Statistical Analysis of Longitudinal/Panel Data Structures Arising from Complex
Date: April 14 - 17, 2010
Organizers: Göran Kauermann (Bielefeld), Harry Haupt (Bielefeld), Jost Reinecke (Bielefeld), Mark Stemmler (Bielefeld)
In many fields of empirical research where statistical methods are applied to model individual behaviour, there exists the problem of unobserved and/or unobservable individual heterogeneity, leading to biased results and hence unreliable inferences and subject matter conclusions drawn from the data. A solution to this problem is the availability of multiple observations on each individual by using longitudinal, or panel data, as they are characterized by a number of remarkable advantages which make them superior to research relying solely on cross-sectional data: they
- allow the identification of causal relationships,
- provide information on intra-individual development, that is constancy and change within the individual across time; thus through longitudinal analysis the constancy and change in relationship among other factors and covariates can be investigated,
- (repeated within-subjects designs) permit the evaluation of the course and outcome of program interventions (e.g., programs which reintegrate unemployed workers into the workforce),
- the study of critical life events or periods and their impact on the development of an individual,
- allow the investigation of developmental trajectories or pathways in certain (sub-)populations.
The past methodological developments in the field of panel data analysis gave rise to a plethora of different concepts and definitions, sometimes parallel, in different disciplines such as Economics, Sociology, Psychology, and so on. The aim of the workshop thus was to bring together specialists from those various fields of panel data research in order to
- present recent discipline-specific advances in methodological (formal and computational) and empirical statistical analysis of panel data,
- provide a platform to explore both conceptual differences as well as common ideas across disciplines, and
- develop some common understanding of modelling assumptions and strategies to foster interdisciplinary exchange and discussion.
In summary, after ten impressive one-hour presentations followed by intensive discussions as well as intensive round-table discussions (Topic: “Spatial and peer group association and modelling of inter-individual in panel data”) and the overwhelming feedback of the participants it can be concluded that the workshop was successful in
- demonstrating and promoting the central and interdisciplinary role of panel data analysis both in statistical and methodological developments, and
- revealing areas, forms, and extent where panel data methods and panel data sets are currently used to explain substance matter questions, and
- demonstrating a vast interdisciplinary overlap, despite a different terminology, in terms of similar statistical approaches or models.
Finally, organizers and participants agreed to continue the discussion about different strategies on panel data analysis in future workshops. One of the future goals will be the integration of different traditions in econometric, psychometric and sociometric analyses.
Badi H. Baltagi (Syracuse, NY), Ursula Berger (Bielefeld), Eldad Davidov (Zürich), Christian Heinze (Bielefeld), Cheng Hsiao (Los Angeles, CA), Volker Lang (Tübingen), Herbert Matschinger (Leipzig), John J. McArdle (Los Angeles, CA), Joachim Möller (Nürnberg), Johan H. L. Oud (Nimwegen), Peter Schmidt (Gießen), Daniel Seddig (Bielefeld), Hermann Singer (Hagen), Martin Spieß (Hamburg), Peter Valet (Leonberg), Sven Voigtländer (Bielefeld), Gert G. Wagner (Berlin)