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We could thus find no link between handedness and depression on the meta-analytical level. We found no differences in left- (OR = 1.04, 95% CI =, p =. Here, an OR > 1 signifies higher rates of atypical handedness in depressed compared to healthy samples. Here, we collected data from 87 studies totaling 35,501 individuals to provide a precise estimate of differences in left-, mixed- and non-right-handedness between depressed and healthy samples and computed odds ratios (ORs) between these groups. While a few studies found evidence that handedness and depression are associated, both the effect size and the direction of this association remain elusive. For depression, however, this relationship is largely unclear. Publikationen Handedness and depression: A meta-analysis across 87 studiesĪlterations in functional brain lateralization, often indicated by an increased prevalence of left- and/or mixed-handedness, have been demonstrated in several psychiatric and neurodevelopmental disorders like schizophrenia or autism spectrum disorder. Existiert in Biologische Persönlichkeitspsychologie Furthermore, the data suggest that footedness is a valuable phenotype for the study of lateral motor biases, its underlying genetics and neurodevelopment.
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Overall, this study provides new and useful reference data for laterality research. Finally, we showed that footedness is only marginally infuenced by cultural and social factors, which play a crucial role in the determination of handedness.
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Furthermore, the presence of mixed-footedness was higher in children compared to adults and leftfootedness was increased in athletes compared to the general population. Individuals with psychiatric and neurodevelopmental disorders exhibited a higher prevalence of non-right-footedness. Males were 4.1% more often non-right-footed compared to females. As many as 60.1% of left-handers were left-footed whereas only 3.2% of right-handers were left-footed. We showed that the prevalence of atypical footedness ranges between 12.10% using the most conservative criterion of left-footedness to 23.7% including all left- and mixed-footers as a single nonright category. The study aimed to determine a reliable point estimate of footedness, to study the association between footedness and handedness, and to investigate moderating factors infuencing footedness. Here, we conducted meta-analyses with four diferent classifcation systems for footedness on 145,135 individuals across 164 studies including new data from the ALSPAC cohort. Furthermore, the relationship between footedness and handedness still remains elusive. While there are good estimates on the prevalence of handedness in the population, there is no large-scale estimation on the prevalence of footedness. Evolution 59 (2):464-468, 2005.Human lateral preferences, such as handedness and footedness, have interested researchers for decades due to their pronounced asymmetries at the population level. The file-drawer problem revisited: a general weighted method for calculating fail-safe numbers in meta-analysis. He suggests a general, weighted fail-safe N using either the fixed- or random-effects frameworks that are far more commonly used for meta-analysis. As Rosenberg points out in the abstract of the paper below, they "are unweighted and are not based on the framework in which most meta-analyses are performed". Having said all that, I don't like either Rosenthal's or Orwin's 'fail-safe N' myself (though I prefer Orwin's to Rosenthal's). Orwin's method allows you to choose this, but the null effect size remains the simplest choice. Rosenthal's original fail-safe N based on statistical significance assumed the mean effect size in missing studies was the null effect size. If you're looking at an invasive or costly intervention or one with severe side-effects, or a condition that is an irritation or of short duration, the trivial reduction would be very much larger. If you're looking at an intervention that may be given to a considerable segment of the population with few side-effects and may prevent early death in a few (statins are one example that come to my mind, but you're the medic), then a small reduction in death rates might still be important, so a trivial reduction could perhaps be 1% or less, i.e. The criterion for a 'trivial' effect size (odds ratio in your example) should be decided based on the size of effect that would be considered 'trivial' in the particular scenario, rather than on statistical grounds.