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      On the Multilevel Nature of Meta-Analysis: A Tutorial, Comparison of Software Programs, and Discussion of Analytic Choices

      1 , 2
      Multivariate Behavioral Research
      Informa UK Limited

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          Abstract

          <p class="first" id="d3534466e69">The term "multilevel meta-analysis" is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term "multilevel meta-analysis" is redundant since all meta-analysis can be formulated as a special kind of multilevel model. To clarify the multilevel nature of meta-analysis the four standard meta-analytic models are presented using multilevel equations and fit to an example data set using four software programs: two specific to meta-analysis (metafor in R and SPSS macros) and two specific to multilevel modeling (PROC MIXED in SAS and HLM). The same parameter estimates are obtained across programs underscoring that all meta-analyses are multilevel in nature. Despite the equivalent results, not all software programs are alike and differences are noted in the output provided and estimators available. This tutorial also recasts distinctions made in the literature between traditional and multilevel meta-analysis as differences between meta-analytic choices, not between meta-analytic models, and provides guidance to inform choices in estimators, significance tests, moderator analyses, and modeling sequence. The extent to which the software programs allow flexibility with respect to these decisions is noted, with metafor emerging as the most favorable program reviewed. </p>

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          Most cited references30

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          Distribution Theory for Glass's Estimator of Effect Size and Related Estimators

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            Gender differences in scholastic achievement: a meta-analysis.

            A female advantage in school marks is a common finding in education research, and it extends to most course subjects (e.g., language, math, science), unlike what is found on achievement tests. However, questions remain concerning the quantification of these gender differences and the identification of relevant moderator variables. The present meta-analysis answered these questions by examining studies that included an evaluation of gender differences in teacher-assigned school marks in elementary, junior/middle, or high school or at the university level (both undergraduate and graduate). The final analysis was based on 502 effect sizes drawn from 369 samples. A multilevel approach to meta-analysis was used to handle the presence of nonindependent effect sizes in the overall sample. This method was complemented with an examination of results in separate subject matters with a mixed-effects meta-analytic model. A small but significant female advantage (mean d = 0.225, 95% CI [0.201, 0.249]) was demonstrated for the overall sample of effect sizes. Noteworthy findings were that the female advantage was largest for language courses (mean d = 0.374, 95% CI [0.316, 0.432]) and smallest for math courses (mean d = 0.069, 95% CI [0.014, 0.124]). Source of marks, nationality, racial composition of samples, and gender composition of samples were significant moderators of effect sizes. Finally, results showed that the magnitude of the female advantage was not affected by year of publication, thereby contradicting claims of a recent "boy crisis" in school achievement. The present meta-analysis demonstrated the presence of a stable female advantage in school marks while also identifying critical moderators. Implications for future educational and psychological research are discussed.
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              Fitting three-level meta-analytic models in R: A step-by-step tutorial

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                Author and article information

                Journal
                Multivariate Behavioral Research
                Multivariate Behavioral Research
                Informa UK Limited
                0027-3171
                1532-7906
                December 08 2017
                January 02 2018
                September 27 2017
                January 02 2018
                : 53
                : 1
                : 74-89
                Affiliations
                [1 ] Center for Assessment and Research Studies, James Madison University
                [2 ] The College Board
                Article
                10.1080/00273171.2017.1365684
                28952787
                fa99e6d6-bc9d-4b9b-8f3c-c5a756f56303
                © 2018
                History

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