How to Create an Online Survey for Academic Research

# How to Create an Online Survey for Academic Research

The first survey you build will probably not be a good one. And that is perfectly fine. Nearly every researcher starts by grabbing a tool, writing whatever questions come to mind, sending out the survey, and then realizing the data are unusable. The problem is almost always the same: survey creation started from the questions instead of from the research problem.

A quality survey is not built in an afternoon. It requires planning, methodological knowledge, pilot testing, and iteration. But when done right, an online survey is an incredibly efficient tool for collecting data from a large number of participants at minimal cost.

Before You Open Any Tool

Before you start creating questions, you need clear answers to three things.

1. What Is Your Research Problem?

"I'm interested in student attitudes toward online instruction" is not a research problem. That is a topic. A research problem is precise and specific:

• •"Is there a difference in satisfaction with online instruction between students in the natural sciences and the social sciences?"
• •"Which factors predict students' intention to use AI tools for studying?"
• •"Is self-regulated learning associated with academic performance among students enrolled in online courses?"

2. What Are Your Hypotheses?

Hypotheses follow from your theoretical framework and prior research. They determine which variables you will measure and which statistical tests you will need. Without hypotheses, you do not know which questions to include in the survey.

3. Who Is Your Target Population?

You need to know who the survey is intended for, how you will reach those people, and how many participants you need. For detailed guidance on planning your sample size, see the article on determining how many participants you need.

Survey Structure: From Start to Finish

Every academic survey should have a clear structure that guides participants through a logical flow.

1. Introduction and Informed Consent

This is the first thing the participant sees and probably the most important part of the survey from an ethical standpoint. The introduction must contain:

• •Who is conducting the research (researcher's name, institution)
• •Purpose of the study (general aim, without revealing hypotheses)
• •Estimated time for completion
• •Whether participation is anonymous or confidential
• •That participation is voluntary and the participant may withdraw at any time
• •Researcher's contact information
• •Explicit consent (a button or checkbox: "I agree to participate")

Without informed consent, your survey is ethically unacceptable. This is not a formality. University ethics committees will reject your research if this is missing.

2. Demographic Questions

Place demographic questions at the beginning (some researchers prefer the end, but placing them first is more common practice in many regions). Typical demographic questions for a student population:

• •Gender
• •Age (range or exact)
• •Year of study
• •Academic program / faculty
• •Grade point average (optional)
• •Size of hometown (optional)

Important: Ask only for demographic data that you actually need for your analysis. If you do not plan to compare results by year of study, do not ask about it. Every unnecessary question increases the length of the survey and decreases the response rate.

3. Main Section: Scales and Instruments

This is the heart of your survey. Here you measure the variables from your hypotheses. For measuring psychological constructs (attitudes, motivation, anxiety, satisfaction), you will typically use standardized scales.

Tips for the main section:

• •Use validated instruments whenever possible (do not invent questions if a tested questionnaire already exists)
• •Group questions by topic/scale
• •Use clear section headings ("Attitudes Toward Online Instruction," "Self-Efficacy in Learning")
• •Avoid breaks in logic (do not jump from attitudes to demographics and back to attitudes)

For creating your own Likert scales, see the guide on how to build a reliable Likert scale.

4. Open-Ended Questions

Place open-ended questions at the end of the survey (unless they are the central research question). Open-ended questions require more cognitive effort, and participants who are already tired from closed-ended items will not provide quality responses if open-ended questions appear too early.

Limit yourself to 1-3 open-ended questions. More than that discourages participants from completing the survey.

5. Thank-You Page

A brief message of thanks at the end. You can repeat the researcher's contact information and offer participants the option to receive the study results (but not at the same email from which they completed the survey, as that would compromise anonymity).

Question Types: When to Use Which

Closed-Ended Questions (Quantitative)

Likert scales are the most common question type in academic surveys. The participant rates their level of agreement with a statement (e.g., from 1 "strongly disagree" to 5 "strongly agree"). Likert scales are ideal for measuring attitudes, opinions, and perceptions.

Multiple-choice questions are used when there is a finite set of answers and the participant selects one. For example: "Which type of instruction do you prefer? (a) Online (b) In-person (c) Hybrid."

Multiple-response questions (checkboxes) are used when the participant can select more than one answer. For example: "Which sources do you use for studying? (select all that apply)" with options such as textbooks, YouTube, lecture notes, etc.

Ranking questions ask participants to order options by priority. For example: "Rank the following factors by importance for your choice of university (1 = most important, 5 = least important)." Use these sparingly because they are cognitively demanding.

Matrix questions group several similar items into a table with the same response options. This saves space but can lead to "straight-lining" (the participant mechanically selects the same column throughout).

Open-Ended Questions (Qualitative)

Use open-ended questions when you want deeper understanding, when you do not know all possible answers, or when exploring experiences. But be prepared for the fact that open-ended questions require qualitative analysis (coding), which is considerably more time-consuming than quantitative analysis.

How Many Questions Is Too Many?

The general rule: a survey should not take longer than 15-20 minutes. In practice, that means:

• •Up to 30 items: Optimal for most research. High completion rate.
• •30-60 items: Acceptable with clear structure and motivated participants.
• •60-100 items: Expect a significant drop in response rate. Justified only for comprehensive studies with motivated participants.
• •More than 100 items: Problematic. Participant fatigue degrades data quality in the second half of the survey.

Every item that is not directly tied to your hypotheses should be removed. Ask yourself for each question: "What will I do with this data in the analysis?" If you do not have a clear answer, remove the question.

Question Order: From General to Specific

The order of questions affects the quality of responses. Here are the principles to follow.

1. From general to specific. Start with broader questions, then move to more specific ones. This is a more natural cognitive flow.

1. Sensitive questions toward the end. Questions about income, mental health, or controversial topics should go near the end of the survey. When a participant has already invested time, they are less likely to abandon the survey over a sensitive question.

1. Group by topic. Keep related questions together. Jumping between unrelated topics confuses participants.

1. Watch for order effects. Earlier questions influence responses to later questions (known as priming). If you first ask about negative experiences with online instruction, the subsequent satisfaction rating for online instruction will be lower than if you had asked about positive experiences first.

Pilot Testing: The Mandatory Step Everyone Skips

Pilot testing is a trial run of your survey with a small number of participants before the main data collection. This is not optional. It is a mandatory step in research methodology.

Why Is Piloting Essential?

• •Reveals unclear or ambiguous questions
• •Shows whether the survey is too long
• •Tests technical functionality (does everything work on mobile, are data being recorded properly)
• •Identifies questions that everyone answers the same way (no variability)
• •Checks whether participants understand the instructions

How Many Participants for a Pilot?

A minimum of 5, ideally 10 participants who belong to (or closely resemble) the target population. These should not be your departmental colleagues, but people who resemble your intended sample.

What to Do with Pilot Data?

Talk to your pilot participants. Ask them:

• •Were any questions unclear?
• •How long did it take?
• •Was anything confusing or annoying?
• •Would they add or remove anything?

Based on the feedback, revise the survey and repeat the pilot if necessary. Pilot data are not included in the final analysis.

Survey Distribution

Once the survey is finalized and pilot tested, it is time to distribute it.

Email

The most reliable method for academic research. Send a personalized email with an explanation of the study and a link to the survey. Personalized emails have a much higher response rate than generic ones.

Social Media

Fast and free, but the sample is biased (only people in your network, or people who use that platform). Use social media as a supplementary channel, not the only one.

QR Code on Campus

Effective for student populations. Print a poster with a QR code and a brief description of the research and place it on bulletin boards.

Snowball Sampling

Ask participants to forward the survey to others. Useful for hard-to-reach populations, but the sample is not representative.

Response rate: For online surveys in academic research, a response rate of 20-30% is typical. Plan your distribution so that this percentage yields a sufficient sample.

Ethical Considerations

Ethics is not an add-on to research. It is its foundation.

Informed Consent

We have already mentioned it, but it bears repeating: every participant must give explicit consent before completing the survey. Consent must be informed (the participant understands what is being asked and why), voluntary (no coercion or pressure), and revocable (the participant can withdraw at any time).

Anonymity vs. Confidentiality

Anonymity means the researcher cannot link responses to the participant's identity. The survey does not collect names, email addresses, IP addresses, or any other identifying information.

Confidentiality means the researcher knows who the participant is but keeps that information private. For example, if you collect an email address for a follow-up reminder, that is confidentiality, not anonymity.

Do not promise anonymity if you are collecting any identifying information.

Right to Withdraw

Participants can stop completing the survey at any point, without any consequences. This is especially important when participants are students and the researcher is a professor. You must never make course grades contingent on research participation.

Data Protection

Data must be protected from unauthorized access. That means: secure storage, password-protected files, data deletion after the study concludes (if the ethics committee requires it), and compliance with data protection regulations.

Practical Example: Survey on Attitudes Toward AI in Education

Imagine you are a master's student in psychology and want to investigate student attitudes toward the use of AI tools in academic work.

Research question: Is there a difference in attitudes toward AI in education between students who have used ChatGPT and those who have not?

Survey structure:

1. Informed consent (1 page)
2. Demographic data (5 items: gender, age, year of study, faculty, GPA)
3. AI experience (3 items: have you used ChatGPT, how often, for what purpose)
4. Scale of attitudes toward AI in education (15 items, Likert 1-5, adapted from an existing scale)
5. Perceived usefulness of AI for learning (8 items, Likert 1-5)
6. Ethical dilemmas (5 items, Likert 1-5, e.g., "Using AI to write essays is a form of cheating")
7. Open-ended question (1 item: "Describe your experience with AI tools in an educational context")
8. Thank-you page

Total: 37 items + 1 open-ended question. Estimated time: 10-12 minutes. Short enough for a decent response rate, detailed enough for meaningful analysis.

Common Mistake

Collecting responses without pilot testing.

This is a mistake that cannot be corrected after you have collected your data. If participants misunderstood question 14, you have invalid responses for that item and cannot retroactively fix them.

Typical problems that piloting reveals:

• •"How often do you use the internet for studying?" Every day? Every week? Students are unsure whether watching YouTube tutorials counts as "using the internet for studying."
• •Double-barreled questions: "Do you think online instruction is high-quality and accessible?" What if a participant thinks it is accessible but not high-quality?
• •Inappropriate response scale: offering a 1-5 scale for a question that requires a yes/no answer.
• •Technical problems: the survey does not work on mobile devices, mandatory questions that should not be mandatory.

Pilot testing with just 5 participants reveals roughly 80% of these problems. Invest two days in a pilot and save yourself a month of frustration with bad data.

Try the Istrazimo Platform

Istrazimo covers the entire process from survey design to results analysis. Create a professional survey in 30 minutes with 34 question types, a landing page for recruitment, and built-in statistical analysis. The platform includes automatic response quality checks (straight-lining detection, completion time monitoring) and CSV data export for further analysis. Get started.