ERIC Number: EJ1431533
Record Type: Journal
Publication Date: 2024
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1070-5511
EISSN: EISSN-1532-8007
Dynamic Fit Index Cutoffs for Hierarchical and Second-Order Factor Models
Daniel McNeish; Patrick D. Manapat
Structural Equation Modeling: A Multidisciplinary Journal, v31 n1 p27-47 2024
A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models. Through simulation, we show that traditional benchmarks perform poorly at identifying misspecification in hierarchical models. This corroborates previous studies showing that traditional benchmarks do not maintain optimal sensitivity to misspecification as model characteristics deviate from those used to derive the benchmarks. Instead, we propose a hierarchical extension to the dynamic fit index (DFI) framework, which automates custom simulations to derive cutoffs with optimal sensitivity for specific model characteristics. In simulations to evaluate performance, results showed that the hierarchical DFI extension routinely exceeded 95% classification accuracy and 90% sensitivity to misspecification whereas traditional benchmarks applied to hierarchical models rarely exceeded 50% classification accuracy and 20% sensitivity.
Descriptors: Factor Analysis, Goodness of Fit, Hierarchical Linear Modeling, Benchmarking, Simulation, Cutting Scores, Evaluation Methods, Classification, Accuracy, Generalization
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
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305D220003