<i>psychmeta</i>: An R Package for Psychometric Meta-Analysis

Dahlke, Jeffrey A.; Wiernik, Brenton M.

doi:10.1177/0146621618795933

Cited by 201 publications

(148 citation statements)

References 2 publications

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“…The arguments are the same as previously specified. Additional details about these and other functions are provided in the psychmeta documentation and vignettes (Dahlke & Wiernik, 2019c).…”

Section: Resultsmentioning

confidence: 99%

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Obtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts

Wiernik

Dahlke

2020

Advances in Methods and Practices in Psychological Science

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Most published meta-analyses address only artifactual variance due to sampling error and ignore the role of other statistical and psychometric artifacts, such as measurement error variance (due to factors including unreliability of measurements, group misclassification, and variable treatment strength) and selection effects (including range restriction or enhancement and collider biases). These artifacts can have severe biasing effects on the results of individual studies and meta-analyses. Failing to account for these artifacts can lead to inaccurate conclusions about the mean effect size and between-studies effect-size heterogeneity, and can influence the results of meta-regression, publication-bias, and sensitivity analyses. In this article, we provide a brief introduction to the biasing effects of measurement error variance and selection effects and their relevance to a variety of research designs. We describe how to estimate the effects of these artifacts in different research designs and correct for their impacts in primary studies and meta-analyses. We consider meta-analyses of correlations, observational group differences, and experimental effects. We provide R code to implement the corrections described.

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“…The arguments are the same as previously specified. Additional details about these and other functions are provided in the psychmeta documentation and vignettes (Dahlke & Wiernik, 2019c).…”

Section: Resultsmentioning

confidence: 99%

“…R code to reproduce the simulation results and corresponding figures presented in this article is available at https://osf.io/cp6rt/. R code to implement the correction and meta-analytic methods described in this article is available in the psychmeta package (Dahlke & Wiernik, 2019b).…”

Section: Disclosuresmentioning

confidence: 99%

Obtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts

Wiernik

Dahlke

2020

Advances in Methods and Practices in Psychological Science

Self Cite

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“…Using this ratio of standard deviations, we corrected the correlations for range enhancement or restriction, as recommended by Schmidt and Hunter (). This was performed using the correct_r_uvdrr function from the R package psychmeta (Dahlke & Wiernik, ). In addition, an ‘effective’ sample size was calculated to correct the variance of the corrected correlation coefficient.…”

Section: Methodsmentioning

confidence: 99%

Resting Frontal Eeg Asymmetry and Personality Traits: A Meta–Analysis

2019

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Frontal asymmetry has been widely used as a marker of emotion, motivation, and psychopathology. When assessed during the resting state, it is regarded as an index of trait approach and withdrawal motivation. However, the replicability of these associations with personality is currently unclear. The present meta-analysis seeks to provide a comprehensive quantitative review of the relationship between personality traits and resting electroencephalographic (EEG) frontal asymmetry. We distinguished five personality clusters: extraversion, neuroticism, impulsivity, anger, and defensiveness. Data from 79 independent samples with overall 5700 participants were included in the metaanalysis. The results revealed that less than 0.4% of the variance in extraversion and neuroticism could be explained by resting frontal asymmetry. Similarly, a small effect was observed for trait anger, and a small-sized to medium-sized effect was observed for defensiveness, although the number of studies was very low. No significant effect emerged for impulsivity. The effects were further reduced after adjustment for publication bias. Given some evidence for heterogeneity, sub-traits were analysed, and methodological moderators were investigated. Based on the results, we conclude that the validity of resting frontal asymmetry as a marker for personality is not supported. Finally, recommendations are given to increase the replicability of frontal asymmetry research.

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“…Point mean estimates of effect sizes were interpreted as follows: values from 0.00 to 0.10 suggest trivial effect; from 0.10 to 0.30 small effect; from 0.30 to 0.50 moderate effect; and >0.50 large effect [ 34 ]. The described statistical analyses were estimated using Hunter–Schmidt method (i.e., dividing by k − 1 rather than k) in R environment with the “Psychmeta” package [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

A Systematic Review and Meta-Analysis on the Relationship between Body Dissatisfaction and Morbid Exercise Behaviour

Alcaraz‐Ibáñez

Paterna

Sicilia

et al. 2021

IJERPH

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Background: The present study aimed to quantify the relationship between body dissatisfaction and morbid exercise behaviour (MEB). Methods: The electronic databases MEDLINE, PsycINFO, Web of Science, SciELO, and Dissertations & Theses Global were searched from inception to September 2020. Pooled effect sizes corrected for sampling errors (r+) were computed using a bare-bones meta-analysis. The robustness of the results was examined by influence analyses. The presence of moderators was examined by inspection of the variance in r+ attributable to sampling errors and 80% credibility intervals, followed by subgroup analysis and univariable/multivariable meta-regressions. Publication bias was examined by visual inspection of funnel plot symmetry, cumulative meta-analysis, and Egger’s test. Results: A total of 41 effect sizes from 33 studies (n = 8747) were retrieved. Results showed a significant and near to moderate effect size (r+ = 0.267, 95% CI = 0.226 to 0.307), and this did not differ by gender, BMI, age, percentage of Whites, study quality, or MEB measure. Conversely, effect sizes were found to be stronger in published and more recently conducted studies. Conclusion: The findings indicate that body dissatisfaction is one of the likely causes underlying MEB. This suggests the need for further longitudinal research aimed at confirming the potential causal nature of this relationship.

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psychmeta: An R Package for Psychometric Meta-Analysis

Cited by 201 publications

References 2 publications

Obtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts

Obtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts

Resting Frontal Eeg Asymmetry and Personality Traits: A Meta–Analysis

A Systematic Review and Meta-Analysis on the Relationship between Body Dissatisfaction and Morbid Exercise Behaviour

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