Power Analysis for Meta‐Analysis

2021 ◽  
pp. 295-312
Keyword(s):  
2002 ◽  
Vol 38 (3) ◽  
pp. 274-280 ◽  
Author(s):  
Steven Muncer ◽  
Shirley Taylor ◽  
Mark Craigie
Keyword(s):  

1998 ◽  
Vol 21 (2) ◽  
pp. 216-217 ◽  
Author(s):  
Joseph S. Rossi

Chow's (1996) defense of the null-hypothesis significance-test procedure (NHSTP) is thoughtful and compelling in many respects. Nevertheless, techniques such as meta-analysis, power analysis, effect size estimation, and confidence intervals can be useful supplements to NHSTP in furthering the cumulative nature of behavioral research, as illustrated by the history of research on the spontaneous recovery of verbal learning.


Psychology ◽  
2019 ◽  
Author(s):  
David B. Flora

Simply put, effect size (ES) is the magnitude or strength of association between or among variables. Effect sizes (ESs) are commonly represented numerically (i.e., as parameters for population ESs and statistics for sample estimates of population ESs) but also may be communicated graphically. Although the word “effect” may imply that an ES quantifies the strength of a causal association (“cause and effect”), ESs are used more broadly to represent any empirical association between variables. Effect sizes serve three general purposes: research results reporting, power analysis, and meta-analysis. Even under the same research design, an ES that is appropriate for one of these purposes may not be ideal for another. Effect size can be conveyed graphically or numerically using either unstandardized metrics, which are interpreted relative to the original scales of the variables involved (e.g., the difference between two means or an unstandardized regression slope), or standardized metrics, which are interpreted in relative terms (e.g., Cohen’s d or multiple R2). Whereas unstandardized ESs and graphs illustrating ES are typically most effective for research reporting, that is, communicating the original findings of an empirical study, many standardized ES measures have been developed for use in power analysis and especially meta-analysis. Although the concept of ES is clearly fundamental to data analysis, ES reporting has been advocated as an essential complement to null hypothesis significance testing (NHST), or even as a replacement for NHST. A null hypothesis significance test involves making a dichotomous judgment about whether to reject a hypothesis that a true population effect equals zero. Even in the context of a traditional NHST paradigm, ES is a critical concept because of its central role in power analysis.


Methodology ◽  
2013 ◽  
Vol 9 (4) ◽  
pp. 137-149 ◽  
Author(s):  
Fernando Marmolejo-Ramos ◽  
Jorge González-Burgos

A power analysis of seven normality tests against the Ex-Gaussian distribution (EGd) is presented. The EGd is selected on the basis that it is a particularly well-suited distribution to accommodate positively skewed distributions such as those observed in reaction times data. A pre-assessment of the power of the selected tests across various types of distributions was done via a meta-analysis and a comparison with other power analyses reported in the literature was also performed. General recommendations are given as to which tests should be used to test normality in data suspected to resemble an EG distribution. Additionally, some topics for future research regarding the use of confidence intervals and the computation of accurate critical values are outlined.


2014 ◽  
Vol 45 (6) ◽  
pp. 1135-1144 ◽  
Author(s):  
M. N. Dalili ◽  
I. S. Penton-Voak ◽  
C. J. Harmer ◽  
M. R. Munafò

Background.Many studies have explored associations between depression and facial emotion recognition (ER). However, these studies have used various paradigms and multiple stimulus sets, rendering comparisons difficult. Few studies have attempted to determine the magnitude of any effect and whether studies are properly powered to detect it. We conducted a meta-analysis to synthesize the findings across studies on ER in depressed individuals compared to controls.Method.Studies of ER that included depressed and control samples and published before June 2013 were identified in PubMed and Web of Science. Studies using schematic faces, neuroimaging studies and drug treatment studies were excluded.Results.Meta-analysis of k = 22 independent samples indicated impaired recognition of emotion [k = 22, g = −0.16, 95% confidence interval (CI) −0.25 to −0.07, p < 0.001]. Critically, this was observed for anger, disgust, fear, happiness and surprise (k's = 7–22, g's = −0.42 to −0.17, p's < 0.08), but not sadness (k = 21, g = −0.09, 95% CI −0.23 to +0.06, p = 0.23). Study-level characteristics did not appear to be associated with the observed effect. Power analysis indicated that a sample of approximately 615 cases and 615 controls would be required to detect this association with 80% power at an alpha level of 0.05.Conclusions.These findings suggest that the ER impairment reported in the depression literature exists across all basic emotions except sadness. The effect size, however, is small, and previous studies have been underpowered.


Sign in / Sign up

Export Citation Format

Share Document