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      The Relation Between Mathematics Anxiety and Mathematics Performance Among School-Aged Students: A Meta-Analysis

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          Abstract

          The purpose of this meta-analysis was to examine the relation between mathematics anxiety (MA) and mathematics performance among school-aged students, and to identify potential moderators and underlying mechanisms of such relation, including grade level, temporal relations, difficulty of mathematical tasks, dimensions of MA measures, effects on student grades, and working memory. A meta-analysis of 131 studies with 478 effect sizes was conducted. The results indicated that a significant negative correlation exist between MA and mathematics performance, r = −.34. Moderation analyses indicated that dimensions of MA, difficulty of mathematical tasks, and effects on student grades differentially affected the relation between MA and mathematics performance. MA assessed with both cognitive and affective dimensions showed a stronger negative correlation with mathematics performance compared to MA assessed with either an affective dimension only or mixed/unspecified dimensions. Advanced mathematical tasks that require multistep processes showed a stronger negative correlation to MA compared to foundational mathematical tasks. Mathematics measures that affected/reflected student grades (e.g., final exam, students’ course grade, GPA) had a stronger negative correlation to MA than did other measures of mathematics performance that did not affect student grades (e.g., mathematics measures administered as part of research). Theoretical and practical implications of the findings are discussed.

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          Bias in meta-analysis detected by a simple, graphical test

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            The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice

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              Robust variance estimation in meta-regression with dependent effect size estimates.

              Conventional meta-analytic techniques rely on the assumption that effect size estimates from different studies are independent and have sampling distributions with known conditional variances. The independence assumption is violated when studies produce several estimates based on the same individuals or there are clusters of studies that are not independent (such as those carried out by the same investigator or laboratory). This paper provides an estimator of the covariance matrix of meta-regression coefficients that are applicable when there are clusters of internally correlated estimates. It makes no assumptions about the specific form of the sampling distributions of the effect sizes, nor does it require knowledge of the covariance structure of the dependent estimates. Moreover, this paper demonstrates that the meta-regression coefficients are consistent and asymptotically normally distributed and that the robust variance estimator is valid even when the covariates are random. The theory is asymptotic in the number of studies, but simulations suggest that the theory may yield accurate results with as few as 20-40 studies. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Review of Educational Research
                Review of Educational Research
                American Educational Research Association (AERA)
                0034-6543
                1935-1046
                June 2019
                April 13 2019
                June 2019
                : 89
                : 3
                : 459-496
                Affiliations
                [1 ]University of Nebraska-Lincoln
                [2 ]The University of Texas at Austin
                Article
                10.3102/0034654319843494
                46d3316a-31b8-4fff-a91e-3d2758550900
                © 2019

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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