Analysing nominal data from a panel survey: Employment transitions of Australian women

Haynes, Michele, Western, Mark, Yu, Laurel and Spallek, Melanie (2008). Analysing nominal data from a panel survey: Employment transitions of Australian women. In: American Sociological Association 103rd Annual Meeting 2008: Worlds of Work, Boston, M.A. USA, (). 1 - 4 August 2008.


Author Haynes, Michele
Western, Mark
Yu, Laurel
Spallek, Melanie
Title of paper Analysing nominal data from a panel survey: Employment transitions of Australian women
Conference name American Sociological Association 103rd Annual Meeting 2008: Worlds of Work
Conference location Boston, M.A. USA
Conference dates 1 - 4 August 2008
Place of Publication not available
Publisher not available
Publication Year 2008
Sub-type Oral presentation
ISBN not available
Total pages 20
Language eng
Abstract/Summary Many processes of interest in social science research are recorded as nominal variables with two or more categories such as employment status, occupation, political preference and self-reported health status. With panel data it is possible to analyse the transitions of individuals between different states of the outcome variable. The generalized linear mixed model (GLMM) often used to analyse nominal variables with repeated observations is the dynamic multinomial logit random effects model. For this model, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data. In this paper we utilise and compare a classical and Bayesian approach to estimate the GLMM, with specific application to analysing employment transitions of women over four waves of an Australian panel survey. We find that Markov chain Monte Carlo simulation allows more flexible model estimation and is less computationally intensive than the classical approach using adaptive Gaussian quadrature.
Subjects 1608 Sociology
160807 Sociological Methodology and Research Methods
Keyword Panel survey
Multinomial logit regression
Employment
Women
Bayesian analysis
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status Unknown
Additional Notes Cited as "Unpublished Manuscript". Section on Methodology Paper Session: Model Comparison, Specification, & Identification

 
Versions
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Access Statistics: 123 Abstract Views  -  Detailed Statistics
Created: Wed, 17 Feb 2010, 09:41:15 EST by Sue Green on behalf of Faculty of Social & Behavioural Sciences