The power of statistical tests in meta-analysis.

By L. Hedges, Terri D. Pigott. Psychological methods, 2001

720 citations recorded by the source corpus.

Subfields: Management, Monitoring, Policy and Law, Social Psychology

View Paper: https://www.semanticscholar.org/paper/d4b21d9321cb68932a5ceeed49330aff1d638042

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Citations in the corpus

What Is the Evidence for Physical Therapy Poststroke? A Systematic Review and Meta-Analysis (2014, neutral): “In addition, the observational nature of the subgroup analyses means they should be interpreted with caution, as it is known that subgroup analyses in meta-analyses can be less highly powered than analyses for main effects [29,579,580].”
Meta-Analysis and Subgroups (2013, background): “In their papers on statistical power for comparing subgroups, Hedges and Pigott (2001, 2004) note that some researchers believe that when we use metaanalysis to compare the effect across subgroups, statistical power is uniformly high.”
How to do a meta-analysis. (2010, background): “However, these tests should be used cautiously as a means to decide on how to conceptualize that data because they typically have low power to detect genuine variation in population effect sizes (Hedges & Pigott, 2001).”
Are perfectionistic strivings in sport adaptive? A systematic review of confirmatory, contradictory, and mixed evidence. (2012, background): “Conducting a meaningful meta-analysis with adequate power, though, requires a body of literature concentrated on investigating similar relationships (Hedges & Pigott, 2001; Johnson & Boynton, 2008).”
Examining the Impact of Service Quality: A Meta-Analysis of Empirical Evidence (2009, background): “A smaller number of effect sizes signifi cantly compromises the power of meta-analytic fi ndings (Hedges and Pigott 2001).”
Cancer survivors and unemployment: a meta-analysis and meta-regression. (2009, background): “Wettergren et al,39 2003 (Sweden) [Hodgkin disease] 90 (45) 47 (12) 14 (6-26) Age and residency Registry-based cross-sectional survey (for both groups)”
How to unring the bell: A meta-analytic approach to correction of misinformation (2018, methodology): “Analogous to the omnibus test that estimates the difference between groups in ANOVAs, the Q test provides a general assessment of variance and specific contrasts need to be used to explore the differences among group means (Hedges & Pigott, 2001).”
The Relative Persuasiveness of Gain-Framed Loss-Framed Messages for Encouraging Disease Prevention Behaviors: A Meta-Analytic Review (2007, background): “With 84 cases, a typical study sample size of 247 (the mean study sample size across these 84 cases), and large heterogeneity (using the conventional v value of Hedges & Pigott, 2001 ), the power for detecting a population effect size of r = .”
Guidelines for Meta-Analyses of Counseling Psychology Research (2006, methodology): “Using Hedges and Pigott (2001), first, the researchers must specify the magnitude of effect size that would be scientifically meaningful or would reflect practical significance.”
Estimates of heterogeneity (I2) can be biased in small meta-analyses (2014, background): “Although the best formulas for calculating confidence intervals are a bit complicated (4,5), they have good coverage and they give a sense of the range of possible values without highlighting a point estimate that may be biased and imprecise.”
Comparison of Three Meta-Analytic Procedures for Estimating Moderating Effects of Categorical Variables (2008, background): “Although previous work has investigated this statistical power issue (Cornwell & Ladd, 1993; Hedges & Pigott, 2001; Overton, 1998; Sackett, Harris, & Orr, 1986; Spector & Levine, 1987), the present study included a more complete set of situations (e.g., measurement error and range restriction) and…”
A meta-analytic review of benefit finding and growth. (2006, methodology): “Ideally, one would conduct a power analysis prior to conducting the meta-analysis and, in fact, Hedges and Pigott (2001) advised against calculating power after the fact.”