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This document includes these contents: Problem definition, document retrieval, effect sizes and computation, analysis of effect sizes, publication bias, cutting edge issues,... Invite you to result.
Practical MetaAnalysis David B Wilson Evaluators’ Institute July 16-17, 2010 Practical MetaAnalysis D. B. Wilson Overview of the Workshop Topics covered will include Review of the basic methods Problem definition Document Retrieval Coding Effect sizes and computation Analysis of effect sizes Publication Bias Cutting edge issues Interpretation of results Evaluating the quality of a meta-analysis Practical Meta Analysis D. B. Wilson Forest Plot from a MetaAnalysis of Correctional BootCamps Favors Comparison Favors Bootcamp Harer & Klein-Saffran, 1996 Jones & Ross, 1997 Fl Dept of JJ (Stuart Co.), 1997 Fl Dept of JJ (Polk Co., Boys), 1997 Jones (FY97), 1998 Jones (FY94-95), 1998 Mackenzie & Souryal (Illinois), 1994 Mackenzie & Souryal (Louisiana), 1994 Jones (FY91-93), 1998 Mackenzie & Souryal (Florida), 1994 Jones (FY96), 1998 Marcus-Mendoza (Men), 1995 Mackenzie, et al 1997 Penn Dept of Corrections, 2001 Flowers, Carr, & Ruback 1991 Bureau of Data and Research, 1996 Mackenzie & Souryal (Oklahoma), 1994 T3 Associates, 2000 Mackenzie & Souryal (New York), 1994 Peters, 1996b Camp & Sandhu, 1995 Mackenzie & Souryal (S.C., New), 1994 Jones, 1996 NY DCS (88-96 Releases), 2000 Marcus-Mendoza (Women), 1995 Austin, Jones, & Bolyard, 1993 Burns & Vito, 1995 Peters, 1996a Fl Dept of JJ (Bay Co.), 1997 NY DCS (96-97 Releases), 2000 NY DCS (97-98 Releases), 2000 Fl Dept of JJ (Pinellas Co.), 1996 Fl Dept of JJ (Manatee Co.), 1996 CA Dept of the Youth Authority, 1997 Boyles, Bokenkamp, & Madura, 1996 Mackenzie & Souryal (S.C., Old), 1994 Fl Dept of JJ (Polk Co., Girls), 1997 Jones, 1997 Thomas & Peters, 1996 Wright & Mays, 1998 Mackenzie & Souryal (Georgia), 1994 Practical Meta Analysis D. B. Wilson Overall Mean Odds-Ratio The Great Debate 1952: Hans J Eysenck concluded that there were no favorable effects of psychotherapy, starting a raging debate 20 years of evaluation research and hundreds of studies failed to resolve the debate 1978: To proved Eysenck wrong, Gene V Glass statistically aggregate the findings of 375 psychotherapy outcome studies Glass (and colleague Smith) concluded that psychotherapy did indeed work Glass called his method “meta-analysis” Practical Meta Analysis D. B. Wilson The Emergence of Metaanalysis Ideas behind meta-analysis predate Glass’ work by several decades Karl Pearson (1904) averaged correlations for studies of the effectiveness of inoculation for typhoid fever R A Fisher (1944) “When a number of quite independent tests of significance have been made, it sometimes happens that although few or none can be claimed individually as significant, yet the aggregate gives an impression that the probabilities are on the whole lower than would often have been obtained by chance” (p 99) Practical Meta Source of the idea of cumulating probability values Analysis D. B. Wilson The Emergence of Metaanalysis Ideas behind meta-analysis predate Glass’ work by several decades W G Cochran (1953) Discusses a method of averaging means across independent studies Laid-out much of the statistical foundation that modern metaanalysis is built upon (e.g., Inverse variance weighting and homogeneity testing) Practical Meta Analysis D. B. Wilson The Logic of Metaanalysis Traditional methods of review focus on statistical significance testing Significance testing is not well suited to this task Highly dependent on sample size Null finding does not carry the same “weight” as a significant finding significant effect is a strong conclusion nonsignificant effect is a weak conclusion Meta-analysis focuses on the direction and magnitude of the effects across studies, not statistical significance Isn’t this what we are interested in anyway? Direction and magnitude are represented by the effect size Practical Meta Analysis D. B. Wilson Illustration Table 21 Validity Studies, N = 68 for Each Observed validity Study coefficient 0.04 0.14 0.31 * 0.12 0.38 * 0.27 * 0.15 0.36 * 0.20 10 0.02 11 0.23 12 0.11 13 0.21 14 0.37 * 15 0.14 16 0.29 * 17 0.26 * 18 0.17 19 0.39 * 20 0.22 21 0.21 * p < 05 (two tailed) Simulated data from 21 validity studies Taken from: Schimdt, F L (1996) Statistical significance testing and cumulative knowledge in psychology: implications for training of researchers Psychological Practical Meta Methods, 1, 115-129 Analysis D. B. Wilson Illustration (Continued) Table 95% Confidence Intervals for Correlations From Table 1, N = 68 for Each Observed 95% confidence validity interval Study coefficient Lower Upper 0.39 0.19 0.59 0.38 0.18 0.58 0.37 0.16 0.58 0.36 0.15 0.57 0.31 0.09 0.53 0.29 0.07 0.51 0.27 0.05 0.49 0.26 0.04 0.48 0.23 0.00 0.46 10 0.22 -0.01 0.45 11 0.21 -0.02 0.44 12 0.21 -0.02 0.44 13 0.20 -0.03 0.43 14 0.17 -0.06 0.40 15 0.15 -0.08 0.38 16 0.14 -0.09 0.37 17 0.14 -0.09 0.37 18 0.12 -0.12 0.36 19 0.11 -0.13 0.35 20 0.04 -0.20 0.28 21 0.02 -0.22 0.26 Practical Meta Analysis D. B. Wilson When Can You Do Metaanalysis? Meta-analysis is applicable to collections of research that Are empirical, rather than theoretical Produce quantitative results, rather than qualitative findings Examine the same constructs and relationships Have findings that can be configured in a comparable statistical form (e.g., as effect sizes, correlation coefficients, odds-ratios, proportions) Are “comparable” given the question at hand Practical Meta Analysis D. B. Wilson 10 Forms of Research Findings Suitable to Meta analysis Central tendency research Pre-post contrasts Prevalence rates Growth rates Group contrasts Experimentally created groups Comparison of outcomes between treatment and comparison groups Naturally occurring groups Comparison of spatial abilities between boys and girls Rates of morbidity among high and low risk groups Practical Meta Analysis D. B. Wilson 11 Forms of Research Findings Suitable to Meta analysis Association between variables Measurement research Validity generalization Individual differences research Correlation between personality constructs Practical Meta Analysis D. B. Wilson 12 Effect Size: The Key to Metaanalysis The effect size makes meta-analysis possible It is the “dependent variable” It standardizes findings across studies such that they can be directly compared Practical Meta Analysis D. B. Wilson 13 Effect Size: The Key to Metaanalysis Any standardized index can be an “effect size” (e.g., standardized mean difference, correlation coefficient, odds-ratio) as long as it meets the following Is comparable across studies (generally requires standardization) Represents the magnitude and direction of the relationship of interest Is independent of sample size Different meta-analyses may use different effect size indices Practical Meta Analysis D. B. Wilson 14 The Replication Continuum Conceptual Replications Pure Replications You must be able to argue that the collection of studies you are meta-analyzing examine the same relationship This may be at a broad level of abstraction, such as the relationship between criminal justice interventions and recidivism or between schoolbased prevention programs and problem behavior Alternatively it may be at a narrow level of abstraction and represent pure replications The closer to pure replications your collection of studies, the easier it is to argue comparability Practical Meta Analysis D. B. Wilson 15 Which Studies to Include? It is critical to have an explicit inclusion and exclusion criteria (see pages 20-21) The broader the research domain, the more detailed they tend to become Refine criteria as you interact with the literature Components of a detailed criteria distinguishing features research respondents key variables research methods cultural and linguistic range time frame publication types Practical Meta Analysis D. B. Wilson 16 Methodological Quality Dilemma Include or exclude low quality studies? The findings of all studies are potentially in error (methodological quality is a continuum, not a dichotomy) Being too restrictive may restrict ability to generalize Being too inclusive may weaken the confidence that can be placed in the findings Methodological quality is often in the “eye-of-the-beholder” You must strike a balance that is appropriate to your research question Practical Meta Analysis D. B. Wilson 17 Searching Far and Wide The “we only included published studies because they have been peer-reviewed” argument Significant findings are more likely to be published than nonsignificant findings Critical to try to identify and retrieve all studies that meet your eligibility criteria Practical Meta Analysis D. B. Wilson 18 Searching Far and Wide (continued) Potential sources for identification of documents Computerized bibliographic databases “Google” internet search engine Authors working in the research domain (email a relevant Listserv?) Conference programs Dissertations Review articles Hand searching relevant journal Government reports, bibliographies, clearinghouses Practical Meta Analysis D. B. Wilson 19 A Note About Computerized Bibliographies Rapidly changing area Get to know your local librarian! Searching one or two databases is generally inadequate Use “wild cards” (e.g., random? will find random, randomization, and randomize) Throw a wide net; filter down with a manual reading of the abstracts Practical Meta Analysis D. B. Wilson 20 Strengths of Metaanalysis Imposes a discipline on the process of summing up research findings Represents findings in a more differentiated and sophisticated manner than conventional reviews Capable of finding relationships across studies that are obscured in other approaches Protects against over-interpreting differences across studies Can handle a large numbers of studies (this would overwhelm traditional approaches to review) Practical Meta Analysis D. B. 21 Wilson Weaknesses of Metaanalysis Requires a good deal of effort Mechanical aspects don’t lend themselves to capturing more qualitative distinctions between studies “Apples and oranges” criticism Most meta-analyses include “blemished” studies to one degree or another (e.g., a randomized design with attrition) Selection bias posses a continual threat Negative and null finding studies that you were unable to find Outcomes for which there were negative or null findings that were not reported Analysis of between study differences is fundamentally Practical Meta correlational Analysis D. B. 22 Wilson ... psychotherapy did indeed work Glass called his method meta-analysis Practical Meta Analysis D. B. Wilson The Emergence of Metaanalysis Ideas behind meta-analysis predate Glass’ work by several... Publication Bias Cutting edge issues Interpretation of results Evaluating the quality of a meta-analysis Practical Meta Analysis D. B. Wilson Forest Plot from a MetaAnalysis of Correctional BootCamps... obtained by chance” (p 99) Practical Meta Source of the idea of cumulating probability values Analysis D. B. Wilson The Emergence of Metaanalysis Ideas behind meta-analysis predate Glass’