Statistics II for Social Sciences

6 ECTS credits
156 h study time

Offer 2 with catalog number 1017129BNR for all students in the 1st semester at a (B) Bachelor - advanced level.

Semester

1st semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Enrollment Requirements

Bachelorstudenten hebben 'Statistiek I voor de sociale wetenschappen' gevolgd, alvorens 'Statistiek II voor de sociale wetenschappen' op te nemen.

Taught in

Dutch

Partnership Agreement

Under agreement for exchange of courses

Faculty

Faculty of Social Sciences & SolvayBusinessSchool

Department

Sociology

Educational team

Djiwo Weenas
Juliette De Vestel
Barbara Janssens
Lennert De Boe
Tom De Winter (course titular)

Activities and contact hours

26 contact hours Lecture
40 contact hours Seminar, Exercises or Practicals
139 contact hours Independent or External Form of Study

Course Content

This course consists of 10 course modules that build on the knowledge and skills from the course 'Statistics I for the social sciences' (‘Statistiek I voor de sociale wetenschappen’):

Basic statistical concepts (e.g. refresher module for the content of the course ‘Statistiek I voor de sociale wetenschappen’)
Probability theory
Probability distributions (e.g. binomial distribution, normal distribution)
Sampling distribution & central limit theorem
Hypothesis testing
Confidence intervals
Inferences for proportions (e.g. difference between two proportions, chi-square test for independence)
Inferences for means (e.g. t-test, ANOVA)
Bivariate linear regression (e.g. dummy regression)
Multivariate linear regression

The statistical concepts and techniques in these course modules are introduced and illustrated during the lectures, and practiced during the seminars, supplemented with online exercises.
This course also includes the online course ‘Bootcamp SPSS’ (only available in Dutch). In this self-study package, students learn the basic techniques for data management and data manipulation in the software package IMB SPSS. In addition, students also learn to execute the bivariate and multivariate techniques, which are discussed in the lectures and seminars, in SPSS. In this course, students continue to build on their SPSS skills that they already acquired in the course 'Statistics I for the social sciences' (‘Statistiek I voor de sociale wetenschappen’). The Bootcamp SPSS consists of the following thematic modules:

Introduction to SPSS
Importing and documenting data
Exploring data
Manipulating data
Analyzing data

The online course ‘Bootcamp SPSS’ is supported through a number of Q&A sessions.

Course material

Digital course material (Required) : Slides van hoor- en werkcolleges en aanvullend online infomateriaal, T. De Winter, H. Vandenheede & F. Van Droogenbroeck, Canvas, 2022
Digital course material (Required) : Oefeningenbundel, T. De Winter, J. De Vestel & D. Weenas, Canvas, 2022
Digital course material (Required) : Bootcamp SPSS, T. De Winter, J. De Vestel & D. Weenas, Canvas, 2022

Additional info

Preparation of and participation in the seminars is essential to master the subject matter fully.

WORKING STUDENTS
Working students get access to recordings for all lectures and seminars, so they can watch them in case they cannot be present during the live on campus classes. This is complemented with a number of Q&A sessions. In this way, we aim for a balance between flexibility for the working student on one hand and opportunities for interaction in small groups with the teaching staff on the other hand.

Learning Outcomes

General competencies

This course aims to introduce the basic concepts and techniques of probability theory and inferential statistics, thus laying the foundation for bivariate and multivariate analysis techniques. The course mainly aims at the understanding of statistical techniques and their practical application within research in the social sciences.

After this course, students …

can explain the basic concepts of descriptive and inferential statistics.
can explain the basic concepts of probability theory in their own words and apply them in exercises.
can explain the characteristics of the different discrete and continuous probability distributions and apply them in exercises.
can explain the principles of inferential statistics (including the central limit theorem) in their own words.
can explain in their own words the techniques of inferential statistics (hypothesis tests and confidence intervals) and apply them to both univariate and bivariate examples.
can apply bivariate and multivariate inferential analysis techniques for qualitative and quantitative variables, and interpret the results correctly and critically.
take a critical look at the analysis and interpretation of statistical data.
can input, import, document, merge, explore and manipulate social science data using IBM SPSS, in preparation for further analysis.
can perform the most common bivariate and multivariate analysis techniques using IBM SPSS.

Specific learning objectives are given for each course module during the lectures.

Grading

The final grade is composed based on the following categories:
Written Exam determines 80% of the final mark.
Practical Exam determines 20% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

Written Examen with a relative weight of 1 which comprises 80% of the final mark.

Within the Practical Exam category, the following assignments need to be completed:

Exam SPSS with a relative weight of 1 which comprises 20% of the final mark.

Note: SPSS skills and exercises. Computer exam.

Additional info regarding evaluation

A student must pass for both examination components separately (with a score of at least 10/20). Only after passing both the written and practical exam, the student will pass this course unit.

If the student did not pass for at least one of the examination components, the result of the unsuccessful exam part is the final result.

The partial result of the exam components will be transferred to the second examination period, if the result is at least 10/20. The exam component for which the student has passed cannot be retaken in the second examination period.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context