Modeling Demand For Unionization With Nontraditional Data Analysis Methods |
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Authors: | Email author" target="_blank">Timothy?DeGrootEmail author |
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Institution: | (1) Spears School of Business , Oklahoma State University , Stillwater, OK 74078, USA |
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Abstract: | Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora
of research attention do not converge upon singularly directional findings. This study explores a potential cause of such
an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant
variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional
linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived
variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis
to model union demand explained over four times as much variance as multiple regression analysis. |
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Keywords: | neural network analysis unionization |
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