Meta-analysis is arguably the most important methodological innovation in the social and behavioural sciences in the last 25 years. This revision of Hunter and Schmidt's book, Methods of Meta-Analysis (SAGE 1990), covers the important new developments in meta-analysis methods over the last 14 years. This edition presents an evaluation of fixed versus random effects models for meta-analysis, new methods for correcting for indirect range restriction in meta-analysis, new developments in corrections for measurement error (including how to select the appropriate reliability coefficients to use), a…mehr
Meta-analysis is arguably the most important methodological innovation in the social and behavioural sciences in the last 25 years. This revision of Hunter and Schmidt's book, Methods of Meta-Analysis (SAGE 1990), covers the important new developments in meta-analysis methods over the last 14 years. This edition presents an evaluation of fixed versus random effects models for meta-analysis, new methods for correcting for indirect range restriction in meta-analysis, new developments in corrections for measurement error (including how to select the appropriate reliability coefficients to use), a discussion of a new Windows-based program package for applying the meta-analysis methods presented in the book, and a discussion of the theories of data underlying different approaches to meta-analysis. Coverage of these topics along with updated coverage of many other topics makes this book the most comprehensive text on meta-analysis methods available today.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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Autorenporträt
John E. (Jack) Hunter (1939--2002) was a professor in the Department of Psychology at Michigan State University. He received his Ph.D. in quantitative psychology from the University of Illinois. Jack coauthored four books and authored or coauthored over 200 articles and book chapters on a wide variety of methodological topics, including confirmatory and exploratory factor analysis, measurement theory and methods, statistics, and research methods. He also published numerous research articles on such substantive topics as intelligence, attitude change, the relationship between attitudes and behavior, validity generalization, differential validity/selection fairness, and selection utility. Much of his research on attitudes was in the field of communications, and the American Communications Association named a research award in his honor. Professor Hunter received the Distinguished Scientific Award for Contributions to Applied Psychology from the American Psychological Association (APA) (jointly with Frank Schmidt) and the Distinguished Scientific Contributions Award from the Society for Industrial/Organizational Psychology (SIOP) (also jointly with Frank Schmidt). He was a Fellow of APA, APS, and SIOP, and was a past president of the Midwestern Society for Multivariate Experimental Psychology. For the story of Jack's life, see Schmidt (2003).
Inhaltsangabe
Preface to 2nd Edition Preface to 1st Edition Acknowledgements Introduction to Meta-Analysis Integration Research Findings Across Studies General problem and an example Problems with statistical significance tests Is statistical power the solution? Confidence intervals Meta-analysis Role of meta-analysis in the behavioral and social sciences Role of meta-analysis in theory development Increasing use of meta-analysis Meta-analysis in industrial-organizational psychology Wider impact of meta-analysis on psychology Impact of meta-analysis outside psychology Meta-analysis and social policy Meta-analysis and theories of data Conclusions Study Artifacts and Their Impact on Study Outcomes Study Artifacts Sampling error, statistical power, and the interpretation of research literatures When and how to cumulate Undercorrection for artifacts in the corrected standard deviation Coding study characteristics and capitalization on sampling error in moderator analysis A look ahead in the book Meta-Analysis of Correlations Meta-Analysis of Correlations Corrected Individually for Artifacts Introduction and Overview Bare bones meta-analysis: Correcting for sampling error only Artifacts other than sampling error Multiple simultaneous artifacts Meta-analysis of individually corrected correlations A worked example: Indirect range restriction Summary of meta-analysis correcting each correlation individually Exercise 1: Bare bones meta-analysis Exercise 2: Meta-analysis correcting each correlation individually Meta-Analysis of Correlations Using Artifact Distributions Full artifact distribution meta-analysis Accuracy of corrections for artifacts Mixed meta-analysis: Partial artifact information in individual studies Summary of artifact distribution of meta-analysis of correlations Exercise: Artifact distribution meta-analysis Technical Questions in Meta-Analysis of Correlations r versus : Which should be used? r vs. regression slopes and intercepts in meta-analysis Technical factors that cause overestimation of Fixed and random models in meta-analysis Credibility vs. confidence intervals in meta-analysis Computing confidence intervals in meta-analysis Range Restriction in meta-analysis: New technical analysis Criticisms of meta-analysis procedures for correlations Meta-Analysis of Experimental Effects and Other Dichotomous Comparisons Treatment Effects: Experimental Artifacts and Their Impact Quantification of the treatment effect: The d statistic and the point-biserial correlation Sampling error in d values: Illustrations Error of measurement in the dependent variable Error of measurement in the treatment variable Variation across studies in treatment strength Range variation on the dependent variable Dichotomization of the dependent variable Imperfect construct validity in the dependent variable Imperfect construct validity in the treatment variable Bias in the effect size (d statistic) Recording, computational, and transcriptional errors Multiple artifacts and corrections Meta-Analysis Methods for d Values Effect size indices: d and r An Alternative to d: Glass' d Sampling error in the d statistic Cumulation and correction of the variance for sampling error Analysis of moderator variables Correcting d values for measurement error in the dependent variable Measurement error in the independent variable in experiments Other artifacts and their effects Correcting for multiple artifacts Summary of meta-analysis of d values Exercise: Meta-Analysis of d-Values Technical Questions in Meta-Analysis of d Values Alternative experimental designs Within-subjects experimental designs Meta-analysis and the within-subjects design Statistical power in the two designs Threats to internal and external validity Bias in observed d values Use of multiple regression in moderation analysis of d values General Issues in Meta-Analysis General Technical Issues in Meta-analysis Fixed eff
Preface to 2nd Edition Preface to 1st Edition Acknowledgements Introduction to Meta-Analysis Integration Research Findings Across Studies General problem and an example Problems with statistical significance tests Is statistical power the solution? Confidence intervals Meta-analysis Role of meta-analysis in the behavioral and social sciences Role of meta-analysis in theory development Increasing use of meta-analysis Meta-analysis in industrial-organizational psychology Wider impact of meta-analysis on psychology Impact of meta-analysis outside psychology Meta-analysis and social policy Meta-analysis and theories of data Conclusions Study Artifacts and Their Impact on Study Outcomes Study Artifacts Sampling error, statistical power, and the interpretation of research literatures When and how to cumulate Undercorrection for artifacts in the corrected standard deviation Coding study characteristics and capitalization on sampling error in moderator analysis A look ahead in the book Meta-Analysis of Correlations Meta-Analysis of Correlations Corrected Individually for Artifacts Introduction and Overview Bare bones meta-analysis: Correcting for sampling error only Artifacts other than sampling error Multiple simultaneous artifacts Meta-analysis of individually corrected correlations A worked example: Indirect range restriction Summary of meta-analysis correcting each correlation individually Exercise 1: Bare bones meta-analysis Exercise 2: Meta-analysis correcting each correlation individually Meta-Analysis of Correlations Using Artifact Distributions Full artifact distribution meta-analysis Accuracy of corrections for artifacts Mixed meta-analysis: Partial artifact information in individual studies Summary of artifact distribution of meta-analysis of correlations Exercise: Artifact distribution meta-analysis Technical Questions in Meta-Analysis of Correlations r versus : Which should be used? r vs. regression slopes and intercepts in meta-analysis Technical factors that cause overestimation of Fixed and random models in meta-analysis Credibility vs. confidence intervals in meta-analysis Computing confidence intervals in meta-analysis Range Restriction in meta-analysis: New technical analysis Criticisms of meta-analysis procedures for correlations Meta-Analysis of Experimental Effects and Other Dichotomous Comparisons Treatment Effects: Experimental Artifacts and Their Impact Quantification of the treatment effect: The d statistic and the point-biserial correlation Sampling error in d values: Illustrations Error of measurement in the dependent variable Error of measurement in the treatment variable Variation across studies in treatment strength Range variation on the dependent variable Dichotomization of the dependent variable Imperfect construct validity in the dependent variable Imperfect construct validity in the treatment variable Bias in the effect size (d statistic) Recording, computational, and transcriptional errors Multiple artifacts and corrections Meta-Analysis Methods for d Values Effect size indices: d and r An Alternative to d: Glass' d Sampling error in the d statistic Cumulation and correction of the variance for sampling error Analysis of moderator variables Correcting d values for measurement error in the dependent variable Measurement error in the independent variable in experiments Other artifacts and their effects Correcting for multiple artifacts Summary of meta-analysis of d values Exercise: Meta-Analysis of d-Values Technical Questions in Meta-Analysis of d Values Alternative experimental designs Within-subjects experimental designs Meta-analysis and the within-subjects design Statistical power in the two designs Threats to internal and external validity Bias in observed d values Use of multiple regression in moderation analysis of d values General Issues in Meta-Analysis General Technical Issues in Meta-analysis Fixed eff
Rezensionen
"Clearly written and compellingly argued, this book explains the procedures and benefits of correcting for measurement error and range restriction and details the methodological developments in meta-analysis over the last decade. No one should consider conducting a meta-analysis without first reading this book. It is essential reading for all scientists." Michael A. McDaniel
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