Structural Equation Modeling (SEM)

Validate causal relationships among latent variables — from measurement to paths and moderation, in one flow.

From reliability/validity checks to structural path analysis, bootstrap mediation, and multi-group moderation, SEM automates the full survey-research pipeline. Export the results straight to paper format.

At a glance

Best for

Survey-based causal models · Mediation/moderation · Theses & papers

Model structure

Latent variables (factors) + measurement items + structural paths

Engine

semopy (SEM) · factor_analyzer (EFA)

Data needed

CSV / XLSX, rows = respondents, cols = survey items (e.g. Likert)

Recommended sample

5–10× the number of items (e.g. 30 items → 150–300 respondents)

Plan

Available on PREMIUM plan and above

Workflow

  1. 1Data preprocessing (missing-value handling · item selection)
  2. 2Research-variable setup (define categories → hypotheses → item mapping → structural model)
  3. 3Reliability analysis (Cronbach's α)
  4. 4Exploratory & confirmatory factor analysis (EFA → CFA fit → standardized loadings)
  5. 5Convergent & discriminant validity (CR · AVE)
  6. 6Structural model (fit → path coefficients → bootstrap mediation)
  7. 7Multi-group analysis (measurement invariance → group paths → structural invariance → group mediation)

Supported analyses

  • Reliability analysis

    Cronbach's α for the internal consistency of items measuring the same factor

  • Exploratory factor analysis (EFA)

    KMO·Bartlett tests + factor loadings to explore whether items group as intended

  • Confirmatory factor analysis (CFA)

    Validate the measurement model with fit indices + standardized loadings (β)

  • Convergent & discriminant validity

    Evaluate factor reliability/validity, including discriminant validity, via CR·AVE

  • Structural path analysis

    Accept/reject hypotheses from causal path coefficients (β) and significance among latent variables

  • Mediation & multi-group

    Bootstrap mediation + group moderation tests (measurement/structural invariance)

Use cases

  • Brand-loyalty causal model

    Validate the brand image → satisfaction → loyalty path and confirm the mediating effect of satisfaction via bootstrapping.

  • Technology Acceptance Model (TAM)

    Fit the perceived usefulness/ease-of-use → intention-to-use structural model and accept/reject each hypothesis.

  • Group moderation analysis

    Test path-coefficient differences across groups (gender, age, etc.) with multi-group analysis to identify moderation.

What you get

  • Reliability & validity tables (Cronbach α · CR · AVE)
  • EFA / CFA results (factor loadings · fit indices)
  • Structural path coefficients + hypothesis acceptance table
  • Bootstrap mediation (direct · indirect · total effects + CIs)
  • Multi-group analysis (measurement invariance · group paths · moderation)
  • Auto-generated paper (LaTeX → PDF)