Application of Multinomial and Ordinal Regressions to the Data of Japanese Female Labor Market

Fujimoto, Kayo (2003) Application of Multinomial and Ordinal Regressions to the Data of Japanese Female Labor Market. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

This paper describes the application of ordered and unordered multinomial approaches to Japanese Female Labor Market data with the goal of examining how inter-organizational networks linking schools to large corporations supersede labor market processes in the Japanese female labor market. Two sets of response categories were used for a proportional odds model, a non-proportional odds model, and a multinomial logit model. The results from the six combinations of these models were compared in terms of their goodness of model fit. The results showed that the proportional odds assumption was weakly supported, and the Wald test indicates that the violation of proportional odds assumption seems to be limited to a single variable. My study implies that partially proportional odds model would yield a better fit to my female labor market data.

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Details

Item Type:	University of Pittsburgh ETD
Status:	Unpublished
Creators/Authors:	Creators Email Pitt Username ORCID Fujimoto, Kayo fujimoto@pitt.edu FUJIMOTO
ETD Committee:	Title Member Email Address Pitt Username ORCID Committee Chair Doreian, Patrick pitpat@pitt.edu PITPAT Committee Member Gleser, Leon ljg@stat.pitt.edu GLESER Committee Member Stone, Roslyn roslyn@pitt.edu ROSLYN
Date:	16 December 2003
Date Type:	Completion
Defense Date:	12 December 2003
Approval Date:	16 December 2003
Submission Date:	16 December 2003
Access Restriction:	No restriction; Release the ETD for access worldwide immediately.
Institution:	University of Pittsburgh
Schools and Programs:	Dietrich School of Arts and Sciences > Statistics
Degree:	MS - Master of Science
Thesis Type:	Master's Thesis
Refereed:	Yes
Uncontrolled Keywords:	AIC; BIC; cumulative logit model; deviance; employment outcome; Fu's generalized odds model; latent variable; McFadden’s R2; parallel regression assumption
Other ID:	http://etd.library.pitt.edu/ETD/available/etd-12162003-153155/, etd-12162003-153155
Date Deposited:	10 Nov 2011 20:11
Last Modified:	15 Nov 2016 13:54
URI:	http://d-scholarship.pitt.edu/id/eprint/10387

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