Research Methodology Guides, Explained with Real Data

Research methods explained in plain language, for beginners and PhD students. Every guide uses a real, live dataset to show the idea in action: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Guides

Research design and methods

• Independent vs Dependent VariablesWhat independent, dependent, control, confounding, moderator and mediator variables are, shown on real citation data.
• Control Group vs Experimental GroupWhat control and experimental groups are, why randomisation matters, and how often trials appear in real research.
• Mediator vs Moderator VariablesHow a mediator explains a relationship and a moderator changes its strength, with real published examples.
• Validity vs Reliability (and Generalizability) in Research, with Real DataInternal and external validity, reliability and generalizability, explained through the limits of a real dataset.
• Retrospective vs Prospective StudiesLooking back versus following forward: how the two designs differ, their strengths and real examples.
• Data Collection Methods in ResearchSurveys, interviews, experiments, simulations and existing data, and which disciplines use which.
• Types of Study DesignThe main research designs side by side, with how often each appears in real published research.
• Research HypothesisNull and alternative hypotheses, and how a real hypothesis is tested on live data.
• Literature Review vs Systematic Review vs Meta-Analysis, with Real DataHow the three kinds of review differ, how they are conducted, and how often they are cited.
• Sampling Methods in ResearchRandom, stratified, cluster, convenience and purposive sampling, and what sampling bias looks like in real data.
• Experimental vs Quasi-Experimental DesignTrue experiments, quasi-experiments and simulations as virtual experiments, with real examples.
• Deductive vs Inductive Reasoning in Research, with Real ExamplesTop-down theory testing versus bottom-up pattern finding, applied to one real question both ways.
• OperationalizationHow abstract ideas become measurable variables, shown through how our own data was measured.
• Surveys and Questionnaires in ResearchQuestion types, questionnaire design and real survey studies by PhD researchers.
• Qualitative Research MethodsInterviews, focus groups, case studies, phenomenology and grounded theory, with real studies.
• Bias in ResearchSelection, measurement, confounding and publication bias, found and handled in a real dataset.
• Research Gap, Problem Statement and Research Question, Explained with an ExampleHow a research gap becomes a problem statement and a clear research question, with a real gap.

Statistics

• Correlation vs CausationWhy two things moving together does not prove one causes the other, shown with a real correlation.
• Incidence vs PrevalenceNew cases versus existing cases, explained through how researchers adopted machine learning.
• Descriptive vs Inferential Statistics, Explained with Real DataSummarising your data versus drawing conclusions from it, with real descriptive statistics and a real test.
• P-Value and Statistical SignificanceWhat a p-value means, with one significant and one non-significant test on live data.
• Pearson Correlation Coefficient (Pearson’s r)What Pearson’s r measures, how to read it, and why it can mislead with skewed data, shown live.
• Chi-Square Test ExplainedTesting whether two categorical variables are related, run live on real research data.
• ANOVA and the Kruskal-Wallis Test, Explained with Real DataComparing three or more groups, with a live ANOVA and Kruskal-Wallis test.
• Regression Analysis ExplainedHow regression estimates the effect of several factors at once, with a live model.
• Standard Deviation and VarianceMeasuring spread, and why skewed data needs more than the mean and standard deviation.
• Post Hoc Tests ExplainedFinding which groups differ after ANOVA, with live pairwise comparisons.
• Confidence Intervals ExplainedWhat a 95% confidence interval means, and how sample size changes it, shown live.
• t-Test vs Mann-Whitney U TestComparing two groups: means versus ranks, with both tests run on the same data.
• Effect Size ExplainedWhy the size of an effect matters as much as its p-value, with live examples.
• Heteroskedasticity ExplainedWhen the spread of data is not constant, why it matters, and how to fix it.

How researchers in different disciplines work

The research methods visible in our mentors’ paper titles, by discipline:

The rise of machine learning in research

Use this data