Lehrveranstaltungen im Wintersemester 2022/2023
(I) Statistik
1) Applied data analysis
Applied data analysis for psychology using the open-source software R (Data Analysis using R)
Dozent/in
Angaben
Seminar
Rein Pr?senz
2 SWS
Englischsprachig
Zeit und Ort: Mo 12:00 - 14:00, M3N/-1.19
Voraussetzungen / Organisatorisches
No programming background necessary, basic knowledge on statistical analysis is advantages but not strictly necessary. For Ba / Ma Psychology only!
Inhalt
An introductory hands-on course that shows how to use R to analyze a typical psychophysical and social psychology research data. The course will walk you through all the analysis stages from importing a raw data to compiling a nice looking final report that automatically incorporates all the figures and statistics. If description below looks intimidating, do not despair! R wraps all these steps into simple easy-to-understand procedures.
Learning Goals: This introductory course into R, will teach you everything you need to know; how to import the data, preprocess it, summarize it, plot it, analyze it and create a visually appealing final report. At the end, you will able to preprocess, analyze, and plot data for you Bachelor/Master/PhD thesis.
Course Method: This seminar assumes no prior knowledge on your part. We will start with basic concepts of variables and functions and proceed to advanced topics. The course will introduce different ways to perform typical data analysis tasks. You can bring your own data but you do not have to.
Grading: In the course, you will complete numerous practical exercises. Completing 80% of them is required to pass the course.
Empfohlene Literatur
Course syllables are available online: https://alexander-pastukhov.github.io/data-analysis-using-r-for-psychology/
"R for Data Science" by Garrett Grolemund and Hadley Wickham available freely at http://r4ds.had.co.nz/
Englischsprachige Informationen:
Title:
Applied data analysis for psychology using the open-source software R
Credits: 3
Zus?tzliche Informationen
Schlagw?rter: statistical analysis, data science, statistics
Institution: Lehrstuhl für Allgemeine 球探足球比分 und Methodenlehre
2) Statistical Rethinking
Bayesian Statistics
Dozent/in
Angaben
Seminar
Rein Pr?senz
2 SWS
Englischsprachig
Zeit und Ort: Mo 10:00 - 12:00, M3/-1.13
Voraussetzungen / Organisatorisches
Some Bachelor level knowledge R is beneficial, but no prior knowledge beyond high school algebra is required. For Ba / Ma Psychology only!
Inhalt
In this seminar, you build your understanding of (Bayesian) statistics from ground up (we start with a concept of probability as counting). It focuses on a linear model design that underpins all classic statistical test: t-test, ANOVA, rm ANOVA, ANCOVA, MANOVA, Pearson correlation, etc. You will learn about their simple common structure, understand how to design such models by hand (much simpler than you think), and, most importantly how to interpret and evaluate these models (much harder than you think) using causal calculus tools and information criteria.
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, practice build models, 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:
Bayesian Statistics
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) Deep Learning
Introduction to Deep Learning for Psychology (Deep Learning)
Dozent/in
Dr.