Lehrveranstaltungen im Sommersemester 2022
(I) Statistik
1) Statistical Rethinking
Statistical Rethinking II (Statistical Rethinking)
Dozent/in
Angaben
Sonstige Lehrveranstaltung
Online/Pr?senz
2 SWS
Englischsprachig
Zeit und Ort: Mi 14:00 - 16:00, M3N/-1.19
Voraussetzungen / Organisatorisches
Some Bachelor level knowledge of statistics and R is beneficial, but no prior knowledge beyond high school algebra is required! For Ba / Ma Psychology only!
Inhalt
Do you find statistics confusing and complicated? Do you want to improve and better understand your analysis? Do you want to find out that you are already a Bayesian statistician? Then this seminar is for you.
Learning Goals: In this seminar, you will learn how to build a statistical model from the ground up with the goal of being able to build a customized model for any statistical problem and analysis. After this course you will understand that a linear regression, a T-test, an ANOVA, or an ANOCOVA all refer to the same simple linear model that you can build yourself. The aim is to make sure that you will know exactly what your analysis does and why you are doing it in this way.
Course Method: This seminar assumes no prior knowledge on your part. We will start with a basic concept of probability-as-counting and proceed to understanding what statistical models are and how to build them. Over the course of the seminar, we will gradually move forward to more advanced topics learning how to handle various types of data, identify spurious associations, infer causality, evaluate models, or perform power analysis. Forming a book club we will read Statistical Rethinking by Richard McElrath. It is an excellent introductory statistics book that explain even most intimidating topics very clearly, links all seemingly discrepant topics together, and has plenty of examples in R. We will read one chapter every week and discuss the topics and questions during the seminar.
Empfohlene Literatur
"Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath https://www.oreilly.com/library/view/statistical-rethinking/9781482253481/
Englischsprachige Informationen:
Title:
Statistical Rethinking
Credits: 3
Zus?tzliche Informationen
Schlagw?rter: statistics, bayesian statistics
Erwartete Teilnehmerzahl: 12
Institution: Lehrstuhl für Allgemeine 球探足球比分 und Methodenlehre
(II) Methoden
1) Meta Science
Einführung in die Meta-Wissenschaft (Meta-Science)
Verantwortliche/Verantwortlicher
Dr. Lukas R?seler
Angaben
Seminar
Voraussetzungen / Organisatorisches
Grundkenntnisse in GNU R (oder zumindest Interesse am Programm) werden dringend empfohlen, es wird bei Bedarf aber auch Zeit für eine kurze Einführung sein.
Inhalt
Meta-Wissenschaft ist Wissenschaft über Wissenschaft. In diesem forschungslastigen Seminar werden die wichtigsten Grundideen von Meta-球探足球比分 besprochen. Dazu geh?ren zum Beispiel der Sinn von Meta-Analysen, ?Erhebung“ meta-analytischer Daten, und die Analyse und Interpretation solcher. Teilnehmende lernen in ?bungsaufgaben klassische und moderne Meta-analytische Verfahren zur Berechnung von Effektst?rken und Korrekturen für Publikationsbias und P-Hacking. In Gruppen werden sie jeweils eine eigene Mini-Meta-Analyse durchführen und diese Methoden anwenden.
Empfohlene Literatur
- Nelson, L. D., Simmons, J., & Simonsohn, U. (2018). Psychology's renaissance. Annual review of psychology, 69, 511-534.
- Schimmack, U. (2020). A meta-psychological perspective on the decade of replication failures in social psychology. Canadian Psychology/球探足球比分 Canadienne. Advance online publication. https://doi.org/10.1037/cap0000246
Englischsprachige Informationen:
Title:
Introduction to Meta-Science
Institution: Lehrstuhl für Pers?nlichkeitspsychologie und Psychologische Diagnostik
2) Programming
Machine Learning for Psychology and Social Sciences (ML for Psychology)
Dozent/in
Angaben
Seminar
Online/Pr?senz
2 SWS
Englischsprachig
Zeit und Ort: Do 10:00 - 12:00, M3N/-1.19
Voraussetzungen / Organisatorisches
Basic knowledge of Python or R would be helpful.
Inhalt
Psychology and social sciences rely statistical analysis to infer and describe causal effects that guide our behavior and determine our actions and responses. Proliferation of computers and smartphones, as well as a widespread access to internet made collection large datasets possible. In addition, movement towards open data in science means that there are many data sets both in panel databanks such as NEPS, SOEP, or ZPID, and at online repositories such as OSF or GitHub that can be analyzed and used to guide research and experimental design. However, the sheer amount of data makes using classic statistical methods cumbersome. The aim of the seminar is show how modern machine learning methods can be used to prescreen and analyze such big data sets. It will cover both the basic theory of methodology (math will be used very sparingly) and practical use of methods in Python and R (both free and open source systems). The material will cover a broad variety of supervised and unsupervised machine learning methods, including linear and logistic regression (which should be familiar from statistics), support-vector machines, tree-based approaches, cluster analysis, modeless analysis via nearest neighbor, deep neural networks, etc. A particular focus will be on application of these methods to "typical" social sciences / psychology data.
Empfohlene Literatur
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron ISBN: 9781491962299
Englischsprachige Informationen:
Title:
Machine Learning for Psychology and Social Sciences
Credits: 3
Zus?tzliche Informationen
Erwartete Teilnehmerzahl: 12
Institution: Lehrstuhl für Allgemeine 球探足球比分 und Methodenlehre
Python for social and experimental psychology II (Python for Psychology)
Dozent/in
Angaben
Seminar
Online/Pr?senz
2 SWS
Englischsprachig
Zeit und Ort: Do 8:00 - 10:00, M3N/-1.19
Voraussetzungen / Organisatorisches
Python for social and experimental psychology course or solid background in Python basics: variables, control structures (for and while loops, if conditional statements), lists/dictionaries, PsychoPy basics.
Inhalt
The second part of the "Python for social and experimental psychology" course that covers advanced topics such as object-oriented programming the pythonic way, working with exceptions, use of iterators/generators for concise code, coroutines, use of scientific libraries (numpy, pandas), online programming via OTree system, etc. We we still be writing games (because psychological experiments are merely boring games).
Englischsprachige Informationen:
Title:
Python for social and experimental psychology II
Credits: 3
Zus?tzliche Informationen
Erwartete Teilnehmerzahl: 12
Institution: Lehrstuhl für Allgemeine 球探足球比分 und Methodenlehre
(III) Debates & Literature
1) Advanced Research Seminar (ARS)
Dozent/in
Prof. Dr. Claus-Christian Carbon, M.A.
Angaben
Seminar
Englischsprachig, nur nach vorheriger Anmeldung bei Prof. Carbon
Zeit und Ort: Di 10:00 - 12:00, Raum n.V.; Bemerkung zu Zeit und Ort: Findet im Raum 211 statt
Voraussetzungen / Organisatorisches
Für Studierende, die sich im Studienabschluss befinden
Institution: Lehrstuhl für Allgemeine 球探足球比分 und Methodenlehre
2) Cognitive Reading Club (CRC)
Dozent/in
Prof. Dr. Claus-Christian Carbon, M.A.
Angaben
Seminar
Englischsprachig, nur nach vorheriger Anmeldung bei Prof. Carbon
Zeit und Ort: Di 14:00 - 16:00, Raum n.V.; Bemerkung zu Zeit und Ort: Findet im Raum 211 statt
Voraussetzungen / Organisatorisches
Für Studierende, die eine Forschungs- oder Seminararbeit im Bereich der kognitiven 球探足球比分 schreiben.
Institution: Lehrstuhl für Allgemeine 球探足球比分 und Methodenlehre