With increased data sharing and research collaboration options available through modern technology, there is an increased need to find more advanced techniques to analyze data across multiple studies. A systematic method of pooling participant-level versus study-level data would be particularly valuable as it would allow for more complex statistical analyses, broader assessment of constructs, and a cost effective way to examine new questions and replicate previous findings. One notable difficulty in pooling raw data in the behavioral sciences is the heterogeneity in methodologies and consequent need to establish measurement invariance. The present study explores the feasibility of using Integrative Data Analysis (IDA) to combine 10 heterogeneous eating disorder prevention data sets and establish measurement invariance across the constructs of thin ideal internalization and body dissatisfaction. Using standard multiple groups factor analysis and likelihood-ratio tests to examine differential item functioning, separate one-factor models were established for the three measures used across studies. Partial measurement invariance was established for all measures. Implications for future IDA studies based on this process are discussed, particularly regarding the clinical impact of measurement invariance.
College and Department
Family, Home, and Social Sciences; Psychology
BYU ScholarsArchive Citation
Green, Kat Tumblin, "Establishing Measurement Invariance of Thin Ideal Internalization and Body Dissatisfaction Across Studies: An Integrative Data Analysis" (2013). All Theses and Dissertations. 4240.
Integrative Data Analysis, mega-analysis, measurement invariance