The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations
This article offers a correlation matrix of meta-analytic estimates between various employee job attitudes (i.e., Employee engagement, job satisfaction, job involvement, and organizational commitment) and indicators of employee effectiveness (i.e., Focal performance, contextual performance, turnover...
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2016-09-01
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340916305017 |
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doaj-18048dc5cb9d48d5abdf13d895aea1c12020-11-24T21:47:21ZengElsevierData in Brief2352-34092016-09-01813911394The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlationsMichael M. Mackay0The University of Memphis, United StatesThis article offers a correlation matrix of meta-analytic estimates between various employee job attitudes (i.e., Employee engagement, job satisfaction, job involvement, and organizational commitment) and indicators of employee effectiveness (i.e., Focal performance, contextual performance, turnover intention, and absenteeism). The meta-analytic correlations in the matrix are based on over 1100 individual studies representing over 340,000 employees. Data was collected worldwide via employee self-report surveys. Structural path analyses based on the matrix, and the interpretation of the data, can be found in “Investigating the incremental validity of employee engagement in the prediction of employee effectiveness: a meta-analytic path analysis” (Mackay et al., 2016) [1]. Keywords: Meta-analysis, Job attitudes, Job performance, Employee, Engagement, Employee effectivenesshttp://www.sciencedirect.com/science/article/pii/S2352340916305017 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Michael M. Mackay |
spellingShingle |
Michael M. Mackay The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations Data in Brief |
author_facet |
Michael M. Mackay |
author_sort |
Michael M. Mackay |
title |
The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations |
title_short |
The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations |
title_full |
The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations |
title_fullStr |
The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations |
title_full_unstemmed |
The link between employee attitudes and employee effectiveness: Data matrix of meta-analytic estimates based on 1161 unique correlations |
title_sort |
link between employee attitudes and employee effectiveness: data matrix of meta-analytic estimates based on 1161 unique correlations |
publisher |
Elsevier |
series |
Data in Brief |
issn |
2352-3409 |
publishDate |
2016-09-01 |
description |
This article offers a correlation matrix of meta-analytic estimates between various employee job attitudes (i.e., Employee engagement, job satisfaction, job involvement, and organizational commitment) and indicators of employee effectiveness (i.e., Focal performance, contextual performance, turnover intention, and absenteeism). The meta-analytic correlations in the matrix are based on over 1100 individual studies representing over 340,000 employees. Data was collected worldwide via employee self-report surveys. Structural path analyses based on the matrix, and the interpretation of the data, can be found in “Investigating the incremental validity of employee engagement in the prediction of employee effectiveness: a meta-analytic path analysis” (Mackay et al., 2016) [1]. Keywords: Meta-analysis, Job attitudes, Job performance, Employee, Engagement, Employee effectiveness |
url |
http://www.sciencedirect.com/science/article/pii/S2352340916305017 |
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