| Title: |
Signaling Model Misspecification in Latent Class Analysis |
| Language: |
English |
| Authors: |
Zachary K. Collier; Joshua Sukumar; Yiqin Cao; Nicholas Bell |
| Source: |
Structural Equation Modeling: A Multidisciplinary Journal. 2025 32(5):941-948. |
| Availability: |
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: |
Y |
| Page Count: |
8 |
| Publication Date: |
2025 |
| Document Type: |
Journal Articles; Reports - Descriptive |
| Descriptors: |
Statistical Analysis; Classification; Structural Equation Models; Algorithms; Adults; African Americans |
| DOI: |
10.1080/10705511.2024.2435995 |
| ISSN: |
1070-5511; 1532-8007 |
| Abstract: |
Modification indices, common in structural equation modeling, are not applicable to latent class analysis due to differences in estimation and fit assessment. To address this, we propose using gradient descent optimization to identify sensitive parameters. By aligning predictions with observed data through iterative adjustments, gradient descent reveals inadequacies in the original model's representation of relationships, prompting alternative specifications. Our method primarily detects misspecifications in latent class models related to variable selection but also aids in class enumeration. Gradient descent shows promise for offering a robust tool to mitigate issues like omitted variable bias and improve the accuracy of study conclusions. |
| Abstractor: |
As Provided |
| Entry Date: |
2026 |
| Accession Number: |
EJ1501486 |
| Database: |
ERIC |