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.

Women, computing and the 'ideal worker': A comparison of professional occupations

In this paper we use recent census data supplemented with case study evidence to investigate the extent to which professional computing occupations in Australia are constructed around the notion of an ‘ideal’ worker. Census data are used to compare computer professionals with other selected professional occupational groups, illustrating different models of accommodating (or not accommodating) workers who do not fit the ideal model. The computer professionals group is shown to be distinctive in combining low but consistent levels of female representation across age groups, average rates of parenthood and minimal provisions for working-time flexibility. One strategy employed by women in this environment is selection of relatively routine technical roles over more time intensive consultancy based work.

Energy and calcium stores are preserved in girls and young women with CF, independent of disease severity: An explanation for the 'gender gap'?

Bioenergetics differ between males and females of many species. Human females apportion a substantial proportion of energy resources towards gynoid fat storage, to support the energetic burden of reproduction. Similarly, axial calcium accrual is favoured in females compared with males. Nutritional status is a prognostic indicator in cystic fibrosis (CF), but girls and young women are at greater risk of death despite equivalent nutritional status to males. The aim of this study was to compare fat (energy) and calcium stores (bone density) in males and females with CF over a spectrum of disease severity. Methods: Fat as % body weight (fat%) and lumbar spine (LS) and total body (TB) bone mineral density (BMD) were measured using dual absorption X-ray photometry in 127(59M) control and 101(54M) CF subjects, aged 9–25 years. An equation for predicted age at death had been determined using survival data and history of pulmonary function for the whole clinic, based on a trivariate normal model using maximum likelihood methods (1). For the CF group, a disease severity index (predicted age at death) was calculated from the derived equations according to each subjects history of pulmonary function, current age, and gender. Disease severity was classified according to percentile of predicted age at death (‘mild’ ≥75th, ‘moderate’ 25th–75th, ‘severe’ ≤25th percentile). Wt for age z-score was calculated. Serum testosterone and oestrogen were measured in males and females respectively. Fat% and LSBMD were compared between the groups using ANOVA. Results: There was an interaction between disease severity and gender: increasing disease severity was associated with greater deficits in TB (p=0.01), LSBMD (p