A Further Proposal to Perform Multiple Imputation on a Bunch of Polytomous Items Based on Latent Class Analysis

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

Abstract

This work advances an imputation procedure for categorical scales which relays on the results of Latent Class Analysis and Multiple Imputation Analysis. The procedure allows us to use the information stored in the joint multivariate structure of the data set and to take into account the uncertainty related to the true unobserved values. The accuracy of the results is validated in the Item Response Models framework by assessing the accuracy in estimation of key parameters in a data set in which observations are simulated Missing at Random. The sensitivity of the multiple imputation methods is assessed with respect to the following factors: the number of latent classes set up in the Latent Class Model and the rate of missing observations in each variable. The relative accuracy in estimation is assessed with respect to the Multiple Imputation By Chained Equation missing data handling method for categorical variables.

References

  1. Linzer, D. A., & Lewis, J. B. (2011). poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software, 42(10), 1–29. http://www.jstatsoft.org/v42/i10/.Google Scholar
  2. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd edn.). New York: Wiley.MATHGoogle Scholar
  3. Rizopoulos, D. (2006). ltm: latent trait models under IRT. R package version 0.5–0.Google Scholar
  4. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.CrossRefGoogle Scholar
  5. Schafer, J. (1997). Analysis of incomplete multivariate data. Boca Raton, FL: Chapman and Hall.CrossRefMATHGoogle Scholar
  6. Sulis, I., & Porcu, M. (2008). Assessing the effectiveness of a stochastic regression imputation method for ordered categorical data. Working paper. Quaderni di Ricerca CRENoS, 4. http://crenos.unica.it/crenos/it/node/269.
  7. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. http://www.jstatsoft.org/v45/i03/.Google Scholar
  8. Vermunt, J. K., Van Ginkel, J. R., Van der Ark, L. A., & Sijtsma, K. (2008). Multiple imputation of categorical data using latent class analysis. Sociological Methodology, 33, 269–297.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Dipartimento di Scienze Sociali e delle IstituzioniUniversità di CagliariCagliariItaly

Personalised recommendations