Tuesday 14th July      Wednesday 15th July      Thursday 16th July      Friday 17th July     

Thursday 16th July, 11:00 - 12:30 Room: L-101

Recent developments in the analysis of panel data 1
Convenor	Dr Klaus Pforr (GESIS – Leibniz-Institute for the Social Sciences )
Coordinator 1	Professor Josef Brüderl (Department of Sociology, University of Munich)
Coordinator 2	Dr Jette Schröder (GESIS – Leibniz-Institute for the Social Sciences)

Session Details

Panel data offer two major advantages compared to cross-sectional data:
1) They allow to identify causal effects under weaker assumptions (within estimation)
2) They allow to estimate individual trajectories over time (growth curve modeling)
Not all model classes that are available for panel data analysis, exploit these advantages fully.
There is much uncertainty amongst users, which kind of models to use. On the other side, there
are new model classes, for which it is quite unclear what the assumptions are that they need to
identify a causal effect (e.g. structural equation models for panel data, and multi-level models).
Therefore, we especially welcome papers that
1) compare different model classes and their usefulness for panel data analyses, or that
2) apply recently developed model classes and explicate their assumptions.

Paper Details

1. Modelling change as an event or as discrete state? A comparison of event-history analysis and panel regression
Professor Marco Giesselmann (DIW Berlin / University of Bielefeld)
Professor Michael Windzio (University of Bremen)

This presentation compares the technical and substantial differences of logisitic panel regression and discrete event history analysis in the social sciences. From these differences, we derive certain strategies for empirical practioners to make accurate and functional decions between these two methods in different research situations. We illustrate our suggestions by a research example based on the Socio-Economic Panel Study (SOEP), analyzing the impact of employment status on cohabiting in early adulthood. We perform this analysis in both analytical frameworks and discuss the surprising differences in results.

2. Can Panel Data Resolve Questions of Causal Ordering–and If So, How?
Mr Lars Leszczensky (MZES, University of Mannheim)

Many social scientists turn to panel data for resolving questions of causal ordering. But conventional panel models rely on the assumption of strict exogeneity, which rules out reverse causality. While this assumption can be relaxed in some circumstances, alternative model may also yield biased estimates if the lags between panel waves do not match the actual causal lags of the process under study. Using a substantive example with real data, I compare results obtained by different panel models and discuss the plausibility of the underlying assumptions of these models.

3. Parenthood and life satisfaction in Switzerland. Methodological challenges and substantive results.
Mrs Malgorzata Mikucka (Université catholique de Louvain)
Mrs Ester Rizzi (Université catholique de Louvain)

Estimation of the effect of parenthood on real-life outcomes, such a life satisfaction, is challenging due to (1) selection, (2) heterogeneity of the effects, and (3) the outcomes changing with the ages of children. This paper uses 14 waves of the Swiss Household Panel and fixed-effect models to estimate the effect of parenthood on life satisfaction. We discuss the consequences of methodological choices involved, including the choice of the sample, coding of independent variable, choice of the reference category, and functional form of relationship.

4. Statistical analysis of multivariate longitudinal data
Dr Xin-yuan Song (The Chinese University of Hong Kong)

This work develops a hidden Markov latent variable model for analyzing multivariate longitudinal data. To reveal the dynamic patterns and possible heterogeneity of the associations and interrelationships among longitudinal observed and latent variables, a mixed hidden Markov model is introduced to model the transition probabilities across different latent states. We develop sound statistical methods to perform parameter estimation, hypothesis testing, and model selection. We conduct simulation studies to assess the empirical performance of the proposed methodologies and apply the model to a longitudinal study of cocaine use. Important insights into effective prevention of cocaine use are obtained.