Applied data analysis for psychology using the open-source software R [Data Analysis using R]

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar, 2 SWS, ECTS: 3

Termine:

Di, 10:00 - 12:00, M3N/-1.19

Voraussetzungen / Organisatorisches:

No programming background necessary, basic knowledge on statistical analysis is advantages but not strictly necessary.

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. These include:

• data import, whether it is a CSV-file, Excel, SPSS, SAS, or Matlab and merging multiple data files into a single easy-to-use table (and saving it)
• data preprocessing: filtering out bad data and transforming values, e.g. turning continuous data into ordinal, degrees to radians, skewed data to a normally distributed ones, renaming and relabeling conditions, working with dates, etc.
• grouping and summarizing data: grouping data based on various combinations of conditions, computing group statistics or transforming data within each group
• plotting: R plotting package ggplot2 makes exploratory analysis easy and generates production-quality figures that you can use directly for your thesis or a publication
• statistical analysis: you will learn how to use various parametric (ANOVA, t-test, linear-mixed models) and non-parametric (traditional and permutation based) statistical tests, as well as the Bayesian approach to statistics and modern predictive modelling approaches (regression and classification using generalized linear models, linear discriminant analysis, support vector machines, etc.).
• putting it all together into a report: R helps you to automatically generate a nice looking report that includes all the analysis steps, figures, and statistics and export it for direct presentation (PDF, HTML) or for further editing (Word). Best part, if you change your mind and analysis, your final looking report is one button press away.

The best way to attend the course is with your own dataset. Bring it to the course and see how R will allow you to understand it deeper or reduce the time you need to analyze it. I am happy to help you all along with such statistical, methodological and graphical problems.

Empfohlene Literatur:

"R for Data Science" by Garrett Grolemund and Hadley Wickham available freely at http://r4ds.had.co.nz/

Schlagwörter:

statistical analysis, data science, statistics

Heureka! Creativity and the curious mind [Heureka]

Dozent/in:

Claudia Muth

Angaben:

Seminar, 2 SWS, Seminar auf Englisch

Termine:

Mi, 16:00 - 18:30, MG2/01.10

Voraussetzungen / Organisatorisches:

This seminar is held in English language.

Inhalt:

The insight that the earth turns around the sun and not vice versa, the discovery of hidden figures in an image, the experience of a marvelous line in a poem that opens up a whole new world or that thrilling moment within a piece of music that induces shivers down the spine: How is it that we gain pleasure by such surprising events considering that humans seek the opposite - namely stability and familiarity? Don't we prefer objects and situations that we can handle and understand easily because they affirm what we already know? In this seminar, we will explore the theoretical basis for human curiosity and creativity by drawing on literature from various fields like psychology, philosophy, cognitive science and neuroscience. We will link this literature to experiences and exercises and discuss topics like:
1) From creative robots and rigid humans: What makes a creative agent and is there something like creativity at all?
2) Creativity and cognition: psychological and neurological findings on divergent thinking and insight
3) Aesthetics in art and design: Why we seek familiar designs but can be thrilled by innovation

Empfohlene Literatur:

Boden, M. (1998). Creativity and artificial intelligence. Artificial Intelligence 103. 347-356
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181- 204. doi: 10.1017/S0140525X12000477
Dietrich, A. & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136(5), 822 848. doi: 10.1037/a0019749
Guilford, J.P. (1950) Creativity. American Psychologist, 5(9), 444 454.
Knoblich, G., & Öllinger, M. (2006). Einsicht und Umstrukturierung beim Problemlösen. In J. Funke (Ed.), Denken und Problemlösen. Göttingen: Hogrefe.

Statistical Rethinking [Statistical Rethinking]

Dozent/in:

Alexander Pastukhov

Angaben:

Sonstige Lehrveranstaltung, 2 SWS, ECTS: 3

Termine:

Mi, 14:00 - 16:00, M3N/03.28

Inhalt:

This is a book-club style seminar dedicated to reading and understanding "“Statistical Rethinking” book (online version with full text can be found here https://ebookcentral.proquest.com/lib/ub-bamberg/detail.action?docID=4648054).
It is an excellent introductory statistics book with a focus on Bayesian statistics, information criteria, and hierarchical models. It explains the principles slowly and clearly and has plenty of examples in R. Prior to each meeting everyone must read a chapter, do exercises. We meet to discuss the chapter and to help each other understand the tricky bits.

Empfohlene Literatur:

"Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath

Schlagwörter:

statistics, bayesian statistics