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.

Recent advances in genotyping technology coupled with an improved understanding of the architecture of linkage disequilibrium across the human genome have resulted in genome-wide association studies (GWAS) becoming a useful and widely applied tool for discovering common genetic variants associated with both quantitative traits and disease risk. After each GWAS was completed, it left behind a set of genotypes and phenotypes, often including anthropometric measures used as covariates. Genetic associations with anthropometric measures are not well characterized, perhaps due to lack of power to detect them in the sample sizes of individual studies. To improve power to detect variants associated with complex phenotypes such as anthropometric traits, data from multiple GWAS can be combined. This thesis describes the methods and results of several such analyses performed as part of the Genome-wide Investigation of ANThropemtric measures (GIANT) consortium, and compares various different methods that can be used to perform combined analyses of GWAS. In particular, the comparisons focus on comparing differences between meta-analysis methods, in which only summary statistics that result from within-study association testing are shared between studies, and mega-analysis methods in which individual-level genotype and phenotype data is analysed together. Finally, a brief discussion of technological means that have the potential to help overcome some of the challenges associated with performing mega-analyses is offered in order to suggest future work that could be undertaken in this area.