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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 Analysemethoden der quantitativen Sozialforschung: Ereignisanalyse II Diskrete Modelle (Vorlesung) -
- Dozent/in:
- Hans-Peter Blossfeld
- Angaben:
- Vorlesung, 2 SWS
- Termine:
- Di, 14:00 - 16:00, FMA/00.07
- Voraussetzungen / Organisatorisches:
- Besuch der Vorlesung ist nur parallel mit der Übung Ereignisanalyse möglich. Eine Anmeldung ist für die Vorlesung und Übung, über FlexNow, erforderlich!
The seminar is divided into a lecture and computer exercises. In the WS 2019/20, the aim of the lecture (2 hours each week) and the computer exercises (2 hours each week) is to introduce students into the models of discrete-time event history analysis, including data preparation, statistical considerations as well as substantive applications. We will in detail discuss the analysis of data with different time-aggregations and the potential impact of these aggregations on the estimated coefficients of time-constant and time-varying covariates. All discrete-time models are demonstrated on the basis of various concrete application examples in the social sciences.
It is not necessary that students took part in the SS 2019 lecture/exercises on continuous-time event history analysis. However, researchers are expected to have a basic knowledge of Stata. Students also have the possibility to discuss analysis problems and present their own event history applications. Participants can get credits by submitting defined exercises that will be specified during the course. The Stata computer exercises will be given by Dr. Gwendolin J. Blossfeld. She will also assist students if they have Stata problems and help them in making their exercises. More details on the assignments will be given during the introductory meetings.
Depending on participants’ requests, the lectures/exercises will be given in German or English.
- Inhalt:
- Description
Over the last two decades, social scientists have been collecting and analyzing event history data with increasing frequency. Illustrative examples of this type of substantive process can be given for a wide variety of social research fields: in labor market studies, workers move between unemployment and employment, full-time and part-time work, or among various kinds of jobs; in social inequality studies, people become a home-owner over the life course; in demographic analyses, men and women enter into consensual unions, marriages, or into father/motherhood, or are getting a divorce; in sociological mobility studies, employees shift through different occupations, social classes, or industries; in studies of organizational ecology, firms, unions, or organizations are founded or closed down; in political science research, governments break down, voluntary organizations are founded, or countries go through a transition from one political regime to another; in migration studies, people move between different regions or countries; in marketing applications, consumers switch from one brand to another or purchase the same brand again; in criminological studies, prisoners are released and commit another criminal act after some time; in communication analysis, interaction processes such as inter- personal and small group processes are studied; in educational studies, students drop out of school before completing their degrees, enter into a specific educational track, or later in the life course, start a program of further education; in analyses of ethnic conflict, incidences of racial and ethnic confrontation, protest, riot, and attack are studied; etc.
Depending on theoretical considerations about the process time axis and the degree of measurement accuracy of event times, the literature often distinguishes between discrete-time and continuous-time event history models. Discrete-time event history methods (1) either theoretically presuppose a discrete process time axis, i.e. that the events can only occur at fixed time intervals; (2) or they assume a continuous process time axis, i.e. that events can happen at any point in time, but that the measurement of these event times could only be achieved in a sequence of quite crude time intervals. For example, in some national panel studies events such as job shifts are only recorded as happening in yearly intervals. Various publications on discrete-time models are available in the literature. In the seminar, we will use a new book manuscript by Blossfeld, G.J. (forthcoming) and refer to Yamaguchi (1991), Singer and Willett (1993), Blossfeld and Blossfeld (2015a, 2015b)), and Blossfeld, Rohwer and Schneider (2019).
- Empfohlene Literatur:
- Blossfeld, H-P., and Blossfeld, G.J. (2015a): Life Course and Event History Analysis (pp. 51–58). In: James D. Wright (ed.), International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Vol 14. Oxford: Elsevier.
Blossfeld, H-P., and Blossfeld, G.J. (2015b): Event History Analysis (pp. 359-385), in: Henning Best and Christof Wolf (eds.): The Sage Handbook of Regression Analysis and Causal Inference, Los Angeles (CA) et al.: Sage.
Blossfeld, H.-P., Rohwer, G., and Schneider, T. (2019) Event History Analysis with Stata, 2nd Edition), Oxford: Routledge (Taylor & Francis Group) (Paperback: 9781138070851 (pub: 2019-04-30); Hardback: 9781138070790 (pub: 2019-04-30)) 2019.
Blossfeld, G.J. (forthcoming): Discrete-Time Event History Analysis (DTEHA). A Comparison with Continuous-Time Event History Analysis (CTEHA). Bamberg University: Manuscript.
Singer, Judith D., and John B. Willett (1993): “It’s About Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events.” Journal of Educational Statistics, 18: 155-195.
Yamaguchi, Kazuo. 1991. Event History Analysis (only pp. 1-28). Newbury Park: Sage.
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Fortgeschrittene Verfahren der Längsschnittanalyse: Ereignisanalyse II Diskrete Modelle (Vorlesung) -
- Dozent/in:
- Hans-Peter Blossfeld
- Angaben:
- Vorlesung, 2 SWS
- Termine:
- Di, 14:00 - 16:00, FMA/00.07
- Voraussetzungen / Organisatorisches:
- Besuch der Vorlesung ist nur parallel mit der Übung Ereignisanalyse möglich. Eine Anmeldung ist für die Vorlesung und Übung, über FlexNow, erforderlich!
The seminar is divided into a lecture and computer exercises. In the WS 2019/20, the aim of the lecture (2 hours each week) and the computer exercises (2 hours each week) is to introduce students into the models of discrete-time event history analysis, including data preparation, statistical considerations as well as substantive applications. We will in detail discuss the analysis of data with different time-aggregations and the potential impact of these aggregations on the estimated coefficients of time-constant and time-varying covariates. All discrete-time models are demonstrated on the basis of various concrete application examples in the social sciences.
It is not necessary that students took part in the SS 2019 lecture/exercises on continuous-time event history analysis. However, researchers are expected to have a basic knowledge of Stata. Students also have the possibility to discuss analysis problems and present their own event history applications. Participants can get credits by submitting defined exercises that will be specified during the course. The Stata computer exercises will be given by Dr. Gwendolin J. Blossfeld. She will also assist students if they have Stata problems and help them in making their exercises. More details on the assignments will be given during the introductory meetings.
Depending on participants’ requests, the lectures/exercises will be given in German or English.
- Inhalt:
- Description
Over the last two decades, social scientists have been collecting and analyzing event history data with increasing frequency. Illustrative examples of this type of substantive process can be given for a wide variety of social research fields: in labor market studies, workers move between unemployment and employment, full-time and part-time work, or among various kinds of jobs; in social inequality studies, people become a home-owner over the life course; in demographic analyses, men and women enter into consensual unions, marriages, or into father/motherhood, or are getting a divorce; in sociological mobility studies, employees shift through different occupations, social classes, or industries; in studies of organizational ecology, firms, unions, or organizations are founded or closed down; in political science research, governments break down, voluntary organizations are founded, or countries go through a transition from one political regime to another; in migration studies, people move between different regions or countries; in marketing applications, consumers switch from one brand to another or purchase the same brand again; in criminological studies, prisoners are released and commit another criminal act after some time; in communication analysis, interaction processes such as inter- personal and small group processes are studied; in educational studies, students drop out of school before completing their degrees, enter into a specific educational track, or later in the life course, start a program of further education; in analyses of ethnic conflict, incidences of racial and ethnic confrontation, protest, riot, and attack are studied; etc.
Depending on theoretical considerations about the process time axis and the degree of measurement accuracy of event times, the literature often distinguishes between discrete-time and continuous-time event history models. Discrete-time event history methods (1) either theoretically presuppose a discrete process time axis, i.e. that the events can only occur at fixed time intervals; (2) or they assume a continuous process time axis, i.e. that events can happen at any point in time, but that the measurement of these event times could only be achieved in a sequence of quite crude time intervals. For example, in some national panel studies events such as job shifts are only recorded as happening in yearly intervals. Various publications on discrete-time models are available in the literature. In the seminar, we will use a new book manuscript by Blossfeld, G.J. (forthcoming) and refer to Yamaguchi (1991), Singer and Willett (1993), Blossfeld and Blossfeld (2015a, 2015b)), and Blossfeld, Rohwer and Schneider (2019).
- Empfohlene Literatur:
- Blossfeld, H-P., and Blossfeld, G.J. (2015a): Life Course and Event History Analysis (pp. 51–58). In: James D. Wright (ed.), International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Vol 14. Oxford: Elsevier.
Blossfeld, H-P., and Blossfeld, G.J. (2015b): Event History Analysis (pp. 359-385), in: Henning Best and Christof Wolf (eds.): The Sage Handbook of Regression Analysis and Causal Inference, Los Angeles (CA) et al.: Sage.
Blossfeld, H.-P., Rohwer, G., and Schneider, T. (2019) Event History Analysis with Stata, 2nd Edition), Oxford: Routledge (Taylor & Francis Group) (Paperback: 9781138070851 (pub: 2019-04-30); Hardback: 9781138070790 (pub: 2019-04-30)) 2019.
Blossfeld, G.J. (forthcoming): Discrete-Time Event History Analysis (DTEHA). A Comparison with Continuous-Time Event History Analysis (CTEHA). Bamberg University: Manuscript.
Singer, Judith D., and John B. Willett (1993): “It’s About Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events.” Journal of Educational Statistics, 18: 155-195.
Yamaguchi, Kazuo. 1991. Event History Analysis (only pp. 1-28). Newbury Park: Sage.
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Forschungsdesigns: Research Design (Übung) -
- Dozent/in:
- Jonas Voßemer
- Angaben:
- Übung, 2 SWS
- Termine:
- Einzeltermin am 15.10.2019, Einzeltermin am 5.11.2019, Einzeltermin am 3.12.2019, Einzeltermin am 17.12.2019, Einzeltermin am 21.1.2020, Einzeltermin am 28.1.2020, 10:00 - 14:00, F21/02.55
- Voraussetzungen / Organisatorisches:
- 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 classroom sessions (Oct 15, 2019; Nov 05, 2019; Dec 03, 2019; Dec 17, 2019; Jan 21, 2020). 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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Forschungsdesigns: Research Design (Vorlesung) -
- Dozent/in:
- Peter Valet
- Angaben:
- Vorlesung, 2 SWS
- Termine:
- Mo, 14:00 - 16:00, KÄ7/00.10
- Voraussetzungen / Organisatorisches:
- 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 classroom sessions (Oct 15, 2019; Nov 05, 2019; Dec 03, 2019; Dec 17, 2019; Jan 21, 2020). 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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Fortgeschrittene Analysemethoden der quantitativen Sozialforschung: Advanced Regression Analysis using Stata (Part AB) -
- Dozent/in:
- May Samy
- Angaben:
- Seminar, 2 SWS
- Termine:
- Do, 10:00 - 12:00, RZ/01.02
- Voraussetzungen / Organisatorisches:
- Requirements:
You should be familiar with the statistics package Stata. If you are not familiar with it, you can either acquire or refresh the necessary skills via self-studies or attend a tutorial course (taught in German) at the beginning of the winter term. Link to the Stata tutorial course
Registration:
An advanced registration for the seminar is not required (e.g. via Flexnow or via e-mail). Further information will be shared during the first meeting.
Module exam:
Portfolio in English (time: 3 months).
- Inhalt:
- Course content:
We will shortly repeat the foundations of bivariate and multiple linear regression analysis and, then, focus on advanced topics of multiple regression analysis. The course is structured around four key topics of cross-sectional data analysis using parametric regression techniques: (1) multiple linear regression, (2) binary logistic regression, (3) ordinal logistic regression, and (4) multinomial logistic regression.
In lab sessions, participants will learn how to implement regression analyses using the statistics package Stata. The lab sessions and the seminar theses will draw on sociological questions and data of the German Social Survey (ALLBUS).
Learning targets:
The aim of this course is to empower participants:
- to critically discuss basic concepts and assumptions of multiple linear and logistic regression analyses,
- to choose the appropriate regression models following the ideas of modern causal analysis,
- to carry out regression analyses (multiple linear, binary logistic, ordinal logistic, and multinomial logistic) using the statistics package Stata,
- to interpret and present the results of regression analyses in tables and graphs.
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Fortgeschrittene Analysemethoden der quantitativen Sozialforschung: Advanced Regression Analysis using Stata (Part B) -
- Dozent/in:
- May Samy
- Angaben:
- Seminar, 2 SWS
- Termine:
- Do, 12:00 - 14:00, RZ/01.02
- Voraussetzungen / Organisatorisches:
- Requirements:
You should be familiar with the statistics package Stata. If you are not familiar with it, you can either acquire or refresh the necessary skills via self-studies or attend a tutorial course (taught in German) at the beginning of the winter term. Link to the Stata tutorial course
Registration:
An advanced registration for the seminar is not required (e.g. via Flexnow or via e-mail). Further information will be shared during the first meeting.
Module exam:
Portfolio in English (time: 3 months).
- Inhalt:
- Course content:
We will shortly repeat the foundations of bivariate and multiple linear regression analysis and, then, focus on advanced topics of multiple regression analysis. The course is structured around four key topics of cross-sectional data analysis using parametric regression techniques: (1) multiple linear regression, (2) binary logistic regression, (3) ordinal logistic regression, and (4) multinomial logistic regression.
In lab sessions, participants will learn how to implement regression analyses using the statistics package Stata. The lab sessions and the seminar theses will draw on sociological questions and data of the German Social Survey (ALLBUS).
Learning targets:
The aim of this course is to empower participants:
- to critically discuss basic concepts and assumptions of multiple linear and logistic regression analyses,
- to choose the appropriate regression models following the ideas of modern causal analysis,
- to carry out regression analyses (multiple linear, binary logistic, ordinal logistic, and multinomial logistic) using the statistics package Stata,
- to interpret and present the results of regression analyses in tables and graphs.
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Fortgeschrittene Verfahren der Querschnittsanalyse: Advanced Regression Analysis using Stata -
- Dozent/in:
- May Samy
- Angaben:
- Seminar, 4 SWS
- Termine:
- Do, 10:00 - 14:00, RZ/01.02
Einzeltermin am 13.2.2020, 10:00 - 14:00, RZ/01.02
- Voraussetzungen / Organisatorisches:
- Requirements:
You should be familiar with the statistics package Stata. If you are not familiar with it, you can either acquire or refresh the necessary skills via self-studies or attend a tutorial course (taught in German) at the beginning of the winter term. Link to the Stata tutorial course
Registration:
An advanced registration for the seminar is not required (e.g. via Flexnow or via e-mail). Further information will be shared during the first meeting.
Module exam:
Portfolio in English (time: 3 months).
- Inhalt:
- Course content:
We will shortly repeat the foundations of bivariate and multiple linear regression analysis and, then, focus on advanced topics of multiple regression analysis. The course is structured around four key topics of cross-sectional data analysis using parametric regression techniques: (1) multiple linear regression, (2) binary logistic regression, (3) ordinal logistic regression, and (4) multinomial logistic regression.
In lab sessions, participants will learn how to implement regression analyses using the statistics package Stata. The lab sessions and the seminar theses will draw on sociological questions and data of the German Social Survey (ALLBUS).
Learning targets:
The aim of this course is to empower participants:
- to critically discuss basic concepts and assumptions of multiple linear and logistic regression analyses,
- to choose the appropriate regression models following the ideas of modern causal analysis,
- to carry out regression analyses (multiple linear, binary logistic, ordinal logistic, and multinomial logistic) using the statistics package Stata,
- to interpret and present the results of regression analyses in tables and graphs.
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Research Design (Übung) -
- Dozent/in:
- Jonas Voßemer
- Angaben:
- Übung, 2 SWS
- Termine:
- Einzeltermin am 15.10.2019, Einzeltermin am 5.11.2019, Einzeltermin am 3.12.2019, Einzeltermin am 17.12.2019, Einzeltermin am 21.1.2020, Einzeltermin am 28.1.2020, 10:00 - 14:00, F21/02.55
- Voraussetzungen / Organisatorisches:
- 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 classroom sessions (Oct 15, 2019; Nov 05, 2019; Dec 03, 2019; Dec 17, 2019; Jan 21, 2020). 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:
- Mo, 14:00 - 16:00, KÄ7/00.10
- Voraussetzungen / Organisatorisches:
- 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 classroom sessions (Oct 15, 2019; Nov 05, 2019; Dec 03, 2019; Dec 17, 2019; Jan 21, 2020). 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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