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Vorlesungsverzeichnis >> Fakultät Sozial- und Wirtschaftswissenschaften >> Bachelor-/Masterstudiengang Soziologie >> Master Soziologie >>
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Methoden der empirischen Sozialforschung inkl. Studienschwerpunkt
Lehrveranstaltungen der Modulgruppe B] Methoden der empirischen Sozialforschung und des Kernbereichs der Modulgruppe C3] Empirische Sozialforschung
Zur aktuellen Zusammensetzung der Module
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Fortgeschrittene Verfahren der Längsschnittanalyse: Applied Panel Data Analysis -
- Dozentinnen/Dozenten:
- Michael Gebel, Chen-Hao Hsu
- Angaben:
- Seminar, 4 SWS
- Termine:
- Mo, 8:00 - 12:00, Raum n.V.
- 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]
Registration:
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:
English
Module exam:
Portfolio in English (time: 3 months)
- Inhalt:
- 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.
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Fortgeschrittene Verfahren der Längsschnittanalyse: Sequences of life: Depicting the life-course as a sequence of events -
- Dozent/in:
- Sophia Fauser
- Angaben:
- Seminar, 4 SWS
- Termine:
- Mo, 14:00 - 18:00, RZ/00.05
- Voraussetzungen / Organisatorisches:
- Voraussetzungen / Organisatorisches:
Students have to be familiar with the contents of the compulsory lecture “Research design”.
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
Moreover, familiarity with basic quantitative methods, such as (cross-sectional) multiple linear and binary regression analysis would be an advantage but is not required.
Registration:
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:
English.
Module-related examination:
Portfolio (time: 3 months); can be either written in English or German
- Inhalt:
- Learning targets:
The central aim of this course is to introduce students to the interdisciplinary method of sequence analysis and enable them to independently apply it to sociological research topics using the statistics package Stata. Specifically, students will receive an overview of working with longitudinal data of the Socio-Economic Panel (SOEP), and learn to prepare panel data for sequence analysis, the various ways of illustrating and describing sequences, as well as how to use the identified sequences for further theory-driven empirical analysis (using groups of sequences both as an independent or dependent variable) and to correctly interpret and clearly present the results of sequence analyses in tables and graphs.
Course contents:
Originating in biology, the interdisciplinary method of sequence analysis has become a popular tool in the social sciences to depict and study life-course events and processes such as the transition from school to work, employment careers or family formation. Generally, the basic idea of sequence analysis is to study successions of states and events, thus offering a more holistic approach than the cross-sectional comparison of single states (e.g. comparing the employed to unemployed).This course is designed to introduce students to sequence analysis and its application to life-course research questions, especially concerning work trajectories (e.g. transitions in and out of unemployment) and family formation processes (e.g., transitions from single to first marriage or childbirth). The course gives an overview of the foundations of working with the Socio-Economic Panel (SOEP) and on how to prepare panel data for sequence analysis. Moreover, the course covers the description and illustration of sequences, measuring the similarity of sequences, clustering sequences, and approaches to further work with sequences as a dependent variable or as an independent variable in a regression framework. Exemplary empirical studies and current debates in the sequence analysis literature are discussed. Additionally, in lab sessions students will learn how to practically implement sequence analyses using the statistics package Stata. The lab sessions and the seminar theses will draw exclusively on topical sociological questions of life course research (determinants and consequences of life course events such as unemployment, fixed-term employment, childbirth or marriage) and data of the SOEP. Specifically, the course offers an applied hands-on approach to the preparation of panel data for sequence analysis and the statistical analyses of life-course sequences during the lab sessions.
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Research Design (Übung) -
- Dozent/in:
- Daniel Zeddel
- Angaben:
- Übung, 2 SWS
- Termine:
- Mi, 14:00 - 16:00, Raum n.V.
- Voraussetzungen / Organisatorisches:
- Registration:
Please register for the course via the VC until October 14, 2021. All students who are registered will receive information about this course via the VC.
The course Research Design is composed of a lecture and a complimentary exercise course. The lecture takes place every week; the exercise course consists of e-learning exercises and occasional online classroom sessions (dates will be announced in first lecture/exercise). It is recommended to take this course at the beginning of the Master's studies.
It is not necessary to register for the course (lecture and exercise course) in advance (e.g., via FlexNow, via email, etc.). More information about the course and the registration guidelines will only be provided during the first lecture.
Language of instruction: English.
Module-related examination: Exam (time: 60 min).
- Inhalt:
- Learning targets:
After successfully attending the lecture and the exercise course, participants will be able to
- postulate research questions,
- deduct and formulate hypotheses following the principles of theory-driven empirical research,
- explain and critically discuss advanced topics of causality and causal inference in experimental and non-experimental cross-sectional and longitudinal research designs.
Course contents:
Participants will learn to formulate research hypotheses and to distinguish between different kinds of causal hypotheses. They will reflect on how to deduct hypotheses from theory according to the principles of theory-driven empirical social research. Rubin's notation of potential outcomes, which has become the backbone of modern causal analysis in the social sciences, will be introduced. Moreover, directed acyclic graphs (DAGs) will be introduced, because they offer an illustrative graphical approach to the problem of causal inference. Advanced topics of common-cause confounding, overcontrol bias, and endogenous selection bias will be discussed in the framework of DAGs. Advanced issues of validity, especially drawing causal inferences, will be addressed in experimental and non-experimental cross-sectional and longitudinal research designs. Throughout the course practical examples from empirical sociological research will be critically discussed.
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Research Design (Vorlesung) -
- Dozent/in:
- Peter Valet
- Angaben:
- Vorlesung, 2 SWS
- Termine:
- Di, 10:00 - 12:00, Raum n.V.
- Voraussetzungen / Organisatorisches:
- Registration:
Please register for the course via the VC until October 14, 2021. All students who are registered will receive information about this course via the VC.
The course Research Design is composed of a lecture and a complimentary exercise course. The lecture takes place every week; the exercise course consists of e-learning exercises and occasional online classroom sessions (dates will be announced in first lecture/exercise). It is recommended to take this course at the beginning of the Master's studies.
It is not necessary to register for the course (lecture and exercise course) in advance (e.g., via FlexNow, via email, etc.). More information about the course and the registration guidelines will only be provided during the first lecture.
Language of instruction: English.
Module-related examination: Exam (time: 60 min).
- Inhalt:
- Learning targets:
After successfully attending the lecture and the exercise course, participants will be able to
- postulate research questions,
- deduct and formulate hypotheses following the principles of theory-driven empirical research,
- explain and critically discuss advanced topics of causality and causal inference in experimental and non-experimental cross-sectional and longitudinal research designs.
Course contents:
Participants will learn to formulate research hypotheses and to distinguish between different kinds of causal hypotheses. They will reflect on how to deduct hypotheses from theory according to the principles of theory-driven empirical social research. Rubin's notation of potential outcomes, which has become the backbone of modern causal analysis in the social sciences, will be introduced. Moreover, directed acyclic graphs (DAGs) will be introduced, because they offer an illustrative graphical approach to the problem of causal inference. Advanced topics of common-cause confounding, overcontrol bias, and endogenous selection bias will be discussed in the framework of DAGs. Advanced issues of validity, especially drawing causal inferences, will be addressed in experimental and non-experimental cross-sectional and longitudinal research designs. Throughout the course practical examples from empirical sociological research will be critically discussed.
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