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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 C.3] Empirische Sozialforschung
Zur aktuellen Zusammensetzung der Module
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Fortgeschrittene Analysemethoden der quantitativen Sozialforschung: Ereignisanalyse II Diskrete Modelle (Vorlesung) Introduction to History Analysis II – Discrete-Time Models -
- Dozent/in:
- Hans-Peter Blossfeld
- Angaben:
- Vorlesung, 2,00 SWS, ECTS: 6, Vorlesung mit zugehöriger Übung
- Termine:
- Di, 14:00 - 16:00, FMA/00.07
vom 15.10.2018 bis zum 9.2.2019
- Inhalt:
- 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 use Yamaguchi (1991), Singer and Willett (1993), Blossfeld and Blossfeld (2015a) as well as Blossfeld and Blossfeld (2015b).
The aim of the lecture (2 hours) with exercises (2 hours) in the WS 2018/19 is first to continue and finish the presentation of continuous-time event history models (e.g., parametric models of time-dependence such as the Gompertz, the Weibull, the Log-Logistic, the Log-Normal, and the semi-parametric models such as the Cox model). Then the students will be introduced into discrete-time event history analysis, including descriptions of data preparation and various discrete-time models. We will in detail discuss the analysis of aggregated data and the potential impact of aggregation 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 certainly helpful but not necessary that students took part in the SS 2018 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.
- Empfohlene Literatur:
- Blossfeld, H-P., and Blossfeld, G.J. (2015a) Life Course and Event History Analysis. In: James D. Wright (ed.), International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Vol 14. Oxford: Elsevier. pp. 51–58.
Blossfeld, H-P., and Blossfeld, G.J. (2015b) Event History Analysis, in: Henning Best and Christof Wolf (eds.): the Sage Handbook of Regression Analysis and Causal Infernece, Los Angeles (CA) et al. 359-385.
Blossfeld, H.-P., K. Golsch, and G. Rohwer (2007): Event History Analysis with Stata, Mahwah (NJ) and London: Erlbaum, pp. 182-215 and 223-270.
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.” Newbury Park: Sage, pp. 1-28.
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Fortgeschrittene Verfahren der Längsschnittanalyse: Ereignisanalyse II Diskrete Modelle (Vorlesung) Introduction to History Analysis II – Discrete-Time Models -
- Dozent/in:
- Hans-Peter Blossfeld
- Angaben:
- Vorlesung, 2,00 SWS, ECTS: 6, Vorlesung mit zugehöriger Übung
- Termine:
- Di, 14:00 - 16:00, FMA/00.07
vom 15.10.2018 bis zum 9.2.2019
- Inhalt:
- 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 use Yamaguchi (1991), Singer and Willett (1993), Blossfeld and Blossfeld (2015a) as well as Blossfeld and Blossfeld (2015b).
The aim of the lecture (2 hours) with exercises (2 hours) in the WS 2018/19 is first to continue and finish the presentation of continuous-time event history models (e.g., parametric models of time-dependence such as the Gompertz, the Weibull, the Log-Logistic, the Log-Normal, and the semi-parametric models such as the Cox model). Then the students will be introduced into discrete-time event history analysis, including descriptions of data preparation and various discrete-time models. We will in detail discuss the analysis of aggregated data and the potential impact of aggregation 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 certainly helpful but not necessary that students took part in the SS 2018 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.
- Empfohlene Literatur:
- Blossfeld, H-P., and Blossfeld, G.J. (2015a) Life Course and Event History Analysis. In: James D. Wright (ed.), International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Vol 14. Oxford: Elsevier. pp. 51–58.
Blossfeld, H-P., and Blossfeld, G.J. (2015b) Event History Analysis, in: Henning Best and Christof Wolf (eds.): the Sage Handbook of Regression Analysis and Causal Infernece, Los Angeles (CA) et al. 359-385.
Blossfeld, H.-P., K. Golsch, and G. Rohwer (2007): Event History Analysis with Stata, Mahwah (NJ) and London: Erlbaum, pp. 182-215 and 223-270.
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.” Newbury Park: Sage, pp. 1-28.
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Forschungsdesigns: Research Design -
- Dozent/in:
- Jonas Voßemer
- Angaben:
- Vorlesung, 2,00 SWS, ECTS: 6
- Termine:
- Di, 10:00 - 12:00, F21/02.55
Einzeltermin am 9.11.2018, 10:00 - 12:00, F21/01.57
Einzeltermin am 16.11.2018, 10:00 - 12:00, F21/01.35
- Voraussetzungen / Organisatorisches:
This is a compulsory course in the new Master's program in Sociology. It is recommended to take this course at the beginning of the Master's studies.
It is not necessary to register for the course in advance (e.g. via FlexNow, via email, etc.). More information about the course and 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 this lecture, 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 A) -
- Dozent/in:
- Peter Valet
- Angaben:
- Seminar, 2,00 SWS, ECTS: 6
- Termine:
- Mi, 14:00 - 16:00, RZ/00.07
- 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 [https://univis.uni-bamberg.de/form?__s=2&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&dir=sowi/sozwiss/metho&ref=lecture&sem=2018w&__e=751].
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 or German (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:
- Peter Valet
- Angaben:
- Seminar, 2,00 SWS, ECTS: 6
- Termine:
- Mi, 16:00 - 18:00, RZ/00.07
- 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 [https://univis.uni-bamberg.de/form?__s=2&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&dir=sowi/sozwiss/metho&ref=lecture&sem=2018w&__e=751].
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 or German (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: Applied Panel Data Analysis (Part A) -
- Dozentinnen/Dozenten:
- Michael Gebel, Sonja Scheuring
- Angaben:
- Seminar, 2,00 SWS, ECTS: 6
- Termine:
- Mo, 8:00 - 10:00, RZ/00.07
- Voraussetzungen / Organisatorisches:
The seminar starts at October 22, 2018.
Participants are expected to be familiar with multiple linear and binary logistic regression analysis. Students are also required to be familiar with the statistics software Stata. These skills could either be acquired or refreshed in self-studies or by attending an additional compact tutorial course (teaching in German) at the beginning of the winter term. Link to Stata tutorial [https://univis.uni-bamberg.de/form?__s=1113&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&founds=sowi/sozwiss/metho/einfhr&__e=1&sem=2018w&codeset=utf8]
If you like to participate in the compact tutorial course, please register via the virtual campus (VC). Link to VC: https://vc.uni-bamberg.de/moodle/course/view.php?id=31887
It is not necessary to register for the seminar in advance (e.g. via Flexnow, via email, etc.). More information about the course and registration guidelines will only be provided during the first seminar session.
Module-related examination: Portfolio (time: 3 months); could be either written in English or German
- Inhalt:
- Learning targets: The central aim of this course is to empower participants to critically discuss basic concepts and assumptions of linear and binary logistic fixed effect and random effect panel data analyses, 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 linear and binary logistic fixed effect and random effect panel data analyses using the statistics software Stata, to correctly interpret the results and clearly present the results of regression analyses in tables and graphs.
Course contents: In general, the course quickly repeats the logic of the longitudinal research design and introduces the foundations of applied panel data analyses. Specifically, linear fixed effect and random effect models and binary logistic fixed effect and random effect models are presented and discussed. In addition, more complex hybrid models (e.g. FEIS) will be part of the seminar to ensure an up-to-date understanding of panel data analysis and to offer possible solutions for more advanced problems. In lab sessions participants will learn how to practically implement panel data analyses using the statistics software 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 Analysemethoden der quantitativen Sozialforschung: Applied Panel Data Analysis (Part B) -
- Dozentinnen/Dozenten:
- Michael Gebel, Sonja Scheuring
- Angaben:
- Seminar, 2,00 SWS, ECTS: 6
- Termine:
- Mo, 10:00 - 12:00, RZ/00.07
- Voraussetzungen / Organisatorisches:
The seminar starts at October 22, 2018.
Participants are expected to be familiar with multiple linear and binary logistic regression analysis. Students are also required to be familiar with the statistics software Stata. These skills could either be acquired or refreshed in self-studies or by attending an additional compact tutorial course (teaching in German) at the beginning of the winter term. Link to Stata tutorial [https://univis.uni-bamberg.de/form?__s=1113&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&founds=sowi/sozwiss/metho/einfhr&__e=1&sem=2018w&codeset=utf8]
If you like to participate in the compact tutorial course, please register via the virtual campus (VC). Link to VC: https://vc.uni-bamberg.de/moodle/course/view.php?id=31887
It is not necessary to register for the seminar in advance (e.g. via Flexnow, via email, etc.). More information about the course and registration guidelines will only be provided during the first seminar session.
Module-related examination: Portfolio (time: 3 months); could be either written in English or German
- Inhalt:
- Learning targets: The central aim of this course is to empower participants to critically discuss basic concepts and assumptions of linear and binary logistic fixed effect and random effect panel data analyses, 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 linear and binary logistic fixed effect and random effect panel data analyses using the statistics software Stata, to correctly interpret the results and clearly present the results of regression analyses in tables and graphs.
Course contents: In general, the course quickly repeats the logic of the longitudinal research design and introduces the foundations of applied panel data analyses. Specifically, linear fixed effect and random effect models and binary logistic fixed effect and random effect models are presented and discussed. In addition, more complex hybrid models (e.g. FEIS) will be part of the seminar to ensure an up-to-date understanding of panel data analysis and to offer possible solutions for more advanced problems. In lab sessions participants will learn how to practically implement panel data analyses using the statistics software 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: Applied Panel Data Analysis -
- Dozentinnen/Dozenten:
- Michael Gebel, Sonja Scheuring
- Angaben:
- Seminar, 4,00 SWS, ECTS: 12
- Termine:
- Mo, 8:00 - 12:00, RZ/00.07
- Voraussetzungen / Organisatorisches:
The seminar starts at October 22, 2018.
Participants are expected to be familiar with multiple linear and binary logistic regression analysis. Students are also required to be familiar with the statistics software Stata. These skills could either be acquired or refreshed in self-studies or by attending an additional compact tutorial course (teaching in German) at the beginning of the winter term. Link to Stata tutorial [https://univis.uni-bamberg.de/form?__s=1113&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&founds=sowi/sozwiss/metho/einfhr&__e=1&sem=2018w&codeset=utf8]
If you like to participate in the compact tutorial course, please register via the virtual campus (VC). Link to VC: https://vc.uni-bamberg.de/moodle/course/view.php?id=31887
It is not necessary to register for the seminar in advance (e.g. via Flexnow, via email, etc.). More information about the course and registration guidelines will only be provided during the first seminar session.
Module-related examination: Portfolio (time: 3 months); could be either written in English or German
- Inhalt:
- Learning targets: The central aim of this course is to empower participants to critically discuss basic concepts and assumptions of linear and binary logistic fixed effect and random effect panel data analyses, 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 linear and binary logistic fixed effect and random effect panel data analyses using the statistics software Stata, to correctly interpret the results and clearly present the results of regression analyses in tables and graphs.
Course contents: In general, the course quickly repeats the logic of the longitudinal research design and introduces the foundations of applied panel data analyses. Specifically, linear fixed effect and random effect models and binary logistic fixed effect and random effect models are presented and discussed. In addition, more complex hybrid models (e.g. FEIS) will be part of the seminar to ensure an up-to-date understanding of panel data analysis and to offer possible solutions for more advanced problems. In lab sessions participants will learn how to practically implement panel data analyses using the statistics software 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 Querschnittsanalyse: Advanced Regression Analysis using Stata -
- Dozent/in:
- Peter Valet
- Angaben:
- Seminar, 4,00 SWS, ECTS: 12
- Termine:
- Mi, 14:00 - 18:00, RZ/00.07
- 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 [https://univis.uni-bamberg.de/form?__s=2&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&dir=sowi/sozwiss/metho&ref=lecture&sem=2018w&__e=751].
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 or German (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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Grundlagen der Wissenschaftstheorie -
- Dozent/in:
- Susann Sachse-Thürer
- Angaben:
- Seminar, 2,00 SWS
- Termine:
- Do, 10:00 - 12:00, F21/02.31
- Voraussetzungen / Organisatorisches:
- Der Kurs richtet sich an Studierende der Soziologie im Masterstudium. Ausnahmen bilden Studierende anderer Fachrichtungen im MA, sofern sich der Kurs sinnvoll in die jeweilige Studienordnung integriert.
Es gibt keine Voraussetzungen für die Teilnahme am Kurs, jedoch basiert er auf regelmäßiger Lektüre der angegebenen Literatur und auf der aktiven Teilnahme an der Seminardiskussion.
Vergabe von Leistungspunkten nach ECTS
Für das Bestehen des Moduls mit Prüfungsleistung müssen Sie
- die Kursliteratur lesen,
- aktiv im Seminar mitarbeiten,
- ein Portfolio erstellen
Einbringen des Moduls:
1. MA Soziologie PO 2012: als Modul MA Soz B.1 Wissenschaftstheoretische Grundlagen in B.] Modulgruppe Methoden der empirischen Sozialforschung im Umfang von 6 ECTS
2. MA Soziologie PO 2017: Als Lehrveranstaltung im Modul MASOZ-ST3 Allgemeine Soziologie in MASOZ D.1 Soziologische Theorie im Umfang von 0 oder 12 ECTS, kombinierbar mit der Lehrveranstaltung Ausgewählte Themen der soziologischen Theorie
- Inhalt:
- Wie man sozialwissenschaftliche Daten erhebt und auswertet, lernt man in den ersten Jahren des Soziologiestudiums. Was aber beim Erwerb dieser konkreten Techniken oft aus dem Blick gerät, ist die Frage, wieso wir überhaupt wissenschaftliche Erkenntnis aus empirisch-wissenschaftlicher Arbeit ableiten (können). Auf welchen Annahmen beruht unsere Neigung, das, was beim wissenschaftlichen Forschen herauskommt, als neues Wissen zu bezeichnen, also als wissenschaftliche Erkenntnis anzunehmen? Und warum ziehen wir so genannte wissenschaftliche Erkenntnismethoden anderen Methoden der Erkenntnis vor? Nähert man sich diesen Fragen, wird man sich schnell bewusst, wie vage das Terrain ist, auf dem wir uns in Studium und Wissenschaft alltäglich bewegen. Sich diesem Risiko einmal auszusetzen ist Grundlage dafür, sich selbst sozialwissenschaftlich betätigen zu können. In diesem Kurs wollen wir (mehr oder weniger) aus der Perspektive der Soziologie Grundlagen der Wissenschaftstheorie erarbeiten.
- Empfohlene Literatur:
- Adorno, Theodor W., Ralf Dahrendorf, Jürgen Habermas und Karl R. Popper (1974): Der Positivismusstreit in der deutschen Soziologie. Luchterhand Verlag. Neuwied.
Bourdieu, Pierre, Jean-Claude Chamboredon und Jean-Claude Passeron (1991): Soziologie als Beruf. Wissenschaftstheoretische Voraussetzungen soziologischer Erkenntnis. Walter de Gruyter. Berlin.
Chalmers, Alan F. (2001): Wege der Wissenschaft. Einführung in die Wissenschaftstheorie. Springer. Berlin [u.a.].
Chalmers, Alan F. (2013): What is this thing called science? Open University Press. Maidenhead.
Detel, Wolfgang (2007): Erkenntnis- und Wissenschaftstheorie. Reclam. Stuttgart.
Ernst, Gerhard (2012): Einführung in die Erkenntnistheorie. Wissenschaftliche Buchgesellschaft. Darmstadt.
Feyerabend, Paul (1983): Wider den Methodenzwang. Suhrkamp. Frankfurt/M. Kuhn, Thomas S. (2011): Die Struktur wissenschaftlicher Revolutionen. Suhrkamp. Frankfurt a. Main. Lakatos, Imre und Alan Musgrave (1974): Kritik und Erkenntnisfortschritt. Vieweg. Braunschweig.
Opp, Karl Dieter (2005): Methodologie der Sozialwissenschaften: Einführung in die Probleme ihrer Theorienbildung und praktischen Anwendung. VS Verlag. Opladen.
Popper, Karl R. (2005): Logik der Forschung. Mohr Siebeck. Tübingen.
Poser, Hans (2012): Wissenschaftstheorie. Eine philosophische Einführung. Reclam. Stuttgart.
Schneider, Norbert (1998): Erkenntnistheorie im 20. Jahrhundert. Klassische Positionen. Reclam. Stuttgart.
Schülein, Johann August und Simon Reitze (2012): Wissenschaftstheorie für Einsteiger. facul-tas.wuv. Wien.
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Research Design -
- Dozent/in:
- Jonas Voßemer
- Angaben:
- Vorlesung, 2,00 SWS, ECTS: 6
- Termine:
- Di, 10:00 - 12:00, F21/02.55
Einzeltermin am 9.11.2018, 10:00 - 12:00, F21/01.57
Einzeltermin am 16.11.2018, 10:00 - 12:00, F21/01.35
Einzeltermin am 5.2.2019, 10:00 - 12:00, KÄ7/00.10
- Voraussetzungen / Organisatorisches:
This is a compulsory course in the new Master's program in Sociology. It is recommended to take this course at the beginning of the Master's studies.
It is not necessary to register for the course in advance (e.g. via FlexNow, via email, etc.). More information about the course and 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 this lecture, 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 Methoden der Datenerhebung: Mixed-Mode-Surveys -
- Dozentinnen/Dozenten:
- Daniel Zeddel, Paul Löwe
- Angaben:
- Vorlesung, 2,00 SWS, ECTS: 6
- Termine:
- Mi, 10:00 - 12:00, F21/03.81
- Inhalt:
- Das Seminar gibt zunächst einen Überblick über die wichtigsten Erhebungsmodi (Face-to-Face, telefonisch, postalisch, Web). Die Besonderheiten bei der Durchführung von Erhebungen in den unterschiedlichen Modi (z.B. modusabhängige Stichprobenpläne) werden ebenso behandelt wie die Auswirkungen der Modi auf verschiedene Fehlerquellen wie Coverage Error, Nonresponse, Messfehler, Stichprobenvarianzen und Interviewereffekte.
Im zweiten Teil des Seminars liegt der Fokus dann auf Mixed-Mode-Erhebungen. Verschiedene Mixed-Mode-Designs werden anhand von Praxisbeispielen vorgestellt. Ein Schwerpunkt liegt auf der neueren Literatur zur Untersuchung der Datenqualität solcher Erhebungen, insbesondere zur Trennung modusbedingter Messfehler von der möglichen Selbstselektion der Teilnehmer in die unterschiedlichen Modi.
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Mixed-Mode-Surveys -
- Dozentinnen/Dozenten:
- Daniel Zeddel, Paul Löwe
- Angaben:
- Vorlesung, 2,00 SWS, ECTS: 6
- Termine:
- Mi, 10:00 - 12:00, F21/03.81
- Inhalt:
- Das Seminar gibt zunächst einen Überblick über die wichtigsten Erhebungsmodi (Face-to-Face, telefonisch, postalisch, Web). Die Besonderheiten bei der Durchführung von Erhebungen in den unterschiedlichen Modi (z.B. modusabhängige Stichprobenpläne) werden ebenso behandelt wie die Auswirkungen der Modi auf verschiedene Fehlerquellen wie Coverage Error, Nonresponse, Messfehler, Stichprobenvarianzen und Interviewereffekte.
Im zweiten Teil des Seminars liegt der Fokus dann auf Mixed-Mode-Erhebungen. Verschiedene Mixed-Mode-Designs werden anhand von Praxisbeispielen vorgestellt. Ein Schwerpunkt liegt auf der neueren Literatur zur Untersuchung der Datenqualität solcher Erhebungen, insbesondere zur Trennung modusbedingter Messfehler von der möglichen Selbstselektion der Teilnehmer in die unterschiedlichen Modi.
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UnivIS ist ein Produkt der Config eG, Buckenhof |
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