Advertisement

Latent Regression in Rasch Framework

  • Silvia BacciEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Rasch-type measurement models are an important and widespread instrument in estimating latent variables. In this contribution the attention is set on a special type of Rasch models: the latent regression Rasch models. The interest is focused on two main problems: the latent regression modelling of longitudinal data and latent regression modelling with missing not at random responses. The empirical study concerns the measurement of Health related Quality of Life in cancer patients.

Keywords

Latent Trait Complete Case Analysis Random Response Latent Regression Difficulty Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Adams, R., Wilson, M., & Wang, W. (1997). The multidimensional random coeffcients multinomial logit model. Applied Psycholigical Measurement,21, 1–23.CrossRefGoogle Scholar
  2. Andersen, E. B., & Madsen, M. (1977). Estimating the parameters of the latent population distribution. Psychometrika,42, 357–374.zbMATHCrossRefMathSciNetGoogle Scholar
  3. Bacci, S. (2008). Analysis of longitudinal Health related Quality of Life using latent regression in the context of Rasch modelling. In C. Huber, N. Limnios, M. Mesbah, & M. Nikuline, (Eds.), Mathematical Methods for Survival Analysis, Reliability and Quality of Life (pp. 277–292). Hermes.Google Scholar
  4. Fisher G. H., & Molenaar, I. W. (1995). Rasch models. foundations, recent developments and applications. New York: Springer-Verlag.Google Scholar
  5. Holman, R., & Glas, C. A. W. (2005). Modelling non-ignorable missing-data mechanisms with item response theory models. British Journal of Mathematical and Statistical Psychology,58, 1–17.CrossRefMathSciNetGoogle Scholar
  6. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. Hoboken: Wiley Series in Probability and Statistics.zbMATHGoogle Scholar
  7. Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.Google Scholar
  8. Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed framework for item response theory. Psychological Methods,8, 185–205.CrossRefGoogle Scholar
  9. Tamburini, M., Rosso S. et al. (1992). A therapy impact questionnaire for quality-of-life assessment in advanced cancer research. Annals of Oncology,5, 565–570.Google Scholar
  10. Ware, J. E., Kosinski, M., Turner-Bowker, D. M., & Gandek, B. (2002). SF-12v2. How to score version 2 of the SF-12 health survey. Lincoln: QualityMetric Incorporated.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Statistics “G. Parent”FirenzeItaly

Personalised recommendations