Modelling error dependence in categorical longitudinal data

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

Hidden Markov models (HMMs) oer an attractive way of accounting and correcting
for measurement error in longitudinal data as they do not require the use of a "gold
standard" data source as a benchmark. However, while the standard HMM assumes
the errors to be independent or random, some common situations in survey and
register data cause measurement error to be systematic. HMMs can correct for
systematic error as well if the local independence assumption is relaxed. In this
chapter, we present several (mixed) HMMs that relax this assumption with the use
of two independent indicators for the variable of interest. Finally, we illustrate the
results of some of these HMMs with the use of an example of employment mobility.
For this purpose, we use linked survey-register data from the Netherlands.
Original languageEnglish
Title of host publicationMeasurement Error in Longitudinal Data
EditorsAlexandru Cernat, Joseph Sakshaug
PublisherOxford University Press
Publication statusAccepted/In press - Jun 2020

Keywords

  • measurement error
  • longitudinal analysis
  • Hidden Markov Model
  • latent class analyses
  • register data

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  • Cite this

    Pavlopoulos, D., Pankowska, P. K., Bakker, B. F. M., & Oberski, D. (Accepted/In press). Modelling error dependence in categorical longitudinal data. In A. Cernat, & J. Sakshaug (Eds.), Measurement Error in Longitudinal Data Oxford University Press.