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Research Research School of Behavioural and Cognitive Neurosciences Education PhD Training Programme A. BCN Standard Courses and Activities

5. BCN Statistics Course 2021

Compulsory

Preferably in the second year. Important: other statistics courses more tailored to your own background may be used to get exemption for this course. For example, if you’d like to learn non-linear statistics (Generalized Additive Modeling) you may follow the BCN Advanced (non)linear regression techniques in R course instead.

Theme

Statistics in a bird’s eye view

Content

This course provides an overview of several statistical concepts and methods. The software used in this course is R, which is freely available and provides excellent facilities for sophisticated statistical analyses. The topics treated in this course are: statistical concepts, data exploration (basic visualization), t-tests, ANOVA, non-parametric tests, regression analysis, logistic regression analysis, and mixed-effects regression analysis (for repeated measures).

Target Group

PhD students within BCN.

Application

Click here to register.

Credits

2 EC.

Objectives

The objectives of this course are to refresh and augment your statistical knowledge. The course provides you with an overview of the relevant aspects in using statistics. The course will be relatively hands-on, meaning that the focus of the course lies on determining which test to use, how to use it, and how to interpret the results. Given that the teacher of the course is a linguist, the examples used in this course will focus on linguistic material. However, it is straightforward to apply them to data from your own field. Furthermore, feel free to bring your own data to get feedback about which type of analysis you could use.

Form

Online Q&A and lab sessions, during 5 days. Instead of live lectures, prerecorded online lectures are made available which can be watched online (via Nestor), during the scheduled sessions or (ideally) beforehand. During the five scheduled sessions, the lecturer is available online to answer any questions you have about the prerecorded lecture, the associated lab session of that day. The course consists of five sessions of four hours each. Please make sure you have the latest version of R (and optionally RStudio) installed on your computer.

The schedule of the course is as follows:
Session 1: Basic concepts of statistics
Session 2: Introduction to R + data exploration
Session 3: Various statistical tests (t-tests, ANOVA, non-parametric alternatives)
Session 4: Linear regression and logistic regression
Session 5: Mixed-effects regression (multilevel modeling)

Credits are assigned to students who viewed all lectures and attended at least 4 sessions (until the end of each session, or until the lab exercises have been made and checked, in case the student watched the recorded lecture at an earlier earlier).

Period

Wednesday March 31, Thursday April 1, Monday April 5, Wednesday April 7, Thursday April 8, from 1 PM – 5 PM. The link to the online sessions will be provided via Nestor before the start of the course.

Final attainment level

After the course, you are able to select an appropriate statistical method for the most frequent occurring data analytic problems in the cognitive and behavioral sciences.

Required Entrance Knowledge

Required entrance knowledge:
The course is not an elementary course in statistics, but a refresher course. Consequently, it is assumed that you have some knowledge about basic statistical concepts (i.e. you know what a p-value is, what hypothesis testing is, etc.), and know what ANOVA and regression are. If you think you might not have enough knowledge, please make sure to cover the recommended literature for the first three lectures *before the course*
Recommended literature:
* Lecture 1-4: https://benjamins.com/#catalog/books/z.195/main (Ch. 1-9 + 12)
* Lecture 5: http://www.sfs.uni-tuebingen.de/~hbaayen/publications/baayenCUPstats.pdf (Ch. 7)

Of course, you are also welcome to use other general statistical textbooks such as Field, Moore & McCabe, or Zar.

Exemption

Students who have already participated in a similar statistics course may ask their supervisor to send a brief e-mail to the PhD coordinator explaining why he or she should receive exemption for this course.

Last modified:26 February 2021 1.49 p.m.