What This Document Is
This document contains detailed lecture notes from EVAL 6970: Research on Evaluation, offered at Western Michigan University. Specifically, these notes focus on advanced meta-analytic techniques – meta-regression and the handling of complex data structures within meta-analysis. The material presented builds upon foundational meta-analysis concepts and delves into methods for exploring the reasons *why* effect sizes vary across studies. It appears to be based on a Spring 2011 course offering, presenting a snapshot of the topics covered at that time.
Why This Document Matters
These notes are invaluable for graduate students and researchers in fields like education, psychology, public health, and social work who utilize meta-analysis as a core research methodology. If you are undertaking a systematic review and meta-analysis, or are seeking to understand the nuances of synthesizing research findings, this resource will be particularly helpful. It’s especially relevant when you need to move beyond simply calculating an average effect size and begin investigating potential moderating variables or dealing with non-standard data arrangements. Understanding these advanced techniques can significantly strengthen the rigor and interpretability of your meta-analytic work.
Common Limitations or Challenges
These notes represent a specific instructor’s presentation of the material and do not substitute for a comprehensive textbook or hands-on statistical training. The notes are a record of lecture content and in-class activities, meaning they may not include exhaustive explanations of every statistical assumption or derivation. Furthermore, the examples and software outputs presented are specific to the tools available at the time of the lecture and may require adaptation for current software versions. This resource does not provide step-by-step instructions for conducting meta-regression or handling complex data; it outlines the concepts and approaches.
What This Document Provides
* An overview of meta-regression, including its purpose and application.
* Discussion of appropriate sample size considerations when employing meta-regression.
* Presentation of fixed-effect model outputs and associated statistical tables (ANOVA).
* Exploration of random-effects models for meta-regression.
* Analysis of variance components within and between studies.
* Examination of the proportion of variance explained by covariates.
* Details regarding in-class activities designed to reinforce learning.
* Illustrative examples of statistical output from meta-analytic software.
