12 Apr module 08: basic non-parametric statistics
- module 8: basic non-parametric statistics
14/09/2020 - 18/09/2020
9:30 am - 11:00 am
Mon 14/09: 9:30 am - 12:45 pm | online
Tue 15/09: 9:30 am - 12:45 pm | online
Wed 16/09: 9:30 am - 12:45 pm | online
Thu 17/09: 9:30 am - 12:45 pm | online
Fri 18/09: 9:30 am - 11:00 am | online
week 2 | register now
This course aims at introducing the traditional nonparametric techniques in statistical analysis and the use of these techniques in a variety of disciplines. Participants will get acquainted with the fundamentals, basic properties and computing tools of the most important nonparametric techniques. The course will first review some of the basic concepts of probability, such as probability distribution, the binomial and normal distributions, summary statistics, confidence intervals and hypothesis testing. Methods that compare measure of centrality or measures of location to a hypothesized value or estimate an interval for the desired measure will be discussed. These will then be extended to methods that compare two measures of location for equality from independent samples and from two paired samples.
At the end of this module, participants will have a better understanding of:
- how to summarize data using both graphical and numerical methods for use in nonparametric statistical methods
- which situations involve paired data and which involve independent samples
- how to conduct a non-parametric test for one-sample and two-sample data (the use of Mann-Whitney U test, Wilcoxon rank-sum test, Wilcoxon signed-rank test, Kruskal-Wallis one way)
- perform the necessary calculations using statistical software (R or SPSS, to be chosen by the participant)
- present and communicate, both orally and in written form, the results of statistical analyses of nonparametric data.
The module is taught in 9 classes, from which 5 are practical sessions. The last practical session is a hands-on session during which participants are provided with a real data set to be analyzed individually using the acquired skills during this course. You will need a laptop with R (or Rstudio) installed on it.
Limited knowledge of basic statistics (meaning: you have had a course in statistics before (for example in your bachelor or master), but you do not know all details anymore). Basic knowledge of the software package of your choice (R or SPSS): importing data and basic data manipulation. Especially for the package R a basic knowledge is necessary!
Cristina Cametti is the Flames coordinator at KU Leuven. She holds a Master degree in Statistics (KU Leuven) and in Crime and Security Sciences (Università Cattolica del Sacro Cuore – Milan). Her interests focused mainly on teaching statistical software courses (R, Python, SAS), on multivariate data analysis and data cleaning techniques.
Dr. Jef Vlegels is a sociologist and statistician working at Ghent University as a post-doc (CHEGG research group) and as a lecturer at Flames training network for Methodology and Statistics. In his research he specializes in quantitative methods for the social sciences, with a specific focus on Social Network Analysis. He is an experienced lecturer in methodology and statistics, with previous experience in introductory courses as well as advanced specialized courses for Bachelor, Master and PhD students.