The Experts below are selected from a list of 2295 Experts worldwide ranked by ideXlab platform
Isabella Giammusso - One of the best experts on this subject based on the ideXlab platform.
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‘You play like a Woman!’ Effects of gender stereotype threat on Women's performance in physical and sport activities: A meta-analysis
Psychology of Sport and Exercise, 2018Co-Authors: Ambra Gentile, Stefano Boca, Isabella GiammussoAbstract:Abstract Objectives The purpose of this quantitative review was to provide an estimation of the effect of stereotype threat on women's performance in sport. Design This review employed a meta-analytic technique. Method a meta-analysis with random effects model was performed on 24 effects. Publication bias was tested through funnel plots and Egger's regression test. Results Findings show a symmetric distribution of effects, making it possible to conclude that no File-Drawer Problem affected the collected sample of effects. Aggregating the results of the reviewed studies, a medium effect of stereotype threat manipulation on women's sport performances emerged (d = 0.33). Collected studies were coded for stereotypicality of threatened exercise. The effect of stereotype threat was significantly higher for sports activities perceived as masculine. Conclusions This meta-analysis reveals that gender stereotype affects the sport activities of women and that this is particularly true for sports typically considered suited to males.
Charles A. Pierce - One of the best experts on this subject based on the ideXlab platform.
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revisiting the File Drawer Problem in meta analysis an assessment of published and nonpublished correlation matrices
Personnel Psychology, 2012Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem rests on the assumption that statistically non-significant results are less likely to be published in primary-level studies and less likely to be included in meta-analytic reviews, thereby resulting in upwardly biased meta-analytically derived effect sizes. We conducted 5 studies to assess the extent of the File Drawer Problem in nonexperimental research. In Study 1, we examined 37,970 correlations included in 403 matrices published in Academy of Management Journal (AMJ), Journal of Applied Psychology (JAP), and Personnel Psychology (PPsych) between 1985 and 2009 and found that 46.81% of those correlations are not statistically significant. In Study 2, we examined 6,935 correlations used as input in 51 meta-analyses published in AMJ, JAP, PPsych, and elsewhere between 1982 and 2009 and found that 44.31% of those correlations are not statistically significant. In Study 3, we examined 13,943 correlations reported in 167 matrices in nonpublished manuscripts and found that 45.45% of those correlations are not statistically significant. In Study 4, we examined 20,860 correlations reported in 217 matrices in doctoral dissertations and found that 50.78% of those correlations are not statistically significant. In Study 5, we compared the average magnitude of a sample of 1,002 correlations from Study 1 (published articles) versus 1,224 from Study 4 (dissertations) and found that they were virtually identical (i.e., .2270 and .2279, respectively). In sum, our 5 studies provide consistent empirical evidence that the File Drawer Problem does not produce an inflation bias and does not pose a serious threat to the validity of meta-analytically derived conclusions as is currently believed.
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Revisiting the File Drawer Problem in meta‐analysis: An assessment of published and nonpublished correlation matrices.
Personnel Psychology, 2012Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem rests on the assumption that statistically non-significant results are less likely to be published in primary-level studies and less likely to be included in meta-analytic reviews, thereby resulting in upwardly biased meta-analytically derived effect sizes. We conducted 5 studies to assess the extent of the File Drawer Problem in nonexperimental research. In Study 1, we examined 37,970 correlations included in 403 matrices published in Academy of Management Journal (AMJ), Journal of Applied Psychology (JAP), and Personnel Psychology (PPsych) between 1985 and 2009 and found that 46.81% of those correlations are not statistically significant. In Study 2, we examined 6,935 correlations used as input in 51 meta-analyses published in AMJ, JAP, PPsych, and elsewhere between 1982 and 2009 and found that 44.31% of those correlations are not statistically significant. In Study 3, we examined 13,943 correlations reported in 167 matrices in nonpublished manuscripts and found that 45.45% of those correlations are not statistically significant. In Study 4, we examined 20,860 correlations reported in 217 matrices in doctoral dissertations and found that 50.78% of those correlations are not statistically significant. In Study 5, we compared the average magnitude of a sample of 1,002 correlations from Study 1 (published articles) versus 1,224 from Study 4 (dissertations) and found that they were virtually identical (i.e., .2270 and .2279, respectively). In sum, our 5 studies provide consistent empirical evidence that the File Drawer Problem does not produce an inflation bias and does not pose a serious threat to the validity of meta-analytically derived conclusions as is currently believed.
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REVISITING THE File Drawer Problem IN META-ANALYSIS
Academy of Management Proceedings, 2011Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem is considered one of the biggest threats to the validity of meta-analytic conclusions. The assumption is that null results are less likely to be published in primary-level s...
Ambra Gentile - One of the best experts on this subject based on the ideXlab platform.
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‘You play like a Woman!’ Effects of gender stereotype threat on Women's performance in physical and sport activities: A meta-analysis
Psychology of Sport and Exercise, 2018Co-Authors: Ambra Gentile, Stefano Boca, Isabella GiammussoAbstract:Abstract Objectives The purpose of this quantitative review was to provide an estimation of the effect of stereotype threat on women's performance in sport. Design This review employed a meta-analytic technique. Method a meta-analysis with random effects model was performed on 24 effects. Publication bias was tested through funnel plots and Egger's regression test. Results Findings show a symmetric distribution of effects, making it possible to conclude that no File-Drawer Problem affected the collected sample of effects. Aggregating the results of the reviewed studies, a medium effect of stereotype threat manipulation on women's sport performances emerged (d = 0.33). Collected studies were coded for stereotypicality of threatened exercise. The effect of stereotype threat was significantly higher for sports activities perceived as masculine. Conclusions This meta-analysis reveals that gender stereotype affects the sport activities of women and that this is particularly true for sports typically considered suited to males.
Dan R. Dalton - One of the best experts on this subject based on the ideXlab platform.
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revisiting the File Drawer Problem in meta analysis an assessment of published and nonpublished correlation matrices
Personnel Psychology, 2012Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem rests on the assumption that statistically non-significant results are less likely to be published in primary-level studies and less likely to be included in meta-analytic reviews, thereby resulting in upwardly biased meta-analytically derived effect sizes. We conducted 5 studies to assess the extent of the File Drawer Problem in nonexperimental research. In Study 1, we examined 37,970 correlations included in 403 matrices published in Academy of Management Journal (AMJ), Journal of Applied Psychology (JAP), and Personnel Psychology (PPsych) between 1985 and 2009 and found that 46.81% of those correlations are not statistically significant. In Study 2, we examined 6,935 correlations used as input in 51 meta-analyses published in AMJ, JAP, PPsych, and elsewhere between 1982 and 2009 and found that 44.31% of those correlations are not statistically significant. In Study 3, we examined 13,943 correlations reported in 167 matrices in nonpublished manuscripts and found that 45.45% of those correlations are not statistically significant. In Study 4, we examined 20,860 correlations reported in 217 matrices in doctoral dissertations and found that 50.78% of those correlations are not statistically significant. In Study 5, we compared the average magnitude of a sample of 1,002 correlations from Study 1 (published articles) versus 1,224 from Study 4 (dissertations) and found that they were virtually identical (i.e., .2270 and .2279, respectively). In sum, our 5 studies provide consistent empirical evidence that the File Drawer Problem does not produce an inflation bias and does not pose a serious threat to the validity of meta-analytically derived conclusions as is currently believed.
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Revisiting the File Drawer Problem in meta‐analysis: An assessment of published and nonpublished correlation matrices.
Personnel Psychology, 2012Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem rests on the assumption that statistically non-significant results are less likely to be published in primary-level studies and less likely to be included in meta-analytic reviews, thereby resulting in upwardly biased meta-analytically derived effect sizes. We conducted 5 studies to assess the extent of the File Drawer Problem in nonexperimental research. In Study 1, we examined 37,970 correlations included in 403 matrices published in Academy of Management Journal (AMJ), Journal of Applied Psychology (JAP), and Personnel Psychology (PPsych) between 1985 and 2009 and found that 46.81% of those correlations are not statistically significant. In Study 2, we examined 6,935 correlations used as input in 51 meta-analyses published in AMJ, JAP, PPsych, and elsewhere between 1982 and 2009 and found that 44.31% of those correlations are not statistically significant. In Study 3, we examined 13,943 correlations reported in 167 matrices in nonpublished manuscripts and found that 45.45% of those correlations are not statistically significant. In Study 4, we examined 20,860 correlations reported in 217 matrices in doctoral dissertations and found that 50.78% of those correlations are not statistically significant. In Study 5, we compared the average magnitude of a sample of 1,002 correlations from Study 1 (published articles) versus 1,224 from Study 4 (dissertations) and found that they were virtually identical (i.e., .2270 and .2279, respectively). In sum, our 5 studies provide consistent empirical evidence that the File Drawer Problem does not produce an inflation bias and does not pose a serious threat to the validity of meta-analytically derived conclusions as is currently believed.
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REVISITING THE File Drawer Problem IN META-ANALYSIS
Academy of Management Proceedings, 2011Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem is considered one of the biggest threats to the validity of meta-analytic conclusions. The assumption is that null results are less likely to be published in primary-level s...
Herman Aguinis - One of the best experts on this subject based on the ideXlab platform.
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Revisiting some “established facts” in the field of management
BRQ Business Research Quarterly, 2014Co-Authors: Herman AguinisAbstract:Abstract Although management is now becoming a mature scientific field and much theoretical and methodological progress has been made in the past few decades, management scholars are not immune to received doctrines and things we “just know to be true.” This article revisits an admittedly selected set of these “established facts” including how to deal with outliers, conducting field experiments with real entrepreneurs in real settings, the File-Drawer Problem in meta-analysis, and the distribution of individual performance. For each “established fact,” I describe its nature, the negative consequences associated with it, and best-practice recommendations in terms of how to address each. I hope this article will serve as a catalyst for future research challenging “established facts” in other substantive and methodological domains in the field of management.
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revisiting the File Drawer Problem in meta analysis an assessment of published and nonpublished correlation matrices
Personnel Psychology, 2012Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem rests on the assumption that statistically non-significant results are less likely to be published in primary-level studies and less likely to be included in meta-analytic reviews, thereby resulting in upwardly biased meta-analytically derived effect sizes. We conducted 5 studies to assess the extent of the File Drawer Problem in nonexperimental research. In Study 1, we examined 37,970 correlations included in 403 matrices published in Academy of Management Journal (AMJ), Journal of Applied Psychology (JAP), and Personnel Psychology (PPsych) between 1985 and 2009 and found that 46.81% of those correlations are not statistically significant. In Study 2, we examined 6,935 correlations used as input in 51 meta-analyses published in AMJ, JAP, PPsych, and elsewhere between 1982 and 2009 and found that 44.31% of those correlations are not statistically significant. In Study 3, we examined 13,943 correlations reported in 167 matrices in nonpublished manuscripts and found that 45.45% of those correlations are not statistically significant. In Study 4, we examined 20,860 correlations reported in 217 matrices in doctoral dissertations and found that 50.78% of those correlations are not statistically significant. In Study 5, we compared the average magnitude of a sample of 1,002 correlations from Study 1 (published articles) versus 1,224 from Study 4 (dissertations) and found that they were virtually identical (i.e., .2270 and .2279, respectively). In sum, our 5 studies provide consistent empirical evidence that the File Drawer Problem does not produce an inflation bias and does not pose a serious threat to the validity of meta-analytically derived conclusions as is currently believed.
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Revisiting the File Drawer Problem in meta‐analysis: An assessment of published and nonpublished correlation matrices.
Personnel Psychology, 2012Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem rests on the assumption that statistically non-significant results are less likely to be published in primary-level studies and less likely to be included in meta-analytic reviews, thereby resulting in upwardly biased meta-analytically derived effect sizes. We conducted 5 studies to assess the extent of the File Drawer Problem in nonexperimental research. In Study 1, we examined 37,970 correlations included in 403 matrices published in Academy of Management Journal (AMJ), Journal of Applied Psychology (JAP), and Personnel Psychology (PPsych) between 1985 and 2009 and found that 46.81% of those correlations are not statistically significant. In Study 2, we examined 6,935 correlations used as input in 51 meta-analyses published in AMJ, JAP, PPsych, and elsewhere between 1982 and 2009 and found that 44.31% of those correlations are not statistically significant. In Study 3, we examined 13,943 correlations reported in 167 matrices in nonpublished manuscripts and found that 45.45% of those correlations are not statistically significant. In Study 4, we examined 20,860 correlations reported in 217 matrices in doctoral dissertations and found that 50.78% of those correlations are not statistically significant. In Study 5, we compared the average magnitude of a sample of 1,002 correlations from Study 1 (published articles) versus 1,224 from Study 4 (dissertations) and found that they were virtually identical (i.e., .2270 and .2279, respectively). In sum, our 5 studies provide consistent empirical evidence that the File Drawer Problem does not produce an inflation bias and does not pose a serious threat to the validity of meta-analytically derived conclusions as is currently believed.
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REVISITING THE File Drawer Problem IN META-ANALYSIS
Academy of Management Proceedings, 2011Co-Authors: Dan R. Dalton, Herman Aguinis, Catherine M. Dalton, Frank A. Bosco, Charles A. PierceAbstract:The File Drawer Problem is considered one of the biggest threats to the validity of meta-analytic conclusions. The assumption is that null results are less likely to be published in primary-level s...