The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling
Datos Bibliográficos
| ID | 20282978 |
|---|---|
| Autores | Lisa J Jobst (0000-0002-4088-5451, Institute of Psychology and Education, Ulm University, Ulm, Germany, autor de correspondencia), Max Auerswald (0000-0001-6992-7400, Institute of Psychology and Education, Ulm University, Ulm, Germany), Morten Moshagen (0000-0002-2929-7288, Institute of Psychology and Education, Ulm University, Ulm, Germany) |
| Año | 2022 |
| Volumen | 82 |
| Número | 5 |
| Páginas | 911-937 |
| Fecha de publicación | 2022-10-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Educational and Psychological Measurement (JOURNAL) |
| Identificadores de la revista | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Editorial | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/00131644211046201 |
| PMID | 35989731 |
| OpenAlex | W3201412136 |
| Idioma | EN |
| Citas recibidas | 8 |
| Referencias citadas | 37 |
Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of non-normality is considered. We conducted a Monte Carlo simulation manipulating the underlying multivariate distribution to assess the effect of the source of non-normality (latent, error, and marginal conditions with either multivariate normal or non-normal marginal distributions) on different measures of fit (empirical rejection rates for the likelihood-ratio model test statistic, the root mean square error of approximation, the standardized root mean square residual, and the comparative fit index). We considered different estimation methods (maximum likelihood, generalized least squares, and (un)modified asymptotically distribution-free), sample sizes, and the extent of non-normality in correctly specified and misspecified models to investigate their performance. The results show that all measures of fit were affected by the source of non-normality but with varying patterns for the analyzed estimation methods
Asymptotic distribution · Econometrics · Estimator · Goodness of fit · Latent variable · Latent variable model · Likelihood-ratio test · Multivariate normal distribution · Multivariate statistics · Normal distribution · Normality · Normality test · Residual · Statistic · Statistical hypothesis testing · Statistics · Structural equation modeling · Test statistic · Advanced Statistical Modeling Techniques · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Applications
Is (critical) health literacy a key to better psychosomatic functioning in patients with inflammatory bowel disease? Testing a mediation model
SemPower
Cyber Behaviors and Well‐Being in Emerging Adults
A literature review of model fit and model comparisons with confirmatory factor analysis
Discriminating Between Attribute, Item-Position, and Wording Effects by the Congeneric and Tau-Equivalent Confirmatory Factor Analysis Models
Humility Throughout the Lifespan and a Global Pandemic
Rethinking aversive personality
Improving University Students’ Academic Development Satisfaction Through Interpersonal Relationship
Skewness and Kurtosis in Real Data Samples
Reporting practices in confirmatory factor analysis
Applications of Structural Equation Modeling in Psychological Research
Item factor analysis
The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality
Scaled test statistics and robust standard errors for non‐normal data in covariance structure analysis
The Effect of Varying Degrees of Nonnormality in Structural Equation Modeling
Asymptotically distribution‐free methods for the analysis of covariance structures
Simulating Multivariate Nonnormal Distributions
Cutoff criteria for fit indexes in covariance structure analysis
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
Structural Equations with Latent Variables
Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables
Revised Parallel Analysis With Nonnormal Ability and a Guessing Parameter
Evaluating Small Sample Approaches for Model Test Statistics in Structural Equation Modeling
Univariate and multivariate skewness and kurtosis for measuring nonnormality
Scale Development Research
Can test statistics in covariance structure analysis be trusted
Comparative fit indexes in structural models
Alternative Ways of Assessing Model Fit
| Obras citantes distintas | 8 |
|---|---|
| Citas por año | 2,67 |
| Intervalo de citas | 2023 - 2026 (4) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 7 |