Modeling Demand for Unionization with Nontraditional Data Analysis Methods

Autor/in	DeGroot, Timothy
Titel	Modeling Demand for Unionization with Nontraditional Data Analysis Methods
Quelle	In: Social Indicators Research, 79 (2006) 2, S.275-289 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0303-8300
DOI	10.1007/s11205-005-4128-4
Schlagwörter	Correlation; Data Analysis; Network Analysis; Models; Multiple Regression Analysis; Unions; Labor Demands + Suchen Sie Ihr Suchwort? Korrelation; Auswertung; Netzplantechnik; Analogiemodell; Arbeitskampf; Tarifkonflikt
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. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10