caa a22 4500 467917426 CHVBK 20180406152907.0 cr unu---uuuuu 170328e20061101xx s 000 0 eng 10.1007/s11205-005-4128-4 doi (NATIONALLICENCE)springer-10.1007/s11205-005-4128-4 DeGroot Timothy Spears School of Business, Oklahoma State University, 74078, Stillwater, OK, USA aut Modeling Demand For Unionization With Nontraditional Data Analysis Methods [Elektronische Daten] [Timothy DeGroot] 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. Springer, 2006 neural network analysis nationallicence unionization nationallicence Social Indicators Research Kluwer Academic Publishers 79/2(2006-11-01), 275-289 0303-8300 79:2<275 2006 79 11205 https://doi.org/10.1007/s11205-005-4128-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11205-005-4128-4 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- DeGroot Timothy Spears School of Business, Oklahoma State University, 74078, Stillwater, OK, USA aut NATIONALLICENCE 773 0- Social Indicators Research Kluwer Academic Publishers 79/2(2006-11-01), 275-289 0303-8300 79:2<275 2006 79 11205 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer