Publication Bias and the Funnel Plot
If striking results are published and null ones disappear, the literature is a biased sample—and any meta-analysis on it overstates the effect. How the funnel plot helps reveal it.
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Meta-Analysis & Synthesis
Publication Bias and the Funnel Plot
Why unpublished null results inflate a meta-analysis, how the funnel plot detects it, and the best defence.
Panel & Longitudinal
Multilevel Models Explained: When Your Data Has Structure
What nested data is, why ignoring it breaks your standard errors, and what random effects and partial pooling do.
Econometrics & Regression
Logistic Regression Explained
Why binary outcomes need an S-curve, how to interpret odds ratios, and why an odds ratio isn’t a risk ratio.
Causal Inference
Regression Discontinuity Design Explained
How a cutoff creates a near-experiment, the testable smoothness assumption, sharp vs fuzzy designs, and local effects.
SEM & Psychometrics
Mediation and Moderation: What’s the Difference?
Mechanism vs condition, how each is analysed, and how they combine as moderated mediation.
Research Methods
Qualitative vs Quantitative Research: How to Choose
Measuring vs understanding, when to use each, how mixed methods combines them, and letting the question decide.
Accreditation & Research Impact
How to Measure Research Impact: Metrics, Frameworks, and Limitations
The layers of impact, academic metrics and their limits, altmetrics, impact case studies, and responsible use.
Statistical Audit & Data
P-Hacking and Researcher Degrees of Freedom
How selecting analyses for significance manufactures false positives—and how pre-registration prevents it.
Survey, Experimental & Qualitative
Common Method Bias: What It Is and How to Reduce It
What it is, where it comes from, how to design against it, and why statistical checks can’t fix it.
Journals & Publication
How to Respond to Peer Review: A Practical Guide
Reading reviews, the point-by-point response letter, handling disagreement, and the tone that wins acceptance.
Forecasting & Time Series
How to Evaluate a Forecast: Accuracy Metrics and Out-of-Sample Testing
Out-of-sample testing, MAE/RMSE/MAPE and their trade-offs, and why you must beat a naive benchmark.
Machine Learning & Data
Overfitting in Machine Learning: What It Is and How to Avoid It
What overfitting is, why it happens, how to detect it with cross-validation, and how to prevent it.
Meta-Analysis & Synthesis
Understanding Heterogeneity in Meta-Analysis
What it is, how I-squared and tau-squared measure it, random-effects models, and when not to pool.
SEM & Psychometrics
Reliability and Validity in Measurement
Consistency vs accuracy, the types of each, and why measurement quality must be established before analysis.
Causal Inference
Instrumental Variables Explained
How an instrument recovers a causal effect, the two validity conditions, and what IV actually estimates.
Panel & Longitudinal
Panel Data vs Cross-Sectional Data: What’s the Difference?
The three data structures compared, and why panel data supports far more credible causal claims.
Econometrics & Regression
Heteroskedasticity: What It Is and How to Handle It
Why it invalidates the usual standard errors (but not the coefficients), how to detect it, and the robust-SE fix.
Econometrics & Regression
Multicollinearity: When Is It a Problem?
What it affects, what it doesn’t, how to detect it with VIF, and when it actually matters.
Econometrics & Regression
Regression Assumptions: What Researchers Should Check
A practical checklist: how to test linearity, homoskedasticity, autocorrelation, collinearity, and exogeneity.
Econometrics & Regression
OLS Regression: What Researchers Should Know
What OLS does, why it's BLUE, the assumptions that decide if results can be trusted, and what to do when they fail.
Econometrics & Regression
Correlation vs Regression: What’s the Difference?
What each measures, how they relate, and why neither, on its own, proves causation.
Statistical Audit & Data
What a Statistical Audit Checks Before Submission
An independent review of assumptions, specification, robustness, reproducibility, and reporting — before you submit.
Survey, Experimental & Qualitative
Designing a Survey That Produces Valid Data
Validity vs reliability, writing good questions, validated scales, representative sampling, and biases to avoid.
Journals & Publication
How to Choose the Right Journal for Your Paper
Why fit beats prestige, how to assess scope and audience, building a shortlist, and verifying before you submit.
Machine Learning & Data
Prediction vs Explanation: Using Machine Learning in Research
Why high accuracy is not evidence of cause, out-of-sample evaluation, and using interpretable and causal ML.
Forecasting & Time Series
Making Time Series Stationary (and Why It Matters)
What stationarity is, the spurious-regression trap, unit-root testing, differencing, and cointegration.
Meta-Analysis & Synthesis
How to Conduct a Systematic Review and Meta-Analysis
Protocol, systematic search, risk-of-bias, pooling, heterogeneity, and reporting to PRISMA standards.
SEM & Psychometrics
CB-SEM vs PLS-SEM: Which Should You Use?
The real difference between covariance- and variance-based SEM, why sample size shouldn't decide it, and how to choose.
Causal Inference
Difference-in-Differences Explained
How it estimates causal effects, the parallel-trends assumption, and why staggered adoption needs modern estimators.
Econometrics & Regression
Understanding Endogeneity and How to Address It
What it means, its three sources, and how to address it with controls, fixed effects, instruments, and design.
Panel & Longitudinal
Fixed Effects vs Random Effects: Which Should You Use?
What each does, the assumption that separates them, the limits of the Hausman test, and how to choose.
Accreditation & Research Impact
How Business Schools Can Evidence Research Impact for AACSB & EQUIS Accreditation
What the 2026 standards ask for, the outputs-outcomes-impact distinction, and how to build a credible evidence base.
Research Methods
How to Choose the Right Statistical Method for Your Research Question
A decision framework: start from the claim, match it to your data, and account for what your field expects.
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Research Methods
Choosing and applying the right method for your question and data.
ExploreEconometrics & Regression
OLS, diagnostics, endogeneity, and the assumptions behind credible estimates.
ExplorePanel & Longitudinal
Fixed vs random effects, dynamic panels, and data observed over time.
ExploreCausal Inference
Difference-in-differences, RDD, IV, and establishing genuine effects.
ExploreSEM & Psychometrics
Measurement, factor analysis, reliability, and structural models.
ExploreMeta-Analysis & Synthesis
Systematic reviews, effect sizes, heterogeneity, and forest plots.
ExploreForecasting & Time Series
Stationarity, ARIMA, VAR, and evaluating forecasts honestly.
ExploreMachine Learning & Data
Prediction vs explanation, overfitting, and interpretable models.
ExploreJournals & Publication
Journal selection, quartiles, and responding to reviewers.
ExploreAccreditation & Research Impact
AACSB & EQUIS evidence, research impact, and institutional research intelligence.
ExploreSurvey, Experimental & Qualitative
Questionnaire design, experiments, sampling, and qualitative and mixed methods.
ExploreStatistical Audit & Data
Independent methodological review, robustness checks, and reproducible workflows.
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