Max Auerswald
Biographic Data
| ID | 9252299 |
|---|---|
| NAME | Max Auerswald |
| GIVEN NAMES | Max |
| FAMILY NAME | Auerswald |
| SIGNATURE | AUERSWALD M |
| AFFILIATIONS | Universität Ulm |
| ORCID | 0000-0001-6992-7400 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
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…
Team Regulatory Focus and its Role for Idea Generation, Idea Implementation, and Innovative Performance
In an experimental study, we explored the relationships between team regulatory focus and temporal patterns of innovative activities as well as innovative performance. We manipulated regulatory focus in 44 student teams and assessed idea generation and implementation activities over time based on video data. External raters assessed innovative performance. Latent growth curve models revealed that higher team promotion focus increased idea generat…
How to determine the number of factors to retain in exploratory factor analysis
Exploratory factor analyses are commonly used to determine the underlying factors of multiple observed variables. Many criteria have been suggested to determine how many factors should be retained. In this study, we present an extensive Monte Carlo simulation to investigate the performance of extraction criteria under varying sample sizes, numbers of indicators per factor, loading magnitudes, underlying multivariate distributions of observed vari…
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How to determine the number of factors to retain in exploratory factor analysis
Exploratory factor analyses are commonly used to determine the underlying factors of multiple observed variables. Many criteria have been suggested to determine how many factors should be retained. In this study, we present an extensive Monte Carlo simulation to investigate the performance of extraction criteria under varying sample sizes, numbers of indicators per factor, loading magnitudes, underlying multivariate distributions of observed vari…
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…
Team Regulatory Focus and its Role for Idea Generation, Idea Implementation, and Innovative Performance
In an experimental study, we explored the relationships between team regulatory focus and temporal patterns of innovative activities as well as innovative performance. We manipulated regulatory focus in 44 student teams and assessed idea generation and implementation activities over time based on video data. External raters assessed innovative performance. Latent growth curve models revealed that higher team promotion focus increased idea generat…
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…
Advanced Statistical Modeling Techniques (3 works) · Econometrics (3 works) · Mathematics (3 works) · Multivariate statistics (3 works) · Statistics (3 works) · Structural equation modeling (3 works) · Computer Science (2 works) · Engineering (2 works) · Multivariate analysis (2 works) · Multivariate normal distribution (2 works)