Charles L Fisk
Biographic Data
| ID | 9251793 |
|---|---|
| NAME | Charles L Fisk |
| GIVEN NAMES | Charles L |
| FAMILY NAME | Fisk |
| SIGNATURE | FISK C L |
| AFFILIATIONS | University of Maryland |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Sensitivity Analyses for Omitted Confounders in Structural Equation Models with Tabu Search Optimization
Structural equation modeling (SEM) is a popular methodological approach for representing and testing hypothesized relationships among observed and unobserved, or latent, variables. Researchers in the social and behavioral sciences commonly encounter three general situations for testing such structural equation models—strictly confirmatory, testing alternative or competing hypothesized models, and model generation. However, none of these involve t…
Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification
Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published st…
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to…
Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling
Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity par…
No prominent works on this page.
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to…
Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling
Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity par…
Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification
Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published st…
Sensitivity Analyses for Omitted Confounders in Structural Equation Models with Tabu Search Optimization
Structural equation modeling (SEM) is a popular methodological approach for representing and testing hypothesized relationships among observed and unobserved, or latent, variables. Researchers in the social and behavioral sciences commonly encounter three general situations for testing such structural equation models—strictly confirmatory, testing alternative or competing hypothesized models, and model generation. However, none of these involve t…
Structural equation modeling (4 works) · Algorithm (3 works) · Computer Science (3 works) · Mathematics (3 works) · Statistics (3 works) · Advanced Multi-Objective Optimization Algorithms (2 works) · Advanced Text Analysis Techniques (2 works) · Ant colony optimization algorithms (2 works) · Artificial Intelligence (2 works) · Engineering (2 works)