses. Use specific examples or quotes to support your conclusions whenever possible. Discuss the implications of the findings. Analyze and interpret the results in the context of the research objectives and prior literature. Identify any significant insights or unexpected discoveries that emerged from the questionnaire analysis. Assess the questionnaire's strengths and limitations. Discuss the effectiveness and appropriateness of the questionnaire design, as well as any potential biases or limitations in the data collected. Addressing these aspects helps establish the credibility and reliability of the findings. Recapitulate the significance of the study. Reiterate the importance of the topic and how the questionnaire sample contributes to the existing body of knowledge. Discuss any practical, theoretical, or methodological implications arising from the findings. Propose recommendations or areas for future research. Identify opportunities for further investigation based on the insights gained from the questionnaire results. Suggest potential modifications to the questionnaire or additional research avenues that could enhance understanding in the field. Conclude with a strong final statement that highlights the overall impact and significance of the questionnaire review. Emphasize the value of the study in advancing knowledge and understanding within the researched area. Remember, a conclusion is the last opportunity to leave a lasting impression on the reader. By effectively summarizing the key findings, highlighting their implications, and suggesting future directions, you can ensure that your conclusion brings a sense of closure to the questionnaire review.

How can i conclude quesionnaire design thinking?

When concluding a questionnaire design process using design thinking, you may follow these steps: Summarize the goal: Start by restating the overall goal or objective of the questionnaire design process. This helps provide clarity on what you wanted to achieve. Highlight the design thinking approach: Discuss the application of design thinking principles throughout the questionnaire design process. Explain how you followed the iterative process of understanding, ideation, prototyping, and testing to create an effective questionnaire. Explain the research and understanding phase: Describe the research and analysis conducted in the initial stage to understand the target audience, their needs, and their expectations. Briefly explain how this understanding influenced the questionnaire design decisions. Discuss the ideation and iteration phase: Share the brainstorming sessions and ideation techniques used to generate multiple questionnaire concepts. Highlight any iterations made based on feedback and testing during this phase. Describe the prototyping and testing phase: Explain how the selected questionnaire concept was converted into a prototype. Discuss the methods used for testing the prototype, such as pilot surveys or interviews, to validate and refine the design. Present the final questionnaire design: Describe the final questionnaire design and layout, including any visual elements, question types, or scales used. Discuss the rationale behind design choices and how they align with the research insights and target audience needs. Explain the validity and reliability considerations: Discuss any steps taken to ensure the questionnaire's validity and reliability. This might include pre-testing the survey, considering question wording and response scales, or addressing potential biases. Reflect on feedback and improvements: Briefly discuss any feedback received during the testing phase and how it influenced the final design. Highlight any changes made based on user input and lessons learned throughout the process. Summarize key findings: Finally, summarize the key findings or insights you expect to gain from administering the questionnaire. Emphasize how the design thinking approach used will help achieve these goals effectively. By following these steps, you will be able to conclude the questionnaire design process using design thinking, showcasing the considerations and efforts put into creating an engaging and effective survey instrument.

What to write to conclude a questionnaire?

Thank you for taking the time to complete this questionnaire. Your input is valuable to us and will assist in improving our product/service. We appreciate your honest responses and assure you that all answers will remain confidential. If you have any additional comments or suggestions, please feel free to include them in the space provided. Your feedback is important to us and will be thoroughly considered. Once again, thank you for your participation and contribution. Your input is instrumental in helping us meet your needs and provide you with a better experience.

What can youwhat can you conclude from your observations in ques?

Unfortunately, you did not provide any specific observations or context to draw conclusions from. Could you please provide more information or specify the observations you are referring to?

What is questionnaire checklist?

A questionnaire checklist is a tool used for collecting important data from a survey respondent. It typically consists of a list of questions that a respondent must answer in order to complete the survey. The questionnaire checklist can be used to gather information on different subjects such as customer satisfaction, market research, or employee satisfaction.

How to conclude a questionnaire?

In concluding your questionnaire, it is important to thank respondents for their time, encourage them to continue to participate in surveys and provide any necessary contact information for follow-up questions. Additionally, you can summarize the purpose of the survey and provide a timeline for when results will be available. Finally, you can let participants know that their responses are appreciated and that their feedback will help shape the future of your project.

How to write the conclussion of a questionnaire?

The conclusion of a questionnaire should summarize the main findings of your survey. Start by highlighting the key takeaways based on the responses you collected. For example, you could summarize the most common answers or the top preferences among your respondents. You could also discuss any areas of improvement uncovered by the survey or provide any recommendations that emerged from the data. Finally, thank your respondents for their participation and provide any contact information should they have any further questions.

What can you write to a conclusion of questionnaire?

In conclusion, thank you for taking the time to answer our questionnaire. Your responses are greatly appreciated and will help us to better understand the needs of our customers. Your feedback is essential to us, as we strive to make our products and services even better.

What are the main check lists in a conclusion?

Verify that all conclusions are backed with data and evidence. Review any assumptions or limitations of the study. Include recommendations for potential solutions or next steps. Be specific whenever possible. Do not introduce new information in the conclusion. Summarize the main ideas. Stay focused on the research topic. Provide a sense of closure.

• Privacy Policy

Home » Research Paper Conclusion – Writing Guide and Examples

Research Paper Conclusion – Writing Guide and Examples

Table of Contents

Research Paper Conclusion

Definition:

A research paper conclusion is the final section of a research paper that summarizes the key findings, significance, and implications of the research. It is the writer’s opportunity to synthesize the information presented in the paper, draw conclusions, and make recommendations for future research or actions.

The conclusion should provide a clear and concise summary of the research paper, reiterating the research question or problem, the main results, and the significance of the findings. It should also discuss the limitations of the study and suggest areas for further research.

Parts of Research Paper Conclusion

The parts of a research paper conclusion typically include:

Restatement of the Thesis

The conclusion should begin by restating the thesis statement from the introduction in a different way. This helps to remind the reader of the main argument or purpose of the research.

Summary of Key Findings

The conclusion should summarize the main findings of the research, highlighting the most important results and conclusions. This section should be brief and to the point.

Implications and Significance

In this section, the researcher should explain the implications and significance of the research findings. This may include discussing the potential impact on the field or industry, highlighting new insights or knowledge gained, or pointing out areas for future research.

Limitations and Recommendations

It is important to acknowledge any limitations or weaknesses of the research and to make recommendations for how these could be addressed in future studies. This shows that the researcher is aware of the potential limitations of their work and is committed to improving the quality of research in their field.

Concluding Statement

The conclusion should end with a strong concluding statement that leaves a lasting impression on the reader. This could be a call to action, a recommendation for further research, or a final thought on the topic.

How to Write Research Paper Conclusion

Here are some steps you can follow to write an effective research paper conclusion:

• Restate the research problem or question: Begin by restating the research problem or question that you aimed to answer in your research. This will remind the reader of the purpose of your study.
• Summarize the main points: Summarize the key findings and results of your research. This can be done by highlighting the most important aspects of your research and the evidence that supports them.
• Discuss the implications: Discuss the implications of your findings for the research area and any potential applications of your research. You should also mention any limitations of your research that may affect the interpretation of your findings.
• Provide a conclusion : Provide a concise conclusion that summarizes the main points of your paper and emphasizes the significance of your research. This should be a strong and clear statement that leaves a lasting impression on the reader.
• Offer suggestions for future research: Lastly, offer suggestions for future research that could build on your findings and contribute to further advancements in the field.

Remember that the conclusion should be brief and to the point, while still effectively summarizing the key findings and implications of your research.

Example of Research Paper Conclusion

Here’s an example of a research paper conclusion:

Conclusion :

In conclusion, our study aimed to investigate the relationship between social media use and mental health among college students. Our findings suggest that there is a significant association between social media use and increased levels of anxiety and depression among college students. This highlights the need for increased awareness and education about the potential negative effects of social media use on mental health, particularly among college students.

Despite the limitations of our study, such as the small sample size and self-reported data, our findings have important implications for future research and practice. Future studies should aim to replicate our findings in larger, more diverse samples, and investigate the potential mechanisms underlying the association between social media use and mental health. In addition, interventions should be developed to promote healthy social media use among college students, such as mindfulness-based approaches and social media detox programs.

Overall, our study contributes to the growing body of research on the impact of social media on mental health, and highlights the importance of addressing this issue in the context of higher education. By raising awareness and promoting healthy social media use among college students, we can help to reduce the negative impact of social media on mental health and improve the well-being of young adults.

Purpose of Research Paper Conclusion

The purpose of a research paper conclusion is to provide a summary and synthesis of the key findings, significance, and implications of the research presented in the paper. The conclusion serves as the final opportunity for the writer to convey their message and leave a lasting impression on the reader.

The conclusion should restate the research problem or question, summarize the main results of the research, and explain their significance. It should also acknowledge the limitations of the study and suggest areas for future research or action.

Overall, the purpose of the conclusion is to provide a sense of closure to the research paper and to emphasize the importance of the research and its potential impact. It should leave the reader with a clear understanding of the main findings and why they matter. The conclusion serves as the writer’s opportunity to showcase their contribution to the field and to inspire further research and action.

When to Write Research Paper Conclusion

The conclusion of a research paper should be written after the body of the paper has been completed. It should not be written until the writer has thoroughly analyzed and interpreted their findings and has written a complete and cohesive discussion of the research.

Before writing the conclusion, the writer should review their research paper and consider the key points that they want to convey to the reader. They should also review the research question, hypotheses, and methodology to ensure that they have addressed all of the necessary components of the research.

Once the writer has a clear understanding of the main findings and their significance, they can begin writing the conclusion. The conclusion should be written in a clear and concise manner, and should reiterate the main points of the research while also providing insights and recommendations for future research or action.

Characteristics of Research Paper Conclusion

The characteristics of a research paper conclusion include:

• Clear and concise: The conclusion should be written in a clear and concise manner, summarizing the key findings and their significance.
• Comprehensive: The conclusion should address all of the main points of the research paper, including the research question or problem, the methodology, the main results, and their implications.
• Future-oriented : The conclusion should provide insights and recommendations for future research or action, based on the findings of the research.
• Impressive : The conclusion should leave a lasting impression on the reader, emphasizing the importance of the research and its potential impact.
• Objective : The conclusion should be based on the evidence presented in the research paper, and should avoid personal biases or opinions.
• Unique : The conclusion should be unique to the research paper and should not simply repeat information from the introduction or body of the paper.

Advantages of Research Paper Conclusion

The advantages of a research paper conclusion include:

• Summarizing the key findings : The conclusion provides a summary of the main findings of the research, making it easier for the reader to understand the key points of the study.
• Emphasizing the significance of the research: The conclusion emphasizes the importance of the research and its potential impact, making it more likely that readers will take the research seriously and consider its implications.
• Providing recommendations for future research or action : The conclusion suggests practical recommendations for future research or action, based on the findings of the study.
• Providing closure to the research paper : The conclusion provides a sense of closure to the research paper, tying together the different sections of the paper and leaving a lasting impression on the reader.
• Demonstrating the writer’s contribution to the field : The conclusion provides the writer with an opportunity to showcase their contribution to the field and to inspire further research and action.

Limitations of Research Paper Conclusion

While the conclusion of a research paper has many advantages, it also has some limitations that should be considered, including:

• I nability to address all aspects of the research: Due to the limited space available in the conclusion, it may not be possible to address all aspects of the research in detail.
• Subjectivity : While the conclusion should be objective, it may be influenced by the writer’s personal biases or opinions.
• Lack of new information: The conclusion should not introduce new information that has not been discussed in the body of the research paper.
• Lack of generalizability: The conclusions drawn from the research may not be applicable to other contexts or populations, limiting the generalizability of the study.
• Misinterpretation by the reader: The reader may misinterpret the conclusions drawn from the research, leading to a misunderstanding of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

• Survey & Questionnaire Introduction: Examples + [5 Types]

Whether online or offline, you need to politely approach survey respondents and get them excited to fill your questionnaire when carrying out a research survey. Therefore, before going into the questions you want to ask, you need to kickstart your data collection process with a compelling survey or questionnaire introduction.  

Generally, only a few people would even listen to you if you shoved your survey in their faces without a simple introduction first. Survey introductions in online questionnaires help you prepare the minds of your respondents ahead of time and gather the best responses. 

What is a Survey Introduction?

A survey introduction is a concise description with relevant information about a survey. It is the first part of the survey that prospective respondents interact with and it helps them decide whether to fill your questionnaire or not. 

Think of survey introductions as abstracts that communicate the entire essence of the data collection process. Without a good abstract, your thesis gets delayed or unapproved. 

Following through with this thought means that the more exciting your survey introduction is, the higher your chances of collecting the right number of quality survey responses.

Features of a Survey Introduction

A good survey introduction must answer these 5 questions: 

• Who is conducting the survey?

Here, you should include the name of the person or organization that is carrying out the research. 

• What is the research about?

Survey respondents need to understand the aims and objectives of your research. This shows them why your survey is important and why they need to be part of it.  

• How long will the survey take?

Prepare their minds ahead of time by adding an estimated survey-completion time. While shorter surveys are likely to have more respondents, don’t give a false estimation to bait people to fill your survey. 

• Is my data safe?

Data privacy and protection is a huge concern for everyone. Since you plan to collect data from respondents, you need to tell them how you will use this information. You can include a link to your company’s privacy policy.

• How will I fill the survey?

Include instructions on how to fill the survey. Include information about relevant documents for the survey too.  

Your survey should be written in simple language your audience understands. It should be friendly, human and show the respondents how much impact they’ll make by taking part in the survey. Always include a nice “thank you” note in your survey introduction. 

Types of Survey Introduction  

Market survey introduction.

If you’re conducting market research using a survey , then you need a market survey introduction. To get more information about your customers/ target market, you need to conduct a market research survey. A market survey introduction gives your target audience a clear picture of what you want to achieve and how their participation is an important part of it.

Market research serves multiple purposes—sometimes, it is all about getting real-time data to inform product launches. Other times, it is for business expansion or product improvement. With a market survey introduction, you can get your audience on the same page and let them know the exact information you need from them. 

A market survey introduction should answer all the questions we looked at when we discussed the features of a survey introduction. After naming your organization, you should also introduce your product or product idea for brand awareness. 

Because of the type of information, market surveys are longer than other types of surveys ; sometimes, they have multiple sections. So, in your market survey introduction, give respondents a heads-up and let them know completing your survey will take more time than the average. You can add a nice reward they can claim after filling the survey. 

Example of Market Survey Introduction  

At Formplus, we are working to improve online data collection for you. We’d really like to know what you feel about online data gathering tools . Take this 20-minute survey and win a free 1-month Formplus premium subscription. Your data will be collected anonymously and only used for this research. Thank You! 

Student Survey Introduction

A student survey is a method of sampling students’ opinions about the school, teachers, and overall learning experiences. From measuring student satisfaction to evaluating courses, student surveys help you to make the right changes to your school. 

A student survey introduction is the first step in getting the best responses from your students. Encourage students to provide objective feedback and let them know how the information will be used.

In the survey introduction, indicate that all responses will be recorded anonymously. Students need to be sure that they can provide honest feedback in the survey without getting harassed or victimized. 

Example of Student Survey Introduction  

Thank you for being one of our students at Salthill College. Please complete this short 3-minutes survey to let us know how satisfied you are with your overall student experience at our college. All responses are recorded anonymously so feel free to provide honest feedback. Your responses will help us improve our teaching and learning environment. 

Research Questionnaire Introduction  

You need a good research questionnaire introduction during the data-collection phase of your research. People are more likely to fill your questionnaire when they clearly understand what you want to achieve and why your research is important. 

In the research questionnaire introduction, you can include facts, data, or statistics about the research problem. Then, show how the data collected via the questionnaire will contribute to solving the problem. The introduction should also address data privacy, data protection, and participant’s consent. 

Even if you plan to share the questionnaire physically, a good research questionnaire introduction will help collect responses faster and save time. 

Example of Research Questionnaire Introduction  

Hello, I am a postgraduate researcher at the London School of Tropical Medicine. I am conducting a study on effective treatment options for communicable diseases in West Africa and I would like to know your experiences with the signs, symptoms, and treatment of communicable diseases. Please complete this 30-minute survey. Your responses are anonymous and you can skip any questions you are not comfortable with. Thank you for your participation. 

Customer Satisfaction Survey Introduction  

Your customer satisfaction survey introduction should communicate 2 things—appreciation and brevity. First, you should let your customers know how much you love their patronage. Next, tell them that the survey will take just a few minutes. 

Throw in an honorary mention of your brand and then, go through some of the information you’ll need from them in the survey. To increase response rates, you can reward respondents with a gift, discount, or special offer. 

Example of Customer Satisfaction Survey Introduction  

Thank you for shopping at Wreaths and Flowers! We’ll like to ask you a few questions about your shopping experience. Your responses will help us make shopping more enjoyable for you. This will only take 1 minute and you get 30% off your next order when you complete the survey! 

Importance of Survey Introduction

• It outlines the most important information about your survey

People need to know what they are getting into before filling your survey or questionnaire, and that’s exactly why you need a great survey introduction. 

• It’s a great way to welcome respondents

You wouldn’t just walk up to someone to ask for something without a proper introduction so why would you want to do this with your survey or questionnaire ? A questionnaire welcome page sets the mood for requesting responses from your respondents. 

• Quality survey introductions help you gain respondents’ trust

Many people are not excited about filling surveys and questionnaires, which is why they need a push. A survey or questionnaire introduction helps respondents to trust you and heightens their interest in filling your survey. 

A survey introduction answers all the questions participants may have about the questionnaire. Think of it as some sort of FAQs that allows respondents to have a full grasp of your data collection process. 

A questionnaire welcome page boosts survey participation and reduces survey dropout rates. 

It helps survey participants to feel like an important part of the overall data gathering process. Survey introductions show participants that you value their opinions. 

Survey introductions build the participants’ interest in your survey or questionnaire. 

Why Use Formplus to Create Surveys?

• Pre and Post Submission Page

Formplus allows you to add exciting survey introductions to your questionnaire. On the form’s intro page, you can provide a brief description of your survey, information on data privacy, and any other thing they need to know before filling the form. 

You can also customize the form’s post-submission page and include a nice “thank you” note for respondents after they complete the survey or questionnaire. Learn more about our intro and post-submission pages here:

• Intuitive Easy to Use Survey Maker  

The Formplus builder is easy to use and you can build surveys and questionnaires from scratch in no time without writing a single line of code. It has a drag-and-drop feature that allows you to add more than 30 different fields to your form seamlessly. 

• Conditional Logic

Survey participants do not have to see or fill out all the fields in your form. With conditional logic, you can show or hide form fields and pages based on answers provided by respondents. This means survey respondents only have to fill the fields that are relevant to them. 

Conditional logic helps you collect the right type of information from different survey participants. This way, you can avoid extra clutter and collect as much data as you want. 

• Offline Surveys

Formplus supports offline data collection and this means you can collect data in areas with poor or no internet access. Survey participants can fill and submit your questionnaire when they are offline. The data they provide will be automatically synced with our servers or your preferred cloud storage when internet access is restored. 

• Customized Surveys and Questionnaires

Formplus allows you to create beautiful and unique surveys with zero design experience. With the flexible design options, you can change the questionnaire’s background, colors, fonts, and create visually appealing designs. You can also add images and your organization’s logo. 

• Share Forms Easily

With multiple form-sharing options, you can send out your survey and collect responses in many ways. Apart from adding your questionnaire to your website, you can also share it using the social media direct sharing buttons and via email invitations. 

• Google Sheets Integration

With Google sheets integration, you can automatically update form responses in your spreadsheet and keep all form collaborators up to date. This makes it easy for you to import and export data, and collaborate with multiple people at the same time. 

• Custom Subdomain

Sharing your questionnaire via a custom subdomain adds an air of professionalism to your overall data collection process. When creating your custom URL, you can include the name of your organization as a means of promoting your brand. 

Custom subdomains are simple and easy to remember too. Hosting your survey on a custom subdomain also serves as an extra layer of security; especially when you share the link via email. 

• Autoresponder Emails  

After receiving a new response to your questionnaire, you can send out an automated automatic confirmation email to the survey participant in the form of autoresponder messages. In your autoresponder email, you should include a thank you message and any links to special offers and rewards. 

• Mobile-Friendly Forms

Many people fill out surveys and questionnaires on their mobile devices and this is why all Formplus forms are mobile-friendly. Participants can complete the survey right on their mobile devices without having to bother about pinching out or zooming in on your form. Formplus forms can be viewed and filled out on any smartphone, tablet, or internet-enabled mobile device. 

In this article, we’ve looked at different survey introductions for different types of questionnaires and surveys including customer satisfaction surveys and research questionnaires. Whether you are collecting data online or offline, the right survey introduction will boost participants’ interest in completing your survey. 

With Formplus, you can add unique survey introductions to your form before sharing it with respondents. On the post-submission page, you can include a beautiful “thank you” note for respondents who complete your survey. Try out the pre and post-submission page option as well as other exciting features when you sign up for a free Formplus account. 

Connect to Formplus, Get Started Now - It's Free!

• accuracy surveys
• age survey questions
• brand survey examples
• compatibility questionnaires
• survey methods
• survey question types
• survey questionnaire
• survey research
• survey types
• busayo.longe

You may also like:

25 Training Survey Questions + Free Form Templates

Asking the right training survey questions before, during, and after a training session is an effective way to gather valuabl

Pilot Survey: Definition, Importance + [ Question Examples]

Before launching a new product or feature into the market, it is a good idea to find out what you

Survey Methods: Definition, Types, and Examples

Data gathering is a flexible and exciting process; especially when you use surveys. There are different survey methods that a

33 Event Survey Questions + [Template Examples]

Read this article to learn how to create an event survey with Formplus

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

How to Analyze Questionnaire Data: A Step by Step Guide

Approaching your questionnaire with the right principles in mind and tools in hand will produce easily-understood results packed with actionable insights.

In this guide you'll be led through the basics behind questionnaire data, then move on to a step-by-step approach for analyzing your responses. 

What is Questionnaire Data?

Types of questionnaire data, how to analyze questionnaire data.

Survey data , aka questionnaire data, is data collected during a survey campaign. This data can be analyzed and broken down, yielding statistics and insights that can be used to boost business.

What is Questionnaire Data Analysis?

The end all be all of customer feedback collection, whether questionnaires, online reviews, or other data, should always be the improvement of your overall customer experience for the benefit of existing and future customers.

The modern market has shown customer experience (CX) to be the number one differentiator between competitors. A large amount of this is by virtue of active customer experience management's attentiveness to existing customers -- companies who are able to convert existing customers into 'Promoters' (on the NPS scale ) improve their lifetime value by 6 to 14 times according to Bain & Co .

This is especially relevant when it comes to customer surveys as surveys are invariably distributed to existing and/or past users. The data they collect and the insights they derive apply directly to the customer journey.

By actively listening to the voices of your customers and analyzing survey data you are getting strategic tips from the best, and most honest, possible source. 

Questionnaire data, or survey data, comes in one of two formats: close-ended data and open-ended data .

Close-ended Questionnaire Data

Close-ended data is what people think of first when they imagine a survey result. It is data that translates directly into numbers. The 'big three' feedback questions ( NPS, CSAT and CES surveys ) all start with a close-ended question. They vary in format, with CSAT being a yes/no binary, NPS a 1-10 scale and CES a 1-5, but the responses can be tabulated in a straightforward manner and analyzed using basic software such as Excel.

From there, close-ended data can be interpreted using basic statistics to derive clear insights. This is basic survey analysis , and there are a ton of tools out there to help you quickly and effectively break down, cross tabulate, and display your results.

However, you aren't getting the most out of your surveys unless you pair your close-ended approach with open-ended questions , which draw out otherwise unseen but invaluable data .

Open-ended Questionnaire Data

Open-ended data is the 'why' behind your close-ended metrics, and for this reason it is key to excellent questionnaire analysis .

You know those additional written comments at the end of surveys? Those are open-ended questions. Throwing out these responses means missing out on the context behind whatever rating the customer is giving you.

The next logical question is, 'How can I measure text-based responses?'. 

Until a few years ago, each dataset's answers would require manual tabulation, which is both tedious and inaccurate. Now, with the power of machine learning and utilizing  techniques such as sentiment analysis and keyword extraction you can interpret your open-ended responses right alongside your close-ended metrics at scale, and in real time .

Having the right customer feedback analysis tools at your disposal can help make sure your survey analysis approaches, both close and open ended, are properly paired and integrated. This is crucial as losing which open-ended comment is tied to which close-ended score can mean losing the depth behind that data, making accurate analysis impossible.

That in mind, let's move on to the main course, our step-by-step approach to survey data analysis.

• Interrogate your question
• Cross tabulate quantitative results
• Expand with open-ended questions
• Analyze your open-ended data
• Visualize your results
• Interpret actionable insights

We landed on these particular steps because they convey a clear journey from the inception of your survey campaign to the implementation of your survey's insights.

1. Interrogate your question

An easy first mistake some businesses make is not knowing what they are looking for out of their survey. This of course directly affects the question(s) you are going to ask within your survey.

So, to form the best possible question and get clear answers, interrogate what you are looking for.  Are you curious as to customer opinion of your price point? Or is it something else entirely.

Deciding on the main goal or goals of your survey before distributing it ensures that you will, at bare minimum, answer your main concerns. That is not to say drilling down on what you are asking limits the possibilities of your survey. With additional comment or thought bubbles for customers to fill out, yielding open-ended response data, you are sure to uncover other, related but hidden, trends. But clarity as to purpose makes sure you don't confuse yourself, or worse, your customers with your survey.

2. Cross tabulate quantitative results

Cross tabulate is just a fancy word for filtering your survey so that you can compare customer groups aka subgroups. Think of it as the process of sorting your data by demographic so that you can unearth trends.

Take a look at this table for instance which reflects the answers to whether attendees of a conference think they will attend again next year, breaking the answers into three sub groups (Administrators, Teachers, and Students):

What at first might have remained hidden if you only looked at the total percentage that wanted to return now becomes clear.

Administrators, as reflected in their 40% 'No' responses and their 46% 'Yes' responses (compared to 86% Students and 80% Teachers) clearly didn't get what they were looking for out of the conference.

Curious questionnaire/survey analysis is good practice -- by taking a deeper look at the data, in this survey's case, uncovered a hidden trend. However, referencing our first step, this wouldn't be possible without asking the right question and keeping track of the three distinct demographic groups.

With this discovery in hand, it would be wise to continue to compare and contrast your data. This could also be a form of benchmarking -- meaning viewing your data in contrast to other surveys. You could compare the number of attendees this year to those in the ten years previous, and, if possible, isolate the subgroups from those years (if they were surveyed). Doing so would let you know which years were most popular with each subgroup.

Now it's one thing to know the Administrators, in this year's case, were the least likely to come back, and quite another to know what made them feel this way. Here's where those pesky open-ended questions come in, and why they are so critical to obtain and dissect.

3. Expand with open-ended questions

While this is third in our list it really needs to be a priority from the jump. Taking every step possible to solicit written feedback will truly take your questionnaire/survey campaigns to the next level.

Attaching open-ended questionnaires to your survey campaigns will add depth to your data and inform you of the 'why' behind your scores.

Luckily, it's easier than ever with advancements in artificial intelligence. Which brings us to our next step, accurately and effectively analyzing your data.

4. Analyze your open-ended data

Machine learning-backed software, such as Monkeylearn takes heaps of text data and transforms it into objective insights.

Analyzing your data using sentiment analysis and keyword extraction text analysis techniques can make your questionnaire analysis best in class.

These, and other open-ended analysis techniques such as topic analysis make sure you get the absolute most of your data, deepening and adding context to your extant quantitative data. These include plug-and-play templates, designed for no-code users to be able to access and mold questionnaire data - Monkeylearn even offers a ready-made survey data template - book your demo today and try it out for free.

5. Visualize your results

Insights are worthless if they cannot be conveyed to the appropriate decision-makers. Look no further than complete visualization suites to get the graphs, stats, and charts that keep modern businesses ahead of the curve.

Monkeylearn's all-in-one dataviz suite, as seen below, embraces the ideal that best-practice visualization means having up-to-the-minute data visualization at your fingertips at all times. 

If you have all the right graphs, and the ability to transform them at all times, you are able to deliver whatever graphs you need to your strategy times, rest assured that they are up to date and accurate.

6. Interpret accurate insights

Here is where we double down on the difference between insight and market data. Insights are the end product of any well-run questionnaire/survey campaign. But they require diligence in regards to what kind of questions you are asking and how deep you are digging to get actionable answers.

Great survey analysis/questionnaire campaigns ensure the applicability of their end data by maintaining a clear idea from the start of what kind of consumer insights they are looking for , while taking care to find the reasons behind their data via open-ended analysis along the way.

Just like that, if applied with care, you have an effective methodology for questionnaire analysis.\ Monkeylearn is here to help with the most powerful survey analysis software. Sign up for a free demo with one of our data analysis experts to get a custom model built for your business, or jump right in with a free trial today.

Rachel Wolff

March 24th, 2022

Posts you might like...

Survey Types for Success in 2022

How customer surveys are performed has changed radically for businesses. And this trend will continue long into the future. Fillable…

What Is Survey Data Processing?

Designing and distributing the perfect survey can take a lot of precious resources. So, it's important that you get a return on this…

Survey Responses: How & What to Ask to Get The Responses You Need

According to data from SurveyMonkey , two thirds of customers say they complete more than half of the surveys they receive. While that…

Text Analysis with Machine Learning

Turn tweets, emails, documents, webpages and more into actionable data. Automate business processes and save hours of manual data processing.

surveys | July 8, 2020

Questionnaire: Types, Definition, Examples & How to Design Your Own

Daniel Ndukwu

A well-designed and considered questionnaire can be the difference between success and failure. 

Customers have wants and needs that are constantly changing and evolving. It’s no longer enough to be reactive when situations arise. Now, your customers expect you to solve problems before they become problems.

Questionnaires make it possible to better understand the wants and needs of your customers so you’re in a position to meet them.

This article walks you through what a questionnaire is, the pros & cons, and how to properly design them so you can unlock deep insights.

You may also like: 

Featured Training

This guide is long, detailed, and should serve as a constant reference. Instead of constantly coming back to check it, download the PDF version. When you download it, you’ll also get access to our free email training that shows how to use questionnaires, quizzes, and popups to understand, engage, and convert visitors to leads and customers.

Table of Contents

What is a questionnaire?

A questionnaire is a research device or instrument that is made up of a series of questions which are closed-ended or open-ended . The goal is to   collect relevant data   from respondents which can then be used for a variety of purposes. When you give the respondent the ability to give a longer answer, it can yield more insights because they can elaborate on their thoughts.

It was first developed by the Statistical Society of London in 1838 and has been in continuous use ever since.

Questionnaires, though versatile, aren’t ideal in every situation – especially when you need to understand specific issues.

In today’s digital era, the role of a business website goes beyond just visual appeal, emerging as a critical channel for engaging and communicating with potential customers.

It’s about transforming your online space into a dynamic portal that mirrors user expectations and preferences, fostering a sense of trust and connection with your visitors.

Crafting a business website that effectively intertwines with your audience’s journey is key to not only drawing them in but also keeping them engaged.

Therefore, choosing a design partner who can bring your vision to life, ensuring your site is a true extension of your brand’s ethos and a robust pillar of your digital marketing efforts, is essential

It’s not advisable to use a questionnaire to ask specific questions about a product or service you’re still considering. This may lead to bias and false positives about the feasibility of the product.

Instead, questionnaires should be used to collect more general information – qualitative or quantitative data – regarding features and preferences. For example, instead of asking if they’d buy a new pink button down shirt with a unique collar, ask if they like to wear the color pink or if they like the type of collar you’re considering.

closed-ended

• Levels of Measurement: Nominal, Ordinal, Interval, and Ratio Scales
• What is Market Segmentation? The 5 Types, Examples, and Use Cases
• Target Market Examples, Definition, and Finding the Right one
• The 11 Best Free YouTube to MP3 Converters
• Behavioral Segmentation Defined + How to Use it for Explosive Results

7 Data Collection Methods for Qualitative and Quantitative Data

• Best YouTube Downloaders of the Year
• Top 13 Demographic Survey Questions you Should be Using
• 15 Best Landing Page Builder Software
• How to grow your podcast audience with simple surveys

Surveys vs questionnaires

Over time, surveys and questionnaires have gotten mixed up and are used interchangeably. They’re not the same thing. The difference is subtle but important.

A questionnaire is a list of questions used to collect data about someone or something. It’s not used to do statistical analysis or find trends and patterns. An example, would be when you sign up for a gym or go for a checkup and have to answer a series of questions about your current physical condition.

The answers you provide are used to understand your overall health, assess risk, and in some cases help find or diagnose issues. It’s not being used as part of a larger data set to clarify the bigger picture or find trends in a population.

A survey is a bit different. Instead of looking at individual questionnaires, it’s used to understand trends, do detailed analysis, and reveal deep insights. The key with a survey is that it’s collecting data with the express purpose of analysis.

As you can see, surveys deal with a lot of data which highlights the importance of a solid data governance strategy. What is data governance ? In a nutshell, it’s a standardized way you handle data to ensure you maintain the quality throughout the entire lifecycle. 

An example would be customer feedback surveys , demographic surveys , market research surveys, NPS surveys , etc. If only one person were to respond to these types of surveys , it would severely limit its usefulness. The more respondents, the easier it is to spot patterns and make informed decisions.

Why do they get mixed up?

Previously, researchers and professional marketers were the main groups who used surveys and questionnaires regularly. They made a clear distinction about what they are and when they were to be used.

​In the past, the realm of surveys and questionnaires was predominantly inhabited by researchers and seasoned marketers, who meticulously understood their nuances and precisely when to employ them. 

However, as the digital age unfolds, the landscape has transformed dramatically. Today, surveys and questionnaires have evolved into versatile tools, transcending traditional boundaries.

They are now readily accessible to businesses and individuals seeking insights into perplexing customer behaviors, steering marketing strategies with a burstiness that adapts to the ever-shifting dynamics of the market.

This democratization of data collection is reshaping the way we gather information, with surveys and questionnaires becoming indispensable instruments for decision-makers of all backgrounds.

With the advent of easy to access survey software , more and more businesses have started to handle their own research. The terms became interchangeable.

A questionnaire is when you ask someone a series of questions and don’t use it for data analysis.

A survey is when you ask someone a series of questions and you use it for data analysis.

For example, if you send an employee a series of questions about the working environment, it’s a questionnaire. When you send out that same questionnaire to 500 employees then compile the data to find trends, it’s a survey.

Make sense?

Let’s dive into the types of questionnaires.

Types of questionnaires

There are two main types of questionnaires and the one you’ll use depends on what kind of information you want and purpose of that information.

Exploratory questionnaire (qualitative)

These are also known as unstructured questionnaires . They’re used to collect qualitative data which is information that can be observed and recorded but isn’t numerical in nature. It’s used to approximate and characterize.

An example of qualitative data would be someone giving your feedback about your writing. They may mention things about the tone, clarity, word choice, etc. it helps you categorize your writing but you can’t attach a number to the feedback.

In the realm of content creation, the quest for the best writing apps becomes an essential journey. Just as perplexity and burstiness define human expression, these apps offer writers a palette of tools to navigate complexity and embrace stylistic variation. Exploring a range of options allows writers to balance intricately crafted sentences with bursts of concise brilliance.

Exploratory questionnaires are ideal when you’re in the early stages and want to learn more about a topic before designing a solution or hypothesis. For example, if you’re in the early stages of product development and don’t know enough about the market then exploratory questionnaires are ideal.

Formal standardized questionnaire (quantitative)

They’re also known as structured questionnaires. These ones are used to collect quantitative data which is information recorded as a count or numerical value.

The data is quantifiable which means it can be used for mathematical calculations or statistical analysis.  In essence, it answers the question of how much, how many, or how often.

An example of quantitative data would be the answer to the following question, “how old are you?” which requires a numerical reply.

Standardized questionnaires are best used when you’ve already formed an initial hypothesis or built out a prototype for a product. You’ll use it to stress test your assumptions, designs, use cases, etc. before going further with product development. Because of its clear focus, the questions you ask are narrow in scope and solicit specific information.

Just as important as the questionnaire type are the question types you choose.

Questionnaire question types

Not all question types are ideal in every situation. That’s why it’s important to understand the type of questionnaire you’re creating first. With that information, it becomes easier to choose the right question types.

Open ended questions

As the name implies, these questions are open for the respondent to answer with more freedom. Instead of presenting a series of answers choices, the respondent writes as much are as little as they want. This is ideal for exploratory questionnaires which collect qualitative data.

Multiple choice questions

Picture your questionnaire functioning like an engaging online brochure for your enterprise, accumulating responses while simultaneously captivating your target audience. As you formulate your queries, don’t forget to consider your overarching brand persona and communication, mirroring the thoughtfulness inherent in a meticulously designed online brochure .

This question presents the respondent with a list of answer options and they can select one or more. The challenge with multiple-choice questions is providing incomplete answer options.

For example, you may ask what industry do you work in and list out 5 of the most common industries. There are more than 5 industries in the world so some people won’t be represented in this situation. A simple solution to this problem is adding an “other” option.

Dichotomous questions

This is a question with only two possible answers. It tends to be a yes or no question but it can also be something like agree/disagree or true/false. Use this when all you need is basic validation without going too deeply into the motivations.

Scaled questions

Scaled questions are common in questionnaires and are often used to judge the degree of a feeling. This can be used in both exploratory and standardized questionnaires because there are many different types of scaled questions such as:

• Rating scale
• Likert scale
• Semantic differential scale

Pictorial questions

The final type of question used in questionnaires substitutes text for images. Respondents are asked a question and shown pictures to choose from. It usually has a higher response rate than other question types.

Similarly, the technique of background removal can significantly enhance the effectiveness of visual data collection. By isolating the subject from any distracting elements, background removal ensures that respondents’ attention is focused exactly where it needs to be.

Furthermore, integrating an AI background remover can revolutionize the way visuals are utilized in questionnaires. This tool automatically extracts the main subject from its background, allowing for clearer and more impactful visual questions.

The clarity gained through this technology can lead to more accurate respondent perceptions and, consequently, more reliable data. Utilizing an AI background remover ensures that every visual element serves its intended purpose without unnecessary distractions.

This method not only enriches the quality of the visual stimuli but also aligns with the precision and purposefulness of a well-structured questionnaire

Questions to avoid in a questionnaire

While you can ask almost anything in your questionnaire, it may not be a good idea to do so. Some questions may give you poor data while others may stop people from completing the questionnaire.

Here are a few question types to avoid.

Hypothetical questions

A hypothetical question asks a respondent what they would do, think, or feel about a situation that may happen in the future. It’s asking people to talk about their future actions and behavior which we’re notoriously bad at. This kind of question may give you data that can’t be used or will skew your overall understanding of the topic.

Elevating your content’s quality can be achieved by utilizing complimentary tools for background removal . Such a strategy enhances the visual attractiveness and professionalism of your creations, thereby distinguishing your content and more efficiently engaging your audience.

Embarrassing or offensive

Even though questionnaires can be anonymous, it’s not a good idea to embarrass or offend the respondent. It may lead to them dropping the questionnaire without completing it or giving you poor answers on purpose. Neither one is a good scenario.

Extreme positive/negative

You don’t want to bias your respondents before they’ve had a chance to form their own opinion on a topic. If a question is presented as extremely positive or negative then it may create a bias that should always be avoided. In the end, your data will be skewed.

Proper product pricing is a very important and useful thing in business. With the help of proper pricing, you can earn much more. To build your pricing you need to do a detailed analysis of your target audience.

Designing your own questionnaire

There are quite a few factors to consider when you’re designing a questionnaire that gives you the exact information you’re looking for.

At the very least, think about the goal, audience, distribution method, etc. Let’s look at the factors to consider while creating a well thought out questionnaire.

1.    What’s the goal of the questionnaire?

This may be the most important aspect of the questionnaire creation process. The goal of your questionnaire will determine both the type and questions to ask your respondents.

As mentioned earlier, if you’re in the beginning stages and are still trying to form a hypothesis, it’s an exploratory questionnaire with open-ended questions . If you’re trying to prove or disprove an already formulated solution or hypothesis then a standardized questionnaire with closed-ended questions would be used.

A clear goal also makes it easier to determine if a specific question is necessary or not. For example, if you’re doing initial product research for a dog toy, a question about the kinds of toys they’ve purchased in the past may be useful. When you have an initial prototype dog toy and want to gauge market response, that question wouldn’t be as useful to you.

2.    Who is the target group?

Whether or not it’s obvious, every market has multiple groups within it. Let’s take an average SaaS company for example. It usually has  pricing tiers  that are mapped to different personas. The customers on each subscription plan have different wants and needs.

The questionnaire you create and send out should reflect that. If you have the resources, create more than one so you can cater to the specific needs of different groups in your customer base.

In a situation where you’ve not seen different customer groups, it may be worth it for you to identify and  segment your customers . Not only will your messaging become more effective, any time you send out a questionnaire or a survey, but it’ll also be more targeted and get a higher response rate. On average, you can expect only 12.5% of an external audience (nonemployees) to respond to your survey.

Developing an ideal questionnaire is greatly dependent on identifying the appropriate questions to ask . This process includes not only choosing questions that closely match your research objectives but also crafting them in a way that garners straightforward and neutral answers.

Employing a tactful questioning technique can reveal more profound insights, thereby enhancing the efficacy of your data-gathering efforts. To gain a broader perspective on establishing connections via skillful questioning, it’s recommended to explore the nuances of formulating highly influential questions.

3.    How will you reach the target group?

This is often overlooked until the last minute but it’s an important consideration. If you have an email list full of past and present customers then this may not be an issue for you.

What about when you’re trying to enter a new market with a new type of product and don’t have customers there? How will you be able to reach them? Can you even reach them online?

This can have major implications on the design of your questionnaire. For example, if it’s a paper-based questionnaire, the design will necessarily be different and the questions won’t be as dynamic. If you’re using ads to get people to take your questionnaire, you may need to provide an incentive and make it shorter.

4.    Do you have a clear question progression?

The way your questions are ordered sets the tone for the entire questionnaire. You don’t want to start with a deep philosophical question that challenges the meaning of life. That’s too heavy. Almost everyone will bounce.

Instead, you want to start with simple questions that almost anyone can answer without too much thought. These are questions like age, sex, and geography – demographic information . These answers can also be used to further segment your respondents.

After discussing the foundational elements of questionnaires, it’s essential to consider modern advancements. ChatGPT Integration represents a significant leap in this direction.

With the rise of AI and chatbots, integrating tools like ChatGPT into your questionnaire process can greatly enhance user engagement. By utilizing ChatGPT, businesses can provide real-time assistance to respondents, clarifying questions, offering instant feedback, and ensuring a more interactive and smoother experience.

Such integrations not only streamline the response process but also pave the way for richer data collection and insights.

After you’ve built up some momentum, move into the core questions you want an answer to. The questions you ask here will depend on your goals but it should relate to your products and services. These questions help you flesh out your product development initiatives as well as create better and more focused marketing messages.

Finally, tie up any loose ends with your final questions. A common but subpar question is “is there anything else you think we should know?” try to avoid this one. Instead, ask things like how they found you, their experience with buying another similar product, how they’d describe a specific problem, etc.

5.    What kind of questions will you use?

Do you want well thought out answers that give you deep insights into the inner workings of the respondent’s mind? Or, do you want a narrow but easily analyzed response? The type of questions you use will determine the type of data you get.

As a rule of thumb, open-ended questions are often used earlier in the research process. Closed-ended questions tend to be used to prove or disprove hypothesis or solutions. Of course, you can use both of them but be sure to pay close attention to question progression so respondents aren’t put off or confused.

6.    Length of questionnaire

There are no hard and fast rules about how long your questionnaire should be. Some of them are hundreds of questions while others are less than five questions. The more questions, the lower your completion rate.

On average, it takes 5 minutes to answer 10 questions. Depending on whether the answers are open-ended or close-ended, the time could be considerably more.

Your customers are busy and most of them won’t sit through a long questionnaire without some form of incentive or compensation. If you’re able to provide that then fine but most customer surveys shouldn’t require it.

Instead, be considerate of the time of others. Keep your questionnaires less than 15 questions and ideally under 10 questions. It makes it easier for respondents to complete the survey and easier for you and your team to analyze the information.

Navigating the complexities of data collection in the digital realm requires a keen understanding of legal boundaries, particularly when dealing with sensitive financial information. Engaging a credit report lawyer becomes essential when your surveys delve into areas that might influence someone’s credit standing.

These legal advisors are adept at ensuring your methods are in line with stringent standards, such as those outlined by the Fair Credit Reporting Act. Their counsel is vital in crafting data handling procedures that not only respect individual privacy rights but also uphold the integrity of your business operations, thereby mitigating risks associated with credit information mismanagement and privacy infractions.

7.    Presentation

Contrary to popular belief, you don’t need a thousand bells and whistles to get people to take your questionnaire. A simple design that emphasizes the questions is more valuable than a flashy one. Of course, you can go flashy if you like. The thing is, most people just don’t care.

Select a font that’s easy for people to read and make sure the size is large enough to be legible on all devices. Apart from that, keep the number of pages to a minimum. 2 pages is much better than 30 pages when it comes to a questionnaire.

When you’re ready to present your findings, that’s when you can get flashy. You can use one of these presentation websites to create slides that display the insights you’ve gathered.

8.    Choose language carefully

If you ask a question that creates bias or confuses your respondents then you may accidentally contaminate your data. Use clear terms, be concise, and avoid industry jargon.

For example, “We’ve been told we make great eggs, would you agree or disagree?” this question causes bias before the customer can answer. An unbiased question would be “how would you rate our eggs on a scale of 1 – 5?”

Also, avoid combining multiple questions into one. An example of a combination question would be “how did you enjoy your stay and would you recommend us to a friend?” These are two distinct questions bundled into one.

Advantages & disadvantages of questionnaires

It’s important to understand both the pros and cons of questionnaires and put proper safeguards in place before you start using them to make important business decisions.

Let’s start with a few of the good things.

Inexpensive

Sending out an online questionnaire is one of the cheapest customer research strategies available. Unless you’re offering some type of incentive or are using ads to get in front of respondents, there are few costs associated with it.

Self-administered questionnaires avoid the need for hiring people to administer it, remove the cost of in-person interviews, and have versatile distribution methods.

Results come in quickly & can reach a large audience

Business moves fast so one of the most powerful advantages of a questionnaire is the ability to get it in the hands of a large group of people quickly. You don’t need to start mailing it out and waiting days for it to get to the intended recipient.

Instead, you can send an email, post it on your website, or share it on social media and start getting responses you can use almost instantly. Also, there’s no real upper limit to the number of people who can respond to the questionnaire.

Easy to analyze the results

The majority of questionnaires are quantitative in nature which allows for quick analysis of the answers. This is even more important when you have a larger pool of respondents.

With a survey tool like KyLeads , you can easily spot trends and derive insights from your questionnaire with our easy to use & understand reporting features.

Respondents can remain anonymous

If respondents are unable to remain anonymous, they may not answer some of the questions truthfully. As long as you’ve done proper targeting and they’re not answering for an incentive, it’s ideal to leave the respondents anonymous. They’ll be more comfortable and answer honestly and thoroughly.

Can cover all aspects of a topic

This is an overlooked aspect of questionnaires. With them, it’s possible to ask 100 questions. Of course, we don’t advise this because almost no one will finish an online questionnaire of that length.

With that being said, you can ask as many questions and solicit as much detail as you want. Play around with the number of questions you ask but try not to overdo it.

For instance, a real estate agent might use a questionnaire to understand the specific needs and preferences of potential homebuyers. By asking targeted questions about desired locations, types of homes, budget constraints, and must-have features, the agent can gather valuable insights.

This information not only helps in tailoring property suggestions but also in refining marketing strategies to attract the right clientele. Moreover, such questionnaires can be a great tool for building a database of client preferences, aiding in future property recommendations and personalized service offerings.

Disadvantages

There are a few disadvantages to questionnaires which you should be aware of.

Unanswered questions

Sometimes, people will just skip answers or drop off halfway. Since the questions are online and no one is there to prompt the respondent, this happens fairly often.

There is any number of reasons for this like unclear or confusing questions, irrelevant questions, incomplete answer options, etc. Making the answer required can help with this but it also increases the chances of someone abandoning the questionnaire altogether.

Questionnaire fatigue

Fatigue with your survey as well as the other surveys being sent out by other companies. More and more companies are using surveys and customers can’t answer all of them. This results in a lower overall response rate to surveys or questionnaires as a whole.

Conversely, someone may start your survey but drop off because there are too many questions or the questions seem to be irrelevant. You can’t get rid of the fatigue 100% but you can reduce it by creating shorter questionnaires and making your questions easy to answer.

Little personalization

Everyone who takes the questionnaire gets, for the most part, the same series of questions presented in the same way. Now, technology is making this better with features like logic branching and answer piping so the experience can be personalized a bit more.

In the end, it’s still limited because there’s a predetermined series of questions and the questionnaire can’t react to open ended statements.

Improper interpretation of questions

This is why it’s so important to choose your question language so carefully. It’s easy to misinterpret a written question and give a wrong answer or skip the question entirely. Another thing to consider is that certain words have multiple meanings and, without context, a different meaning may be applied.

Prevent this by using simple direct language in your questions and avoiding jargon.

Difficult to analyze certain types of questions

Multiple choice questions and dichotomous questions are simple to analyze. Open ended questions can’t be analyzed so easily.

They’ll require a human touch to ensure you’ve understood what the person is trying to tell you. This isn’t a bad thing but it can get tedious when there are a lot of answers to sift through.

Examples of questionnaires

There are countless types of questionnaires and surveys you can use to get deep insights about your customers and business. In this section, you’ll learn 6 common types that’ll help you improve your business immediately.

Brand awareness

This questionnaire example is ideal when you’re actively focusing on building awareness and doing demand generation . It helps you gauge whether or not your efforts are yielding fruit.

It’s one thing for people to end up on your website through a search on Google or a random post on social media. It’s another thing for there to be brand recall or positive associations about your business.

It’s impossible to stay on the sidelines when social media is taking over the world.

That’s why it makes sense to consider this option: include the latest social media trends in your questionnaire and gain powerful insights into how these trends are influencing consumer wants and shaping their expectations and associations with your business.

In addition to evaluating traditional website traffic and search engine rankings, this is also a great technique for gleaning relevant information.

The brand awareness questionnaire will give you a better understanding of whether people looking for solutions you provide think of your brand, the kind of associations your name creates, and if you’re considered a leader in your field.

The NPS questionnaire has become popular over the last few years and it helps you measure customer loyalty and satisfaction.

It’s important to note that in its original form, it’s measuring loyalty and satisfaction that pertains to your entire business as opposed to specific products.

It uses a scale to measure customer loyalty. You calculate the score by subtracting the percentage of detractors from promoters and it’s expressed as an absolute number.

CSAT questionnaire

The customer satisfaction (CSAT) questionnaire example we’re sharing is just one of many. CSATs are incredibly varied. Even the NPS questionnaire is a type of CSAT. In general, it’s used to understand how satisfied a customer is with specific products and services or your business as a whole.

Use the basic outline below then tweak the questions to apply to your business or specific product lines. For example, if you were a shoe company, you could ask how often they wear shoes purchased from you.

If you were a hair extensions company, you could ask how satisfied they were with the product or the shopping experience as a whole.

Demographic questionnaire

Demographic questionnaires are often used to identify and segment the groups you have in your audience. This type of questionnaire is ideal if you’re entering a new market and want to start building up a profile of the people who will be your customers.

At the same time, you may want to use this to understand your current customer base so you can create better messaging or product pricing.

Oftentimes, the demographic questions are a small part of larger questionnaires used to understand who’s giving what kind of answer.

For example, if you serve a customer group that varies in age and income, you’d like to know what kind of customers are giving answers so you can make decisions properly.

Psychographic questionnaire

Psychographic segmentation has a firm place in modern business because everyone has demographic data (or can get it).

Demographic segmentation pales in comparison to knowing why a group of people do what they do.

Look at it this way, demographic data helps you understand the characteristics and buying power of your customers.

Psychographics helps you understand the why behind their actions and their attitudes behind certain stances. It can be a goldmine if gathered and used properly.

Post-event questionnaire

Ah, events . If you’re like most of us mere mortals then there’s a love-hate relationship with them. On the one hand, if they go off well then it can power your business to the next level.

On the other hand, everything that can go wrong probably will. As the organizer of the event or someone who had a key role, it may seem like you know exactly what went right and what went wrong.

If you don’t get feedback from as many people as possible then those are just assumptions which may or may not be correct.

Use post-event surveys to talk to as many people as humanly possible to get a clear picture of how you can improve.

This guide has covered a lot of ground so don’t expect to cram everything in one sitting.

Questionnaires are the backbone of surveys. Without them, there’s nothing to analyze. Before you dive in and start designing your questionnaire to collect all that juicy customer data, there are a number of things to do.

Decide on the type of questionnaire and your goals, focus on the right questions, figure out who the target group is, and so much more. Be sure to revisit this guide whenever you’re in doubt.

Related Articles

Data collection is essential for businesses, organizations, and even personal use. In the digital age data is one of the […]

March 18, 2022 | surveys

The Guide To Good Employee Engagement Survey Questions (+25 Examples)

Employee satisfaction surveys are inconclusive. No matter how many polls you conduct, the results aren’t revealing anything substantial about the […]

December 6, 2021 | surveys

How to Analyze Survey Results and Display Them Visually

How to present your survey data in a way that’s visually appealing, engaging, and informative? Through the magic of visualization. […]

Mariya Koracheva

May 19, 2021 | surveys

4 Best Practices of Asking for Customer Feedback with Emails

Satisfied customers are the lifeblood of any business. Happy customers spend more on brands they support which means increased sales […]

Hugh Beaulac

May 14, 2021 | Email Marketing

A Columbia professor wanted to document history. NYPD arrested him outside his home

Gregory Pflugfelder had just finished the final class of his career at Columbia. In 28 years at the university, he achieved many accolades as a professor of history who taught a popular course on Japanese monsters – mostly focused on Godzilla and "the role of the monstrous in the cultural imagination."

He didn't know it, but a cultural monster of sorts would soon be at his door.

The next night, on Tuesday, the 64-year-old silver-haired scholar stepped outside his apartment building, located off campus across the street from Columbia. He wanted to record iPhone video of hundreds of police responding to historic student protests against Israel’s war in Gaza . Fifteen minutes later, the NYPD arrested him.

The New York Police Department listed Pflugfelder among 112 arrests made at Columbia on Tuesday night , according to police records obtained by USA TODAY. But Pflugfelder was never on campus.

“I certainly posed no danger to anybody,” he told USA TODAY. “I was literally standing in the street and not blocking anybody.”

As protests and opposition to the war in Gaza has swept across U.S. campuses, universities and police have increasingly pointed to "outside agitators" and off-campus disruptors as the insurgents behind the campus unrest. Pflugfelder's arrest – on a charge of obstructing government administration – is among the first of 282 people put in custody at or near Columbia and City College of New York during police raids. The arrests have raised claims of heavy-handed police tactics to suppress largely peaceful demonstrations against the Israel-Hamas war.

Columbia University referred questions about the professor’s arrest to NYPD. Neither NYPD nor New York Mayor Eric Adams’ office responded to email requests from USA TODAY. 

Live updates: NYPD says officer fired gun on Columbia campus; NYU, New School protests cleared

'Historic mistake'

Pflugfelder's last class, Introduction to Japanese Civilization, is a course he taught since he started teaching at Columbia in 1996. He's only taught at the Ivy League school. His plan Tuesday was to do “absolutely nothing,” he recalled. This included reading and watching the Hulu show, "Shōgun."

In the afternoon, he heard protests nearby, around the corner from his apartment on West 114th Street. His apartment building is located across the street from campus, where demonstrators gathered for weeks and formed an encampment calling on the university to divest from Israel.

He knew about the heightened police response because of a prior protest at Columbia, on April 18, at which police arrested over 100 people at the encampment in the center of campus. Police buses blocked Pflugfelder's street to take protesters to NYPD headquarters.

He supported students right to demonstrate. He wrote a letter to Columbia President Minouche Shafik, who requested NYPD respond to the encampment on campus. It was his first time writing to the president’s office. “I urge you not to compound the historic mistake you've made by repeating it,” he wrote on April 23.

A week later, on Tuesday, he felt history would be made again, and he wanted to document it. He stepped out of his apartment building to record video on his iPhone.

By about 9 p.m., he estimated hundreds of police, donning helmets and batons, had formed lines on the street. He recorded students forced inside fraternity houses and dorm buildings, with video of them knocking on the windows. Then, he turned to look at the street, where officers formed lines ahead of their siege on campus.

At most, he said, he stood 7 feet onto the street from the curb. Police ordered him inside, but he told them his address was about 300 feet down the block. They told him to go home, but he said he wanted to continue recording. An officer said, “OK, put him down,” Pflugfelder recalled, though he was not forced to the ground. Nonetheless, he ended up cuffed in zip ties.

“I just stayed on my block, relatively well behaved,” he said. “Poorly located, unfortunately.”

He said he told the female police officer arresting him: “You’ve just arrested your first faculty member.” He said she responded, “This is for your protection.”

Police response to NYC schools showed 'precision policing,' Adams says

Mayor Adams has said police acted with professionalism in mass arrests on college campuses, which included police using a SWAT vehicle to enter Hamilton Hall, the occupied Columbia building.

"The NYPD's precision policing ensured that the operation was organized, calm, and that there were no injuries or violent clashes,” Adams told reporters on Wednesday, the day after the arrests.

But Jennvine Wong, supervising attorney at the nonprofit Legal Aid Society’s cop accountability project, said Pflugfelder's arrest raises questions about whether NYPD escalated rather than deescalated situations. It also may have violated laws protecting citizens’ right to record police interactions.

“Generally speaking, there is still a First Amendment right to record in public as long as they’re not interfering with police,” Wong told USA TODAY. “To me, this sounds like a devious arrest.”

War in Gaza: Biden to meet with King of Jordan as US, Israel go 'back and forth' over Rafah invasion

By Pflugfelder’s account, he was the third arrested person to enter an NYPD van. Ten people would fill the van that took him downtown. At 6-foot-5-inches, Pflugfelder said he felt cramped. He also has “claustrophobic tendencies,” and during the ride, he asked others to help him take his mind off his feelings, so they asked about his classes. He gathered during the ride that most people inside were Columbia students, based on the questions they asked.

At NYPD headquarters, he was in a holding cell with about 60 other men. He stayed there for about five hours. One person next to him on the bench said he was from Columbia and had been at Hamilton Hall, the occupied school building police raided using flash-bang grenades, and where police errantly fired a gunshot indoors. The man Pflugfelder saw was visibly bruised, including a black eye.

“The violence against protesters was extreme,” said Corinna Mullin, an adjunct assistant professor of political science at John Jay College of Criminal Justice, part of CUNY, at a recent news conference. Mullin was among those arrested Tuesday night at City College.

Conclusions drawn, then data collected

Police released Pflugfelder from custody at about 5 a.m. with a ticket to appear in Manhattan Criminal Court on May 20. He called an Uber and went home, though since has found it hard to rest. He’s hasn't yet communicated with university administration. He’s not looking forward to it.

“I would not put myself in a vulnerable situation vis-a-vis an institution that has assaulted me,” he said.

Irene Mulvey, the president of the American Association of University Professors, said the group has several firsthand, eyewitness accounts of what she called unnecessarily violent and disproportionate responses to what started as peaceful protests. 

Information released by police, including on the number of “outside agitators,” has not answered important questions about the rationale to send police to college campuses, including at Columbia, said Mulvey, a mathematician and professor emeritus at Fairfield University, in Connecticut. A central reason police responded to Columbia was people from outside who indoctrinated students with training and ideology, though officials have disclosed little evidence to date.

“Scientists, we would collect data and draw conclusions,” she told USA TODAY. “In this case, it seems a conclusion was drawn, and then data was collected, which may or may not justify it.”

Pflugfelder has yet to have the relaxing day he’s sought after nearly three decades of teaching. In jail, police made him remove the shoelaces of his black and white Vessi sneakers. He’s kept them unlaced since then, as a reminder.

Contributing: Mike James, USA TODAY

German Election Candidate Petr Bystron Alleged of Receiving €20,000 Bribes from Putin's Aide

I n recent news, Petr Bystron, a prominent member of the far-right party Alternative for Germany (AfD), has come under investigation for alleged money laundering. This investigation, led by the Munich public prosecutor’s office, has sent shockwaves through the political landscape of Germany, raising questions about the integrity of the AfD and its members.

The Allegations Against Petr Bystron

Petr Bystron, who is the second candidate on the AfD’s election list for the European elections, is facing allegations of receiving up to €20,000 from individuals linked to Russian President Vladimir Putin. The funds, it is claimed, were intended to spread Kremlin propaganda. These allegations, if proven true, could have significant implications for the party’s future, potentially leading to a loss of public trust and a decline in support. The very nature of these allegations brings to light the potential for foreign interference in domestic politics, a concern that has been growing in recent years.

Lifting of Parliamentary Immunity

The German parliament, in response to these allegations, voted to lift Petr Bystron’s parliamentary immunity. This move has enabled the police to conduct searches in Berlin, Bavaria, and on the Spanish island of Majorca in pursuit of evidence. The lifting of his parliamentary immunity simply allows for a thorough and unbiased investigation to take place. This is a significant development, as it underscores the seriousness of the allegations and the commitment of the German authorities to uphold the rule of law.

Impact on the AfD Party

This investigation marks a fresh blow against the AfD party, which is currently under scrutiny over allegations that it has links to China and Russia. The AfD has been a controversial presence in German politics, with its far-right ideologies often clashing with the more centrist views of other parties. This investigation into one of its prominent members could further tarnish the party’s reputation and lead to a decline in its popularity. The potential fallout from this investigation could be far-reaching, affecting not only the AfD but also the broader political landscape in Germany.

Implications and Consequences

The allegations against Petr Bystron raise serious questions about the integrity of the AfD and its members. If proven true, they could have significant implications for the party’s future, potentially leading to a loss of public trust and a decline in support. However, it is important to note that these are still allegations, and the investigation is ongoing. Bystron, like any individual, is innocent until proven guilty. The outcome of this investigation could have a profound impact on the political career of Bystron and the future of the AfD.

Broader Issues

The case also brings to light the broader issue of foreign interference in domestic politics. The alleged links between Petr Bystron and individuals connected to Putin suggest a potential breach of national security . This raises concerns about the vulnerability of political systems to external influences and underscores the need for robust measures to prevent such occurrences. The potential for foreign interference in domestic politics is a global concern, and this case serves as a stark reminder of the need for vigilance and strong safeguards.

In conclusion, the investigation into Petr Bystron serves as a stark reminder of the challenges that modern democracies face. As the world becomes increasingly interconnected, the lines between domestic and foreign affairs blur, making it imperative for nations to safeguard their political systems against external influences. The outcome of this investigation will undoubtedly have far-reaching implications, not just for Bystron and the AfD but for German politics as a whole. It underscores the importance of transparency, accountability, and the rule of law in maintaining the integrity of our democratic institutions.

Large Language Models with Azure AI Search and Python for OpenAI RAG

By: Hristo Hristov   |   Updated: 2024-05-15   |   Comments   |   Related: More > Artificial Intelligence

You have a vast amount of data on an Azure SQL Server and would like to take advantage of the newest architectural pattern for AI-infused apps—the retrieval augment generation (RAG). RAG enables large language models (LLMs) such as GPT to be grounded in your company-specific data and provide answers to complex questions and queries that would otherwise require time-consuming data mining.

With the newest features in Azure AI Search, we can connect an Azure SQL data source, define an index, and create an automated indexer to vectorize and store the source data. Then, we can configure an Azure OpenAI model to use the vectorized data to provide grounded answers and references based on them.

Each task can be accomplished from a VS Code Jupyter notebook. From that to a fully serverless automation agent, the gap is minimal but outside this article's scope.

Resource Requirements

For this solution, you will need the following resources:

• Azure SQL Server with a target table containing a column with long text subject to indexing.
• A Jupyter notebook and a local Python environment .
• Azure AI Search for data indexing and vector store.
• Azure OpenAI for an embedding model that will vectorize our data.

Let's start with examining each component's setup.

Azure SQL Server

I have configured a managed instance database with a single table called IntVect . The table has three columns: id , title , and content . I have imported some of Paul Graham's essays – they are good examples of long, non-synthetic string data with lots of complex ideas. This is what the data looks like:

You can use this (or any other data) if the table schema stays the same. In an actual setup, the database will be populated by an upstream analytical process.

Python Environment

From VS Code, open your project folder. Create a requirements.txt file with the following lines and save it:

Hit Ctrl+Shift+P, select Python: Create environment , select venv , then your global Python interpreter. Check the requirements file for installing the required packages:

Wait until your environment is created and selected.

Azure AI Search

Create an instance of Azure AI Search, Microsoft's indexing service and vector store. You should use the Basic pricing tier to ensure the service can later be coupled with an OpenAI GPT model. However, ideally, you want to use the Standard tier at a minimum, which also enables semantic reranking. The image below is what my Azure AI Search instance looks like:

Azure OpenAI

Create an instance of Azure OpenAI. Depending on your subscription settings, this resource may not be directly available to you. If this is the case, refer to the following article: Limited access to Azure OpenAI Service . Then, fill in the form to request access to the platform. Below is what my Azure OpenAI resource looks like:

Next, open the Azure Studio and go to Deployments. Click Create new deployment and fill in the form:

Once done, note down the name of your embeddings model, the URI of the Azure OpenAI resource, and your API key. Alternatively, you can follow this detailed tutorial for creating the resource and deploying an embeddings model.

At this point, all the resources are in place. Below is a high-level overview of how these resources will interact with each other:

• The notebook will interact programmatically with the Azure AI Search API via the Python SDK.
• The search service will ingest data from the database by splitting it into chunks.
• The search service will call the Azure OpenAI API to produce an embedding representation of the data.
• Azure OpenAI will respond with an embedding representation, which will be stored in Azure AI Search.

Data Vectorization

With all the pieces of the puzzle, let's get back to the meat of this article – setting up an automated data vectorization pipeline for Azure SQL data.

Environment Variables

This is our first code block. Create a .env file and add lines for the required variables.

Then refer to these variables using the dotenv and os packages:

Create a Data Source Connector

Using the following code snippet, create a data source connector in your Azure AI Search:

This is what my code looks like with the sensitive connection string information blurred out:

In a production setup, the connection string should not be as exposed. It may come from another environment variable, or another type of authentication may be used.

Checking the data sources in the Azure portal will reveal the newly created one:

Create a Search Index

The next step is to create a search index. The Azure AI Search Python SDK gives us all the necessary data types and functions to do so:

Let's break it down (skipping the obvious imports):

• 21: Create an index client using the instance we have.
• parent_id : which is like a key column for Azure AI Search.
• title : which will hold the text stored in the SQL title column.
• chunk_id : identifier of the chunk in the text needs to be chunked.
• chunk : will hold text stored in the SQL content column.
• vector : will contain the numerical representation of the text.
• 49: In the list of algorithms, we provide the two powerful algorithms that will enable the correct information retrieval.
• 66: Give the vector search profile a name and reference to the algorithms
• 78: Finally, configure the vectorizers. Here, we are using the Azure OpenAI embedding model. Therefore, we reference the environment variables provided earlier.
• 91 – 98: Configure the semantic search profile, enabling hybrid querying.
• 100: Instantiate the index.
• 105: Create the index.

Running this code will result in the index being created (or updated):

We have not populated this index with data yet. Nevertheless, we can examine the fields it has. They correspond to the configuration we provided:

Create a Skillset

Next comes the creation of two skillsets: chunking and embedding. Skillsets are reusable resources attached to the index and use built-in AI capabilities. These two skillsets are also why we call the vectorization "integrated." We do not need to chunk and embed the data separately prior to storing them in the index. The index will take care of all of this for us.

Let's break it down:

• 13: Give the skillset a name.
• 17: Text split mode: either pages or sentences .
• 18: Context: the 'root' of our data called 'document' .
• 19: Maximum page length in characters.
• 20: Page overlap length. It should be adjusted according to the use case and expected input text length. If you go back and examine the length of essay five, you will see that it is close to 60,000 characters. Therefore, some chunks are expected to appear.
• 21 – 24: Inputs: the source is the content column.
• 25 – 28: Outputs: an array of substrings called pages . This output will be used as a source for the embedding skill.
• 35: Context: note the context here are all the pages coming from the upstream skill denoted by /pages/* .
• 36 – 38: Azure OpenAI service configuration.
• 39 – 42: The input is the text from the split pages from the upstream skill.
• 43 – 46: The output is the embedding vector, which will end up in the embedding field vector .
• 49 – 65: Define the index projections. In short, this means mapping the skillset to an index. The index projections define a secondary index that outlines the AI capabilities coupled with the front-facing index.
• 67 – 72: Define the skillset with a name, description, list of skills just defined, and an index projection.
• 74: Instantiate a client.
• 75: Add or update the skillset to the client.

Running this code block will result in the skillset being created. We can check the result in the Azure portal:

Create an Indexer

Finally, we must create an indexer. This is the agent that will run the index and ingest the target data to it. The creation is straightforward:

• 06: Give the indexer a name. The good practice is to name it based on the index name.
• 08 – 17: Define the indexer. We must give it a name, a description, a skillset (in this case, but not necessarily), the data source, and the field mappings between the data source and the index destination fields. The source_field_name values correspond to the SQL columns, and the target_field_name values correspond to the index destination fields.
• 19: Instantiate the indexer client.
• 20: Create the indexer.
• 22: Run the indexer.

Using this code block, an indexer automation can also be easily implemented later. Going back to the Azure portal, we can examine the indexer. We see that 10 out of 10 documents have successfully been indexed. The count corresponds to the number of rows in the source SQL table:

When we go back to the index, we now see that the indexer has populated the index:

The 213 documents (much more than the count of rows in the source data) are, in fact, the chunks that the splitter determined to divide the source data into.

Having our data indexed, we are ready to vector-query it. Let's see that in action:

• 01: Define the query.
• 03: Instantiate a search client.
• 04: Get a vectorized query out of the string one. We use the VectorizableTextQuery class with settings for how many k-nearest neighbors to return, the field to search against ( vector ), and the exhaustive set to True to search across all vectors within the vector index.
• 06 – 11: Using both the string and vector representation of the query, we search the index. We select only three fields for display: parent_id, chunk_id , and chunk . Note: The top value is set to two, which corresponds to the value for k-nearest neighbors. In other words, from the two possible results, we select both.
• 13 – 17: the result is an iterable paged search item so we can loop over it.

Running this code block will give us the top two results (chunks) that contain text like our query. Later, when we connect this index to an LLM, it will use the chunks to generate relevant answers.

Using the Python SDK for Azure AI Search, we have programmatically created a data source, defined an integrated vectorized index, and run an indexer. As a result, we can vector-query the data. Even more, the index is now ready for integration within an AI chatbot using RAG.

• Request Access to Azure OpenAI Service
• RAG in Azure AI Search
• Vector search configuration overview
• Azure AI Search Integrated Vectorization
• Azure AI Search split skill
• Azure AI Search embedding skill
• Azure AI Search Index Projections
• Indexers in AI Search

About the author

Comments For This Article

Related Content

LangChain Prompt Templates with OpenAI LLMs

LLMOps and Model Tracking with MLFlow with Open AI

Creating Applications with ChatGPT, LLMs and Generative AI - Overview

Large Language Models (LLMs) to train artificial intelligence (AI) tools such as ChatGPT

Exploring AI Tools for Writing SQL Queries - Are they reliable?

Google Gemini - Generative AI Tool for SQL Professionals

GitHub Copilot to Auto Generate Code for SQL Server Stored Procedures and Functions - Part 2

Related Categories

SQL Reference Guide

Artificial Intelligence

Data Science

Data Warehousing

Import and Export

Development

Date Functions

System Functions

JOIN Tables

SQL Server Management Studio

Database Administration

Performance

Performance Tuning

Locking and Blocking

Data Analytics \ ETL

Microsoft Fabric

Azure Data Factory

Integration Services

Popular Articles

Date and Time Conversions Using SQL Server

Format SQL Server Dates with FORMAT Function

SQL Server CROSS APPLY and OUTER APPLY

SQL Server Cursor Example

SQL CASE Statement in Where Clause to Filter Based on a Condition or Expression

DROP TABLE IF EXISTS Examples for SQL Server

SQL Convert Date to YYYYMMDD

Rolling up multiple rows into a single row and column for SQL Server data

SQL NOT IN Operator

Resolving could not open a connection to SQL Server errors

Format numbers in SQL Server

SQL Server PIVOT and UNPIVOT Examples

Script to retrieve SQL Server database backup history and no backups

How to install SQL Server 2022 step by step

An Introduction to SQL Triggers

Using MERGE in SQL Server to insert, update and delete at the same time

How to monitor backup and restore progress in SQL Server

List SQL Server Login and User Permissions with fn_my_permissions

SQL Server Loop through Table Rows without Cursor

SQL Server Database Stuck in Restoring State

X-Men '97's Finale Just Answered a 30-Year-Old Question

The X-Men '97 season finale answered some major questions fans have had for three decades while raising more questions and setting up Season 2.

Quick Links

X-men '97 featured the return of the phoenix, marvel cameos that answer a 30-year-old spider-man mystery, an epic conclusion in x-men '97, x-men '97's post-credit scene, what the x-men '97 finale means for season 2.

• X-Men '97 is a hit, with strong writing, great animation, and exciting action, breathing new life into the X-Men brand.
• Season 1 tackled iconic storylines, featured powerful character moments, and ended with a cliffhanger setting up Season 2.
• Season 2 seems to be Apocalypse-centric, exploring different timelines and featuring familiar Marvel cameos, promising more X-Men excitement.

This article contains major spoilers for X-Men '97.

X-Men '97 has concluded its first season and has wrapped up what many would call the best entry in a Marvel franchise since Avengers: Endgame . Continuing the storyline from X-Men: The Animated Series , X-Men '97 featured incredible animation, strong writing emphasizing character and political allegory, and some of the best action in the superhero genre while adapting some of the most iconic X-Men storylines of all time with their own spin. The series has breathed new life into the X-Men brand, and the merry mutants are now the most popular they have been in years.

X-Men '97 featured a massive 10-episode first season that drew from many iconic storylines like "The X-Cutioner Song," "Lifedeath," "Inferno," "E is for Extinction," and "Operation Zero Tolerance." The series saw the X-Men face off against the threat of the human/sentinel hybrid Bastion as he looked to turn humans into living sentinels to wipe out the human race in a conflict that took the lives of many mutants, including Gambit. Meanwhile, Magneto, who had tried to walk the path of a hero, turned his back on humanity and threatened to destroy the world. The X-Men, fractured, looked to save the day, and while the season ended on a triumphant note, it had a massive cliffhanger with many teases for what is to come for X-Men '97 Season 2.

X-Men '97

*Availability in US

Not available

Throughout X-Men '97 , they have been teasing the return of the Phoenix . Not only has it been in various versions of the opening credits, but it has been referenced in multiple episodes. When Bastion thinks he has the mutants defeated, having used Mister Sinister's control over Cable to overpower Jean Grey, he begins his Prime Sentinel attack. Yet as soon as he destroys the device that Beast and Forge made to sever his connection, Jean Grey rises from the water and indulges in the Phoenix flames . She uses her vast cosmic powers to restore the device and equip it to Bastion while also burning through all the mutant DNA Mister Sinister has spliced into himself to stay alive all these years, leaving him a white-red version of himself.

X-Men: 20 Mutants Who Must be on the Team in the MCU Reboot

X-Men: The Animated Series ' adaptation of the "Phoenix Saga" is seen as the peak of the original series, and notably, it was never brought back. The Phoenix in the comics has often returned, typically in major event storylines like Avengers vs. X-Men and in Grant Morrison's New X-Men , which X-Men '97 drew from. While Jean Grey says the Phoenix is gone now (hence giving the final episode a sense of tension so Bastion can still be a threat), it is likely not the end of the Phoenix in X-Men '97 , particularly with the reveal of Mother Askani at the end of the series, who in the comics is Rachel Summers, daughter of Jean Grey and Cyclops and another user of the Phoenix.

X-Men '97 has delivered not only plenty of X-Men cameos from fan-favorite mutants, like this episode featuring Psylocke and Alpha Flight, but all corners of the Marvel Universe. The seventh episode, "Bright Eyes," introduced Captain America with the following episode, "Tolerance is Extinction Pt. 1," featuring cameos by the likes of Spider-Man, Doctor Doom, and Baron Zemo. "Tolerance is Extinction Pt. 2" then featured Morph turning into the Hulk, the first non-X-Men-related character they have transformed into.

"Tolerance is Extinction Pt. 3" delivered plenty more cameos as the massive Prime Sentinel attack showed Captain America alongside Iron Man, rocking his signature armor from Iron Man: The Animated Series , with the President of the United States. In addition, Daredevil was shown fighting in the streets of New York City alongside Cloak and Dagger. Doctor Strange was performing surgery with his magic at a hospital following the blackout.

Blade's MCU Delays & Production Issues Explained

The Prime Sentinels also attacked the nation of Wakanda, where The Black Panther and his Dora Milaje fended them off, yet notably, this was not T'Challa but instead King T'Chacka, the father of T'Challa. This is a bit of a departure as Black Panther was previously seen in the Fantastic Four animated series from the 1990s, which shared continuity with X-Men: The Animated Series . This is highlighted in the finale, as Asteroid M falls to Earth, Morph turns into Mr. Fantastic of the Fantastic 4.

Yet one blink and you'll miss it cameo answered a 30-year-old question fans have had for years related to another Marvel animated series from the 1990s. While citizens are watching Asteroid M fall to Earth, fans will notice two individuals watching that look familiar: Peter Parker and Mary Jane Watson from Spider-Man: The Animated Series . While this might seem like a regular Easter egg, it resolves the famous cliffhanger from Spider-Man: The Animated Series .

Peter Parker goes into the multiverse looking for Mary Jane Watson after he learns that the Mary Jane he married at the start of the final season was a clone and the real Mary Jane was lost in limbo after getting pulled through a portal during a confrontation with the Green Goblin two seasons prior. The series ended without a proper resolution and one that has left fans disappointed for years, but now the storyline is resolved. While it might have happened off-screen, it is nice to know these two finally had a happy ending.

The X-Men '97 Season 1 finale is an epic conclusion . The United States launched the Magneto protocols, which fired a series of missiles at Asteroid M, causing its destruction and falling from Earth. Bastion would rather die than join the X-Men, and he vanquishes in the destruction. Jubilee falls out of the falling astroid, but Sunspot saves her, safely getting them to the ground. Meanwhile, the X-Men pool all of their powers together to stop destruction in an impressive show of power from everyone.

In case they don't make it, Cyclops and Jean Grey communicate with Cable telepathically to say goodbye. With a final assist from Magneto , who, along with Professor X, was trapped in a coma having a psychic talk, they stop the Asteroid...but then, in a blink, it vanishes out of the sky. It is not an explosion, but to the rest of the world, it looks like it.

The show then jumps forward six months with the X-Men presumed dead. While Forge looks over making a new X-Men team, the time traveler Bishop shows up, missing in the time stream since Episode 3 of X-Men '97 . Bishop tells Forge the X-Men are not dead but, in fact, lost in time, and they need to go looking for them. In one timeline, the year is 3960 A.D., and Cyclops and Jean Grey find themselves in an Apocalyptic wasteland. They are greeted by a woman in a hood named Mother Askani and, more importantly, a young Nathan Summers, Cyclops son who will grow up to be Cable .

Meanwhile, Rogue, Nightcrawler, Beast, Xavier, and Magneto come across a young mutant fighting a group of people in a desert. He reveals himself to be En Sabah Nur, a younger version of the mutant villain Apocalypse, and the team realizes they have arrived in Egypt in 3,000 B.C. Worth noting is that Storm, Morph, or the weakened Wolverine are not seen anywhere, leaving their fate and where they landed in time unknown. The X-Men team is now split up across almost 6,000 years , and one villain ties it all together: Apocalypse.

For most of X-Men '97 's run, once the credits start rolling, there is no additional scene, but for the season finale, they followed the format of other Disney+ MCU series and MCU films by featuring a tease for what is to come in X-Men '97 Season 2 while also tying into the two timelines teased at the end of the episode. The mutant villain Apocalypse is shown in the ruins of Genosha, in the very same crater that Rogue held Gambit after he died in Episode 5, "Remember It." Apocalypse talks about how much "death" there was while picking up a torn-up version of Gambit's signature playing card, the Queen of Hearts.

10 Best X-Men Villains We Haven't Seen in Marvel Movies

In both the comics and X-Men: The Animated Series , Apocalypse would transform mutants into becoming his Horsemen, genetically altered and mentally conditioned to serve him and granted enhanced powers. The most famous are the Angel, who becomes Archangel, and the Horsemen of Death. The other members all share the same name as their biblical counterparts: Famine, War, and Pestilence. Many different mutants in the comics have served as Apocalypse horsemen, including Gambit, which X-Men '97 Season 2 appears to be building at.​​​​​​

Between Apocalypse origin in 3,000 B.C., the tease of Apocalypse in the present timeline reviving Gambit, and then the 3960 future timeline that is inspired by the "Age of Apocalypse" comic book storyline , Season 2 of X-Men '97 will be Apocalypse-centric . This certainly makes sense as he was one of the biggest villains in X-Men: The Animated Series , and he was the most significant character/plotline shown in the various changing opening credits that never played into the plot of X-Men '97 .

This is interesting as the original storyline, a groundbreaking and seminal work in Marvel Comics, was published in 1995, a year before X-Men: The Animated Series ended. The comic itself was inspired by X-Men: The Animated Series , specifically the episode "One Man's Worth," which saw the villainous Nimrod go back in time and kill Professor X, creating a post-apocalyptic timeline. "One Man's Worth" is the episode that was foundational for now, retroactively being the origin story of X-Men '97 Season 1 villain Bastion. Between that and serving as the inspiration for the "Age of Apocalypse" comic storyline, which now will be adapted into X-Men '97 , it might be the most important X-Men: The Animated Series episode.

X-Men '97 Season 2 now has the chance to be split up across three different timelines, all centered around Apocalypse. The X-Men in the past will deal with his origin, while a new X-Men team in the present day can face off against Apocalypse and his new Horseman, including Gambit. Finally, Cyclops and Jean Grey might have stopped Bastion's future from coming to pass, but in his absence, Apocalypse ushers in a new, equally terrible one, as seen by the inclusion of their daughter Rachel Summers, who was glimpsed in Bastion's future back in Episode 7 while here she is now an older woman going by the name Mother Askani.

The entire team might not be reunited until the end of Season 2 . While no set date has been listed for X-Men '97 Season 2, following the extremely positive reaction and the massive cliffhanger, fans (and likely Marvel Studios now) will want it to arrive sooner rather than later. X-Men '97 is streaming now on Disney+ .

Trump’s lawyer charges Michael Cohen lied to jury

Angry defense lawyer shouts in confrontation with Donald Trump’s former fixer, who stayed calm in his third day of testimony.

NEW YORK — The central witness against Donald Trump withstood a withering cross-examination Thursday from the former president’s defense lawyer, who accused Michael Cohen of lying as recently as two days ago to realize his dreams of revenge against his ex-boss.

The confrontation between Cohen and Trump lawyer Todd Blanche was the most anticipated moment in the month-long trial, which is now speeding toward a conclusion. Because the trial is off Friday, the jurors will have three days to weigh Cohen’s answers. His cross-examination will continue Monday morning, setting the stage for closing arguments next week.

New York Supreme Court Justice Juan Merchan told the lawyers that he would try to make sure those arguments don’t stretch out over more than one day, but he warned that they might because of scheduling demands of the jurors and other logistics issues.

The day’s testimony was closely watched by a cadre of Trump’s political allies sitting behind him in court, including Reps. Matt Gaetz (R-Fla.) and Lauren Boebert (R-Colo.). There were so many congressional Republicans in court in New York that a House Oversight Committee hearing in Washington was delayed .

Trump, who paid close attention Thursday to Blanche’s questioning of Cohen, has still not decided whether he will take the stand, Blanche told the judge. Most defendants do not testify at their trials, believing the risks of being questioned by prosecutors under oath are simply too great.

Trump hush money trial

In his third day on the witness stand, Cohen remained calm and quiet — speaking in a slow, sometimes raspy voice as Blanche challenged his truthfulness again and again. At one point, Blanche shouted that Cohen was a liar.

Cohen’s ability to keep his cool under pressure is an important measuring stick for the prosecutors’ chances of success.

Perhaps more importantly, the jury must decide whether they believe the only witness who directly ties Trump to an alleged scheme to falsify business records to cover up hush money payments to a porn star.

Cohen, a disbarred and convicted former lawyer , has admitted that he lied for Trump for years; it would be a far more serious threat to the prosecution case if jurors came to suspect he lied to them.

Trump is charged with 34 felony counts of falsifying business records by Manhattan District Attorney Alvin Bragg . The indictment accuses Trump of creating a false paper trail to hide the fact that adult-film star Stormy Daniels was paid $130,000 in October 2016 to stay silent about her claim to have had sex with Trump years earlier. Trump denies the two had sex.

Cohen is instrumental to the prosecution case because he paid Daniels with his own money; the following year, the lawyer was given monthly payments from Trump in what prosecutors say was a corrupt scheme to reimburse him and keep Daniels’s allegations under wraps. Cohen is the only witness who has described conversations with Trump in which he said it was clear that his boss understood they would create the false paper trail.

The angriest and potentially most consequential moment in Thursday’s testimony came when Blanche confronted Cohen over his claim that he spoke to Trump on the evening of Oct. 24, 2016, when he called the phone of Trump’s security chief, Keith Schiller.

Cohen testified Tuesday that during the phone call, he told Trump the plan to pay hush money to Daniels was moving forward.

Blanche, however, presented text messages between Schiller and Cohen that preceded that call and suggested an entirely different reason for the conversation. In those texts, Cohen complained about getting harassing phone calls and asked for Schiller’s help. “Call me,” Schiller replied.

After hours of mild-mannered and patient questioning of Cohen, Blanche erupted as he confronted Cohen over the Schiller texts. Accusing Cohen of fabricating key evidence against his client, the lawyer angrily grabbed the microphone and raised his voice.

“That was a lie! You did not talk to President Trump that night!” Blanche bellowed.

Blanche suggested the call was simply too short for it to have included Schiller handing his phone to his boss so he and Cohen could discuss a financial transaction that would ultimately be the genesis of criminal charges against Trump.

“I’m not sure that’s accurate,” Cohen said.

He tried to revise his earlier account, saying he “also spoke to Mr. Trump and told him that everything regarding the Stormy Daniels matter was being worked on and it’s going to be resolved.”

The back-and-forth was the most tense moment yet of Cohen’s cross-examination, and of the entire trial.

But a quieter exchange may prove more damaging to Cohen’s credibility. It happened when Blanche asked Cohen if he had been willing to lie under oath while pleading guilty to tax crimes “because the stakes affected you personally.”

Cohen agreed that he had been.

A few minutes later, Blanche asked Cohen whether “the outcome of this trial affects you personally.”

Again, Cohen said: “Yes.”

Throughout the day, Blanche tried to methodically rip apart the prosecution portrait of Cohen as a remorseful, reformed henchman , using elements of Cohen’s prior testimony to suggest to the jury that he is a singularly selfish person .

Wearing a pale yellow tie, dark suit and dark-rimmed glasses, Cohen met Blanche’s indignation with a calm insistence that whatever his faults, his story about Trump’s guilt was true.

Yet he also struggled to explain why he told a congressional committee in 2019 that he never sought and would never seek a pardon from Trump, when his lawyer was doing just that . (Cohen called it a “misstatement.”) Or how he could claim to have accepted responsibility for financial crimes, but also call the prosecutor and judge in that case corrupt.

On the stand, Cohen said the fault for what happened lay with his bank, his accountant and others.

“You’ve blamed a lot of people over the years for the conduct you were convicted of, yes?” Blanche asked.

“I blame people, yes,” he replied.

Cohen also admitted that he often recorded his conversations with people without their knowledge, including at one point Trump, who at the time was his legal client.

Blanche played for the jury two short recordings of a bombastic Cohen talking about how joyful he was over Trump’s indictment and the prospect of the former president possibly going to jail. Cohen has continued to rail publicly against Trump, on podcasts, social media and in news interviews, despite repeated entreaties from the prosecutors for him to stop.

“I truly f---ing hope that this man ends up in prison,” Cohen said on a podcast excerpt played for the jury Thursday. “Revenge is a dish best served cold, and you best believe I want this man to go down and rot inside for what he did to my family.”

In another podcast clip, this one from May of last year, weeks after Trump’s indictment in this case, Cohen declared: “I want to thank the Manhattan district attorney’s office and their fearless leader Alvin Bragg, with whom I spent countless hours.”

On the witness stand, Cohen conceded that he had not in fact met or spent time with Bragg.

The district attorney has attended the trial intermittently but was not in court Thursday. The trial is off Friday so that Trump can attend his son’s high school graduation.

Just before court ended for the day, Blanche asked about a 2016 conversation in which Cohen reassured a reporter that the story about a Trump-Daniels encounter was false. In the phone call, Cohen told the reporter to believe Cohen because he is “a really bad liar.”

On the stand, Cohen acknowledged that he was lying at the time.

Trump New York hush money case

Former president Donald Trump’s criminal hush money trial is underway in New York. Follow live updates from the trial .

Key witnesses: Several key witnesses, including David Pecker and Stormy Daniels, have taken the stand. Here’s what Daniels said during her testimony . Read full transcripts from the trial .

Gag order: New York Supreme Court Justice Juan Merchan has twice ruled that Trump violated his gag order , which prohibits him from commenting on jurors and witnesses in the case, among others. Here are all of the times Trump has violated the gag order .

The case: The investigation involves a $130,000 payment made to Daniels, an adult-film actress , during the 2016 presidential campaign. It’s one of many ongoing investigations involving Trump . Here are some of the key people in the case .

The charges: Trump is charged with 34 felony counts of falsifying business records. Falsifying business records is a felony in New York when there is an “intent to defraud” that includes an intent to “commit another crime or to aid or conceal” another crime. He has pleaded not guilty . Here’s what to know about the charges — and any potential sentence .