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:

Mi, 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

Python for social and experimental psychology

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar, ECTS: 3

Inhalt:

An introductory hands-on course that teaches how to use Python to create offline or online experimental programs. No prior knowledge is required, as we will start with introduction to programming in Python by writing games (because psychological experiments are merely boring games). We will progress from basic text based interaction to using graphics and sound and creating online studies using OTree system.

Statistical Rethinking [Statistical Rethinking]

Dozent/in:

Alexander Pastukhov

Angaben:

Seminar, 2 SWS, ECTS: 3

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://learning.oreilly.com/library/view/statistical-rethinking/9781482253481/). This is an excellent introductory statistics book that builds your understanding of statistics from ground up, explains the principles slowly and clearly and has plenty of examples. Which is why, no prior knowledge of statistics is required. In fact, we will start completely from scratch, rethinking the concepts of probability and inferences, learning how to build models by hand, making sure we understands them at every step.
As noted, this a book-club style seminar. Prior to each meeting everyone must read a chapter and do the 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: https://learning.oreilly.com/library/view/statistical-rethinking/9781482253481/

Schlagwörter:

statistics, bayesian statistics