180159 VO+UE Tools in Cognitive Science II: Basic Statistics for Cognitive Scientists (2024S)

LEARNING GOAL 1
Basic understanding of the principles of statistical procedures, including:
- Understanding what kind of questions can (and cannot) be worked on with statistical methods
- Understanding what kind of questions correspond to what research design and what the appropriate statistical tests are, as well as the requirements for those tests
(aka “know what data to collect in order to actually be able to work on your research questions and hypotheses before you start your analysis”
aka “how to avoid collecting data only to later realise that you cannot use it to work on your research question/hypothesis due to some tests’ data requirements”)
- Overview of different types of statistic and when/how to use them (descriptive statistics, inferential statistics, including testing and exploratory approaches/methods)
- Basic understanding of hypotheses testing, significance testing, confidence intervals (including a brief intro to Frequentist VS Bayesian statistics)
- How to read and interpret statistical results (i.e. reporting of results, in a paper or a similar context)

LEARNING GOAL 2
Introduction to the statistics software IBM SPSS Statistics and using it for a variety of tests and interpret their results, including:
- Descriptive statistics (measures such as mean, median, mode, standard deviation, variance, curtosis, skewness; frequency tables, cross tables, when and how to use them depending on the level of measurement)
- Graphs and diagrams
- Basic and advanced methods (inferential statistics), including: chi square (and associated measures/association coefficients), T-Test (unpaired and matched pairs) and its non-parametric alternatives (Mann-Whitney-U-Test, Wilcoxon Test), correlation (Pearson’s R, Spearman correlation), Regression (linear, if time/interest also logistic and ordinal regression), ANOVA (Analysis of Variance, with and without repeated measures, mixed ANOVA), ANCOVA (Analysis of Covariance), how to work with Indices, PCA (Principal Component Analysis), as well as a brief outlook of some mutli-variat methods (multivariate regression, MANOVA)
- How to interpret and report above procedures/tests/methods and their results
- Additionally: Basics in SPSS Syntax (a scripting language used to analyse and edit data in SPSS), outlook on using Python (programming language) in SPSS

LEARNING GOAL 3
Basis for
a) future self-studies in statistics, and
b) a general notion of how to use (what kind of) statistics in students' respective research area/field of interest (or to find out how to!).
This course is designed to provide students with a “good enough” understanding of statistics to enable them to continue learning about and using statistics on their own (e.g. knowing and understanding the principles and requirements of tests, knowing the correct terminology and “what to google”, being able to judge if an online tutorial is “good” or “bad”, etc.).

METHOD OF INSTRUCTION
During the course, we will conduct our own miniature research project: discuss its design, collect data, learn how to get the data into SPSS, how to prepare and clean it, how to analyse it, and, finally, how to interpret and report the results of our analysis. In general, the course is intended to have a “hands-on”, applied, and practical character -- we will be “doing statistics” from the very beginning!
Additionally, several methods and how they may be used with regard to different research questions and corresponding hypotheses stemming from different cognitive science disciplines will be discussed. The course will also feature datasets from actual cognitive science research projects from behavioural/cognitive biology, computer science, and neuroscience that can be worked on in an exemplary manner.
The time in class will mostly be spent on working with SPSS, the homework/exercises and related discussions will prompt students to reflect on and think through what they learnt on a more general and theoretical level.