Quantitative Research
Quantitative research done properly — survey design, measurement and scales, hypothesis formulation, sampling and power, and the statistical tests that turn clean data into defensible findings.
All Quantitative Research guides
8 articles
Convergent & Discriminant Validity: AVE, HTMT and Fornell-Larcker Thresholds
The exact thresholds examiners check: AVE ≥ 0.50, loadings ≥ 0.708, HTMT below 0.85/0.90, and the Fornell-Larcker criterion — what each means, and what to do when a construct fails.
Sampling Methods in Research: Types, Selection & Justification
Probability or non-probability? Random, stratified, purposive or snowball? How to choose a sampling method you can defend — and write the justification your committee expects.
How to Choose the Right Statistical Test: A Decision Guide
Every statistical test answers three questions: what are you testing, how many groups, and what type of data? Answer those and the right test chooses itself — here's the complete decision map.
Qualitative vs Quantitative vs Mixed Methods: Which Fits Your Study?
Your method should follow your question, not your comfort zone. A clear guide to choosing between qualitative, quantitative and mixed-methods designs.
Reliability and Validity Explained (With Examples)
If your measures aren't reliable and valid, your results mean nothing. A plain-English guide to both — and how to report them in your thesis.
How to Determine Your Sample Size (Without the Guesswork)
Too small and your study is underpowered; too large and you waste effort. Here's how sample size is actually decided — for surveys, experiments and SEM models.
Understanding P-Values and Statistical Significance (Plainly)
P-values are the most used and most misunderstood number in research. Here's what a p-value actually means, what it doesn't, and how to report it honestly.
How to Design a Research Questionnaire That Gives You Clean Data
A weak questionnaire quietly ruins good research. Here's how to design questions that are clear, unbiased and aligned to your variables — so your data holds up.
Put it into practice
Free tools, templates and mentoring connected to quantitative research.
Frequently asked
What sample size do I need for a quantitative study?+
It depends on your analysis: rules of thumb exist (e.g. 10 cases per predictor for regression), but the defensible route is a power analysis based on expected effect size, desired power (usually 0.80) and significance level — our sample size calculator gives you a starting point.
Should I use a 5-point or 7-point Likert scale?+
Both are defensible. Seven points capture slightly more variance and suit SEM well; five points are simpler for respondents. What matters more is using validated scales and being consistent across constructs.
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