how do questionnaires help in research

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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.

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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.

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Questionnaire: The ultimate guide, advantages & examples

What is a Questionnaire?

A questionnaire is a research instrument that consists of a set of questions or other types of prompts that aims to collect information from a respondent. A research questionnaire is typically a mix of close-ended questions  and  open-ended questions .

Open-ended, long-form questions offer the respondent the ability to elaborate on their thoughts. Research questionnaires were developed in 1838 by the Statistical Society of London.

LEARN ABOUT: Candidate Experience Survey

The data collected from a data collection questionnaire can be both  qualitative  as well as  quantitative  in nature. A questionnaire may or may not be delivered in the form of a  survey , but a survey always consists of a questionnaire.

LEARN ABOUT: Testimonial Questions

Advantages of a good questionnaire design

• With a survey questionnaire, you can gather a lot of data in less time.
• There is less chance of any bias(like selection bias ) creeping if you have a standard set of questions to be used for your target audience. You can apply logic to questions based on the respondents’ answers, but the questionnaire will remain standard for a group of respondents that fall in the same segment.
• Surveying online survey software is quick and cost-effective. It offers you a rich set of features to design, distribute, and analyze the response data.
• It can be customized to reflect your brand voice. Thus, it can be used to reinforce your brand image.
• The responses can be compared with the historical data and understand the shift in respondents’ choices and experiences.
• Respondents can answer the questionnaire without revealing their identity. Also, many survey software complies with significant data security and privacy regulations.

LEARN ABOUT: Structured Questionnaire

Characteristics of a good questionnaire

Your survey design depends on the type of information you need to collect from respondents. Qualitative questionnaires are used when there is a need to collect exploratory information to help prove or disprove a hypothesis. Quantitative questionnaires are used to validate or test a previously generated hypothesis. However, most questionnaires follow some essential characteristics:

• Uniformity:  Questionnaires are very useful to collect demographic information, personal opinions, facts, or attitudes from respondents. One of the most significant attributes of a research form is uniform design and standardization. Every respondent sees the same questions. This helps in  data collection  and  statistical analysis  of this data. For example, the  retail store evaluation questionnaire template  contains questions for evaluating retail store experiences. Questions relate to purchase value, range of options for product selections, and quality of merchandise. These questions are uniform for all customers.

LEARN ABOUT: Research Process Steps

• Exploratory:  It should be exploratory to collect qualitative data. There is no restriction on questions that can be in your questionnaire. For example, you use a data collection questionnaire and send it to the female of the household to understand her spending and saving habits relative to the household income. Open-ended questions give you more insight and allow the respondents to explain their practices. A very structured question list could limit the data collection.

LEARN ABOUT: Best Data Collection Tools

• Question Sequence:  It typically follows a structured flow of questions to increase the number of responses. This sequence of questions is screening questions , warm-up questions, transition questions, skip questions, challenging questions, and classification questions. For example, our  motivation and buying experience questionnaire template  covers initial demographic questions and then asks for time spent in sections of the store and the rationale behind purchases.

Types & Definitions

As we explored before, questionnaires can be either structured or free-flowing. Let’s take a closer look at what that entails for your surveys.

• Structured Questionnaires:  Structured questionnaires collect  quantitative data . The questionnaire is planned and designed to gather precise information. It also initiates a formal inquiry, supplements data, checks previously accumulated data, and helps validate any prior hypothesis.
• Unstructured Questionnaires:  Unstructured questionnaires collect  qualitative data . They use a basic structure and some branching questions but nothing that limits the responses of a respondent. The questions are more open-ended to collect specific data from participants.

Types of questions in a questionnaire

You can use multiple question types in a questionnaire. Using various question types can help increase responses to your research questionnaire as they tend to keep participants more engaged. The best customer satisfaction survey templates are the most commonly used for better insights and decision-making.

Some of the widely used  types of questions  are:

• Open-Ended Questions:   Open-ended questions  help collect qualitative data in a questionnaire where the respondent can answer in a free form with little to no restrictions.
• Dichotomous Questions:  The  dichotomous question  is generally a “yes/no”  close-ended question . This question is usually used in case of the need for necessary validation. It is the most natural form of a questionnaire.
• Multiple-Choice Questions:   Multiple-choice questions  are a close-ended question type in which a respondent has to select one (single-select multiple-choice question) or many (multi-select multiple choice question) responses from a given list of options. The multiple-choice question consists of an incomplete stem (question), right answer or answers, incorrect answers, close alternatives, and distractors. Of course, not all multiple-choice questions have all of the answer types. For example, you probably won’t have the wrong or right answers if you’re looking for customer opinion.
• Scaling Questions:  These questions are based on the principles of the four measurement scales –  nominal, ordinal, interval, and ratio . A few of the question types that utilize these scales’ fundamental properties are  rank order questions ,  Likert scale questions ,  semantic differential scale questions , and  Stapel scale questions .

LEARN ABOUT: System Usability Scale

• Pictorial Questions:  This question type is easy to use and encourages respondents to answer. It works similarly to a multiple-choice question. Respondents are asked a question, and the answer choices are images. This helps respondents choose an answer quickly without over-thinking their answers, giving you more accurate data.

Types of Questionnaires

Questionnaires can be administered or distributed in the following forms:

• Online Questionnaire : In this type, respondents are sent the questionnaire via email or other online mediums. This method is generally cost-effective and time-efficient. Respondents can also answer at leisure. Without the pressure to respond immediately, responses may be more accurate. The disadvantage, however, is that respondents can easily ignore these questionnaires. Read more about online surveys .
• Telephone Questionnaire:  A researcher makes a phone call to a respondent to collect responses directly. Responses are quick once you have a respondent on the phone. However, a lot of times, the respondents hesitate to give out much information over the phone. It is also an expensive way of conducting research. You’re usually not able to collect as many responses as other types of questionnaires, so your sample may not represent the broader population.
• In-House Questionnaire:  This type is used by a researcher who visits the respondent’s home or workplace. The advantage of this method is that the respondent is in a comfortable and natural environment, and in-depth data can be collected. The disadvantage, though, is that it is expensive and slow to conduct.

LEARN ABOUT: Survey Sample Sizes

• Mail Questionnaire:  These are starting to be obsolete but are still being used in some  market research studies. This method involves a researcher sending a physical data collection questionnaire request to a respondent that can be filled in and sent back. The advantage of this method is that respondents can complete this on their own time to answer truthfully and entirely. The disadvantage is that this method is expensive and time-consuming. There is also a high risk of not collecting enough responses to make actionable insights from the data.

How to design a Questionnaire

Questionnaire design is a multistep process that requires attention to detail at every step.

Researchers are always hoping that the responses received for a survey questionnaire yield useable data. If the questionnaire is too complicated, there is a fair chance that the respondent might get confused and will drop out or answer inaccurately.

LEARN ABOUT: Easy Test Maker

As a  survey creator , you may want to pre-test the survey by administering it to a focus group during development. You can try out a few different questionnaire designs to determine which resonates best with your target audience. Pre-testing is a good practice as the survey creator can comprehend the initial stages if there are any changes required in the survey .

Steps Involved in Questionnaire Design

1. identify the scope of your research:.

Think about what your questionnaire is going to include before you start designing the look of it. The clarity of the topic is of utmost importance as this is the primary step in creating the questionnaire. Once you are clear on the purpose of the questionnaire, you can begin the design process.

LEARN ABOUT:  Social Communication Questionnaire

2. Keep it simple:

The words or phrases you use while writing the questionnaire must be easy to understand. If the questions are unclear, the respondents may simply choose any answer and skew the data you collect.

3. Ask only one question at a time:

At times, a researcher may be tempted to add two similar questions. This might seem like an excellent way to consolidate answers to related issues, but it can confuse your respondents or lead to inaccurate data. If any of your questions contain the word “and,” take another look. This question likely has two parts, which can affect the quality of your data.

4. Be flexible with your options:

While designing, the survey creator needs to be flexible in terms of “option choice” for the respondents. Sometimes the respondents may not necessarily want to choose from the answer options provided by the survey creator. An “other” option often helps keep respondents engaged in the survey.

5. The open-ended or closed-ended question is a tough choice:

The survey creator might end up in a situation where they need to make distinct choices between open or close-ended questions. The question type should be carefully chosen as it defines the tone and importance of asking the question in the first place.

If the questionnaire requires the respondents to elaborate on their thoughts, an  open-ended q u estion  is the best choice. If the surveyor wants a specific response, then close-ended questions should be their primary choice. The key to asking closed-ended questions is to generate data that is easy to analyze and spot trends.

6. It is essential to know your audience:

A researcher should know their target audience. For example, if the target audience speaks mostly Spanish, sending the questionnaire in any other language would lower the response rate and accuracy of data. Something that may seem clear to you may be confusing to your respondents. Use simple language and terminology that your respondents will understand, and avoid technical jargon and industry-specific language that might confuse your respondents.

For efficient market research, researchers need a representative sample collected using one of the many  sampling techniques , such as a sample questionnaire. It is imperative to plan and define these target respondents based on the demographics  required.

7. Choosing the right tool is essential: 

QuestionPro is a simple yet advanced survey software platform that the surveyors can use to create a questionnaire or choose from the already existing 300+ questionnaire templates.

Always save personal questions for last. Sensitive questions may cause respondents to drop off before completing. If these questions are at the end, the respondent has had time to become more comfortable with the interview and are more likely to answer personal or demographic questions.

Differences between a Questionnaire and a Survey

Read more: Difference between a survey and a questionnaire

Questionnaire Examples

The best way to understand how questionnaires work is to see the types of questionnaires available. Some examples of a questionnaire are:

USE THIS FREE TEMPLATE

The above survey questions are typically easy to use, understand, and execute. Additionally, the standardized answers of a survey questionnaire instead of a person-to-person conversation make it easier to compile useable data.

The most significant limitation of a data collection questionnaire is that respondents need to read all of the questions and respond to them. For example, you send an invitation through email asking respondents to complete the questions on social media. If a target respondent doesn’t have the right social media profiles, they can’t answer your questions.

Learn More: 350+ Free Survey Examples and Templates

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Home » Questionnaire – Definition, Types, and Examples

Questionnaire – Definition, Types, and Examples

Table of Contents

Questionnaire

Definition:

A Questionnaire is a research tool or survey instrument that consists of a set of questions or prompts designed to gather information from individuals or groups of people.

It is a standardized way of collecting data from a large number of people by asking them a series of questions related to a specific topic or research objective. The questions may be open-ended or closed-ended, and the responses can be quantitative or qualitative. Questionnaires are widely used in research, marketing, social sciences, healthcare, and many other fields to collect data and insights from a target population.

History of Questionnaire

The history of questionnaires can be traced back to the ancient Greeks, who used questionnaires as a means of assessing public opinion. However, the modern history of questionnaires began in the late 19th century with the rise of social surveys.

The first social survey was conducted in the United States in 1874 by Francis A. Walker, who used a questionnaire to collect data on labor conditions. In the early 20th century, questionnaires became a popular tool for conducting social research, particularly in the fields of sociology and psychology.

One of the most influential figures in the development of the questionnaire was the psychologist Raymond Cattell, who in the 1940s and 1950s developed the personality questionnaire, a standardized instrument for measuring personality traits. Cattell’s work helped establish the questionnaire as a key tool in personality research.

In the 1960s and 1970s, the use of questionnaires expanded into other fields, including market research, public opinion polling, and health surveys. With the rise of computer technology, questionnaires became easier and more cost-effective to administer, leading to their widespread use in research and business settings.

Today, questionnaires are used in a wide range of settings, including academic research, business, healthcare, and government. They continue to evolve as a research tool, with advances in computer technology and data analysis techniques making it easier to collect and analyze data from large numbers of participants.

Types of Questionnaire

Types of Questionnaires are as follows:

Structured Questionnaire

This type of questionnaire has a fixed format with predetermined questions that the respondent must answer. The questions are usually closed-ended, which means that the respondent must select a response from a list of options.

Unstructured Questionnaire

An unstructured questionnaire does not have a fixed format or predetermined questions. Instead, the interviewer or researcher can ask open-ended questions to the respondent and let them provide their own answers.

Open-ended Questionnaire

An open-ended questionnaire allows the respondent to answer the question in their own words, without any pre-determined response options. The questions usually start with phrases like “how,” “why,” or “what,” and encourage the respondent to provide more detailed and personalized answers.

Close-ended Questionnaire

In a closed-ended questionnaire, the respondent is given a set of predetermined response options to choose from. This type of questionnaire is easier to analyze and summarize, but may not provide as much insight into the respondent’s opinions or attitudes.

Mixed Questionnaire

A mixed questionnaire is a combination of open-ended and closed-ended questions. This type of questionnaire allows for more flexibility in terms of the questions that can be asked, and can provide both quantitative and qualitative data.

Pictorial Questionnaire:

In a pictorial questionnaire, instead of using words to ask questions, the questions are presented in the form of pictures, diagrams or images. This can be particularly useful for respondents who have low literacy skills, or for situations where language barriers exist. Pictorial questionnaires can also be useful in cross-cultural research where respondents may come from different language backgrounds.

Types of Questions in Questionnaire

The types of Questions in Questionnaire are as follows:

Multiple Choice Questions

These questions have several options for participants to choose from. They are useful for getting quantitative data and can be used to collect demographic information.

• a. Red b . Blue c. Green d . Yellow

Rating Scale Questions

These questions ask participants to rate something on a scale (e.g. from 1 to 10). They are useful for measuring attitudes and opinions.

• On a scale of 1 to 10, how likely are you to recommend this product to a friend?

Open-Ended Questions

These questions allow participants to answer in their own words and provide more in-depth and detailed responses. They are useful for getting qualitative data.

• What do you think are the biggest challenges facing your community?

Likert Scale Questions

These questions ask participants to rate how much they agree or disagree with a statement. They are useful for measuring attitudes and opinions.

How strongly do you agree or disagree with the following statement:

“I enjoy exercising regularly.”

• a . Strongly Agree
• c . Neither Agree nor Disagree
• d . Disagree
• e . Strongly Disagree

Demographic Questions

These questions ask about the participant’s personal information such as age, gender, ethnicity, education level, etc. They are useful for segmenting the data and analyzing results by demographic groups.

• What is your age?

Yes/No Questions

These questions only have two options: Yes or No. They are useful for getting simple, straightforward answers to a specific question.

Have you ever traveled outside of your home country?

Ranking Questions

These questions ask participants to rank several items in order of preference or importance. They are useful for measuring priorities or preferences.

Please rank the following factors in order of importance when choosing a restaurant:

• a. Quality of Food
• c. Ambiance
• d. Location

Matrix Questions

These questions present a matrix or grid of options that participants can choose from. They are useful for getting data on multiple variables at once.

Dichotomous Questions

These questions present two options that are opposite or contradictory. They are useful for measuring binary or polarized attitudes.

Do you support the death penalty?

How to Make a Questionnaire

Step-by-Step Guide for Making a Questionnaire:

• Define your research objectives: Before you start creating questions, you need to define the purpose of your questionnaire and what you hope to achieve from the data you collect.
• Choose the appropriate question types: Based on your research objectives, choose the appropriate question types to collect the data you need. Refer to the types of questions mentioned earlier for guidance.
• Develop questions: Develop clear and concise questions that are easy for participants to understand. Avoid leading or biased questions that might influence the responses.
• Organize questions: Organize questions in a logical and coherent order, starting with demographic questions followed by general questions, and ending with specific or sensitive questions.
• Pilot the questionnaire : Test your questionnaire on a small group of participants to identify any flaws or issues with the questions or the format.
• Refine the questionnaire : Based on feedback from the pilot, refine and revise the questionnaire as necessary to ensure that it is valid and reliable.
• Distribute the questionnaire: Distribute the questionnaire to your target audience using a method that is appropriate for your research objectives, such as online surveys, email, or paper surveys.
• Collect and analyze data: Collect the completed questionnaires and analyze the data using appropriate statistical methods. Draw conclusions from the data and use them to inform decision-making or further research.
• Report findings: Present your findings in a clear and concise report, including a summary of the research objectives, methodology, key findings, and recommendations.

Questionnaire Administration Modes

There are several modes of questionnaire administration. The choice of mode depends on the research objectives, sample size, and available resources. Some common modes of administration include:

• Self-administered paper questionnaires: Participants complete the questionnaire on paper, either in person or by mail. This mode is relatively low cost and easy to administer, but it may result in lower response rates and greater potential for errors in data entry.
• Online questionnaires: Participants complete the questionnaire on a website or through email. This mode is convenient for both researchers and participants, as it allows for fast and easy data collection. However, it may be subject to issues such as low response rates, lack of internet access, and potential for fraudulent responses.
• Telephone surveys: Trained interviewers administer the questionnaire over the phone. This mode allows for a large sample size and can result in higher response rates, but it is also more expensive and time-consuming than other modes.
• Face-to-face interviews : Trained interviewers administer the questionnaire in person. This mode allows for a high degree of control over the survey environment and can result in higher response rates, but it is also more expensive and time-consuming than other modes.
• Mixed-mode surveys: Researchers use a combination of two or more modes to administer the questionnaire, such as using online questionnaires for initial screening and following up with telephone interviews for more detailed information. This mode can help overcome some of the limitations of individual modes, but it requires careful planning and coordination.

Example of Questionnaire

Title of the Survey: Customer Satisfaction Survey

Introduction:

We appreciate your business and would like to ensure that we are meeting your needs. Please take a few minutes to complete this survey so that we can better understand your experience with our products and services. Your feedback is important to us and will help us improve our offerings.

Instructions:

Please read each question carefully and select the response that best reflects your experience. If you have any additional comments or suggestions, please feel free to include them in the space provided at the end of the survey.

1. How satisfied are you with our product quality?

• Very satisfied
• Somewhat satisfied
• Somewhat dissatisfied
• Very dissatisfied

2. How satisfied are you with our customer service?

3. How satisfied are you with the price of our products?

4. How likely are you to recommend our products to others?

• Very likely
• Somewhat likely
• Somewhat unlikely
• Very unlikely

5. How easy was it to find the information you were looking for on our website?

• Somewhat easy
• Somewhat difficult
• Very difficult

6. How satisfied are you with the overall experience of using our products and services?

7. Is there anything that you would like to see us improve upon or change in the future?

…………………………………………………………………………………………………………………………..

Conclusion:

Thank you for taking the time to complete this survey. Your feedback is valuable to us and will help us improve our products and services. If you have any further comments or concerns, please do not hesitate to contact us.

Applications of Questionnaire

Some common applications of questionnaires include:

• Research : Questionnaires are commonly used in research to gather information from participants about their attitudes, opinions, behaviors, and experiences. This information can then be analyzed and used to draw conclusions and make inferences.
• Healthcare : In healthcare, questionnaires can be used to gather information about patients’ medical history, symptoms, and lifestyle habits. This information can help healthcare professionals diagnose and treat medical conditions more effectively.
• Marketing : Questionnaires are commonly used in marketing to gather information about consumers’ preferences, buying habits, and opinions on products and services. This information can help businesses develop and market products more effectively.
• Human Resources: Questionnaires are used in human resources to gather information from job applicants, employees, and managers about job satisfaction, performance, and workplace culture. This information can help organizations improve their hiring practices, employee retention, and organizational culture.
• Education : Questionnaires are used in education to gather information from students, teachers, and parents about their perceptions of the educational experience. This information can help educators identify areas for improvement and develop more effective teaching strategies.

Purpose of Questionnaire

Some common purposes of questionnaires include:

• To collect information on attitudes, opinions, and beliefs: Questionnaires can be used to gather information on people’s attitudes, opinions, and beliefs on a particular topic. For example, a questionnaire can be used to gather information on people’s opinions about a particular political issue.
• To collect demographic information: Questionnaires can be used to collect demographic information such as age, gender, income, education level, and occupation. This information can be used to analyze trends and patterns in the data.
• To measure behaviors or experiences: Questionnaires can be used to gather information on behaviors or experiences such as health-related behaviors or experiences, job satisfaction, or customer satisfaction.
• To evaluate programs or interventions: Questionnaires can be used to evaluate the effectiveness of programs or interventions by gathering information on participants’ experiences, opinions, and behaviors.
• To gather information for research: Questionnaires can be used to gather data for research purposes on a variety of topics.

When to use Questionnaire

Here are some situations when questionnaires might be used:

• When you want to collect data from a large number of people: Questionnaires are useful when you want to collect data from a large number of people. They can be distributed to a wide audience and can be completed at the respondent’s convenience.
• When you want to collect data on specific topics: Questionnaires are useful when you want to collect data on specific topics or research questions. They can be designed to ask specific questions and can be used to gather quantitative data that can be analyzed statistically.
• When you want to compare responses across groups: Questionnaires are useful when you want to compare responses across different groups of people. For example, you might want to compare responses from men and women, or from people of different ages or educational backgrounds.
• When you want to collect data anonymously: Questionnaires can be useful when you want to collect data anonymously. Respondents can complete the questionnaire without fear of judgment or repercussions, which can lead to more honest and accurate responses.
• When you want to save time and resources: Questionnaires can be more efficient and cost-effective than other methods of data collection such as interviews or focus groups. They can be completed quickly and easily, and can be analyzed using software to save time and resources.

Characteristics of Questionnaire

Here are some of the characteristics of questionnaires:

• Standardization : Questionnaires are standardized tools that ask the same questions in the same order to all respondents. This ensures that all respondents are answering the same questions and that the responses can be compared and analyzed.
• Objectivity : Questionnaires are designed to be objective, meaning that they do not contain leading questions or bias that could influence the respondent’s answers.
• Predefined responses: Questionnaires typically provide predefined response options for the respondents to choose from, which helps to standardize the responses and make them easier to analyze.
• Quantitative data: Questionnaires are designed to collect quantitative data, meaning that they provide numerical or categorical data that can be analyzed using statistical methods.
• Convenience : Questionnaires are convenient for both the researcher and the respondents. They can be distributed and completed at the respondent’s convenience and can be easily administered to a large number of people.
• Anonymity : Questionnaires can be anonymous, which can encourage respondents to answer more honestly and provide more accurate data.
• Reliability : Questionnaires are designed to be reliable, meaning that they produce consistent results when administered multiple times to the same group of people.
• Validity : Questionnaires are designed to be valid, meaning that they measure what they are intended to measure and are not influenced by other factors.

Advantage of Questionnaire

Some Advantage of Questionnaire are as follows:

• Standardization: Questionnaires allow researchers to ask the same questions to all participants in a standardized manner. This helps ensure consistency in the data collected and eliminates potential bias that might arise if questions were asked differently to different participants.
• Efficiency: Questionnaires can be administered to a large number of people at once, making them an efficient way to collect data from a large sample.
• Anonymity: Participants can remain anonymous when completing a questionnaire, which may make them more likely to answer honestly and openly.
• Cost-effective: Questionnaires can be relatively inexpensive to administer compared to other research methods, such as interviews or focus groups.
• Objectivity: Because questionnaires are typically designed to collect quantitative data, they can be analyzed objectively without the influence of the researcher’s subjective interpretation.
• Flexibility: Questionnaires can be adapted to a wide range of research questions and can be used in various settings, including online surveys, mail surveys, or in-person interviews.

Limitations of Questionnaire

Limitations of Questionnaire are as follows:

• Limited depth: Questionnaires are typically designed to collect quantitative data, which may not provide a complete understanding of the topic being studied. Questionnaires may miss important details and nuances that could be captured through other research methods, such as interviews or observations.
• R esponse bias: Participants may not always answer questions truthfully or accurately, either because they do not remember or because they want to present themselves in a particular way. This can lead to response bias, which can affect the validity and reliability of the data collected.
• Limited flexibility: While questionnaires can be adapted to a wide range of research questions, they may not be suitable for all types of research. For example, they may not be appropriate for studying complex phenomena or for exploring participants’ experiences and perceptions in-depth.
• Limited context: Questionnaires typically do not provide a rich contextual understanding of the topic being studied. They may not capture the broader social, cultural, or historical factors that may influence participants’ responses.
• Limited control : Researchers may not have control over how participants complete the questionnaire, which can lead to variations in response quality or consistency.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Develop a Questionnaire for Research

Last Updated: December 4, 2022 Fact Checked

wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. To create this article, 24 people, some anonymous, worked to edit and improve it over time. There are 13 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 586,735 times. Learn more...

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

Designing Your Questionnaire

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

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

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

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

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

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

Writing your questionnaire

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

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

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

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

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

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

Distributing Your Questionnaire

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

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

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

Community Q&A

You Might Also Like

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

About This Article

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

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Questionnaires

Questionnaires can be classified as both, quantitative and qualitative method depending on the nature of questions. Specifically, answers obtained through closed-ended questions (also called restricted questions) with multiple choice answer options are analyzed using quantitative methods. Research findings in this case can be illustrated using tabulations, pie-charts, bar-charts and percentages.

Answers obtained to open-ended questionnaire questions (also known as unrestricted questions), on the other hand, are analyzed using qualitative methods. Primary data collected using open-ended questionnaires involve discussions and critical analyses without use of numbers and calculations.

There are following types of questionnaires:

Computer questionnaire . Respondents are asked to answer the questionnaire which is sent by mail. The advantages of the computer questionnaires include their inexpensive price, time-efficiency, and respondents do not feel pressured, therefore can answer when they have time, giving more accurate answers. However, the main shortcoming of the mail questionnaires is that sometimes respondents do not bother answering them and they can just ignore the questionnaire.

Telephone questionnaire .  Researcher may choose to call potential respondents with the aim of getting them to answer the questionnaire. The advantage of the telephone questionnaire is that, it can be completed during the short amount of time. The main disadvantage of the phone questionnaire is that it is expensive most of the time. Moreover, most people do not feel comfortable to answer many questions asked through the phone and it is difficult to get sample group to answer questionnaire over the phone.

In-house survey .  This type of questionnaire involves the researcher visiting respondents in their houses or workplaces. The advantage of in-house survey is that more focus towards the questions can be gained from respondents. However, in-house surveys also have a range of disadvantages which include being time consuming, more expensive and respondents may not wish to have the researcher in their houses or workplaces for various reasons.

Mail Questionnaire . This sort of questionnaires involve the researcher to send the questionnaire list to respondents through post, often attaching pre-paid envelope. Mail questionnaires have an advantage of providing more accurate answer, because respondents can answer the questionnaire in their spare time. The disadvantages associated with mail questionnaires include them being expensive, time consuming and sometimes they end up in the bin put by respondents.

Questionnaires can include the following types of questions:

Open question questionnaires . Open questions differ from other types of questions used in questionnaires in a way that open questions may produce unexpected results, which can make the research more original and valuable. However, it is difficult to analyze the results of the findings when the data is obtained through the questionnaire with open questions.

Multiple choice question s. Respondents are offered a set of answers they have to choose from. The downsize of questionnaire with multiple choice questions is that, if there are too many answers to choose from, it makes the questionnaire, confusing and boring, and discourages the respondent to answer the questionnaire.

Dichotomous Questions .  Thes type of questions gives two options to respondents – yes or no, to choose from. It is the easiest form of questionnaire for the respondent in terms of responding it.

Scaling Questions . Also referred to as ranking questions, they present an option for respondents to rank the available answers to questions on the scale of given range of values (for example from 1 to 10).

For a standard 15,000-20,000 word business dissertation including 25-40 questions in questionnaires will usually suffice. Questions need be formulated in an unambiguous and straightforward manner and they should be presented in a logical order.

Questionnaires as primary data collection method offer the following advantages:

• Uniformity: all respondents are asked exactly the same questions
• Cost-effectiveness
• Possibility to collect the primary data in shorter period of time
• Minimum or no bias from the researcher during the data collection process
• Usually enough time for respondents to think before answering questions, as opposed to interviews
• Possibility to reach respondents in distant areas through online questionnaire

At the same time, the use of questionnaires as primary data collection method is associated with the following shortcomings:

• Random answer choices by respondents without properly reading the question.
• In closed-ended questionnaires no possibility for respondents to express their additional thoughts about the matter due to the absence of a relevant question.
• Collecting incomplete or inaccurate information because respondents may not be able to understand questions correctly.
• High rate of non-response

Survey Monkey represents one of the most popular online platforms for facilitating data collection through questionnaires. Substantial benefits offered by Survey Monkey include its ease to use, presentation of questions in many different formats and advanced data analysis capabilities.

Survey Monkey as a popular platform for primary data collection

There are other alternatives to Survey Monkey you might want to consider to use as a platform for your survey. These include but not limited to Jotform, Google Forms, Lime Survey, Crowd Signal, Survey Gizmo, Zoho Survey and many others.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words.

John Dudovskiy

Frequently asked questions

How do you administer questionnaires.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Methods in educational research

Questionnaires

Questionnaires can be used qualitatively or quantitatively. As with all other methods, the value of the questionnaire depends on its ability to provide data which can answer the research question, and the way that a questionnaire is designed and worded can be significant in this. A questionnaire designed to capture levels of student satisfaction may well provide information to this end, but for researchers interested in more than this, such measures could amount to little more than superficial data. Careful consideration needs to be given to what the questionnaire is intended to elicit, and so – depending on their study – some researchers might find it more useful to use pre-existing standardised questionnaires based on validated scales such as those used to measure self-efficacy (Bandura, 2006) or agency (Tapal et al., 2017).

Guidance for developing questionnaires using self-efficacy scales [pdf]

"The questionnaire is a widely used and useful instrument for collecting survey information, providing structured – often numerical – data, able to be administrated without the presence of the researcher and often comparatively straightforward to analyse. These attractions have to be counterbalanced by the time taken to develop, pilot and refine the questionnaire, by the possible unsophistication and limited and superficial scope of the data that are collected […]. The researcher will have to judge the appropriateness of using a questionnaire for data collection, and, if so, what kind of questionnaire it should be." Cohen, Manion and Morrison, 2018, p.471

Cohen, Manion and Morrison (2018) provide a comprehensive overview of the different issues and stages involved in questionnaire design and it is important that each of these is given full consideration from the outset. These issues include:

• Intended population/sample – as this can influence the form, wording and means of administrating the questionnaire
• Intended method of data analysis – to ensure that questions are framed appropriately
• Type of questionnaire: structured/closed, semi-structured or “unstructured”
• Question/response types – e.g. dichotomous questions, multiple choice, Likert/rating scales, constant sum, rank ordering, open ended
• Wording of questions – e.g. need for clarity, risk of leading responses
• Opportunity to pilot and revise questionnaire

As with all educational research, attention must be given to the particular ethical and practical considerations involved in this particular type of research. For many researchers, online survey tools such as Qualtrics provide a convenient means of administering questionnaires but these require attention to particular considerations – which Cohen, Manion and Morrison (2018) provide further detailed guidance on in Chapter 18.

Quantitative questionnaire design

A key priority with quantitative questionnaire design is to be clear from the outset exactly what it is you want to measure, why you want to do this and whether your proposed design is actually going to generate the sort of data you need. Do you want, for instance, to generate inferential or just descriptive statistics? Different question types lend themselves to different scales of data (rating scales to ordinal data, for instance) so thinking ahead to the analysis is an essential part of the design phase. Equally, if a pre- and post- study design is deemed appropriate, then the essential principles of experimental design need to be factored into the design and administration of the questionnaires.

Qualitative questionnaire design

If your area of research renders it necessary to obtain qualitative data, it might be worth considering in the first instance if interviews or focus groups might provide a more appropriate means of eliciting this. Self-completion questionnaires do not provide scope for probing further if questions are left unanswered or incomplete, and participants can vary enormously in terms of the time they are prepared to devote and the amount they are prepared to write in completing open text questionnaires. If questionnaires are most appropriate, however, then the general principles of good questionnaire design (layout, wording, ordering and so on) need to be considered alongside the practicality and feasibility of completing the questionnaire from the participants’ point of view.

How do wildfires affect mental health? A new UW study examines the connection

“there are a lot of vulnerable groups of people and these fires have substantial mental health impact we need to prepare for.”.

Checking air quality and staying indoors when smoke inundates Washington has become second nature during Washington’s wildfire season in recent years. But new research highlights how wildfires can affect a less visible aspect of well-being: mental health.

A University of Washington study published in late February found an increase in prescriptions to treat depression and anxiety or stabilize mood in the six weeks after wildfires. The study used prescription data, commercial insurance claims and pharmacy records to examine the impact of 25 large California wildfires from 2011 to 2018.

“California experienced a substantial burden of wildfires from 2011 to 2018, and as wildfires become more intense and frequent in the context of anthropogenic climate change, it is increasingly important to understand and address their mental health effects,” the authors wrote.

Extensive research has focused on how wildfires and smoke affect cardiovascular and respiratory health; a study published in February found that the overlap of extreme heat and wildfire smoke had a compound effect and increased hospitalizations.

But few studies have examined how fires affect mental health. Previous research on the connection has been more qualitative, using focus groups to examine the effect of one fire in one region, said Zack Wettstein, the lead author of the study and a UW Medicine emergency medicine doctor. He conducted the research as a guest researcher with the Centers for Disease Control and Prevention’s Climate and Health Program.

“All too often we’re overlooking these mental health impacts,” Wettstein said. “There are a lot of vulnerable groups of people and these fires have substantial mental health impact we need to prepare for.”

Sleep disruption and decreased sense of safety likely affect mental health, particularly among those who must evacuate and face property loss, researchers wrote. Wildfires can also cause or exacerbate mental health conditions such as post-traumatic stress disorder, anxiety, depression and complex grief.

Wettstein has practiced emergency medicine in California, Idaho and Washington during wildfire season. Summer is already one of the busiest times in the emergency department, he said, and fires add an influx of patients.

“When smoke waves and heat waves roll through, it feels like we’re being inundated with patients experiencing a range of conditions from these exposures,” Wettstein said. “We’re seeing lots of folks coming in with stress and anxiety related to smoke exposure, let alone folks in closer proximity to fires who are evacuating, or have lost family or property.”

The study recommends “ensuring access to mental health services and supporting programs that promote mental health resilience before, during and after wildfires” as interventions to mitigate mental health effects. Wettstein hopes that hospitals will consider how to allocate resources during wildfire season and plan for potential surges.

“I don’t know how much people have considered upstaffing our mental health teams in the emergency department and otherwise,” Wettstein said. “There are opportunities to consider what to do in advance of these events. Providers can make sure their patients have enough medication on hand, so they don’t find themselves short.”

As in all studies, there are limitations: The data only includes patients enrolled on commercial insurance, meaning it didn’t reflect the experience of uninsured people, or those on Medicaid or Medicare. The data didn’t specify whether the prescriptions were new or refills, making it hard to tell whether smoke exposure led to new diagnoses or exacerbated existing mental health conditions.

Researchers also focused on metropolitan statistical areas (MSAs), a geographic designation the CDC uses to highlight urbanized areas with a population of at least 50,000. This means that rural communities, which “face a disproportionate burden of wildfire exposure and concomitant lack of mental health resources,” the authors wrote, are likely underrepresented.

There hasn’t been a similar study done in Washington, Wettstein said; he sees a “huge opportunity” to use this approach to examine other regions of the U.S. besides California.

The research findings raise a broader question: How do we deliver mental health care, especially when an emergency affects broad swaths of the population?

“Our mental health care system is already overtaxed,” Wettstein said. “With the projections of climate related impacts on temperature, air quality and other conditions in Washington, there’s going to be a greater burden of health impacts related to these events. What can we do to help prepare our system and make it more resilient, so we can treat everybody?”

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Checkboxes for race and ethnicity on government forms will include more choices

Hansi Lo Wang

New checkboxes for "Middle Eastern or North African" and "Hispanic or Latino" are coming to the U.S. census and federal forms. Advocates say these changes will help enforce civil rights protections.

Next U.S. census will have new boxes for 'Middle Eastern or North African,' 'Latino'

DEBBIE ELLIOTT, HOST:

Major changes are coming to how the U.S. government asks about your race and ethnicity. And supporters say these changes could improve the accuracy of statistics about Latinos and people of Middle Eastern or North African descent. NPR's Hansi Lo Wang reports.

HANSI LO WANG, BYLINE: Guillermo Creamer says one of those changes is going to solve a conundrum. On federal government forms he answers, yes, he identifies as Latino, which the government considers to be an ethnicity. And then he has to answer...

GUILLERMO CREAMER: What's your race? I genuinely don't ever know what to answer.

WANG: You see the boxes and none of them quite fit for you?

CREAMER: No because to me, you know, being Latino, that's all-encompassing for me.

WANG: Creamer will soon be able to answer a new combined question that asks about a person's race and/or ethnicity and the checkboxes under it include Hispanic or Latino. The White House's Office of Management and Budget has released an example.

CREAMER: I sent it around to my parents and other members of my family. And I was like, hey, like, finally, like, there's more than just the other for us (laughter).

WANG: And there will be a completely new checkbox for Middle Eastern or North African.

MAYA BERRY: It is progress. It is progress.

WANG: Maya Berry is the executive director of the Arab American Institute, which for more than three decades has been campaigning for this box and to change what research suggests to be an outdated policy for racially categorizing people with roots in the Middle East or North Africa - or MENA.

BERRY: Yes, no longer rendered by definition in terms of formal federal policy as exclusively white. The point is, folks can self-identify with any racial category they feel comfortable with.

WANG: Still, Berry says she is concerned that the government's new definition for Middle Eastern or North African is not inclusive enough of people who are of MENA descent and identify with Black diaspora communities. And she's worried that can affect the data that will be used to redraw voting districts, enforce civil rights and guide policymaking and research across the country.

Hansi Lo Wang, NPR News.

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• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.

Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.

Table of contents

Questionnaires vs surveys, questionnaire methods, open-ended vs closed-ended questions, question wording, question order, step-by-step guide to design, frequently asked questions about questionnaire design.

A survey is a research method where you collect and analyse data from a group of people. A questionnaire is a specific tool or instrument for collecting the data.

Designing a questionnaire means creating valid and reliable questions that address your research objectives, placing them in a useful order, and selecting an appropriate method for administration.

But designing a questionnaire is only one component of survey research. Survey research also involves defining the population you’re interested in, choosing an appropriate sampling method , administering questionnaires, data cleaning and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalise your results to the population. Gather data from a sample that represents the range of views in the population for externally valid results. There will always be some differences between the population and the sample, but minimising these will help you avoid sampling bias .

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Questionnaires can be self-administered or researcher-administered . Self-administered questionnaires are more common because they are easy to implement and inexpensive, but researcher-administered questionnaires allow deeper insights.

Self-administered questionnaires

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Self-administered questionnaires can be:

• Cost-effective
• Easy to administer for small and large groups
• Anonymous and suitable for sensitive topics

But they may also be:

• Unsuitable for people with limited literacy or verbal skills
• Susceptible to a nonreponse bias (most people invited may not complete the questionnaire)
• Biased towards people who volunteer because impersonal survey requests often go ignored

Researcher-administered questionnaires

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents.

Researcher-administered questionnaires can:

• Help you ensure the respondents are representative of your target audience
• Allow clarifications of ambiguous or unclear questions and answers
• Have high response rates because it’s harder to refuse an interview when personal attention is given to respondents

But researcher-administered questionnaires can be limiting in terms of resources. They are:

• Costly and time-consuming to perform
• More difficult to analyse if you have qualitative responses
• Likely to contain experimenter bias or demand characteristics
• Likely to encourage social desirability bias in responses because of a lack of anonymity

Your questionnaire can include open-ended or closed-ended questions, or a combination of both.

Using closed-ended questions limits your responses, while open-ended questions enable a broad range of answers. You’ll need to balance these considerations with your available time and resources.

Closed-ended questions

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables.

Categorical variables can be nominal or ordinal. Quantitative variables can be interval or ratio. Understanding the type of variable and level of measurement means you can perform appropriate statistical analyses for generalisable results.

Examples of closed-ended questions for different variables

Nominal variables include categories that can’t be ranked, such as race or ethnicity. This includes binary or dichotomous categories.

It’s best to include categories that cover all possible answers and are mutually exclusive. There should be no overlap between response items.

In binary or dichotomous questions, you’ll give respondents only two options to choose from.

White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander

Ordinal variables include categories that can be ranked. Consider how wide or narrow a range you’ll include in your response items, and their relevance to your respondents.

Likert-type questions collect ordinal data using rating scales with five or seven points.

When you have four or more Likert-type questions, you can treat the composite data as quantitative data on an interval scale . Intelligence tests, psychological scales, and personality inventories use multiple Likert-type questions to collect interval data.

With interval or ratio data, you can apply strong statistical hypothesis tests to address your research aims.

Pros and cons of closed-ended questions

Well-designed closed-ended questions are easy to understand and can be answered quickly. However, you might still miss important answers that are relevant to respondents. An incomplete set of response items may force some respondents to pick the closest alternative to their true answer. These types of questions may also miss out on valuable detail.

To solve these problems, you can make questions partially closed-ended, and include an open-ended option where respondents can fill in their own answer.

Open-ended questions

Open-ended, or long-form, questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. For example, respondents may want to answer ‘multiracial’ for the question on race rather than selecting from a restricted list.

• How do you feel about open science?
• How would you describe your personality?
• In your opinion, what is the biggest obstacle to productivity in remote work?

Open-ended questions have a few downsides.

They require more time and effort from respondents, which may deter them from completing the questionnaire.

For researchers, understanding and summarising responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorise answers, and you may also need to involve other researchers in data analysis for high reliability .

Question wording can influence your respondents’ answers, especially if the language is unclear, ambiguous, or biased. Good questions need to be understood by all respondents in the same way ( reliable ) and measure exactly what you’re interested in ( valid ).

Use clear language

You should design questions with your target audience in mind. Consider their familiarity with your questionnaire topics and language and tailor your questions to them.

For readability and clarity, avoid jargon or overly complex language. Don’t use double negatives because they can be harder to understand.

Use balanced framing

Respondents often answer in different ways depending on the question framing. Positive frames are interpreted as more neutral than negative frames and may encourage more socially desirable answers.

Use a mix of both positive and negative frames to avoid bias , and ensure that your question wording is balanced wherever possible.

Unbalanced questions focus on only one side of an argument. Respondents may be less likely to oppose the question if it is framed in a particular direction. It’s best practice to provide a counterargument within the question as well.

Avoid leading questions

Leading questions guide respondents towards answering in specific ways, even if that’s not how they truly feel, by explicitly or implicitly providing them with extra information.

It’s best to keep your questions short and specific to your topic of interest.

• The average daily work commute in the US takes 54.2 minutes and costs $29 per day. Since 2020, working from home has saved many employees time and money. Do you favour flexible work-from-home policies even after it’s safe to return to offices?
• Experts agree that a well-balanced diet provides sufficient vitamins and minerals, and multivitamins and supplements are not necessary or effective. Do you agree or disagree that multivitamins are helpful for balanced nutrition?

Keep your questions focused

Ask about only one idea at a time and avoid double-barrelled questions. Double-barrelled questions ask about more than one item at a time, which can confuse respondents.

This question could be difficult to answer for respondents who feel strongly about the right to clean drinking water but not high-speed internet. They might only answer about the topic they feel passionate about or provide a neutral answer instead – but neither of these options capture their true answers.

Instead, you should ask two separate questions to gauge respondents’ opinions.

Strongly Agree Agree Undecided Disagree Strongly Disagree

Do you agree or disagree that the government should be responsible for providing high-speed internet to everyone?

You can organise the questions logically, with a clear progression from simple to complex. Alternatively, you can randomise the question order between respondents.

Logical flow

Using a logical flow to your question order means starting with simple questions, such as behavioural or opinion questions, and ending with more complex, sensitive, or controversial questions.

The question order that you use can significantly affect the responses by priming them in specific directions. Question order effects, or context effects, occur when earlier questions influence the responses to later questions, reducing the validity of your questionnaire.

While demographic questions are usually unaffected by order effects, questions about opinions and attitudes are more susceptible to them.

• How knowledgeable are you about Joe Biden’s executive orders in his first 100 days?
• Are you satisfied or dissatisfied with the way Joe Biden is managing the economy?
• Do you approve or disapprove of the way Joe Biden is handling his job as president?

It’s important to minimise order effects because they can be a source of systematic error or bias in your study.

Randomisation

Randomisation involves presenting individual respondents with the same questionnaire but with different question orders.

When you use randomisation, order effects will be minimised in your dataset. But a randomised order may also make it harder for respondents to process your questionnaire. Some questions may need more cognitive effort, while others are easier to answer, so a random order could require more time or mental capacity for respondents to switch between questions.

Follow this step-by-step guide to design your questionnaire.

Step 1: Define your goals and objectives

The first step of designing a questionnaire is determining your aims.

• What topics or experiences are you studying?
• What specifically do you want to find out?
• Is a self-report questionnaire an appropriate tool for investigating this topic?

Once you’ve specified your research aims, you can operationalise your variables of interest into questionnaire items. Operationalising concepts means turning them from abstract ideas into concrete measurements. Every question needs to address a defined need and have a clear purpose.

Step 2: Use questions that are suitable for your sample

Create appropriate questions by taking the perspective of your respondents. Consider their language proficiency and available time and energy when designing your questionnaire.

• Are the respondents familiar with the language and terms used in your questions?
• Would any of the questions insult, confuse, or embarrass them?
• Do the response items for any closed-ended questions capture all possible answers?
• Are the response items mutually exclusive?
• Do the respondents have time to respond to open-ended questions?

Consider all possible options for responses to closed-ended questions. From a respondent’s perspective, a lack of response options reflecting their point of view or true answer may make them feel alienated or excluded. In turn, they’ll become disengaged or inattentive to the rest of the questionnaire.

Step 3: Decide on your questionnaire length and question order

Once you have your questions, make sure that the length and order of your questions are appropriate for your sample.

If respondents are not being incentivised or compensated, keep your questionnaire short and easy to answer. Otherwise, your sample may be biased with only highly motivated respondents completing the questionnaire.

Decide on your question order based on your aims and resources. Use a logical flow if your respondents have limited time or if you cannot randomise questions. Randomising questions helps you avoid bias, but it can take more complex statistical analysis to interpret your data.

Step 4: Pretest your questionnaire

When you have a complete list of questions, you’ll need to pretest it to make sure what you’re asking is always clear and unambiguous. Pretesting helps you catch any errors or points of confusion before performing your study.

Ask friends, classmates, or members of your target audience to complete your questionnaire using the same method you’ll use for your research. Find out if any questions were particularly difficult to answer or if the directions were unclear or inconsistent, and make changes as necessary.

If you have the resources, running a pilot study will help you test the validity and reliability of your questionnaire. A pilot study is a practice run of the full study, and it includes sampling, data collection , and analysis.

You can find out whether your procedures are unfeasible or susceptible to bias and make changes in time, but you can’t test a hypothesis with this type of study because it’s usually statistically underpowered .

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

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PCOS symptoms are still difficult for doctors to diagnose and treat. Here's why.

Every morning, Jeni Gutke swallows 12 pills. In the evening, she takes 15 more, then another before bed. She also takes an injectable medication once weekly, and two other medications as needed.

Gutke, of Joliet, Illinois, has polycystic ovary syndrome, or PCOS, and the medications and supplements help the 45-year-old cope with migraines, high blood pressure, diabetes, high cholesterol, anxiety and depression that come with the complex hormonal condition. 

Not one of  Gutke’s medications are technically “PCOS drugs.” 

The Food and Drug Administration has not approved a medication specifically for PCOS, which is often linked to infertility, irregular or missed periods, weight problems, and other debilitating symptoms. Gutke’s array of medications is typical of how many of the estimated 5 million women in the U.S. diagnosed with PCOS deal with it.

“It’s such a vast syndrome that affects everything from your head to your toes,” she said. She was diagnosed with endometrial cancer — another risk linked to PCOS — at age 37. 

After nearly a century of disagreements over what, exactly, defines the condition, as well as a lack of research, PCOS is still poorly understood. The symptoms vary so widely that any single drug would be unlikely to help all patients, said Dr. Heather Huddleston, a reproductive endocrinologist at the University of California, San Francisco and director of UCSF’s PCOS Clinic. 

Women with PCOS and the doctors who care for them say they want better options — treatments for the condition’s root causes rather than bandages for individual symptoms. Even as calls for better treatments grow, the lack of investment in PCOS research has limited doctors’ ability to help their patients. 

“It gets very messy to try to identify one treatment that’s going to work for everybody,” Huddleston said. 

Many women with the condition end up taking off-label prescriptions — meaning drugs technically approved for other conditions, like diabetes or obesity — to help PCOS-related symptoms. Navigating insurance coverage for off-label prescriptions can be challenging.

“There’s no magic pill,” said Tallene Hacatoryan, 31, a registered dietician from Orange County, California. “There are too many components for there to be a one-size-fits-all treatment.”

Hacatoryan was diagnosed with PCOS at age 18 and now works as a diet and lifestyle coach for women with PCOS.  

Although research is murky when it comes to the best diet for women with PCOS, the most up-to-date international guidelines recommend exercise and a healthy diet. There’s no evidence that any particular diet improves symptoms, although some women have found lifestyle coaching helpful.

Insufficient funding for research

Among the reported 315 medical conditions that receive federal support from the National Institutes of Health , PCOS ranks near the bottom, with an estimated $10 million earmarked for research in 2024. Until 2022, PCOS was so underfunded that it wasn’t included as a line item in the NIH list.  And the condition is not explicitly included in the $100 million the Department of Health and Human Services announced recently to research neglected areas of women’s health. Neither is PCOS mentioned in  President Joe Biden’s recent executive order to advance women’s health , which includes $200 million for NIH research grants, or the White House’s calls for Congress to allocate $12 billion to fund women’s health research.

A spokesperson at the NIH said that it’s too early to know which women’s health conditions will receive funding under the new initiative. 

“Given how common PCOS is, the amount of funding it’s gotten is proportionately extremely small,” Huddleston said. 

Government funding is just one part of the total research budget for a given disease. While it’s tough to pin down a dollar figure for private industry spending, experts say the lack of FDA-approved PCOS treatments reflects a lack of investment from drugmakers, too. 

Developing PCOS treatments requires a better understanding of the condition. This, in turn, requires far more research tracking thousands of women over many years, which can be extremely expensive, experts say. 

However, there are some promising signs.

Although research is early and only in a few dozen women, there are a handful of small drug companies studying possible PCOS treatments. A Menlo Park, California-based company called May Health , for instance, is developing a one-time surgical procedure it thinks could help with PCOS. Spruce Bio, a San Francisco biotech firm, is running a small clinical trial with a drug called tildacerfont for PCOS. It is not clear yet if the oral drug works. President and CFO Samir Gharib said larger clinical trials will depend on the company’s ability to “secure additional financing” or partner with another drug company. 

More women's health news

• First over-the-counter birth control pills hit store shelves.
• Common medical errors harm or kills hundreds of thousands each year, especially women.
• Increasing protein from plants may help protect women against many chronic diseases.

The FDA recently attended a meeting with advocacy group PCOS Challenge where women shared their experiences with the agency’s scientists and drug companies. No PCOS drug trials were announced after the meeting, but the FDA’s interest shows a growing push for improved treatment, said William Patterson, a spokesperson for PCOS Challenge.

No known cure for PCOS

Doctors recommend hormonal contraceptives — most commonly the birth control pill — to regulate heavy, irregular periods;, acne;, and unwanted hair growth. Others say taking the pill just masks, rather than treats, their PCOS symptoms and the symptoms return as soon as they stop taking it. 

“PCOS is unfortunately not curable, so treatment is about managing its symptoms,” said Dr. Jessica Chan, a reproductive endocrinologist at Cedars-Sinai. Chan said birth control can be a good option for some, but not all, of her PCOS patients. 

For women with PCOS whose main concerns are insulin resistance or stubborn weight gain, Chan often prescribes off-label diabetes medications like metformin. 

Some doctors who treat PCOS, including OB-GYNs or endocrinologists, have also begun prescribing GLP-1 agonists like Ozempic and Wegovy, which have shown promise for some women with PCOS,  although studies have been small and early -stage.

Novo Nordisk, the company that makes Ozempic and Wegovy, said it has no plans as of now to seek FDA approval for PCOS. Still, the company mentions PCOS on its Truth About Weight website, part of its marketing campaign for Wegovy

Causes and symptoms of PCOS

“We don’t know the initial spark leading to PCOS or where it arises from,” Chan said.

PCOS affects an estimated 6% to 12% of reproductive-age women in the U.S. The real prevalence is likely higher since an estimated 70% of cases go undetected. 

Experts generally agree that PCOS, at its core, is a hormone-related condition. Women with PCOS have higher levels of androgen hormones, which can cause a range of symptoms, including:

• Missing, irregular, or heavy periods
• Excess hair growth on the face or body
• Thinning or balding scalp hair

According to endocrinologist Dr. Andrea Dunaif, some doctors have been pushing to separate PCOS into two different diagnoses: one having more to do with the reproductive cycle and fertility issues and another having more to do with metabolism, high body weight, and diabetes. 

“PCOS looks to be at least two or three different conditions we’re lumping together, but they’re genetically distinct,” said Dunaif, the chief of the endocrinology, diabetes and bone disease division of Mount Sinai Health System and the Icahn School of Medicine.

The confusion surrounding PCOS diagnosis is partly why it’s been hard to get large pharmaceutical companies to invest in PCOS treatment, she said.

In Dunaif’s view, it’s not accurate to call the condition “PCOS” at all, because it has more to do with excess hormones than it does with actual cysts on the ovaries. PCOS got its name from the bumps on the ovaries appearing like cysts on an ultrasound image. These are not cysts, but instead egg follicles that are, as Dunaif described them, “arrested in development.” 

As it is, many doctors diagnose the condition based on two of three factors:

• Irregular periods
• High androgen levels
• Multiple follicles on the patient’s ovaries

But these three factors don’t account for some of the most challenging symptoms of PCOS: insulin resistance and stubborn weight gain. Excess androgen hormones can spike insulin levels, which interferes with how the body processes sugar. Doctors aren’t sure whether the hormonal dysregulation causes insulin resistance, or whether insulin resistance causes excess androgen hormones. 

Either way, women with PCOS have a higher risk of diabetes, excess weight gain, high cholesterol, and high blood pressure. Yet these metabolic conditions aren’t included in the criteria many doctors use to diagnose PCOS. The result? A missed diagnosis. 

This was initially the case for Candice Bolden, 35, who started noticing acne and excess facial and body hair several years before she was diagnosed with PCOS in 2021. Bolden, a lifelong dancer, also had unusually low energy.  

“The final straw was excess weight gain that I could not take off no matter what I did,” said Bolden, who lives in Los Angeles. “All the other things I had kind of just stuffed under the rug. I’d just chalked it up to being a hairy, Haitian woman.”

After gaining 35 pounds, the 5-foot-2-inch Bolden, who exercised twice a day and followed strict diets, saw multiple doctors who she said ignored her symptoms. 

“Doctors kept telling me I was fine, and to go home, work out, and eat clean,” she said. “It was the most frustrating thing ever.”

‘We don’t have to live underneath this dark cloud’

Women living with PCOS say the rise of online communities, including on social media apps like TikTok and Instagram, has given them a place to speak out, share the treatment approaches working for them, and meet other women with PCOS. 

When Bolden finally got a diagnosis, she wasn’t sure what to do next. Gutke and Hacatoryan had similar experiences. 

“I was like, ‘Wait, I have so many questions,’ and the doctor just told me, ‘It is what it is,’” Hacatoryan said. 

Hacatoryan calls women in her online community her “cysters.”

Bolden said she’s noticed more women turning to social media to learn how others manage their PCOS and share their own stories.

On her own social media accounts, she’s been trying to change the narrative about PCOS being primarily a fertility problem, which she sees as an outdated perception.

“When I was diagnosed, my doctor mentioned PCOS being the No. 1 reason for infertility, and that shattered me,” said Bolden, who was newly engaged at the time and eager to start a family. “I was happy I was diagnosed, because it showed me something was actually happening and I wasn’t just crazy. But I was heartbroken.”

Things changed after Bolden moved; found a new doctor; and worked closely with her husband and the  online PCOS community to find a system that worked to manage her PCOS symptoms.  

Bolden is now pregnant and expecting a baby girl. 

“I want people diagnosed with PCOS to know there’s hope, and we don’t have to live underneath this dark cloud all the time,” she said.

NBC News contributor Caroline Hopkins is a health and science journalist who covers cancer treatment for Precision Oncology News. She is a graduate of the Columbia University Graduate School of Journalism.  

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Thousands to be offered blood tests for dementia in UK trial

More than 50 clinics will offer tests to about 5,000 people who are worried about their memory in five-year trial

Thousands of people across the UK who are worried about their memory will receive blood tests for dementia in two trials that doctors hope will help to revolutionise the low diagnosis rate.

Teams from the University of Oxford and University College London will lead the trials to research the use of cheap and simple tests to detect proteins for people with early stages of dementia or problems with cognition, with the hope of speeding up diagnosis and reaching more people.

Currently, getting a formal diagnosis in the UK relies on mental ability tests, brain scans or invasive and painful lumbar punctures, where a sample of cerebrospinal fluid is drawn from the lower back.

About 1 million people are living with the condition in Britain, and this is expected to rise to about 1.7 million by 2040 – with potentially grim consequences. In 2022, dementia took the lives of 66,000 people in England and Wales, and it is now the leading cause of death in Britain, with Alzheimer’s accounting for two-thirds of cases.

Patients and their families have been reported to wait for up to four years to get an appointment and the results, according to charities. More than one in three people living with dementia in England are yet to receive a formal diagnosis.

The tests are highly effective in research settings, so if they prove as useful in real life, they could make the diagnosis of Alzheimer’s more accessible.

They could provide results to patients much sooner and accelerate the introduction of new Alzheimer’s drugs that rely on early diagnosis. The trial will help determine if they can be rolled out routinely on the NHS.

Fiona Carragher, the director of research and influencing at the Alzheimer’s Society, said the reliance on specialised tests had led to “unnecessary delays, worry and uncertainty” that meant people often could not access the care they needed early on.

“Dementia is the UK’s biggest killer, yet a third of people living with dementia don’t have a diagnosis, which means they’re not able to access care and support. At the moment, only 2% of people with dementia can access the specialised tests needed to demonstrate eligibility for new treatments, leading to unnecessary delays, worry and uncertainty,” she said.

The research teams are sponsored by Alzheimer’s Research UK and the Alzheimer’s Society, with £5m of funding from the People’s Postcode Lottery.

Dr Sheona Scales, the director of research at Alzheimer’s Research UK, said: “We’ve seen the enormous potential that blood tests are showing for improving the diagnostic process for people and their loved ones in other disease areas. Now we need to see this same step change in dementia, which is the greatest health challenge facing the UK.

“It’s fantastic that through collaborating with the leading experts in the dementia community, we can look to bring cutting-edge blood tests for diagnosing dementia within the NHS. And this will be key to widening access to groundbreaking new treatments that are on the horizon.”

More than 50 memory clinics across the UK will be offering blood tests to about 5,000 volunteers as part of the five-year trial.

Jonathan Schott, the chief medical officer at Alzheimer’s Research UK, will lead a trial on the most promising blood biomarker in tests on 1,100 people across the UK.

The second trial will test for multiple forms of dementia, including Alzheimer’s disease, vascular dementia, frontotemporal dementia and dementia with Lewy bodies on about 4,000 people.

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A quick guide to survey research

1 University of Cambridge,, UK

2 Cambridge University Hospitals NHS Foundation Trust,, UK

Questionnaires are a very useful survey tool that allow large populations to be assessed with relative ease. Despite a widespread perception that surveys are easy to conduct, in order to yield meaningful results, a survey needs extensive planning, time and effort. In this article, we aim to cover the main aspects of designing, implementing and analysing a survey as well as focusing on techniques that would improve response rates.

Medical research questionnaires or surveys are vital tools used to gather information on individual perspectives in a large cohort. Within the medical realm, there are three main types of survey: epidemiological surveys, surveys on attitudes to a health service or intervention and questionnaires assessing knowledge on a particular issue or topic. 1

Despite a widespread perception that surveys are easy to conduct, in order to yield meaningful results, a survey needs extensive planning, time and effort. In this article, we aim to cover the main aspects of designing, implementing and analysing a survey as well as focusing on techniques that would improve response rates.

Clear research goal

The first and most important step in designing a survey is to have a clear idea of what you are looking for. It will always be tempting to take a blanket approach and ask as many questions as possible in the hope of getting as much information as possible. This type of approach does not work as asking too many irrelevant or incoherent questions reduces the response rate 2 and therefore reduces the power of the study. This is especially important when surveying physicians as they often have a lower response rate than the rest of the population. 3 Instead, you must carefully consider the important data you will be using and work on a ‘need to know’ rather than a ‘would be nice to know’ model. 4

After considering the question you are trying to answer, deciding whom you are going to ask is the next step. With small populations, attempting to survey them all is manageable but as your population gets bigger, a sample must be taken. The size of this sample is more important than you might expect. After lost questionnaires, non-responders and improper answers are taken into account, this sample must still be big enough to be representative of the entire population. If it is not big enough, the power of your statistics will drop and you may not get any meaningful answers at all. It is for this reason that getting a statistician involved in your study early on is absolutely crucial. Data should not be collected until you know what you are going to do with them.

Directed questions

After settling on your research goal and beginning to design a questionnaire, the main considerations are the method of data collection, the survey instrument and the type of question you are going to ask. Methods of data collection include personal interviews, telephone, postal or electronic ( Table 1 ).

Advantages and disadvantages of survey methods

Collected data are only useful if they convey information accurately and consistently about the topic in which you are interested. This is where a validated survey instrument comes in to the questionnaire design. Validated instruments are those that have been extensively tested and are correctly calibrated to their target. They can therefore be assumed to be accurate. 1 It may be possible to modify a previously validated instrument but you should seek specialist advice as this is likely to reduce its power. Examples of validated models are the Beck Hopelessness Scale 5 or the Addenbrooke’s Cognitive Examination. 6

The next step is choosing the type of question you are going to ask. The questionnaire should be designed to answer the question you want answered. Each question should be clear, concise and without bias. Normalising statements should be included and the language level targeted towards those at the lowest educational level in your cohort. 1 You should avoid open, double barrelled questions and those questions that include negative items and assign causality. 1 The questions you use may elicit either an open (free text answer) or closed response. Open responses are more flexible but require more time and effort to analyse, whereas closed responses require more initial input in order to exhaust all possible options but are easier to analyse and present.

Questionnaire

Two more aspects come into questionnaire design: aesthetics and question order. While this is not relevant to telephone or personal questionnaires, in self-administered surveys the aesthetics of the questionnaire are crucial. Having spent a large amount of time fine-tuning your questions, presenting them in such a way as to maximise response rates is pivotal to obtaining good results. Visual elements to think of include smooth, simple and symmetrical shapes, soft colours and repetition of visual elements. 7

Once you have attracted your subject’s attention and willingness with a well designed and attractive survey, the order in which you put your questions is critical. To do this you should focus on what you need to know; start by placing easier, important questions at the beginning, group common themes in the middle and keep questions on demographics to near the end. The questions should be arrayed in a logical order, questions on the same topic close together and with sensible sections if long enough to warrant them. Introductory and summary questions to mark the start and end of the survey are also helpful.

Pilot study

Once a completed survey has been compiled, it needs to be tested. The ideal next step should highlight spelling errors, ambiguous questions and anything else that impairs completion of the questionnaire. 8 A pilot study, in which you apply your work to a small sample of your target population in a controlled setting, may highlight areas in which work still needs to be done. Where possible, being present while the pilot is going on will allow a focus group-type atmosphere in which you can discuss aspects of the survey with those who are going to be filling it in. This step may seem non-essential but detecting previously unconsidered difficulties needs to happen as early as possible and it is important to use your participants’ time wisely as they are unlikely to give it again.

Distribution and collection

While it should be considered quite early on, we will now discuss routes of survey administration and ways to maximise results. Questionnaires can be self-administered electronically or by post, or administered by a researcher by telephone or in person. The advantages and disadvantages of each method are summarised in Table 1 . Telephone and personal surveys are very time and resource consuming whereas postal and electronic surveys suffer from low response rates and response bias. Your route should be chosen with care.

Methods for maximising response rates for self-administered surveys are listed in Table 2 , taken from a Cochrane review.2 The differences between methods of maximising responses to postal or e-surveys are considerable but common elements include keeping the questionnaire short and logical as well as including incentives.

Methods for improving response rates in postal and electronic questionnaires 2

• – Involve a statistician early on.
• – Run a pilot study to uncover problems.
• – Consider using a validated instrument.
• – Only ask what you ‘need to know’.
• – Consider guidelines on improving response rates.

The collected data will come in a number of forms depending on the method of collection. Data from telephone or personal interviews can be directly entered into a computer database whereas postal data can be entered at a later stage. Electronic questionnaires can allow responses to go directly into a computer database. Problems arise from errors in data entry and when questionnaires are returned with missing data fields. As mentioned earlier, it is essential to have a statistician involved from the beginning for help with data analysis. He or she will have helped to determine the sample size required to ensure your study has enough power. The statistician can also suggest tests of significance appropriate to your survey, such as Student’s t-test or the chi-square test.

Conclusions

Survey research is a unique way of gathering information from a large cohort. Advantages of surveys include having a large population and therefore a greater statistical power, the ability to gather large amounts of information and having the availability of validated models. However, surveys are costly, there is sometimes discrepancy in recall accuracy and the validity of a survey depends on the response rate. Proper design is vital to enable analysis of results and pilot studies are critical to this process.

L.A. County faces $12.5 billion in climate costs through 2040, study says

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A first-of-its-kind report has estimated that Los Angeles County must invest billions of dollars through 2040 to protect residents from worsening climate hazards, including extreme heat, increasing precipitation, worsening wildfires, rising sea levels and climate-induced public health threats.

The report, published this week by the nonprofit Center for Climate Integrity, identified 14 different climate adaptation measures that authors calculated would cost L.A. taxpayers at least $12.5 billion over the next 15 years, or approximately $780 million per year. The vast majority of those costs — more than $9 billion — will be incurred by local municipal governments, including the cities of Los Angeles, Long Beach and Santa Clarita, the report said.

“These numbers don’t include the costs of recovering from disasters — from extreme weather events that knock out power or damage infrastructure or do all the kinds of things they do,” said Richard Wiles, president of the Center for Climate Integrity. “So it’s a very conservative estimate, and yet it’s a really big number.”

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Wiles said the costs for L.A. County are nearly as high as for the entire state of Pennsylvania, which faces about $15 billion in climate adaptation costs over roughly the same period.

“This is a big number, but this is going to happen,” he said. “These costs will be incurred at some point, and it’s just much better to pay now than it is to pay later. I can’t emphasize that enough.”

The most expensive adaptation categories are related to precipitation and heat, including an estimated $4.3 billion for improved stormwater management, $2.5 billion for cool pavement investments and $1.4 billion for tree canopies to combat urban heat islands, the report found. Other costs include wildfire mitigation; coastal defense and infrastructure protection; building upgrades for cooling and air conditioning; and responses to vector-borne diseases such as West Nile virus.

County officials said the findings weren’t surprising and agreed that they may even be conservative given the scale of the threats.

“The impacts of climate have become more and more visible over the past few years in particular,” said Rita Kampalath, L.A. County’s chief sustainability officer. “We know that we’re facing really, really huge needs in terms of how we prepare our communities to face those, and to be resilient in the face of increased climate impacts. It’s only going to increase from here.”

Stormwater capture in particular has been on the minds of many Angelenos this winter as record-breaking rainfall pounded the region . A monster storm in February saw the Los Angeles River roar to life and funnel millions of gallons into the Pacific Ocean .

But the river — which was encased in concrete nearly a hundred years ago — and other local flood channels will be no match for climate change-enhanced storms of the future. Though the long-term trend in the West is toward hotter and drier conditions, Los Angeles will still see bouts of severe storms and extreme wet years that will increase flood risk significantly, according to the state’s fourth climate change assessment .

To mitigate these impacts, the county must expand its stormwater drainage infrastructure by installing bioswales, porous pavement and other opportunities for stormwater to seep into the ground, the report found. It noted that these “green infrastructure” upgrades are the least expensive option to cope with extreme rainfall events, as opposed to increasing the size and scale of hard infrastructure such as drain pipes.

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The county is making progress on this work through its Safe Clean Water Program, passed by voters as Measure W in 2018 , Kampalath said. The program allocates about $280 million annually to stormwater capture projects, although recent reports have found that progress to date has been slow .

“While it is a big need, I do actually feel like the county has been investing, and our residents and voters in particular have shown that this is a high priority,” she said. “We’re not as far as we would like to be — it’s hard to say that about much of anything when it comes to climate — but I do think that we have resources available to try to address some of these needs.”

Meanwhile, extreme heat continues to pose a significant threat to L.A. County residents, and it is predicted to only get worse in the years and decades ahead. The region is expected to experience an average of 48.5 days above 90 degrees per year between 2024 and 2040, the report says. That’s about 12.5 more hot days per year than communities experienced between 1994 and 2013.

Some of the best methods to combat the dangers of rising heat include installing cool pavements , expanding urban green space, painting railway tracks with reflective paint to keep them at operable temperatures, and upgrading cooling systems for public buildings such as schools, the report says. Converting public parking lots to cool pavements that reflect instead of absorb sunlight can also help lower ambient temperatures.

Heat is “the impact that affects communities of color the most, and people less able to adapt themselves and their personal lives,” Wiles said. He noted that some urban areas can simmer up to 20 degrees hotter than surrounding neighborhoods with heavy tree canopies.

“From a public health perspective, these types of adaptations are increasingly critical just to make neighborhoods livable,” he said.

The report comes at a moment when the state is facing a significant $37.9-billion budget deficit , which has prompted Gov. Gavin Newsom to slash $2.9 billion from California climate programs , delay an additional $1.9 billion and shift $1.8 billion to other funds.

Kampalath said it’s too soon to say whether those cuts will trickle down to L.A. County’s climate efforts, but that they could potentially affect funds officials were hoping to take advantage of through grants and other programs.

However, she noted that many of the county’s climate adaptation strategies can have multiple benefits, such as tree canopy programs that help combat heat and improve stormwater management simultaneously.

“As we’re looking at how to address these impacts, we do need to think about a multi-benefit approach, and what kind of strategies we can put in place that are really going to address a wide range of things — not only climate, but biodiversity and health impacts and the well-being of our communities as well,” she said.

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Ultimately, funding for the projects outlined in the report will come from taxpayers, whether at the municipal, state or federal level, Wiles said. But he also hopes that oil and gas companies will be held accountable for their role in the worsening climate crisis, as fossil fuel emissions are by far the largest driver of global warming.

Last year, California filed a bombshell lawsuit against five of the largest oil and gas companies for their alleged “decades-long campaign of deception” about the risks posed by fossil fuels, which have forced the state to spend billions of dollars to address environmental-related damages. State Atty. Gen. Rob Bonta is seeking to create a nuisance abatement fund to finance climate mitigation and adaptation efforts, among other outcomes.

“Each and every community in Los Angeles County should consider bringing similar legal actions to hold climate polluters accountable and ensure that taxpayers aren’t left to pay the bill alone,” the report says.

Indeed, there are other climate hazards that will cost Angelenos billions in adaptation expenses over the next decade and a half, the report found.

They include an increase in vector-borne diseases such as West Nile virus as more mosquitoes are drawn to the area’s changing temperatures and precipitation patterns. About 500,000 new cases of the virus are expected in the county through 2040, which will cost an estimated $993 million to treat. Climate change will also lead to more pediatric asthma cases due to an increase in pollen, with about 160,000 new cases expected through 2040.

The county also needs about $680 million in road improvements as heat and rain contribute to more cracks, erosion and soft surfaces. A foot of sea level rise along the coast of L.A. County will require at least $576 million for berms, flood walls, bank stabilization and other infrastructure measures to prevent flooding and to avoid infrastructure damage by 2040.

Wildfires, already getting larger, faster and more frequent across California , will necessitate nearly $1 billion just to clear vegetation and other fuels from land around the county’s infrastructure, the report found. It noted that L.A. County will face an average of 36 more high-fire days through 2040 when compared to the 1994-to-2013 baseline.

The estimated $919-million wildfire cost does not account for fighting fires or repairing damage from blazes. The 2018 Woolsey fire racked up an estimated $3 billion to $5 billion in insured losses alone.

Wiles said the expenses outlined in the report won’t solve climate change but will help “hold things where they are today,” or least prevent the hazards from getting worse.

He said he hoped the report would help guide county officials as they face difficult choices about where, how and to what limited funds should be allocated. Investing in climate adaptations now can save money — and lives — later, he said.

“These costs are still coming,” Wiles said. “The next disaster will happen. This is just what it’s going to cost to prepare.”

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Hayley Smith is an environment reporter for the Los Angeles Times, where she covers the many ways climate change is reshaping life in California, including drought, floods, wildfires and deadly heat.

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