Using Longitudinal Data to Model Teachers' Ratings of Classroom Behavior as a Dynamic Process

1981 ◽  
Vol 6 (3) ◽  
pp. 237-255 ◽  
Author(s):  
Ian Plewis
Keyword(s):  
Measurement Error ◽  
Longitudinal Data ◽  
Dynamic Process ◽  
Categorical Data ◽  
Continuous Time ◽  
Classroom Behavior ◽  
Markov Models ◽  
Time Measurement ◽  
Small Sample ◽  
Educational Implications

Simple Markov models are fitted to a small sample of longitudinal categorical data of teachers' ratings of children's classroom behavior. Although the data consist only of observations at 5 occasions, it was possible, after dividing the data into two groups, to fit plausible models in continuous time. Measurement error and alternative longitudinal designs are discussed, and some possible educational implications are noted.

Continuous Time Markov Models for Binary Longitudinal Data

2006 ◽  
Vol 48 (3) ◽  
pp. 411-419 ◽  
Author(s):  
Richard H. Jones ◽  
Stanley Xu ◽  
Gary K. Grunwald
Keyword(s):  
Longitudinal Data ◽  
Continuous Time ◽  
Markov Models ◽  
Binary Longitudinal Data

scholarly journals Modelling Error Dependence in Categorical Longitudinal Data

2021 ◽  
pp. 173-194
Author(s):  
Dimitris Pavlopoulos ◽  
Paulina Pankowska ◽  
Bart Bakker ◽  
Daniel Oberski
Keyword(s):  
Measurement Error ◽  
Longitudinal Data ◽  
Gold Standard ◽  
Markov Models ◽  
Hidden Markov ◽  
Register Data ◽  
Standard Data ◽  
Data Source ◽  
Error Dependence ◽  
Modelling Error

Hidden Markov models (HMMs) offer 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.

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