Einführung in die Methoden der empirischen Sozialforschung Teil I

Dozent/in:

Peter Valet

Angaben:

Vorlesung, 2,00 SWS, ECTS: 5

Termine:

Di, 8:00 - 10:00, F21/01.37

Voraussetzungen / Organisatorisches:

Empfohlen für 1. Semester

Eine Voranmeldung (z. B. über FlexNow oder per E-Mail) ist nicht notwendig! Informationen zur Prüfungsanmeldung werden im Rahmen der ersten Veranstaltung mitgeteilt.

Modulprüfung: Klausur (60 min.)

Inhalt:

Lernziel: Im Anschluss an die Veranstaltung können die TeilnehmerInnen die zentralen Schritte des Forschungsprozesses benennen und die zu treffenden Entscheidungen erörtern, die Grundprinzipien theoriegeleiteter empirischer Forschung nachvollziehen und Hypothesen formulieren, Probleme der Konzeptspezifikation, Operationalsierung und Messung erläutern und anhand von Beispielen praktisch umsetzen, die grundlegende Idee und praktische Umsetzung verschiedener Auswahlverfahren erläutern, verschiedene Datenerhebungsmethoden erklären und deren Vor- und Nachteile kritisch miteinander vergleichen.

Lerninhalte: Die Veranstaltung thematisiert Grundlagen der empirischen Sozialforschung in folgenden Themenfeldern:

Phasen und Ablauf des Forschungsprozesses

Richtlinien zur Generierung und Auswahl von Forschungsfragen

Theoriegeleitete empirische Forschung: Theorien, Formulierung von Hypothesen und ihre empirische Prüfung

Konzeptspezifikation und Operationalisierung

Messung: Gütekriterien, Indexbildung und Skalierungsverfahren

Stichprobenbeziehung und Auswahlverfahren

Die Befragung als Datenerhebungsverfahren und Grundlagen der Fragebogenkonstruktion

Alternative Datenerhebungstechniken: Beobachtung, Inhaltsanalyse und nicht-reaktive Verfahren

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.

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.

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.