The aim of this course is to provide basics skills and knowledge about quantitative data analysis both for descriptive and inferential statistics.
The learning outcomes G4, and to a lesser extent, G2 (G26 & G27) are pursued by this course.
At the end of this course, the students should be able to:
- Translate a concrete issue into a research question that fit quantitative data analysis (G41).
- Identify the different existing variable types (G43).
- Select, apply and interpret descriptive statistics in a concrete research context (G43).
- Understand the underlying reasoning of inferential statistics.
- Select apply and interpret inferential statistics (essentially bivariate procedure) in a concrete research context (G44)
- Critically evaluate research endorsing a quantitative design (G45).
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Individual written evaluation
The course is divided into 30hours of lecture course and 15hours of practical exercises.
The practical exercises sessions aim at facilitating the development of interpretative and selection skills about descriptive and inferential statistical methods.
Both lecture course and practical exercises allows students to get used to the use of statistical software.
Descriptive statistics :
- Nominal variables : mode
- Ordinal variables : median, interquartile range
- Continuous variables : mean, variance, standard deviation.
Inferential statistics: knowledge
- Population and sample
- Inferential test procedure
- Type I and II error, statistical power
- Effect size
Inferential statistics (statistical tests):
- Chi-square & Cramer's V.
- Spearman & Pearson's correlations.
- Simple & multiple linear regression.
- T-test & one-way Anova.
Critical reading:
- Understanding of the most used statistical terms and icons in empirical literature.
- Diagram's, tables and indices' interpretation.
- Critical distance with traditional manipulation of statistical information.
- Awareness of the limitations of the statistical tools.
