Methods for Synthesizing Regression Results

智力价值：在这个项目中，研究者将检查现有的指数来代表回归分析的结果进行荟萃分析，并将开发新的方法来综合回归分析的结果。目前，由于难以比较不同模型的结果，元分析研究往往忽略回归结果。研究者将考虑这些研究指标的适当标准误差，这是大多数研究结果组合的基础。此外，他们将确定现有指标是否可以用来表示结构方程模型和层次线性模型（HLMs）等相关分析的结果，并在必要时提出新的指标。研究中将考虑几个特殊情况。特别是，将考虑表示组差异的结果（即虚拟变量的斜率），包括回归模型包含多个虚拟变量以表示具有多个水平的因素（例如，两个治疗组和一个共同对照组）的情况。由于元分析人员也经常希望总结不同设计的跨研究（例如，HLM研究与回归研究），因此拟议的研究将概述这种跨设计组合是合理的条件。更广泛的影响：本研究将对数学和科学教育的综合研究以及回归模型和其他形式的线性模型常见的许多其他领域（例如，经济学，政治学）产生广泛的影响。本研究提供的方法将使审稿人能够将回归结果与更简单的指标（如相关性、标准化平均差异）一起纳入meta分析。将特别注意综合工作，其中亚人口比较（例如服务不足群体内部和之间的差异）特别重要。

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.