Pre-Master Descriptive and Inferential Statistics


Course Objective

Knowledge and understanding - The student has acquired knowledge and
understanding of:
(1) elementary methods and techniques of descriptive and inferential

Application - The student has acquired the competences to:
(2) use the appropriate descriptive statistics given the available data
(in SPSS);
(3) apply several elaboration models;
(4) use statistical techniques to test the difference between two groups
(i.c. t-test), and to apply them in SPSS;
(5) use statistical techniques to test the association between
variables (i.c. correlation coefficient and regression analysis), and to
apply them in SPSS;
(6) draw a table with relevant statistical information and how to ‘read’
such tables.

Course Content

This course offers an overview of techniques to describe quantitative
data. Topics are, among others, mean, variance, correlation and
regression. Using the elaboration model students learn to interpret the
relation between two variables controlling for the effects of a third
variable. This course also offers an overview of statistical techniques
how to analyze collected data in order to test hypotheses. Afterwards a
student is able to
formulate hypotheses, to test them, to draw correct conclusions, to show
the relation between levels of significance, p-values, statistical
power, and statistical errors. During SPSS tutorials all techniques will
be applied.

Teaching Methods

Lectures and practical tutorials

Method of Assessment

Digital examination and SPSS assignments.


Alan Agresti & Christine Franklin. Statistics: the Art and Science of
Learning from Data (3rd edition, 2013).

Also possible: Alan Agresti, Christine Franklin & Bernhard Klingenberg.
Statistics: the Art and Science of Learning from Data (4th edition,
Also possible: Gerhard G. van de Bunt (compiler). Descriptive and
Inferential Statistics in the Social Sciences (2011). Pearson Custom

Recommended: Software-package SPSS, for instance via Surfspot
( It is always possible to use SPSS at the VU,
but there are not that many public computers and often these computers
are booked. In these cases SPSS cannot be used.

Target Audience

Premaster students

General Information

Course Code S_PMDIS
Credits 6 EC
Period P4
Course Level 0
Language of Tuition English
Faculty Faculty of Social Sciences
Course Coordinator dr. J.C. Muis
Examiner dr. D. Pavlopoulos
Teaching Staff dr. D. Pavlopoulos
dr. J.C. Muis
dr. A. de Wit MSc

Practical Information

You need to register for this course yourself

Teaching Methods Lecture, Practical