Detailed Meta-Analysis Syllabus

 

Topics in Detail

Monday
Introduction to meta-analysis (Lecture 1)
Topics:

Progress in science; A methodology of literature review; A brief history of meta-analysis

Readings: Cooper & Hedges (1994); Glass (1976); Rosenthal (1991), Chapter 1; Schmidt (1992)

Advantages and criticisms of meta-analysis (Lecture 2)
Topics:

Advantages; Criticisms; Limitations; Conclusions

Readings: Cooper & Rosenthal (1980); Lipsey & Wilson (2001), Chapter 1; Rosenthal & DiMatteo (2001);

Sharpe (1997)

Problem formulation and searching the literature (Lecture 3)
Topics:

Formulating a problem; Study eligibility criteria; Introduction to publication bias; Searching literature; Retrieving literature; Beginning a meta-analytic database

Readings: Hall, Tickle-Dregnen, Rosenthal, & Mosteller (1994); Lipsey & Wilson (2001), Chapter 2; M. C. Rosenthal (1994)

Identifying and coding variables in primary literature (Lecture 4)
Topics:

Identifying interesting variables to code; Developing a coding protocol; Evaluating coding; Ciding within a meta-analytic database

Readings: Lipsey (1994); Lipsey & Wilson (2001), Chapter 4, Chapter 5, Appendix E; Orwin (1994); Stock (1994); Woodworth (1994)

Tuesday
Computing effect sizes I: Introduction and computing Z or r from reported data (Lecture 5)
Topics:

Null-hypothesis testing versus effect sizes; Four common metrics; Computing Z from significance; Couting r

Readings: Rosenthal (1991), Chapter 2; Rosenthal (1994)

Computing effect sizes II: Computing d or o from reported data; standard errors (Lecture 6)
Topics:

Null-hypothesis testing versus effect sizes; Four common metrics; Computing Z from significance; Couting r

Readings: Fleiss (1994); Haddock, Rindskopf, & Shadish (1998); Lipsey & Wilson (2001), Chapter 3, Appendix B

Computing effect sizes III: Correcting effect sizes for artifacts (Lecture 7)
Topics:

Types of artifacts; Correcting for artifacts; Controversy of correction

Readings: Hunter & Schmidt (1994)

Wednesday
Combining effect sizes I: Computing (fixed-effects) mean effect sizes (Lecture 8)
Topics:

Creating a set of independent effect sizes; Computing mean effect size Inferences about mean effect size

Readings: Lipsey & Wilson (2001), Chapter 6, Chapter 7; Shadish & Haddock (1994)

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Combining effect sizes II: Evaluating heterogeneity of effect sizes (Lecture 9)
Topics:

Conceptualizing variability in effect sizes; Screening for outliers; Testing homogeneity versus heterogeneity; Implications of heterogeneity

Readings: Harwell (1997)

Combining effect sizes III: Fixed- and random-effects models (Lecture 10)
Topics:

Fixed- versus random-effects models; Computing random-effects models; Which model should I use?

Readings: Hedges & Vevea (1998)

Thursday
Comparing effect sizes I: Categorical moderator analysis (Lecture 11)
Topics:

Conceptualizing systematic variation as moderation; Two methods of comparing effect sizes; ANOVA-based approach

Readings: Lipsey & Wilson (2001), Chapter 6, Chapter 7 (also listed for Lecture 8)

Comparing effect sizes II: Continuous moderator analysis (Lecture 12)
Topics:

Introduction; Computing regression-based moderation (weighting, centering, significance testing, interpretation)

Readings: Continue Lipsey & Wilson (2001), Chapter 6, Chapter 7 (from Lecture 11); Hedges & Prigott (2004)

Comparing effect sizes III: Multiple / confounded moderators (Lecture 13)
Topics:

Confounded moderators; General approach to moderator analysis; Analyzing multiple moderators (colinearity, interpretation, interactions); Limits to interpreting moderators

Readings: Lipsey (2003)

Friday
Diagnostics (e.g., evaluating publication bias) (Lecture 14)
Topics:

Outlier detection; Colinearity of moderators; Publication bias

Readings: Begg (1994); Rosenthal (1979)

Reporting results of a meta-analysis (graphical displays; writing tips) (Lecture 15)
Topics:

Review of two graphical displays; Writing the manuscript

Readings: Bem (1995); Eagly & Wood (1994); Halvorsen (1994); Light, Singer, & Willet (1994); Rosenthal (1995)