Analyzing effect sizes: Random-effects models.

By Stephen Raudenbush. 2009

584 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7f5220bda375b3c436b6fe580113b6c35d3cdafc

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Citations in the corpus

Can we really reduce ethnic prejudice outside the lab? A meta‐analysis of direct and indirect contact interventions (2015, methodology): “The expected overall effect of contact interventions (Hypothesis 1 and 2) was tested under the assumptions of the meta-analytic random effects model (REM; see Hedges & Vevea, 1998; Raudenbush, 2009).”
Active learning increases student performance in science, engineering, and mathematics (2014, methodology): “We used a random effects model (46, 47) to compare effect sizes.”
A Meta-Analysis Integrating 25 Years of Diversity Climate Research (2020, methodology): “More specifically, we used the omnibus multilevel metaregression analyses where individual effect size estimates are nested within samples (Raudenbush, 2009).”
Methods for Second Order Meta-Analysis and Illustrative Applications (2013, background): “This is because FE models assume a priori that there can be no real variation in effects sizes across studies (i.e., no variation beyond sampling error variance), an unrealistic assumption (Hunter & Schmidt, 2000; Raudenbush, 1994, 2009; Schmidt et al., 2009).”
Advancing Theory by Assessing Boundary Conditions With Metaregression: A Critical Review and Best-Practice Recommendations (2018, methodology): “The following equation (e.g., Raudenbush, 2009; Viechtbauer, López-López, SánchezMeca, & Marín-Martínez, 2015) describes the extent to which boundary conditions x predict the relation between two variables, as indexed by the effect size y, in a sample of k studies: y B B x B x u ei i j ij i i= + +…”
Examining the Efficacy of Self-Regulated Strategy Development for Students with Emotional or Behavioral Disorders: A Meta-Analysis (2014, methodology): “A random effects model was selected because it allows for generalization to a larger population (Raudenbush, 2009) and is recommended by Hedges et al.”
Methods to estimate the between‐study variance and its uncertainty in meta‐analysis† (2015, unknown): “The estimate τ̂2REML is produced by setting the derivative of the restricted log-likelihood function (Raudenbush, 2009) lnL τ2 ¼ k 2 ln 2πð Þ 1 2 X ln vi þ τ2 1 2 X yi μ̂RE τ̂2ML 2 vi þ τ2ð Þ 1 2 ln X 1 vi þ τ2ð Þ ; with respect to τ2 equal to zero and solving the resulting equation for τ2.”
Multivariate Meta-Analysis as Structural Equation Models (2013, methodology): “, Kalaian & Raudenbush, 1996; Konstantopoulos, 2008; Kutner, Nachtsheim, Neter, & Li, 2005; Raudenbush et al., 1988). Cheung (2008) applied this transformation on the univariate effect size.”
Racial/ethnic discrimination and well-being during adolescence: A meta-analytic review (2018, unknown): “Random-effects models assume that studies differ beyond sampling errors and that true effect sizes vary across studies (Raudenbush, 2009).”
A Quantitative Review of Physical Activity, Health, and Learning Outcomes Associated With Classroom-Based Physical Activity Interventions (2012, methodology): “, Qwithin was significant), a mixed-effects model with moderator variables was performed by incorporating additional uncertainty within each level of the moderator, which was estimated using the method-of-moment estimation (Raudenbush, 2009).”
Self-esteem and the quality of romantic relationships (2016, methodology): “For computing the weighted mean effect sizes, we used random-effects models and study weights with w = n − 3 (Raudenbush, 2009).”
Meta‐analysis reveals the importance of matrix composition for animals in fragmented habitat (2011, methodology): “The pooled variance estimate was incorporated into the weights for each effect as wi = 1/(vi + s2pooled), and tests for the main effects of connectivity metric class, matrix heterogeneity, habitat cover and interactions were conducted as fixed effects (Raudenbush 2009).”
Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach. (2014, methodology): “Three-level meta-analyses can be conducted in multilevel modeling packages such as HLM (Raudenbush & Bryk, 2002) and SAS (SAS Institute, 2003). Readers might wonder why an SEM approach is preferable. As discussed in M. W.-L. Cheung (2008), many state-of-the-art techniques are available to reviewers ”