Analyzing Data
The Analyze menu is the heart of ChakataStat. This section documents every procedure, grouped the way the menu is. Each page tells you when to use a procedure, how to fill the dialog, and how to read the output, with worked examples on the bundled sample data (File → Open Sample Dataset).
If you are not sure which test you need, start with the procedure chooser.
How every analysis dialog works
The dialogs share one shape, so once you know one you know them all:
- A source list of variables on the left; target list(s) (Variables, Factor, Dependent, …) on the right. Double-click or use the arrows to move variables across.
- An Options section (collapsible) holds the advanced knobs — confidence level, significance level (α), one/two-tailed, missing-data handling, random seed — hidden by default so the common path stays simple.
- A Run button (also Enter) dispatches the analysis; it shows a spinner while the engine works and blocks a double-submit. Esc cancels.
- Results append to the Output View as titled cards. Many dialogs also offer a Plot… checkbox that adds a chart built from the result.
Every analysis respects the active case semantics — Select, Weight and Split — described in Case Semantics & Transforms. And every run is recorded in the Syntax console.
The procedures
| Page | Menu group | Covers |
|---|---|---|
| Descriptive Statistics | Descriptive Statistics | Frequencies, Descriptives, Explore, Crosstabs |
| Compare Means | Compare Means, Bayesian | Means, the three T tests, One-Way ANOVA, and the Bayesian t test / correlation / ANOVA / linear regression (Bayes factors + posteriors) |
| Correlation & Regression | Correlate, Regression, GLM, Mixed | Bivariate/partial correlation; linear, logistic, Poisson, negative-binomial, multinomial and ordinal regression; mediation analysis; factorial GLM/ANCOVA; mixed models; repeated measures & MANOVA |
| Meta-Analysis | Meta-Analysis | Fixed-effect and DerSimonian-Laird random-effects pooling of per-study effect sizes; heterogeneity (Q, I², τ²); forest and funnel plots with Egger's test |
| Nonparametric Tests | Nonparametric Tests | Mann-Whitney, Wilcoxon, Sign, Kruskal-Wallis, Friedman, Kendall's W, Runs, Binomial, Tests of Normality |
| Scale, Reduction & Classify | Scale, Dimension Reduction, Classify, ROC Curve | Reliability; intraclass correlation; factor analysis; k-means, hierarchical & two-step cluster; discriminant analysis; ROC curve / AUC |
| Confirmatory Factor Analysis | Dimension Reduction | Testing a hypothesized measurement structure: ML loadings with standard errors, factor correlations, and the conventional fit block (χ², CFI, TLI, RMSEA, SRMR) |
| Survival & Time Series | Survival, Time Series | Kaplan-Meier, Cox regression; autocorrelation, ARIMA/SARIMA with forecasting |
| Custom Tables | Tables | Custom (pivot) tables; multiple-response tables |
A note on assumptions
Many procedures carry optional assumption checks (Levene's test of equal variances, Mauchly's sphericity test, Tests of Normality, casewise influence diagnostics). Where they exist, the relevant page points them out — turning them on costs nothing and keeps you honest about whether the headline test is valid.