Jewish Population in the United States, 2014

The authors describe the relative benefits of conducting meta-analyses with (a) individual participant data (IPD) gathered from the constituent studies and (b) aggregated data (AD), or the group-level statistics (in particular, effect sizes) that appear in reports of a study's results. Given that both IPD and AD are equally available, meta-analysis of IPD is superior to meta-analysis of AD. IPD meta-analysis permits synthesists to perform subgroup analyses not conducted by the original collectors of the data, to check the data and analyses in the original studies, to add new information to the data sets, and to use different statistical methods. However, the cost of IPD meta-analysis and the lack of available IPD data sets suggest that the best strategy currently available is to use both approaches in a complementary fashion such that the first step in conducting an IPD meta-analysis would be to conduct an AD meta-analysis. Regardless of whether a meta-analysis is conducted with IPD or AD, synthesists must remain vigilant in how they interpret their results. They must avoid ecological fallacies, Simpson's paradox, and interpretation of synthesis-generated evidence as supporting causal inferences. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the pooling of data drawn from multiple existing developmental studies. This approach is characterized by a host of advantages, but this also introduces several new complexities that must be addressed prior to broad adoption by developmental researchers. In this article, the authors focus on methods for fitting measurement models and creating scale scores using data drawn from multiple longitudinal studies. The authors present findings from the analysis of repeated measures of internalizing symptomatology that were pooled from three existing developmental studies. The authors describe and demonstrate each step in the analysis and conclude with a discussion of potential limitations and directions for future research.