How to Generate Non-normal Data for Simulation of Structural Equation Models
Bibliographic Data
| ID | 19290141 |
|---|---|
| Authors | Stefan Mattson (corresponding author) |
| Year | 1997 |
| Volume | 32 |
| Issue | 4 |
| Pages | 355-373 |
| Publication date | 1997-10-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1207/s15327906mbr3204_3 |
| PMID | 26777072 |
| OpenAlex | W2001749998 |
| Language | EN |
| Citations received | 14 |
| References cited | 12 |
A procedure for generating non-normal data for simulation of structural equation models is proposed. A simple transformation of univariate random variables is used for the generation of data on latent and error variables under some restrictions for the elements of the covariance matrices for these variables. Data on the observed variables is then computed from latent and error variables according to the model. It is shown that by controlling univariate skewness and kurtosis on pre-specified random latent and error variables, observed variables can be made to have a relatively wide range of univariate skewness and kurtosis characteristics according to the pre-specified model. Univariate distributions are used for the generation of data which enables a user to choose from a large number of different distributions. The use of the proposed procedure is illustrated for two different structural equation models and it is shown how PRELIS can be used to generate the data
Covariance · Kurtosis · Latent variable · Multivariate statistics · Range (aeronautics) · Skewness · Statistics · Structural equation modeling · Univariate · Advanced Statistical Modeling Techniques · Applied Mathematics · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Psychometric Methodologies and Testing
An empirical comparison of the efficacy of covariance-based and variance-based SEM
The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality
Assessing Common Method Bias
Estimation issues with PLS and CBSEM
Minimum sample size estimation in PLS‐SEM
The relationship between perceived stress and job burnout of police officers during the Covid-19 pandemic
Análisis de las propiedades psicométricas del Cuestionario de Competencias Socioemocionales SEC-Q en estudiantes universitarios chilenos
Generating Nonnormal Distributions via Gaussian Mixture Models
Structured Factor Analysis
A Cautionary Note on the Use of the Vale and Maurelli Method to Generate Multivariate, Nonnormal Data for Simulation Purposes
Generating Non-normal Data for Simulation of Structural Equation Models Using Mattson's Method
Mixture Factor Analysis for Approximating a Nonnormally Distributed Continuous Latent Factor With Continuous and Dichotomous Observed Variables
Estimating Latent Variable Interactions With Nonnormal Observed Data
Generating Nonnormal Multivariate Data Using Copulas
Methodology for Genetic Studies of Twins and Families
Scaled test statistics and robust standard errors for non‐normal data in covariance structure analysis
A Method for Simulating Non-Normal Distributions
A comparison of some methodologies for the factor analysis of non‐normal Likert variables
Simulating Multivariate Nonnormal Distributions
Bootstrapping Goodness-of-Fit Measures in Structural Equation Models
| Citations per year | 0.54 |
|---|---|
| Citation span | 2000 - 2025 (26) |
| Highly cited | No |