Factor analysis for ranked data with application to a job selection attitude survey
Philip Yu (),
F. Lam K. and
M. Lo S.
Journal of the Royal Statistical Society Series A, 2005, vol. 168, issue 3, pages 583-597
Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation-maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top "q" out of "k" items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs. Copyright 2005 Royal Statistical Society.
References: Add references at CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-985X.2005.00363.x link to full text (text/html)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:bla:jorssa:v:168:y:2005:i:3:p:583-597
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X
Access Statistics for this article
Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples
More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society
Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().