Monte Carlo Simulation for Structural Equation Modeling
Specifies the Factor Correlation Matrix for a Model
Fits Structural Equation Models to Simulated Data Using lavaan.
Simulates Data Sets from a Structural Equation Model (SEM) with Normal...
Generates Categorical Data Sets from Continuous Data.
Simulates Correlation matrix by a given SEM model.
Specifies Factor Loading Values for a Model.
Introduces Missing at Random (MAR) Values into Data Sets.
Introduces Missing Completely at Random (MCAR) Values into Data Sets.
Introduces Missing Not at Random (MNAR) Values into Data Sets
Simulates Categorical Data Sets Based on a Structural Equation Model (...
Simulates Data Sets Based on a Structural Equation Model (SEM).
Provides tools to conduct Monte Carlo simulations under different conditions (e.g., varying sample size, data normality) for structural equation models (SEMs). Data can be simulated based on user-defined factor loadings and correlations, with optional non-normality added via Fleishman's power method (1978) <doi:10.1007/BF02293811>. Once generated, models can be estimated using 'lavaan'. This package facilitates testing model performance across multiple simulation scenarios. When data generation is completed (or when generated data sets are given) model tests can also be run. Please cite as "Orçan, F. (2021). MonteCarloSEM An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."