sem-guide

name	sem-guide
description	Structural equation modeling with latent variables guide
metadata	{"openclaw":{"emoji":"🌐","category":"analysis","subcategory":"statistics","keywords":["structural equation modeling","SEM","latent variable model","multilevel model"],"source":"wentor-research-plugins"}}

Structural Equation Modeling Guide

Build, estimate, and evaluate structural equation models (SEM) with latent variables using Python (semopy) and R (lavaan), including confirmatory factor analysis and path analysis.

What Is SEM?

Structural Equation Modeling is a multivariate statistical framework that combines factor analysis and path analysis to test complex theoretical models involving:

• Observed (manifest) variables: Directly measured (e.g., survey items, test scores)
• Latent (unobserved) variables: Theoretical constructs measured indirectly through observed indicators (e.g., "motivation," "intelligence")
• Structural paths: Directional relationships between variables (regression-like)
• Measurement model: How latent variables relate to their indicators (CFA)
• Structural model: How latent variables relate to each other (path analysis)

SEM Components

Component	Description	Diagram Symbol
Observed variable	Measured directly	Rectangle
Latent variable	Inferred from indicators	Oval/circle
Regression path	Directional relationship	Single-headed arrow
Covariance	Non-directional association	Double-headed arrow
Error/residual	Unexplained variance	Small circle with arrow

Step 1: Confirmatory Factor Analysis (CFA)

CFA tests whether observed variables load onto hypothesized latent factors.

In R (lavaan)

library(lavaan)

# Define the measurement model
# =~ means "is measured by"
cfa_model <- '
  # Latent variable definitions
  Motivation =~ mot1 + mot2 + mot3 + mot4
  SelfEfficacy =~ se1 + se2 + se3
  Performance =~ perf1 + perf2 + perf3 + perf4

  # Covariances between latent variables (estimated by default in CFA)
'

# Fit the model
fit <- cfa(cfa_model, data = mydata, estimator = "MLR")

# View results
summary(fit, fit.measures = TRUE, standardized = TRUE)

# Key output to examine:
# - Factor loadings (standardized > 0.5 is desirable)
# - Model fit indices (see table below)
# - Modification indices (for model improvement)
modindices(fit, sort = TRUE, minimum.value = 10)

In Python (semopy)

import semopy
import pandas as pd

# Define model in lavaan-like syntax
model_spec = """
Motivation =~ mot1 + mot2 + mot3 + mot4
SelfEfficacy =~ se1 + se2 + se3
Performance =~ perf1 + perf2 + perf3 + perf4
"""

# Fit the model
model = semopy.Model(model_spec)
result = model.fit(data)

# View parameter estimates
print(model.inspect())

# Get fit statistics
stats = semopy.calc_stats(model)
print(stats.T)

Step 2: Full Structural Model

After confirming the measurement model, add structural (regression) paths.

In R (lavaan)

sem_model <- '
  # Measurement model
  Motivation =~ mot1 + mot2 + mot3 + mot4
  SelfEfficacy =~ se1 + se2 + se3
  Performance =~ perf1 + perf2 + perf3 + perf4

  # Structural model (regressions)
  # ~ means "is regressed on"
  Performance ~ Motivation + SelfEfficacy
  SelfEfficacy ~ Motivation

  # Optional: define indirect effect
  # indirect := a * b
'

fit <- sem(sem_model, data = mydata, estimator = "MLR")
summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)

Mediation Analysis

mediation_model <- '
  # Measurement model
  X =~ x1 + x2 + x3
  M =~ m1 + m2 + m3
  Y =~ y1 + y2 + y3

  # Structural model
  M ~ a*X          # a path
  Y ~ b*M + c*X    # b path + direct effect c

  # Define indirect and total effects
  indirect := a * b
  total := c + a * b
'

fit <- sem(mediation_model, data = mydata, se = "bootstrap", bootstrap = 1000)
summary(fit, standardized = TRUE)

# Bootstrap confidence intervals for indirect effect
parameterEstimates(fit, boot.ci.type = "bca.simple", standardized = TRUE)

Model Fit Assessment

Fit Index Reference Table

Index	Good Fit	Acceptable	What It Measures
Chi-square (p)	p > 0.05	Sensitive to N; use with other indices	Exact fit test
Chi-square/df	< 2	< 3	Parsimony-adjusted exact fit
CFI	> 0.95	> 0.90	Comparative fit vs. null model
TLI	> 0.95	> 0.90	CFI adjusted for parsimony
RMSEA	< 0.06	< 0.08	Approximate fit per df
SRMR	< 0.08	< 0.10	Average residual correlation
AIC/BIC	Lower = better	--	Model comparison (not absolute)

Interpreting Fit

# Extract fit measures in lavaan
fitMeasures(fit, c("chisq", "df", "pvalue", "cfi", "tli", "rmsea",
                    "rmsea.ci.lower", "rmsea.ci.upper", "srmr"))

Reporting template:

The structural equation model demonstrated adequate fit to the data:
chi-square(df) = X.XX, p = .XXX; CFI = .XX; TLI = .XX; RMSEA = .XXX
[90% CI: .XXX, .XXX]; SRMR = .XXX.

Model Modification and Comparison

Modification Indices

# Show top modification indices
mi <- modindices(fit, sort = TRUE)
head(mi, 10)

# Common modifications:
# - Allow error covariances between similarly-worded items
# - Add cross-loadings (if theoretically justified)
# - Remove non-significant paths

Model Comparison

# Compare nested models using chi-square difference test
fit1 <- sem(model1, data = mydata)  # More constrained
fit2 <- sem(model2, data = mydata)  # Less constrained

anova(fit1, fit2)  # Chi-square difference test

# For non-nested models, compare AIC/BIC
fitMeasures(fit1, c("aic", "bic"))
fitMeasures(fit2, c("aic", "bic"))

Common Pitfalls

Issue	Problem	Solution
Small sample size	Unstable estimates, poor fit	Minimum N = 200, or 10-20 per parameter
Too many parameters	Overfitting, non-convergence	Simplify model, use parceling
Non-normal data	Biased standard errors	Use MLR estimator or bootstrapping
Ignoring missing data	Biased results	Use FIML (full information maximum likelihood)
Data-driven respecification	Capitalizing on chance	Cross-validate with holdout sample
Conflating fit with truth	Good fit does not mean correct model	Consider equivalent/alternative models

Assumptions and Diagnostics

1. Multivariate normality: Check with Mardia's test; use robust estimators (MLR) if violated
2. Linearity: SEM assumes linear relationships between variables
3. No multicollinearity: Correlations between latent variables should not exceed 0.85
4. Sufficient sample size: Rule of thumb: N >= 200 or 10-20 observations per estimated parameter
5. Correct model specification: Omitted variables can bias all estimates

# Check multivariate normality
library(MVN)
mvn(mydata[, c("mot1", "mot2", "mot3", "se1", "se2", "se3")],
    mvnTest = "mardia")

# Use robust estimation if non-normal
fit_robust <- sem(sem_model, data = mydata, estimator = "MLR")