Abstract

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.

摘要

综合数据分析 (IDA) 是一种分析工具，允许研究人员将多个独立研究的原始数据结合起来，与单一研究分析或荟萃分析相比，可以改进对潜在结构的测量。这通常是通过实施适度非线性因素分析 (MNLFA) 来实现的，这是一种高级建模方法，允许对项目和因素参数进行协变量调节。当前的论文概述了这种建模技术，突出了最适合 IDA 的独特优势。我们通过使用来自五个独立预防试验的经验数据提供教程，进一步说明了 MNLFA 中涉及的复杂模型构建过程。还提供了用于分析的代码和数据。