The Relative Importance and Interaction of Contextual and Methodological Predictors of Mean rWG for Work Climate |
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Authors: | Burke Michael J. Smith-Crowe Kristin Burke Maura I. Cohen Ayala Doveh Etti Sun Shuhua |
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Affiliation: | 1.Freeman School of Business, Tulane University, New Orleans, LA, 70118, USA ;2.Questrom School of Business, Boston University, Boston, MA, 02215, USA ;3.HumRRO, 700 North Hurstbourne Parkway, Louisville, KY, 40223, USA ;4.Technion – Israel Institute of Technology, Haifa, Israel ;5.Technion – Israel Institute of Technology, Statistics Laboratory, Faculty of Industrial Engineering and Management, 3200003, Haifa, Israel ; |
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Abstract: | 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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