SEM & Structural Equation Modeling Support
Structural equation modeling support for CFA, latent variables, path models, fit assessment, model revision, and reporting.
SEM support should start with a defensible theoretical model and measurement structure. Tezyar helps researchers plan confirmatory factor analysis and structural models, assess identification and fit, interpret parameters, and report results without treating fit indices as a substitute for theory.
Measurement Model and CFA
Latent constructs require indicators with a defensible measurement rationale. CFA is used to evaluate the proposed measurement structure before interpreting structural paths.
Model Identification and Estimation
The number of parameters, scale setting, sample characteristics, distributional assumptions, and estimator choice all affect whether a model can be estimated and interpreted.
Fit Assessment
Model fit should be evaluated using multiple indicators alongside residuals, parameter plausibility, theory, and data quality; no single threshold should drive the entire conclusion.
Model Modification and Reporting
Modification indices can suggest areas to inspect, but changes should be theory-led and transparently reported rather than made only to improve fit.
Frequently Asked Questions
Can you help with AMOS as well as R?
Yes. The statistical reasoning is software-independent; implementation can be aligned with the software appropriate to the project.
Can SEM be used with a small sample?
Feasibility depends on model complexity, estimator, data quality, and parameterization. Sample adequacy should be assessed for the specific model.
Related specialized topics
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R analysis support for reproducible research, statistical modelling, visualization, reporting, and thesis or manuscript workflows.
Regression analysis support for linear, logistic, generalized, multivariable, and research-specific modelling workflows.
Sample size and power analysis support for research design, theses, grant proposals, experiments, surveys, and clinical studies.
Survey analysis support for data cleaning, scale reliability, descriptive analysis, group comparisons, regression, factor analysis, and reporting.
Statistical support for clinical and health research: analysis planning, outcomes, regression, survival methods, repeated measures, and reporting.