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

Termine:

Mi, 8:00 - 10:00, M3N/-1.19