Sozial­wissenschaften – Master (alle)

Elections are the central focus of political activity in democracies. The characteristics of politics, parties and electoral systems are fundamental to the outcome of elections, which differ across and within countries. To better understand elections we need to study them comparatively, therefore this course focuses on comparative research on elections. The course focuses on the context in which elections are fought and how this affects electoral outcomes. A number of contextual effects of electoral behaviour will be covered, such as institutional configurations, election campaigns, the strategies of political parties and the importance of events in understanding the dynamics of electoral outcomes. We will consider competing theoretical and empirical explanations of the electoral process in democratic as well as partially democratic and even non-democratic countries.

Political behavior takes place in context. This statement is a truism and implies several challenges at the same time. Context is a multidimensional concept comprising – inter alia – social, political, and institutional features. At the conceptual and theoretical level, the diversity of dimensions requires careful consideration of how to integrate contextual features into individual-level models of political behavior. Moreover, combining data from different levels of aggregation to examine the role of contexts in individual-level behavior raises several methodological issues. In this seminar, we will address the conceptual, theoretical, and methodological issues in the analysis of contextual effects on individual-level political behavior. Students will review empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets.

This seminar discusses seminal and current work on agents of political violence. We analyze the role and characteristics of the military, including drivers and co­unter­strategies to coup d’états. A large part of the course will focus on irregular armed agents that are aligned with the stage, engaging with the growing research on militias, death squads, civil defense forces and paramilitary groups. We assess national and transnational drivers of their formation, their termination, how they affect political violence during and outside of civil wars. The seminars will be student-led. Over the course of the seminar you will develop your own research question on one of the topics discussed in the seminar and carry out your own research. Additionally, you are expected to write one book review, read all required materials, engage in the discussions and provide feedback on other student’s work.
This seminar  is planned as an online course. If COVID-19 regulations allow for it , it will be held on campus.

Crises and Polarization   Crises play an important role in the causal literature, which is receiving increasing attention in political science. Compared to conventional associational analyses, which draw conclusions from data relations­hips respectively their distributions, questions about causality ask for the efficacy of a policy, the causes for poverty etc., which require knowledge about the data generation process. Because causal analysis goes one step further as its aim is to infer not only beliefs or probabilities under static conditions, but also the dynamics of beliefs under changing conditions, for example, changes induced by treatments or external interventions, crises can serve as a methodological instrument. The structure of the course divided into two parts. The first part devotes exclusive attention to causal inference methods (duration about 45 minutes). The goal is to learn the methodological tools of evidence-based political science. After a short break we will discuss applications in the second part, which mainly investigate shocks/crises and their implications. This part will begin with the session 4 on 28 September.
Requirements: The requirements consist of a presentation and a term paper. To understand causal inference we will mainly read Hernán, Miguel A. and James M. Robins. 2020. Causal Inference. What If and partly Angrist, Joshua D. and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
All participants need to prepare the listed readings (see syllabus in Ilias Group) in a manner that allows to discuss the pros and cons of each topic. Each reading will be presented by one participant (15 minutes presentation). These presentations have to be circulated latest on Mondays 18.00 (in the evening before the session).

The term paper addresses applications regarding mainly crises and polarization. Participants are free to select an article of their choice which applies causal inference methods. They have to summarize the content of this article and to discuss the methodological pros and cons. Ideally, they replicate the findings and discuss their replicability. The term paper have to be submitted latest on 31st December 2021 (about 15 pages).

This course gives an overview of data used in political science and their measurement properties. At the beginning of the course we will focus on survey data and traditional statistics and move to data science approaches for big data towards the end of the course. Topics covered include the Total Survey Error (TSE) framework, operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, sampling, coverage, and nonresponse of survey and big data, and data analytics approaches in data science.

The course is mandatory for students of the M.A. in Political Science and consists of lectures (6 ECTS) and tutorials (2 ECTS). During the lectures the theory covered in the course readings is discussed. The tutorials offer hands-on practice and experience during individual and group exercises.

The objective of this course is to provide students with the basics of formal modeling in political science. The course has some breadth in coverage in the sense that it provides a graduate-level introduction and overview to different areas in game theory. It is also narrow in the sense that the emphasis is not on application and model testing but getting trained in reading and writing down formal models. At the conceptual level the course will cover the following topics: normal form games, Nash equilibria, extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete and imperfect information, Bayesian perfect equilibria, signaling games, preferences and individual choices, basics of decision theory and social choice. At the substantial level, we will use these concepts to study, as examples, candidate competition, political lobbying, and war and deterrence.

The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.

In the accompanying course „Tutorial Multivariate Analyses“ students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

All researchers face similar challenges posed by core issues of research design. A research design is a plan that specifies how you plan to carry out a research project and, particularly, how you expect to use your evidence to answer your research question. This course aims to provide an overview of potential research designs for establishing causal inference and their advantages and disadvantages. Students should also understand the trade-offs involved in choosing a particular research design. Students are expected to have their own ideas about potential research questions to be able to actively participate in seminar-style meetings that are organised within this lecture course

Summary

This course gives an overview of data used in political science and their measurement properties. At the beginning of the course we will focus on survey data and traditional statistics and move to data science approaches for big data towards the end of the course. Topics covered include the Total Survey Error (TSE) framework, operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, sampling, coverage, and nonresponse of survey and big data, and data analytics approaches in data science.

The course is mandatory for students of the M.A. in Political Science and consists of lectures (6 ECTS) and tutorials (2 ECTS). During the lectures the theory covered in the course readings is discussed. The tutorials offer hands-on practice and experience during individual and group exercises.

This tutorial accompanies the graduate-level introductory lecture in game theory. Its main objective is to practice solution concepts for static and dynamic games of complete and incomplete information. The contents are centered around the material covered in the lecture. Thus, the following key areas will be discussed: normal-form games, dominance, Nash equilibria, mixed strategies, continuous strategies, sequential games with perfect and incomplete information, subgames and subgame perfection, Bayesian equilibria, signaling and cheap talk games, expected utility and decision theory, bargaining theory, and social choice. At the substantial level, we will use these concepts to study, as examples, candidate competition, political lobbying, and war and deterrence.

This tutorial accompanies the course “Multivariate Analyses” in the M.A. program in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

This tutorial accompanies the course “Multivariate Analyses” in the M.A. program in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

This tutorial accompanies the course “Multivariate Analyses” in the M.A. program in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

The main focus lies on the introduction to statistical models and estimators beyond linear regression useful to a social scientists. A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will first discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to first the maximum likelihood estimator and second to generalized linear models (GLS) for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial). Classes will be accompanied by lab sessions to repeat and practice the topics from the classes. We will use the statistical package Stata.

Lab sessions will repeat and practice topics from the lecture based on weekly exercises. These exercises follow the lecture, so that you will have a week to solve each exercise. We will do some applied exercises by hand and use the statistical package Stata. The goal of the lab session is:

a) to practice the topics you learned in applied form,
b) offer a opportunity for questions, and
c) give you and discuss with you hands-on approaches for your further empirical work (e.g. graphical representation of results).

Lab sessions will repeat and practice topics from the lecture based on weekly exercises. These exercises follow the lecture, so that you will have a week to solve each exercise. We will do some applied exercises by hand and use the statistical package Stata. The goal of the lab session is:

a) to practice the topics you learned in applied form,
b) offer a opportunity for questions, and
c) give you and discuss with you hands-on approaches for your further empirical work (e.g. graphical representation of results).

Course description
The methodological toolbox that is available to social scientists has considerably increased in recent years. Unfortunately, however, this increased methodological precision is not always accompanied by theoretical precision. In this course, we aim to shift attention from identifying causal mechanisms empirically to theorizing about them.
For this purpose, we discuss how selected exemplary studies tackle the key questions of applied sociological theorizing. Roughly speaking, we proceed in three steps. In a first step, we ask what, exactly, the scientific or social problem under study and the related research question is. This question may sound almost trivial, but it is not, given that everything else depends on a clear understanding of the underlying problem. After establishing the explanandum, our second step is to examine the theoretical explanation that the authors offers to explain the observed phenomena or pattern. In order to do so, we discuss the (sometimes hidden) assumptions and scope conditions of respective theories. We also discuss how convincing these theoretical explanations are, what observable implications they have, and how the authors test these implications empirically. In the final step, we assess whether the study has succeeded in advancing our (theoretical) understanding of the problem it sought to address.

The aim of this research seminar is to conduct an analysis of secondary data and to write a short research paper. Topics may include comparative welfare state research, public opinion, labour market, life course and aging sociology. Students can choose the topic on their own and can either opt for a replication of a published research paper or work on an original research paper. Studies on topics related to the social consequences of the Corona pandemic are highly welcome.

In the first session we will discuss how to a select a research question and a research design. In the following sessions the instructor will briefly introduce two to three datasets (depending on the research interests of the participating students) which are particularly suitable for addressing research questions in the fields mentioned above (e.g., German Internet Panel (GIP, including the Mannheim Corona-Study); German Socio-economic Panel (SOEP); Survey of Health, Ageing, and Retirement in Europe (SHARE); European Social Survey (ESS)). One or two additional sessions will deal with the workflow of data analysis using Stata (focus on writing accessible and effective do-files). In the remaining weeks of the seminar the students are working on their research projects. Students will develop hypotheses, write a review of research related to these hypotheses and conduct an empirical test using secondary data.

The seminar starts on October 13, 2021 and will then take place on a weekly basis as online course (digitally live, not recoded). Later some regular classes will be replaced by individual appointments with the instructor. A one-day workshop at the end of the semester will give students the opportunity to present and discuss their research project (on-site if possible).

Intro
In this applied research seminar, students will learn how to design and implement online behavioral research using a suitable computational approach involving open web technologies, such as (but not limited to): such as: HTML, CSS, JavaScript, nodeGame (https://nodegame.org), REST APIs, web scraping, Chrome Extensions, etc.
After iterating on an initial research idea, students will move onto its implementation. Students are expected to deliver a computational prototype / proof-of-concept of their research strategy. Ideally, the output of this research seminar will provide the foundation of a master thesis in the following semester.
Examples of viable research projects are:

• Online group-behavior experiment / survey with the nodeGame framework.
• New instrument/method for measuring a theoretical psychological or sociological construct to be integrated as a widget for the nodeGame framework.
• A computational solution to a known methodological challenge for conducting online research (e.g., last year a student did a ML classifier for bot detection).
• A Chrome Extension for measuring browsing behavior / running an intervention.
• A statistical approach on data collected from public APIs or scraped from open web sites.

Students are required to actively participate and interact with other fellow students and the instructor during the lectures and the dedicated group chat.
Modules
The course includes the following modules:

1. Design of Online Behavioral Research.
   1. Principles of Experimental Method. Validity, causality, lab-vs-online-vs-field, review of landmark online experiments.
   2. Modeling Human Behavior. Introduction to game theory, social influence processes, heuristics and biases.
   3. Recruiting strategies and ethical considerations. Challenges and opportunities.

 

2. Implementation of Online Behavioral Research.
   1. Fundamentals Computer Programming for the Web. The JavaScript language for the browser and the server (Node.JS), Git, and GitHub.
   2. Advanced Programming for the Web. The nodeGame platform for conducting online behavioral experiments, Chrome Extensions, Web Scraping, REST APIs.

Format
Online course with weekly meetings.
Slides and video tutorials will be available. Some material already available for the students to review on the nodeGame website and YouTube channel:

• https://nodegame.org/courses.htm
• https://www.youtube.com/channel/UC2esqxvayZr80QdUZujLqpA

A group chat will be available on a dedicated Discord channel, a platform available on all major mobile platform and on desktops. In this channel, students can ask technical and administrative questions, and interact with other fellow participants.
Obtaining the Credits
Students will earn credits for successfully defining a research strategy (Module 1) and for implementing a running prototype that involves at least one computational approach from Module 2. The project must be submitted via the GitHub platform; a written report is to be included.
Additionally, participants must deliver two presentations:
Mid-term project pitch. In 5–10minute presentation, students introduce their research design and their implementation plan to the class and receive feedback from the instructor and other fellow participants.
Final project presentation. In a 10–15minute presentation, students show the final the experiment is presented and played online and defend their design choices in a short Q&A session with the instructor and other fellow participants.
Bonus points. Participants can increase their final grade by:
– Actively interacting with other fellow participants through the official discussion media: chat, mailing list, and online presentations.
– Delivering their final project presentation in the form of video to be uploaded to nodeGame TV YouTube channel (https://www.youtube.com/channel/UC2esqxvayZr80QdUZujLqpA).
– Running your code online and collecting early data, performing a brief data analysis of the results showing whether (a) your hypotheses are confirmed, (b) your instrument worked as expected, the behavioral challenge you took on is properly addressed.
Prerequisites
There are no strict prerequisites for this course, but a quantitative background and previous programming knowledge are strong assets for succeeding in this course.

How do we know which research design fits best our research question? What requirements must be in place for good descriptive, causal and predictive inference? How do we estimate causal effects? How do we design and analyze experiments? Can we make causal claims from observational data? Researchers in the social sciences must be able to answer all of these questions.
This course teaches the fundamental concepts behind the estimation of causal effects, including potential obstacles to causal inference. Real-world examples will be discussed in detail and students will apply the techniques learned with real datasets in R. Students will come away with an understanding of how to estimate causal effects in both randomized and observational settings, with a particular focus on the careful design of both types of studies.

In the practice sessions, students will learn how to implement causal inference methods in R. Students should bring their own laptop for the all practice sessions. Previous knowledge in R is not necessary although advantageous. Please make also sure to install R and R studio before the first practice session.

In the practice sessions, students will learn how to implement causal inference methods in R. Students should bring their own laptop for the all practice sessions. Previous knowledge in R is not necessary although advantageous. Please make also sure to install R and R studio before the first practice session.

The class will first introduce classical social science research on population health and health inequalities. We will discuss how (to what extent) and why (through which mechanisms) health, illness and mortality is distributed unequal between different social groups like class, gender, „race“/ethnicity, sexuality, and migration status. We will also discuss how these disparities depend on meso- and macro-level causes. Therefore, we will examine societal systems of domination and discrimination (e.g. Classism, Sexism, Heterosexism, Gender binarism and Racism), and how they structure the distribution of population health and health inequalities. To this end, we will also talk about new concepts that try to incorporate intersectionality theory into population health research.

Up to the mid-1980s immigration was one of the least politicized issues on the political agenda of European countries. Since then, however, it has become one of the most important topics on the political agenda. Mass immigration has resulted in widespread xenophobia and fierce debates on the difficulties of integrating new arrivals. Muslim migration in particular seems to pose a special challenge to Western Europe, leading to widespread Islamophobia throughout the region. In this seminar we will consider reactions to Muslim immigration in Western Europe at various levels. What kind of policies do the European states implement in order to regulate mass immigration and integration? How do nationals react to this and how can we explain Islamophobia?

The purpose of this class is to learn how to conduct cutting-edge experimental social science research by gaining first-hand experience in replicating research published in flagship journals (e.g. American Sociological Review, European Sociological Review, PNAS, American Political Science Review, etc.) as well as extending an existing experimental study with original data. Replications are an important but so far still largely neglected feature of the scientific process whereby we verify and expand our cumulative knowledge of empirical findings. Recently several large-scale replication projects in the social sciences have started to evaluate the validity of our research results, often finding evidence that results cannot be entirely replicated due to various reasons (e.g. coding errors, alternative statistical modelling specification, p-hacking, bugs in statistical software packages, etc.). This course aims to contribute to this open science and replication movement by helping students to develop a transparency routine for their future (research) careers. In addition, this course will improve students’ data literacy by teaching them how to best do replications and provide them with practical knowledge about how to meet the highest standards of reproducibility when conducting and documenting empirical social science research.

„Like father, like son!“ „Someone's a chip off the old block!“ „To follow in his father's foot-steps!“ There is a variety of popular proverbs describing the same (sociological) fact: Social origin, i.e. the class or income position of parents, has a large effect on the future life-chances of children. For example, a child from a working class background has considerably lower prospects of achieving a position as a professional compared to a child from parents who work as professionals. And the same is true vice versa. Asking about this lack of social mobility (or, contrary, asking about the amount of social mobility) across generations is one of the classic questions in sociology. The seminar will offer a comprehensive view on con-cepts of intergenerational (and intragenerational) social mobility, on theoretical approaches to explain social mobility, on quantitative methodological approaches and on empirical re-sults regarding the amount of social mobility in Europe. Students are expected to read and discuss the most prominent articles in the field, including rather technical/statistical state-of-the-art applications of theories and new methodologies. The seminar encourages students to think critically of the concepts, theories and empirical applications and invites students to develop their own research questions.

The aim of this course is to introduce students to structuralist theorizing in the social sciences. One way to characterize structuralist thinking is to posit the primacy of relations over attributes: considering kinship, for example, it is not meaningful to speak of being a father without any reference to sons and daughters. In other words, any social role is inherently relational because it always entails a role complement (father-son, or father-daughter). As we will see throughout the course, much of social life and human behavior can be understood with reference to such relational pairs. Pairs often come in the form of binary oppositions: nature-nurture; female-male; young-elder; us-them; and so forth. Drawing on the work of Claude Lévi-Strauss in particular, the course aims (a) to systematically understand the building blocks of structuralist thinking, and (b) to understand its implications for social organization and cohesion writ large, that is, what holds societies together in the long run. Substantive empirical evidence will come from systems of social exchange, governed by norms of reciprocity; from the contrast between restricted and generalized exchange; the formation and ambiguities of role structures; and from the structural analysis of culture, in particular in the form of myths that provide a foundation for social organization in indigenous societies. Students planning to take this class should be prepared to consider empirical examples that come from rather exotic societies, remote in place and time from our contemporary society.

In dieser Vorlesung werden die formalen Grundlagen und zentralen Verfahren der multivariaten Statistik eingeführt und empirisch illustriert. Dabei werden zunächst die multiple Regression, das Allgemeine Lineare Modell und die Mehrebenenanalyse behandelt. Darauf aufbauend werden multivariate Auswertungs­verfahren für Mittelwertsvergleiche und zur Diskrimination sowie Verfahren der exploratorischen Faktorenanalyse vorgestellt. Abschließend erfolgt eine Einführung in lineare Strukturgleichungs­modelle und konfirmatorische Faktorenanalyse.

Zu den einzelnen Verfahren werden die mathematischen Grundlagen dargelegt und Anwendungs­möglichkeiten und Einsatzgebiete in der psychologischen Forschung diskutiert.

Das Modul vermittelt Kenntnisse über diagnostische Verfahren und ihren Einsatz für fundierte Entscheidungen und Beratungen in unterschiedlichen Anwendungs­bereichen der psychologischen Diagnostik. Damit erlangen die Studierenden eine zentrale Schlüssel­qualifikation für ihre berufliche Tätigkeit im Anschluss an das Studium. Darüber hinaus werden aktuelle Entwicklungen der testtheoretischen Modellbildung und der Testkonstruktion behandelt.

Spezifische Inhalte: Übersicht über Test­verfahren, Strategien, mögliche Fehlerarten, rechtliche und gesellschaft­liche Rahmenbedingungen sowie Kosten-Nutzen-Analysen der diagnostischen Entscheidung und Klassifikation in unterschiedlichen Problemfeldern, wie etwa der beruflichen Eignungs­diagnostik, der pädagogisch-psychologischen Diagnostik oder der neuropsychologischen Diagnostik.

Diese Ringvorlesung vermittelt vertiefte Kenntnisse zu Diagnostik, Ätiologie sowie Störungs- und Behandlungs­wissen der Klinischen Psychologie und Psychopathologie des Kindes-, Jugend- und Erwachsenenalters. Zu den behandelten Themengebieten gehört u.a. die kritische Auseinandersetzung mit der klassifikatorischen Diagnostik und mit aktuellen Kontroversen der Klinische Psychologie. Darüber hinaus befassen sich die Teilnehmerinnen und Teilnehmer mit aktuellen Forschungs­tätigkeiten und etablierten Forschungs­methoden aus ausgewählten Bereichen der Klinischen Psychologie und Psychopathologie.

The lecture will present cutting edge research conducted in Cognitive Psychology at the University of Mannheim.
After an introductory overview of Cognitive Psychology and its advanced methods by A. Bröder, various researchers will present their current work. The following reseacrher are planned as lecturers (changes possible): Dr. Nina Arnold, Dr. Martin Brandt, Prof. A. Bröder, Prof. E. Erdfelder, Dr. Michael Gräf, Dr. Meike Kroneisen, Dr. Lena Naderevic, Prof. Rüdiger Pohl und Dr. Monika Undorf.

Final test: written exam (M.Sc. students), language German or English.

Die Vorlesung wird verschiedene Forschungs­themen der Kognitiven Psychologie anhand der aktuell in Mannheim betriebenen kognitions­psychologischen Forschung vertiefen.
Nach einem einführenden Überblick über die Kognitions­psychologie und spezifische methodische Probleme (4–5 Termine, gestaltet durch A. Bröder) werden in Form einer Ringvorlesung aktuelle Forschungen der Lehr­stühle für Allgemeine Psychologie und für Kognitive und Differentielle Psychologie vorgestellt.  Die Dozentinnen und Dozenten sind voraussichtlich: Dr. Nina Arnold, Dr. Martin Brandt, Prof. A. Bröder, Prof. E. Erdfelder, Dr. Michael Gräf, Dr. Meike Kroneisen, Dr. Lena Naderevic, Prof. Rüdiger Pohl und Dr. Monika Undorf.
Prüfungs­leistung: Klausur
Empfohlen für: Studierende des Master­programms Kognitive und Klinische Psychologie (1. Semester) und des Master­programms Arbeit, Wirtschaft und Gesellschaft (3. Semester)

Inhalt:
Die Metakognitions­forschung untersucht die bemerkenswerte Fähigkeit des Menschen, sein eigenes Denken zu reflektieren. Zutreffende Metakognitionen sind unerlässlich, um sich selbst und andere richtig einzuschätzen und eigenes Verhalten effektiv zu steuern. Aber auf welcher Informations­basis beruhen Metakognitionen? Verfügen auch nichtmenschliche Säugetiere über Metakognitionen? Sind Menschen in der Lage, die Kognitionen anderer Personen zutreffend einschätzen? Wie entwickeln sich Metakognitionen über die Lebens­spanne? Auf welche Weise können Metakognitionen gefördert und trainiert werden? Gehen psychische Störungen mit Einschränkungen im Bereich der Metakognitionen einher? In dem Seminar werden diese und weitere Fragen auf Basis aktueller Fach­literatur diskutiert.

Empfohlen für:
Studierende im M.Sc. Psychologie im 2. Fach­semester.

Erworben werden kann:
Leistungs­nachweis gemäß der Prüfungs­ordnung für den Studien­gang M.Sc. Psychologie.

Anmeldung:
Über das Studierenden­portal

Sprechstunde:
Nach Vereinbarung

Die Metakognitions­forschung untersucht die bemerkenswerte Fähigkeit des Menschen, sein eigenes Denken zu reflektieren. Zutreffende Metakognitionen sind unerlässlich, um sich selbst und andere richtig einzuschätzen und eigenes Verhalten effektiv zu steuern. Aber auf welcher Informations­basis beruhen Metakognitionen? Verfügen auch nichtmenschliche Säugetiere über Metakognitionen? Sind Menschen in der Lage, die Kognitionen anderer Personen zutreffend einschätzen? Wie entwickeln sich Metakognitionen über die Lebens­spanne? Auf welche Weise können Metakognitionen gefördert und trainiert werden? Gehen psychische Störungen mit Einschränkungen im Bereich der Metakognitionen einher? In dem Seminar werden diese und weitere Fragen auf Basis aktueller Fach­literatur diskutiert.

Empfohlen für:
Studierende im M.Sc. Psychologie im 2. Fach­semester.

Erworben werden kann:
Leistungs­nachweis gemäß der Prüfungs­ordnung für den Studien­gang M.Sc. Psychologie.

Anmeldung:
Über das Studierenden­portal

Sprechstunde:
Nach Vereinbarung

Diese Vorlesung bietet eine Einführung in das Fach Arbeits- und Organisations­psychologie und stellt dabei wesentliche Themen vor, unter anderem Arbeits­motivation, Stress, Arbeits­gruppen, Führung, Arbeits­analyse, Leistungs­beurteilung, Personalauswahl, Training. Es wird ein erster Überblick über wichtige forschungs- und praxisbezogene Fragestellungen vermittelt, wobei insbesondere die empirische Basis des Faches betont wird. Diese Veranstaltung empfiehlt sich für alle Studierenden als Einstieg in das Fach „Arbeits- und Organisations­psychologie”.

AG4/BD4: VERTIEFUNG SOZIALPSYCHOLOGIE

Durchgeführt von: Prof. Dr. Herbert Bless/ Prof. Dr. Jochen Gebauer

Raum: online (SOWI-ZOOM-03) 

Teilnahme­möglichkeit: digital live

Zeit: Mo 13.45 – 15.15 Uhr

Erster Termin: 06.09.2021

Inhalt: In der Veranstaltung sollen aufbauend auf dem im Bachelor­studien­gang erworbenen Wissen ausgewählte Forschungs­gebiete vertiefend behandelt werden. Im Einzelnen können Themen behandelt werden wie Umfrageforschung/ Kontexteffekte in Fragebogen, subjektives Wohlbefinden (Glück und Zufriedenheit), Kultur als sozialer Kontext und evolutionäre Erklärungen für Sozial­verhalten.

Literatur: Wird in der Veranstaltung bekannt gegeben

Empfohlen für: Die Veranstaltung ist eine Pflicht­veranstaltung im Master­studien­gang Arbeit, Wirtschaft und Gesellschaft (BD4). Im Master­studien­gang Kognitive und Klinische Psychologie kann die Veranstaltung im Rahmen des Wahlmoduls (AG4) belegt werden.

Erworben werden können: 4 ECTS

Erforderliche Studien­leistung: -

Erforderliche Prüfungs­leistung: Klausur benotet oder Klausur unbenotet

Voraussetzung: Gute Englisch­kenntnisse

Anmeldung: Über das Studierenden­portal

Sprechstunde:
Prof. Dr. Herbert Bless nach Anmeldung, siehe Homepage, A 433
Prof. Dr. Jochen Gebauer: Kontakt­möglichkeiten finden Sie auf www.psy.de

Materialien: Über das Studierenden­portal

Techniken der beruflichen Eignungs­diagnostik können für unterschiedliche Personalmaßnahmen eingesetzt werden. Im Seminar werden zwei Anwendungs­szenarien (in der Personalauswahl und für die berufliche Laufbahn­beratung) genauer untersucht werden. Mit Mittelpunkt stehen zunächst grundlegende Herangehensweisen und Verfahrenstechniken der beruflichen Eignungs­diagnostik. Anhand eines fiktiven Anwendungs­falls sollen dann eignungs­diagnostische Untersuchungen im Grundprinzip geplant und auch praktisch umgesetzt werden. Die Untersuchungs­ergebnisse sollen anwendungs- und zielpersonen­gerecht mündlich und schriftlich kommuniziert werden.

lab.js is a simple, graphical tool to help you build studies for the web and the laboratory – in addition, it is free and open-source. Many standard tasks can be implemented in lab.js using its graphical user interface. Further, more complex tasks can be realized through the underlying programming language JavaScript.

The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of lab.js.

No prior knowledge of the software or JavaScript is required. As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:
http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

The seminar gives an overview of

• standard and advanced linear models (incl. multiple regression with continuous and categorical predictors, product terms, regularization methods, and nonlinear regression),
• generalized linear models (incl. logistic regression, Poisson models, and log-linear models), and
• supervised and unsupervised classification methods (incl. discriminant analysis, clustering methods, regression trees, and mixture models).

Regression and classification models are essential in many fields of psychological research as well as in clinical and epidemiological contexts. In this seminar, the models are introduced with their mathematical and statistical foundations, including model equations, methods of parameter estimation, and criteria of statistical inference. Statistical concepts and model applications are illustrated with simulations and through analyses of real data with R.

The language of instruction is English.

In zunehmend flexibleren Arbeits­umgebungen gewinnt Selbstregulation immer mehr an Bedeutung. In diesem Seminar beschäftigen wir uns daher mit der Frage, welche Rolle Selbstregulation für Leistung im Arbeits­alltag spielt, und wie organisatorische und interpersonelle Umstände solche Prozesse beeinflussen können. Themen können sowohl Selbstkontrolle oder Emotions­regulation, aber auch die Rolle von Führung für selbstregulatorische Prozesse sein.

Im Rahmen von Projekt­gruppen werden die Studierenden im zweiten Teil des Seminars die gelernten und selbst erarbeiteten Inhalte in Form von Broschüren, Postern etc. praxisnah umsetzen. Am Ende des Seminars erfolgt eine Präsentation der Projekt­gruppen­ergebnisse.

Neben einer inhaltlichen Diskussion legt das Seminar auch besonderes Gewicht auf die arbeits- und organisations­psychologischen Methoden, mit denen Selbstregulation im Arbeits­kontext untersucht wird.

Organizations are increasingly concerned about the health of their 
employees.

This is reflected in integrating health-related concepts and 
health-promoting measures into organizational strategies.

The seminar deals with the question of how can organizations act in 
order to maintain the health of their employees in the long term. The 
individual health-related actions of employees should also be discussed.

The seminar consists of two parts. In the first part, we work together 
to investigate state-of-the-art research on the topic using current 
literature.

During this time, the students have the opportunity to decide on a topic 
that they will work on in depth as part of their term paper and in the 
second part of the seminar in project groups.

In the context of project groups, in the second part of the seminar, the 
students will put the learned and self-developed content into practice 
in the form of a practical „product“ (e.g., poster, video, brochure etc.)
At the end of the seminar, the project group results will be presented.

In addition to a content-related discussion, the seminar also places 
particular emphasis on the work and organizational psychological methods 
with which health is examined at work.

Gegenstand des Seminars sind aktuelle Forschungs­trends in der Pädagogischen Psychologie und dabei ganz besondere innerhalb der pädagogischen Motivations­forschung.

In dem Kurs werden aktuelle Forschungs­debatten zu neuen motivationalen Konstrukten (GRIT), sowie zur Erweiterung bestehender Theorien (Zielforschung) kontrovers diskutiert. Weitergehend wird der Wert aktueller Forschung für die Praxis thematisiert (sozialpsychologische Interventionen im Bildungs­kontext).

Das Seminar wird gegenstandsbezogen Forschungs­prozesse und auch Anreizstrukturen im Wissenschafts­system thematisieren, so dass ein tieferer Einblick in den Wissenschafts­betrieb gelingt. Diesbezüglich wird auch aktuelle motivations­psychologische Forschung des Lehr­stuhl für Pädagogische Psychologie zu Betrugs­verhalten, Motivation von Hochschul­dozierenden und Studien­wahlmotivation thematisiert.

Hinweis: Das Seminar wird als synchrone Lehr­veranstaltung abgehalten. Die Durchführung findet voraussichtlich in Präsenz statt. Sollte es zu einer negativen Veränderung der pandemischen Situation kommen, ist angedacht das Seminar digital durchzuführen.

Titel: „Forschungs­methoden in der Sozialpsychologie nach der Replikations­krise“

Durchgeführt von: Dr. Ira Maschmann

Raum: online (SOWI-ZOOM-07)

Teilnahme­möglichkeit: digital live

Zeit: Di, 15:30 – 17:00

Beginn: 07.09.2021

Inhalt: Im Zuge der Replikations­krise in der Psychologie stellt sich die Frage, was gute sozialpsychologische Forschung ausmacht. Dieses Seminar beschäftigt sich mit dieser Frage. Im Rahmen dessen werden Studien und Forschungs­fragen aus der Sozialpsychologie hinsichtlich diverser Kriterien kritisch betrachtet, wie beispielsweise ob wir Comics tatsächlich als lustiger beurteilen, wenn wir durch einen Stift in unserem Mund unbewusst ein Lächeln simulieren, oder ob Frauen sich wirklich mächtiger fühlen, wenn sie eine Körperhaltung wie Wonder Woman einnehmen.
Ziel des Seminars ist, kritisches Lesen und Diskutieren von (sozial-)psychologischen Forschungs­ergebnissen zu vertiefen, sowie methodologische Neuerungen in der Forschungs­praxis wie beispielsweise Open Science kennenzulernen.

Empfohlen für: Studierende des Masters Psychologie

Erworben werden kann: ECTS: 4

Voraussetzung: Die Inhalte der Vorlesung zu Forschungs­methoden werden als bekannt vorausgesetzt.

Erforderliche Studien­leistung: Stundengestaltung

Erforderliche Prüfungs­leistung: Hausarbeit

Anmeldung: über das Studierenden­portal

Sprechstunde: nach Vereinbarung

Materialien: Materialien werden auf Ilias bereitgestellt.

BM1/BN1: Ausgewählte Probleme der Sozialpsychologie I/II
„Psychologie der Beratung“

Modul: BM1/BN1: Ausgewählte Probleme der Sozialpsychologie I/II

Titel: Psychologie der Beratung

durchgeführt von: Dr. Sabine Scholl

Raum: online (SOWI-ZOOM-04)

Teilnahme­möglichkeit: digital live

Termin: Montag, 12:00–13:30 Uhr

Veranstaltungs­zeitraum: 6.9.2021–10.12.2021

Inhalt:
Im Alltag und Beruf werden wir häufig gebeten Ratschläge zu erteilen. In diesem Seminar werden Theorien und Methoden aus der Sozialpsychologie auf verschiedene Beratungs­situationen angewandt. Dabei werden auf der Grundlage sozial-kognitiver Modelle und Theorien Einflussfaktoren auf Ratgebende und Ratsuchende besprochen. Hierbei steht die Verknüpfung von praktischen Fragen und theoriegeleiteten Antworten im Vordergrund. Das Seminar erfordert unter anderem die Lektüre von englischsprachiger Fach­literatur.


Literatur: Wird in der Veranstaltung bekannt gegeben.


Empfohlen für: Studierende der Psychologie des Master­studien­gangs „Arbeit, Wirtschaft und Gesellschaft“. Die Veranstaltung kann als BM1/BN1 oder BM2/BN2 im Rahmen der Module BM/BN „Denken und Verhalten im sozialen Kontext“ besucht werden.

Erworben werden können: 4 ECTS

Erforderliche Studien­leistungen: Hausaufgaben, Referat (Präsentation Projektarbeit)

Erforderliche Prüfungs­leistung: Hausarbeit (schriftliche Ausarbeitung der Projektarbeit)

Voraussetzung: Zulassung zum Master­studien­gang „Arbeit, Wirtschaft und Gesellschaft“

Anmeldung: Portal2

Sprechstunde: Nach Vereinbarung

Die Art und Weise, wie wir miteinander kommunizieren, unsere Freizeit gestalten und unserer Arbeit nachgehen, hat sich in den letzten Jahren aufgrund der zunehmenden Verbreitung der Medien stark verändert. Die Fragen, wie und warum Menschen bestimmte Medienangebote nutzen, auf welche Weise sie diese verarbeiten und welche Folgen daraus resultieren, sind daher von hoher Bedeutung. Einige Beispiele sind: (1) Wieso schauen wir gerne traurige Filme, obwohl Menschen doch eigentlich lieber fröhlich sein wollen? (2) Welchen Einfluss hat politische Satire (z. B. Fernsehsendungen wie die Heute-Show) auf die Einstellungen und Wahlabsichten der WählerInnen? (3) Wie verändert computer­vermittelte Kommunikation (z. B. über Messenger-Dienste wie WhatsApp oder soziale Netzwerkseiten wie Instagram) unsere zwischenmenschlichen Beziehungen? Zur Beantwortung dieser und vieler weiterer Fragen werden die wichtigsten medienpsychologischen Ansätze/Theorien/Modelle aus den Bereichen der Medienselektion, -rezeption und -wirkung behandelt, in einem anwendungs­bezogenen Kontext diskutiert und anhand aktueller Forschungs­ergebnisse aus wissenschaft­lichen Fach­zeitschriften vertieft. Die Studierenden nehmen dabei aktiv an den Sitzungen teil und arbeiten selbstorganisiert auch außerhalb der Sitzungen.