Intellectual Merit: In this project, the investigators will examine existing indices for representing the results of regression analyses for meta-analysis, and will develop new methods for synthesizing results from regression analyses. Currently, meta-analytic studies often omit regression results due to the difficulty in comparing results across different models. The investigators will consider the appropriate standard errors for these study indices, which are fundamental to most combinations of study results. In addition, they will determine whether existing indices can be used to represent results of related analyses such as structural equation models and hierarchical linear models (HLMs), and will propose new indices if needed.
Several special cases will be considered in the research. In particular, results meant to represent group differences (i.e., slopes for dummy variables) will be considered, including cases where regression models contain several dummy variables to represent factors with multiple levels (e.g., two treatment groups and a common control group). Because meta-analysts also often wish to summarize across studies with different designs (e.g., HLM studies with regression studies), the proposed research will outline the conditions under which such cross-design combinations are reasonable.
Broader Impact: This study will have broad impacts on syntheses of research in mathematics and science education, and in many other areas (e.g., economics, political science) where regression models and other forms of the linear model are common. The methods provided by this research will enable reviewers to include regression results in meta-analyses together with simpler indices (e.g., correlations, standardized mean differences). Special attention will be paid to synthesis work where subpopulation comparisons (such as within and across underserved group differences) are particularly important.
