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Factor analysis for ranked data with application to a job selection attitude survey

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  • Philip L. H. Yu
  • K. F. Lam
  • S. M. Lo

Abstract

Summary. 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.

Suggested Citation

  • Philip L. H. Yu & K. F. Lam & S. M. Lo, 2005. "Factor analysis for ranked data with application to a job selection attitude survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 583-597, July.
    • Handle: RePEc:bla:jorssa:v:168:y:2005:i:3:p:583-597
    DOI: 10.1111/j.1467-985X.2005.00363.x
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    References listed on IDEAS

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    1. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
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    Cited by:

    1. Cristina Mollica & Luca Tardella, 2021. "Bayesian analysis of ranking data with the Extended Plackett–Luce model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 175-194, March.
    2. Philip Yu & Paul Lee & W. Wan, 2013. "Factor analysis for paired ranked data with application on parent–child value orientation preference data," Computational Statistics, Springer, vol. 28(5), pages 1915-1945, October.
    3. Xu, Hang & Alvo, Mayer & Yu, Philip L.H., 2018. "Angle-based models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 113-136.

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