Data Simulation Based on Latent Factors
Adds (Substantial) Cross-loadings to simulate_factors Data
Adds Local Dependence to simulate_factors Data
Adds Methods Factors to simulate_factors Data
Adds Population Error to simulate_factors Data
Adds Wording Effects to simulate_factors Data
Categorize Continuous Data
Transforms simulate_factors Data to Zipf's Distribution
Estimate Number of Dimensions using Empirical Kaiser Criterion
Estimates Exploratory Structural Equation Model
Estimates Dimensions using Several State-of-the-art Methods
Estimate Number of Dimensions using Factor Forest
latentFactoR--package
Estimate Number of Dimensions using Next Eigenvalue Sufficiency Test
Obtain Zipf's Distribution Parameters from Data
Simulates Latent Factor Data
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.