Lisa J Jobst
Datos Biográficos
| ID | 9252298 |
|---|---|
| NOMBRE | Lisa J Jobst |
| NOMBRES | Lisa J |
| APELLIDO | Jobst |
| FIRMA | JOBST L J |
| AFILIACIONES | Universität Ulm |
| ORCID | 0000-0002-4088-5451 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2021 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2022 |
| ÍNDICE H | 0 |
Sample Size Requirements for Bifactor Models
Despite the widespread application of bifactor models, little research has considered required sample sizes for this type of model. As universal sample size recommendations are often misleading, we illustrate how to determine sample size requirements of bifactor models using Monte Carlo simulations in R. Furthermore, we present results of an extensive simulation study investigating the effects of the number of specific factors and indicators, loa…
The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling
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 di…
Effects of Multivariate Non-Normality and Missing Data on the Root Mean Square Error of Approximation
The root mean square error of approximation (RMSEA) with various corrections for non-normality is a common fit index in structural equation modeling (SEM). The present study analyzed the performance of the uncorrected, the “sample corrected”, and the “population corrected” RMSEA in misspecified models for both complete and incomplete data sets under multivariate normality and multivariate non-normality. Additionally, the effect of the multivariat…
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Effects of Multivariate Non-Normality and Missing Data on the Root Mean Square Error of Approximation
The root mean square error of approximation (RMSEA) with various corrections for non-normality is a common fit index in structural equation modeling (SEM). The present study analyzed the performance of the uncorrected, the “sample corrected”, and the “population corrected” RMSEA in misspecified models for both complete and incomplete data sets under multivariate normality and multivariate non-normality. Additionally, the effect of the multivariat…
Sample Size Requirements for Bifactor Models
Despite the widespread application of bifactor models, little research has considered required sample sizes for this type of model. As universal sample size recommendations are often misleading, we illustrate how to determine sample size requirements of bifactor models using Monte Carlo simulations in R. Furthermore, we present results of an extensive simulation study investigating the effects of the number of specific factors and indicators, loa…
The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling
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 di…
Econometrics (3 obras) · Mathematics (3 obras) · Statistics (3 obras) · Advanced Statistical Modeling Techniques (2 obras) · Multivariate normal distribution (2 obras) · Multivariate statistics (2 obras) · Normality (2 obras) · Psychometric Methodologies and Testing (2 obras) · Structural equation modeling (2 obras) · Advanced Causal Inference Techniques (1 obras)