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

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing an improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through the implementation of moderated non-linear 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 model 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.