Work

Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial

Year: 2022

Type: article

Abstract: Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through implementation of moderated nonlinear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex modeling building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.

Source: Structural Equation Modeling A Multidisciplinary Journal

DOI: 10.1080/10705511.2022.2070753

Authors: Joseph M. Kush, Katherine E. Masyn, Masoumeh Amin-Esmaeili, Ryoko Susukida, Holly C. Wilcox, Rashelle J. Musci

Institutions: Johns Hopkins University, Georgia State University

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: National Institute of Mental Health

Award: Netting prevention intervention butterfly effects: An integrative data analysis investigating the long-term and cross-over effects of randomized,school-based prevention programs on adult mental health