Data Collection Methods in Research: Types, Examples and Real Data

Home › Research Methodology › Data Collection Methods in Research

Below, we explain each method in plain language, compare their strengths, and show which methods researchers in different disciplines actually use, based on the live publication data of PhD researchers on ScholarsColab.

The main data collection methods at a glance

Methods are often grouped as quantitative (numbers: surveys with fixed answers, experiments, measurements) and qualitative (words and meanings: interviews, open observation). Studies that combine the two use mixed methods.

Example: how we collected the data for these guides

To explain how to choose a data collection method, here is how ScholarsColab collected the data used on these pages: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Step 1
Start from the question. We wanted to know which research methods PhD researchers use and how often their papers are cited.

Step 2
Consider the options. We could have sent a questionnaire to every mentor asking about their methods. That would take weeks, many people would not reply, and memories of old papers are unreliable.

Step 3
Choose the method that fits. Each mentor’s publication list already exists on Google Scholar, with titles, years and citation counts. Using data that someone else has already collected is called secondary data. It was fast and covered tens of thousands of papers.

Step 4
Accept the trade-off. We cannot ask the authors anything, and titles do not always name the method used. Every data collection method has a trade-off like this, so choose the one whose weaknesses matter least for your question.

Step 5
See what methods our mentors used. Counting the methods named in their paper titles gives this picture:

Simulations and experiments lead because most of our mentors work in engineering and the sciences. In psychology or education, questionnaires and interviews would lead instead.

Each method in a little more detail

Surveys and questionnaires

Good for reaching many people with the same questions. Closed questions (multiple choice, rating scales) are easy to analyse; open questions give richer answers. 68 of the 41,792 papers in our data (0.2%) show this in their title: questionnaire / opinion survey. It is most common in Social sciences & education, where 2% of papers show it.

Interviews and focus groups

Good for understanding why people think or act as they do. Structured interviews follow fixed questions; semi-structured ones allow follow-up; focus groups capture discussion between participants.

Experiments

Good for testing cause and effect, by changing one factor while keeping others constant. 356 of the 41,792 papers in our data (0.9%) show this in their title: experimental / laboratory study. It is most common in Physical sciences & mathematics, where 1.8% of papers show it.

Simulation and modelling

Common in engineering and the physical sciences, where building or testing the real system would be costly, slow or dangerous. 738 of the 41,792 papers in our data (1.8%) show this in their title: simulation / modelling. It is most common in Physical sciences & mathematics, where 4.4% of papers show it.

Secondary data

Includes government statistics, hospital records, company reports and research databases. It is efficient, but you must check how the data were collected and whether they really fit your question.

How to choose a data collection method

1. Start from your research question. “How many?” and “how much?” point to quantitative methods; “why?” and “how?” often point to qualitative ones.
2. Check what data already exist before collecting new data.
3. Consider access: can you reach the people, sites or equipment you need?
4. Consider time, budget and ethics approval.
5. Plan how you will analyse the data before collecting it, so you collect the right kind.
6. Pilot your instrument on a small group and refine it.

Common mistakes reviewers point out

• A method that does not match the question, such as a short survey to explore deep personal experiences.
• Unvalidated questionnaires or leading questions.
• Too little detail about how data were collected, so others cannot judge or repeat the study.
• Using secondary data without checking its quality or original purpose.

Frequently asked questions

Use this data in your own work

More research methodology guides

• Independent vs Dependent Variables
• Control Group vs Experimental Group
• Mediator vs Moderator Variables
• Validity vs Reliability (and Generalizability) in Research, with Real Data
• Retrospective vs Prospective Studies
• Types of Study Design
• Research Hypothesis
• Literature Review vs Systematic Review vs Meta-Analysis, with Real Data
• Sampling Methods in Research
• Experimental vs Quasi-Experimental Design
• Correlation vs Causation
• Deductive vs Inductive Reasoning in Research, with Real Examples
• Incidence vs Prevalence
• Descriptive vs Inferential Statistics, Explained with Real Data
• P-Value and Statistical Significance
• Operationalization
• Surveys and Questionnaires in Research
• Qualitative Research Methods
• Bias in Research
• Research Gap, Problem Statement and Research Question, Explained with an Example
• Pearson Correlation Coefficient (Pearson’s r)
• Chi-Square Test Explained
• ANOVA and the Kruskal-Wallis Test, Explained with Real Data
• Regression Analysis Explained
• Standard Deviation and Variance
• Post Hoc Tests Explained
• Confidence Intervals Explained
• t-Test vs Mann-Whitney U Test
• Effect Size Explained
• Heteroskedasticity Explained