A plain-language map of the statistical methods used in management and social-science research — when to use each one, its assumptions, and how to run it in R, Python, SPSS, and Excel.
48 methods
Answer a few questions about your research goal and data. The helper suggests a method — always confirm its assumptions on its page.
Test whether groups or conditions differ on an outcome — two groups, three or more, independent or repeated measures.
8 methodsExamine whether two variables are related — correlations for numeric data, chi-square family for categories.
8 methodsModel how one or more predictors influence an outcome — regression, moderation, mediation.
2 methodsSummarize many variables into a few components or factors — PCA and exploratory factor analysis.
4 methodsConfirm that your measurement model holds — CFA, SEM, PLS-SEM, convergent and discriminant validity.
5 methodsQuantify the consistency of scales and raters — alpha, omega, composite reliability, kappa, ICC.
3 methodsDiscover or assign group membership — cluster analysis and discriminant analysis.
2 methodsUnderstand data ordered in time — trends, seasonality, and forecasting.
2 methodsDiagnostic tests that guard the validity of other methods — normality and homogeneity of variance.
Test whether groups or conditions differ on an outcome — two groups, three or more, independent or repeated measures.
Tests whether the means of two independent groups differ on a continuous outcome — e.g., do male and female employees differ in job satisfaction?
Bivariate
Tests whether the mean of one sample differs from a known or hypothesized value — e.g., does average employee engagement differ from the scale midpoint of 3?
Univariate
Compares two independent group means without assuming equal variances — the safer default when group sizes or spreads differ.
Bivariate
Tests whether the mean of the same group differs across two occasions or conditions — e.g., job satisfaction before vs after a training program.
Bivariate
Non-parametric comparison of two independent groups using ranks — the go-to alternative to the independent t-test for ordinal or non-normal data.
Bivariate
Non-parametric test for paired data — compares two related measurements when difference scores are ordinal or non-normal.
Bivariate
Tests whether three or more independent group means differ on a continuous outcome — e.g., does job satisfaction differ across four departments?
Bivariate
Compares three or more group means without assuming equal variances — the robust cousin of one-way ANOVA.
Bivariate
Tests the effects of two categorical factors — and, crucially, their interaction — on one continuous outcome, e.g., do gender and job level jointly shape pay satisfaction?
Multivariate
Tests whether the same participants' means differ across three or more occasions or conditions — e.g., engagement measured quarterly over a year.
Multivariate
Compares group means on an outcome while statistically controlling one or more continuous covariates — e.g., post-training performance controlling for pre-training scores.
Multivariate
Tests group differences on several related continuous outcomes simultaneously — e.g., do departments differ on the combination of satisfaction, commitment, and engagement?
Multivariate
Non-parametric comparison of three or more independent groups using ranks — the distribution-free counterpart of one-way ANOVA.
Bivariate
Non-parametric test for three or more related measurements — the distribution-free counterpart of repeated-measures ANOVA.
Bivariate
Examine whether two variables are related — correlations for numeric data, chi-square family for categories.
Measures the strength and direction of the linear relationship between two continuous variables — e.g., does job autonomy rise with job satisfaction?
Bivariate
Measures the strength of a monotonic relationship using ranks — the robust alternative to Pearson for ordinal data, outliers, or curved-but-monotonic trends.
Bivariate
Rank correlation based on concordant and discordant pairs — preferred over Spearman for small samples and data with many tied ranks.
Bivariate
The correlation between two variables after statistically removing the influence of one or more control variables — e.g., autonomy and satisfaction controlling for tenure.
Multivariate
The correlation between one true dichotomy and one continuous variable — mathematically equivalent to an independent t-test, e.g., gender and salary.
Bivariate
Tests whether two categorical variables are related — e.g., is employment type (permanent/contract) associated with turnover (stayed/left)?
Bivariate
Exact test of association for small contingency tables — the correct choice when chi-square's expected-count rule fails.
Bivariate
Tests whether the observed frequency distribution of one categorical variable matches expected proportions — e.g., do complaint types occur equally often?
Univariate
Model how one or more predictors influence an outcome — regression, moderation, mediation.
Models a continuous outcome as a straight-line function of one predictor — e.g., predicting sales performance from training hours.
Bivariate
Models a continuous outcome from several predictors at once, giving each predictor's unique contribution — the workhorse of survey-based management research.
Multivariate
Enters predictors in theory-ordered blocks and tests the R² increment of each block — does personality predict performance beyond demographics?
Multivariate
Tests whether the strength or direction of an X→Y relationship depends on a third variable W — e.g., does supervisor support buffer the stress→burnout link?
Multivariate
Tests whether X influences Y through an intermediate variable M — e.g., transformational leadership → psychological empowerment → job performance.
Multivariate
Models the probability of a binary outcome from one or more predictors — e.g., predicting whether an employee quits within a year.
Multivariate
Predicts an ordered categorical outcome — e.g., low/medium/high engagement — respecting the ordering that multinomial models throw away.
Multivariate
Predicts membership in three or more unordered categories — e.g., which benefits package (cash/insurance/leave) employees choose.
Multivariate
Summarize many variables into a few components or factors — PCA and exploratory factor analysis.
Compresses many correlated variables into a few uncorrelated components that retain maximum variance — data reduction, not latent-construct discovery.
Multivariate
Uncovers the latent factors underlying a set of items — the standard first step in developing a new measurement scale.
Multivariate
Confirm that your measurement model holds — CFA, SEM, PLS-SEM, convergent and discriminant validity.
Tests whether your data fit a pre-specified measurement model — the standard evidence that items measure the constructs you claim.
Multivariate
Combines measurement models with structural paths among latent constructs — testing whole theoretical models, measurement error included.
Multivariate
Composite-based structural modeling that maximizes explained variance — suited to prediction goals, complex models, smaller samples, and formative constructs.
Multivariate
Evidence that a scale's items converge on their own construct (AVE, CR) and that constructs are empirically distinct from each other (Fornell–Larcker, HTMT).
Multivariate
Quantify the consistency of scales and raters — alpha, omega, composite reliability, kappa, ICC.
The classic index of internal consistency — how coherently a set of items measures one construct, e.g., a 6-item commitment scale.
Multivariate
Factor-model-based reliability that allows items to load unequally — the recommended modern replacement for (or companion to) Cronbach's alpha.
Multivariate
Reliability computed from CFA/SEM standardized loadings — the standard statistic in measurement-model tables alongside AVE.
Multivariate
Chance-corrected agreement between two raters assigning categories — e.g., two coders classifying interview excerpts into themes.
Bivariate
Reliability of continuous ratings — consistency or agreement among raters, or of repeated measurements; also the aggregation statistic in multilevel research.
Multivariate
Discover or assign group membership — cluster analysis and discriminant analysis.
Partitions cases into k groups so that members are similar within and different between clusters — e.g., segmenting customers by behavior.
Multivariate
Builds a tree (dendrogram) of nested clusters without pre-specifying their number — cut the tree where the structure makes sense.
Multivariate
Finds the weighted combinations of predictors that best separate known groups, and classifies new cases — e.g., which ratios distinguish surviving from failing firms.
Multivariate
Understand data ordered in time — trends, seasonality, and forecasting.
Describes a series over time — separating trend, seasonality, and noise — e.g., is monthly attrition drifting upward once seasonal spikes are removed?
Univariate
Models a series' own past values and shocks to forecast its future — the classic statistical workhorse for univariate forecasting.
Multivariate
Diagnostic tests that guard the validity of other methods — normality and homogeneity of variance.
Tests whether a variable's distribution departs from normality — a gatekeeper check for t-tests, ANOVA, and regression residuals.
Univariate
Tests whether groups have equal variances — the standard gatekeeper before t-tests and ANOVA decide between classic and Welch versions.
Bivariate