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Advanced Meta-Analysis in Psychology

Manual: General · Subject: Psychology

Master the quantitative synthesis of psychological research, addressing heterogeneity and publication bias.

Synthesizing Psychological Evidence

Random-Effects Modeling

Meta-analysis quantitatively synthesizes effect sizes across studies. Because psychological studies rarely share exactly the same true effect (due to different populations or methods), a Random-Effects model is preferred. The weight WiW_i of each study ii incorporates both within-study variance ViV_i and between-study variance τ2\tau^2: Wi=1Vi+τ2W_i = \frac{1}{V_i + \tau^2}

Heterogeneity Metrics

Cochran's Q
A statistical test for the presence of heterogeneity. Highly powered in large meta-analyses.
I-squared (I2I^2)
The percentage of total variation across studies that is due to true heterogeneity rather than sampling error (chance).
Tau-squared (τ2\tau^2)
The absolute variance of the true effect sizes.
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Correcting for Publication Bias

Publication bias can severely distort meta-analytic estimates. Always use visual tools and statistical methods like trim-and-fill, p-curve analysis, or PET-PEESE to assess and correct for small-study effects and publication bias.

In a meta-analysis, what does the I2I^2 statistic represent?

What visual tool is commonly used in meta-analyses to detect publication bias by plotting effect sizes against their standard errors?

PRISMA Guidelines for Systematic Reviews

  1. 1

    Identification: Search databases with predefined boolean strings.

  2. 2

    Screening: Review titles and abstracts against inclusion/exclusion criteria.

  3. 3

    Eligibility: Full-text review of remaining articles.

  4. 4

    Included: Final selection of studies for quantitative synthesis.