Florian Scharf
Biographic Data
| ID | 8867870 |
|---|---|
| NAME | Florian Scharf |
| GIVEN NAMES | Florian |
| FAMILY NAME | Scharf |
| SIGNATURE | SCHARF F |
| AFFILIATIONS | University of Kassel |
| ORCID | 0000-0003-1659-4774 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Model Specification Search in Correlated Factor Models Using Bee Swarm Optimization
We introduce a Bee Swarm Optimization (BSO), a metaheuristic algorithm for correlated factor models that optimizes test construction by simultaneously determining the dimensionality and the optimal item set of a measure. In an extensive simulation study, we systematically varied the number of factors and indicators, the structural complexity, the magnitude of factor correlations, cross-loadings, and the extent of noise items. We benchmarked the B…
Analyzing the Dynamics of Auditory Distraction Using Multigroup Multilevel Structural Equation Modeling
Advanced Structural Equation Models (SEMs) have gained popularity in recent years, but they are rarely applied in experimental psychology. We illustrate how analyzing longitudinal phenomena in repeated-measures experiments using SEM can enhance understanding of trial-level dynamics and fundamental cognitive processes. Previous work shows that children exposed to novel sounds during a visual task display increased response times in that trial and …
Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization
Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in…
Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies
Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of a…
On the relationship of arousal and attentional distraction by emotional novel sounds
Unexpected and task-irrelevant sounds can impair performance in a task. It has been shown that highly arousing emotional distractor sounds impaired performance less compared to moderately arousing neutral distractor sounds. The present study tests whether these differential emotion-related distraction effects are directly related to an enhancement of arousal evoked by processing of emotional distractor sounds. We disentangled costs of orienting o…
Can Ridge and Elastic Net Structural Equation Modeling be Used to Stabilize Parameter Estimates when Latent Factors are Correlated
Multicollinearity between predictors is a common concern in SEM applications. As in linear regression models, high correlations between predictors can lead to unstable parameter estimates (i.e., large standard errors) and reduced statistical power. Regularized estimation methods, which have recently become available for SEMs, may provide more stable estimates in the presence of multicollinearity at the cost of a certain amount of bias in the esti…
No prominent works on this page.
Can Ridge and Elastic Net Structural Equation Modeling be Used to Stabilize Parameter Estimates when Latent Factors are Correlated
Multicollinearity between predictors is a common concern in SEM applications. As in linear regression models, high correlations between predictors can lead to unstable parameter estimates (i.e., large standard errors) and reduced statistical power. Regularized estimation methods, which have recently become available for SEMs, may provide more stable estimates in the presence of multicollinearity at the cost of a certain amount of bias in the esti…
On the relationship of arousal and attentional distraction by emotional novel sounds
Unexpected and task-irrelevant sounds can impair performance in a task. It has been shown that highly arousing emotional distractor sounds impaired performance less compared to moderately arousing neutral distractor sounds. The present study tests whether these differential emotion-related distraction effects are directly related to an enhancement of arousal evoked by processing of emotional distractor sounds. We disentangled costs of orienting o…
Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization
Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in…
Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies
Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of a…
Model Specification Search in Correlated Factor Models Using Bee Swarm Optimization
We introduce a Bee Swarm Optimization (BSO), a metaheuristic algorithm for correlated factor models that optimizes test construction by simultaneously determining the dimensionality and the optimal item set of a measure. In an extensive simulation study, we systematically varied the number of factors and indicators, the structural complexity, the magnitude of factor correlations, cross-loadings, and the extent of noise items. We benchmarked the B…
Analyzing the Dynamics of Auditory Distraction Using Multigroup Multilevel Structural Equation Modeling
Advanced Structural Equation Models (SEMs) have gained popularity in recent years, but they are rarely applied in experimental psychology. We illustrate how analyzing longitudinal phenomena in repeated-measures experiments using SEM can enhance understanding of trial-level dynamics and fundamental cognitive processes. Previous work shows that children exposed to novel sounds during a visual task display increased response times in that trial and …
Advanced Multi-Objective Optimization Algorithms (2 works) · Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Distraction (2 works) · Mathematics (2 works) · Metaheuristic Optimization Algorithms Research (2 works) · Psychology (2 works) · Statistics (2 works) · Advanced Statistical Methods and Models (1 work)