Meta-analysis is the use of statistical methods to combine the results of independent research studies. The results of each study are summarized by one or more indices of effect size and a sampling uncertainty (variance) for each effect. Representing study results by effect sizes permits the use of statistical methods to synthesize these results across studies. This essay describes the most frequently used effect sizes and their properties. It describes how the two principal types of analytic methodology in meta-analysis (fixed and random effects models) are used to estimate an average effect across studies. It also discusses how heterogeneity of effects across studies can be detected via a heterogeneity test and modeled as a function of study characteristics. In addition, this essay describes areas of current research in meta-analysis. One area is the development of methods to handle dependencies that can arise when the results of studies are described by several effect sizes computed from data on the same individuals. Another area involves methods for detecting and correcting publication bias. A third is the development of methods to incorporate more complex study designs into metaanalyses, including multilevel experiments and single case designs used in behavioral psychology, special education, and some medicine.
|Original language||English (US)|
|Title of host publication||Emerging Trends in the Social and Behavioral Sciences|
|Subtitle of host publication||An Interdisciplinary, Searchable, and Linkable Resource|
|Editors||Robert A Scott, Marlis C Buchmann|
|Publisher||John Wiley & Sons, Inc.|
|Number of pages||16|
|State||Published - 2015|