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.