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Presenter Bergsma, Wicher; Dept. of Statistics, London School of Economics and Political Science Authors Wicher Bergsma; London School of Economics and Political Science, U.K Marcel Croon and Jacques Hagenaars; Tilburg School of Social and Behavioral Sciences Title Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data Abstract In the social, behavioural, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. Marginal models provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level. The present talk will focus on the maximum likelihood method, which has many attractive statistical properties, for fitting marginal models. This talk is based on a recent book by the authors in the Springer series Statistics for the Social Sciences, see www.cmm.st.
Presenter Croon, Marcel; Tilburg School of Social and Behavioral Sciences Authors Marcel Croon; Jacques A. Hagenaars; Department Methodology and Statistics, Tilburg University Wicher Bergsma; London School of Economics and Political Science, UK Francesca Bassi; University of Padova, Padova, Italy Title Marginal models for longitudinal categorical data from a complex rotating design Abstract In their book Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data (2009), Bergsma, Croon & Hagenaars discuss several applications of marginal models for categorical data observed in longitudinal studies. They distinguish between the analysis of trend data, when different random samples from the same population are drawn at different time points, and panel data, when the same random sample from a population is observed at different time points. For both types of data, they discuss how various hypotheses about gross and net changes over time can be tested by marginal modeling. These methods can be extended to the case the data are collected in a more complex way, for instance, by means of a rotating design in which different random cross-sectional samples are followed over time at different measurement occasions. The data which will be analyzed come from the Italian Continuous Quarterly Labour Force Survey, which is cross-sectional with a 2-2-2 rotating design. The questionnaire yields multiple indicators of labour force participation for each quarter: (i) each respondent is classified as employed, unemployed or out of the labour market according to the definition of the International Labour Office on the bases of answers given to a group of questions (ii) each respondent is asked to classify himself as employed, unemployed or out of the labour market, the so-called self-perceived condition; and (iii) a retrospective
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