Julia-Kim Walther
Biographic Data
| ID | 6629983 |
|---|---|
| NAME | Julia-Kim Walther |
| GIVEN NAMES | Julia-Kim |
| FAMILY NAME | Walther |
| SIGNATURE | WALTHER J |
| AFFILIATIONS | University of Tübingen |
| ORCID | 0000-0001-5758-1211 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Multilevel Multigroup Structural Equation Modeling In A Single-Level Framework
Finding the Optimal Number of Persons ( N ) and Time Points ( T ) for Maximal Power in Dynamic Longitudinal Models Given a Fixed Budget
Planning longitudinal studies can be challenging as various design decisions need to be made. Often, researchers are in search for the optimal design that maximizes statistical power to test certain parameters of the employed model. We provide a user-friendly Shiny app OptDynMo available at https://shiny.psychologie.hu-berlin.de/optdynmo that helps to find the optimal number of persons (N) and the optimal number of time points (T) for which the p…
To Be Long or To Be Wide
A two-level data set can be structured in either long format (LF) or wide format (WF), and both have corresponding SEM approaches for estimating multilevel models. Intuitively, one might expect these approaches to perform similarly. However, the two data formats yield data matrices with different numbers of columns and rows, and their cols : rows is related to the magnitude of eigenvalue bias in sample covariance matrices. Previous studies have s…
Shrinking Small Sample Problems in Multilevel Structural Equation Modeling via Regularization of the Sample Covariance Matrix
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized estimation approach designed for scenarios with both a small number of groups and small group sizes, an…
Shrinking Small Sample Problems in Multilevel Structural Equation Modeling via Regularization of the Sample Covariance Matrix
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized estimation approach designed for scenarios with both a small number of groups and small group sizes, an…
Finding the Optimal Number of Persons ( N ) and Time Points ( T ) for Maximal Power in Dynamic Longitudinal Models Given a Fixed Budget
Planning longitudinal studies can be challenging as various design decisions need to be made. Often, researchers are in search for the optimal design that maximizes statistical power to test certain parameters of the employed model. We provide a user-friendly Shiny app OptDynMo available at https://shiny.psychologie.hu-berlin.de/optdynmo that helps to find the optimal number of persons (N) and the optimal number of time points (T) for which the p…
To Be Long or To Be Wide
A two-level data set can be structured in either long format (LF) or wide format (WF), and both have corresponding SEM approaches for estimating multilevel models. Intuitively, one might expect these approaches to perform similarly. However, the two data formats yield data matrices with different numbers of columns and rows, and their cols : rows is related to the magnitude of eigenvalue bias in sample covariance matrices. Previous studies have s…
Shrinking Small Sample Problems in Multilevel Structural Equation Modeling via Regularization of the Sample Covariance Matrix
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized estimation approach designed for scenarios with both a small number of groups and small group sizes, an…
Multilevel Multigroup Structural Equation Modeling In A Single-Level Framework
Mathematics (4 works) · Computer Science (3 works) · Psychometric Methodologies and Testing (3 works) · Statistics (3 works) · Structural equation modeling (3 works) · Econometrics (2 works) · Multilevel model (2 works) · Advanced Causal Inference Techniques (1 works) · Advanced Statistical Methods and Models (1 works) · Advanced Statistical Modeling Techniques (1 works)