MindStat SEM/CFA — Structural Equation Modeling & Confirmatory Factor Analysis

Data Input

Enter correlation matrix or raw data

Import / Export Data

ℹ️ CSV / Excel: Row 1 = variable names, next rows = correlation matrix. You can also load a saved session via the (Open) button in the top bar.

Study Variables

Variable names (comma-separated) Sample size n Data Type

Continuous (Pearson) Ordinal / Likert (Polychoric)

Correlation Matrix

💡 Enter the correlation matrix (lower triangle or full). One row per line, values separated by spaces or commas.

Confirmatory Factor Analysis (CFA)

Define the model: which variables load on which factor

Structural Equation Modeling (SEM)

Specify structural paths between latent factors

Mediation Analysis (Bootstrap)

Calculate direct and indirect effects via Bootstrap

Import / Export Data

ℹ CSV / Excel: Row 1 = variable names (X, M, Y), following rows = raw participant data.

Data Input (CSV or Paste)

💡Enter raw data (rows = participants, columns = X, M, Y). First row = headers.

Independent X

Mediator M

Dependent Y

Bootstrap iterations

Alpha (α)

Measurement Invariance Testing

Multi-group CFA: Configural & Metric invariance (Cheung & Rensvold, 2002)

Group 1 (Current Data)

ℹ️ Group 1 uses the data loaded in Data Input. Run CFA first to confirm the factor structure.

Group 2 Data

Sample Size (n2)

Correlation Matrix — Group 2 (lower triangle or full)

ℹ️ Must have same variables and factor structure as Group 1. Expected variables: —

Validity & Reliability

Convergent validity, discriminant validity, and reliability assessment

Full Results Summary

Comprehensive display of all results

⚠️Run CFA first to display results.