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The Relative Importance and Interaction of Contextual and Methodological Predictors of Mean rWG for Work Climate
被引:0
|作者:
Michael J. Burke
Kristin Smith-Crowe
Maura I. Burke
Ayala Cohen
Etti Doveh
Shuhua Sun
机构:
[1] Tulane University,Freeman School of Business
[2] Boston University,Questrom School of Business
[3] HumRRO,undefined
[4] Technion – Israel Institute of Technology,undefined
[5] Technion – Israel Institute of Technology,undefined
[6] Statistics Laboratory,undefined
[7] Faculty of Industrial Engineering and Management,undefined
来源:
关键词:
Within-group agreement;
r;
CART;
Multilevel research;
Meta-analysis;
D O I:
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学科分类号:
摘要:
A variety of collective phenomena are understood to exist to the extent that workers agree on their perceptions of the phenomena, such as perceptions of their organization’s climate or perceptions of their team’s mental model. Researchers conducting group-level studies of such phenomena measure individuals’ perceptions via surveys and then aggregate data to the group level if the mean within-group agreement for a sample of groups is sufficiently high. Despite this widespread practice, we know little about the factors potentially affecting mean within-group agreement. Here, focusing on work climate, we report an investigation of a number of expected contextual (social interaction) and methodological predictors of mean rWG, a common statistic for judging within-group agreement in applied psychology and management research. We used the novel approach of meta-CART, which allowed us to assess the relative importance and possible interactions of the predictor variables. Notably, mean rWG values are driven by both contextual (average number of individuals per group and cultural individualism-collectivism) and methodological factors (the number of items in a scale and scale reliability). Our findings are largely consistent with expectations concerning how social interaction affects within-group agreement and psychometric arguments regarding why adding more items to a scale will not necessarily increase the magnitude of an index based on a Spearman-Brown “stepped-up correction.” We discuss the key insights from our results, which are relevant to the study of multilevel phenomena relying on the aggregation of individual-level data and informative for how meta-analytic researchers can simultaneously examine multiple moderator variables.
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页码:923 / 951
页数:28
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