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Einrichtungen >> Mit der Hochschule verbundene wissenschaftliche Einrichtungen >> Leibniz-Institut für Bildungsverläufe e.V. (LIfBi) an der Universität Bamberg >>

  Fortgeschrittene Verfahren der Längsschnittanalyse: Applied Panel Data Analysis

Prof. Dr. Michael Gebel, Chen-Hao Hsu, M.A.

Rein Online
Zeit: Mo 8:00 - 12:00

Voraussetzungen / Organisatorisches
Voraussetzungen / Organisatorisches Requirements:
Students have to be familiar with the contents of the compulsory lecture "Research design" and multiple linear and binary logistic regression analysis. Moreover, students are required to be familiar with the statistics package Stata. These skills could either be acquired or refreshed in self-studies or by attending an online tutorial course. Link to Stata Tutorial [https://univis.uni-bamberg.de/form?dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr_6&anonymous=1&ref=tlecture&sem=2021w&tdir=sowi/sozwiss/haupts/method]

Please register in the VC of the event until 14.10.2021. Only the persons who are registered in the VC will receive all information about the course.

Type of instruction:
On-site teaching; only the introductory course is given online on 18 October. There will be no video recording. Changes are possible depending on the guidelines of the university.

Language of instruction:

Module exam:
Portfolio in English (time: 3 months)

Learning targets:
The central aim of this course is to empower participants to critically discuss basic concepts and assumptions of various panel regression estimators for linear and binary dependent variables, to conduct theory-driven empirical research, to choose and specify the appropriate regression models according to the ideas of modern causal analysis, to independently carry out panel data analyses using the statistics package Stata and data of the Socio-Economic Panel (SOEP), to correctly interpret the results and clearly present the results of regression analyses in tables and graphs.

Course contents:
The course will first explain the basic logic of panel design as compared to cross-sectional design and practice the preparation and descriptive analysis of panel data in Stata. In the next step, Pooled Ordinary Least Squares (POLS), Fixed Effect (FE), First Difference (FD), Random Effect (RE) and hybrid models for linear dependent variables are presented and explained. Here, the different assumptions for the consistency of the estimators (=causal inference) are compared: the exogeneity assumptions are illustrated by means of causal graphs and the implications of the full rank assumption for the inclusion of time-constant regressors and the need for sufficiently time-varying regressors are discussed. In addition, the assumptions for efficiency of the estimators (=statistical inference) are presented, associated statistical tests (e.g., tests for serial correlation of error terms, Hausman test) are practiced, and implications in the form of the use of panel-robust standard errors are explained. The different models are compared and practical recommendations for model selection are given. Based on the principles of theory-driven empirical research and modern causal analysis, it is explained which time-constant and time-varying control variables should be selected. Regarding model specification, the role of Lagged Dependent Variables (LDV) and Lagged Explanatory Variables (LEV) as control variables as well as causal variables (keywords: dynamic panel models, impact function, anticipation effects) is also discussed. Problems and possible solutions of bidirectional causality in the form of feedback loops and simultaneity are discussed. Aspects of modeling age, period, and cohort effects and growth processes are discussed. Panel data models for binary dependent variables are introduced. Finally, the issue of missing data in panel models is addressed. In addition to theoretical introductions to the models, the logic of the various panel data estimators is illustrated using very simple play data. In lab sessions participants will learn how to practically implement panel data analyses using the statistics package Stata. The lab sessions and the seminar theses will draw exclusively on topical sociological questions of life course research (consequences of life course events) and data of the Socio-Economic Panel (SOEP). Specifically, the course offers an applied introduction and hands-on experience in the complex preparation of panel data for the statistical analyses during the lab sessions.

Englischsprachige Informationen:
Advanced Techniques in Longitudinal Analysis: Applied Panel Data Analysis

Zusätzliche Informationen
Erwartete Teilnehmerzahl: 30

Institution: Lehrstuhl für Soziologie, insbes. Methoden der empirischen Sozialforschung

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