Benedikt Langenberg
Biographic Data
| ID | 8867158 |
|---|---|
| NAME | Benedikt Langenberg |
| GIVEN NAMES | Benedikt |
| FAMILY NAME | Langenberg |
| SIGNATURE | LANGENBERG B |
| AFFILIATIONS | Maastricht University |
| ORCID | 0000-0002-4757-0698 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
How to Use Residual Dynamic Structural Equation Modeling to Study Individual Differences and Intraindividual Variability in Experimental Factorial Designs
This article demonstrates the application of residual dynamic structural equation modeling (RDSEM) for analyzing custom contrasts in experimental factorial designs. Previous applications of RDSEM have often focused on ecological momentary assessment and daily diary data. However, RDSEM was explicitly developed for intensive longitudinal data more generally, including settings with very short time intervals between observations. Beyond these types…
Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM
Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior inform…
Using Structural Equation Modeling in Place of Between-Subjects Analysis of Variance
This article demonstrates how to use structural equation modeling (SEMing) in place of between-subjects analysis of variance (BS-ANOVA). More specifically, this article demonstrates how to closely reproduce the F-values and p-values from ANOVA (for all main and interactions effects) using an SEM model comparison approach (i.e., χ2 difference tests between two SEMs that conform to the main and interaction effects of the null and alternative hypoth…
Repeated Measures Anova with Latent Variables to Analyze Interindividual Differences in Contrasts
Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of interest. It mainly considers contrasts that test standard main and interaction effects, even though more complex contrasts can in principle be used. Analyses, however, only focus on drawing concl…
No prominent works on this page.
Repeated Measures Anova with Latent Variables to Analyze Interindividual Differences in Contrasts
Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of interest. It mainly considers contrasts that test standard main and interaction effects, even though more complex contrasts can in principle be used. Analyses, however, only focus on drawing concl…
Using Structural Equation Modeling in Place of Between-Subjects Analysis of Variance
This article demonstrates how to use structural equation modeling (SEMing) in place of between-subjects analysis of variance (BS-ANOVA). More specifically, this article demonstrates how to closely reproduce the F-values and p-values from ANOVA (for all main and interactions effects) using an SEM model comparison approach (i.e., χ2 difference tests between two SEMs that conform to the main and interaction effects of the null and alternative hypoth…
Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM
Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior inform…
How to Use Residual Dynamic Structural Equation Modeling to Study Individual Differences and Intraindividual Variability in Experimental Factorial Designs
This article demonstrates the application of residual dynamic structural equation modeling (RDSEM) for analyzing custom contrasts in experimental factorial designs. Previous applications of RDSEM have often focused on ecological momentary assessment and daily diary data. However, RDSEM was explicitly developed for intensive longitudinal data more generally, including settings with very short time intervals between observations. Beyond these types…
Analysis of variance (3 works) · Econometrics (3 works) · Mathematics (3 works) · Psychometric Methodologies and Testing (3 works) · Repeated measures design (3 works) · Statistics (3 works) · Advanced Statistical Modeling Techniques (2 works) · Behavioral and Psychological Studies (2 works) · Economic and Environmental Valuation (2 works) · Factorial (2 works)