5. BCN Statistics Course 2018
Preferably in the second year.
Statistics in a bird’s eye view
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: data exploration (basic visualization), t-tests, ANOVA, non-parametric tests, regression analysis, logistic regression analysis, mixed-effects regression analysis (for repeated measures), and generalized additive modeling (for time series analysis).
PhD students within BCN.
Click here to register.
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.
Lectures and lab classes, during 4 days
The course consist of five session of three hours each. Each session starts with a lecture of 1-2 hours, and ends with a lab session. Therefore, please bring your laptop with the latest version of R (and optionally RStudio) installed. The schedule of the course is as follows:
Lecture 1: Introduction to R + data exploration
Lecture 2: Various statistical tests (t-tests, ANOVA, non-parametric alternatives)
Lecture 3: Linear regression and logistic regression
Lecture 4: Mixed-effects regression (multilevel modeling)
Lecture 5: Generalized additive modeling (non-linear regression)
June 2018, click here to see the exact data.
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 behavioural 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*
* Lecture 1-3: https://benjamins.com/#catalog/books/z.195/main (Ch. 1-9 + 12)
* Lecture 4: http://www.sfs.uni-tuebingen.de/~hbaayen/publications/baayenCUPstats.pdf (Ch. 7)
* Lecture 5: - Simon Wood (2006). Generalized Additive Modeling. http://reseau-mexico.fr/sites/reseau-mexico.fr/files/igam.pdf
- Bodo Winter and Martijn Wieling (2016). How to analyze linguistic change using mixed models, Growth Curve Analysis and Generalized Additive Modeling. Journal of Language Evolution, 1(1): 7-18.
Of course, you are also welcome to use other general statistical textbooks such as Field, Moore & McCabe, or Zar.
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:||17 May 2018 3.08 p.m.|