Integrative data analysis in clinical psychology research.
paper
Cited
Public
- Authors
- Hussong, Andrea M; Curran, Patrick J; Bauer, Daniel J
- Year
- 2013
- Journal
- Annual review of clinical psychology
- PMID
- 23394226
- DOI
- 10.1146/annurev-clinpsy-050212-185522
- PMCID
- PMC3924786
Integrative data analysis (IDA), a novel framework for conducting the simultaneous analysis of raw data pooled from multiple studies, offers many advantages including economy (i.e., reuse of extant data), power (i.e., large combined sample sizes), the potential to address new questions not answerable by a single contributing study (e.g., combining longitudinal studies to cover a broader swath of the lifespan), and the opportunity to build a more cumulative science (i.e., examining the similarity of effects across studies and potential reasons for dissimilarities). There are also methodological challenges associated with IDA, including the need to account for sampling heterogeneity across studies, to develop commensurate measures across studies, and to account for multiple sources of study differences as they impact hypothesis testing. In this review, we outline potential solutions to these challenges and describe future avenues for developing IDA as a framework for studies in clinical psychology.
Building Commensurate Measures for IDA
Age and Study Trends in Select Items
Boxplots of factor score estimates by age and study with mean trends depicted by smoothed lines.
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