Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, 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 available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

Comparison of rural and urban attitudes to the conservation of Asian elephants in Sri Lanka: empirical evidence

There has been little study of economic and general attitudes towards the conservation of the Asian elephant. This paper reports and analyses results from surveys conducted in Sri Lanka of attitudes of urban dwellers and farmers towards nature conservation in general and the elephant conservation in particular. The analyses are based on urban and a rural sample. Contingent valuation techniques are used as survey instruments. Multivariate logit regression analysis is used to analyse the respondents' attitudes towards conservation of elephants. It is found that, although some variations occurred between the samples, the majority of the respondents (both rural and urban) have positive attitudes towards nature conservation in general. However, marked differences in attitudes toward elephant conservation are evident between these two samples: the majority of urban respondents were in favour of elephant conservation; rural respondents expressed a mixture of positive and negative attitudes. Overall, considerable unrecorded and as yet unutilised economic support for conservation of wild elephants exists in Sri Lanka. (C) 2002 Elsevier Science Ltd. All rights reserved.

A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.