| Academic year: | 2009-2010 |
| Course code module | 1MSOC_040 |
| Semester: | 1st semester |
| Credits: | 6 |
| Study load (hours) | 168 |
| Theory (hours): | 45,00 |
| Practice/Exercises(hours): | |
| Other (hours): | |
| Part-time program: | 1 |
| Instructor(s) | Dimitri Mortelmans
|
| Language of instruction: | Dutch |
| Semester exam information: | exam in the 1st semester |
| Contract restriction information: | |
1. Prerequisites
*Algemene competenties
Students are expected to have a thorough basic knowledge of quantitative research methods and statistics.
Students know the basics of univariate and bivariate statistics, regression analysis, factor analysis and cluster analysis. Students also know the basics of inductive statistics.
*Sequentiality
None
2. Objectives (expected learning outcomes)
Students gain insight in the applicability of multivariate statistics in social science research. More specifically, they gain insight in the basics of the techniques of this course. They can do basic analyses with these techniques and they can read and interprete a computer output. They can give a methodological description of the choices they make in multivariate statistics and are able to convert the output of a computer program into a publishable form on which they can communicate.
3. Course content
The first part covers the basics of structural equation modelling. Continuing previous courses on path analysis and factor analysis, the measurement model (confirmatory factor analysis) is discussed followed by the standard structural model.
The second part consists of an introduction in multilevel analysis. Starting from the regression model in the course Statistics 2, we gradually extend the model to estimations of the null random intercept, the random intercept en the fully random multilevel models.
A third and last part introduces students in the basics of longitudinal analysis. After a short introduction on time and measurements of time in social scientific research, a basic introduction is given on three often used techniques of longitudinal modelling. In each of these three traditions we link to previous courses (Statistics II or Applied Multivariate Statistics itself). Event-history-modelling will be very shortly discussed.
Students use the ESS (European Social Survey) dataset and use SAS in their programming. An introduction to the use of SAS is given at the start of the course.
4. Teaching method
Direct contact: Lectures
Personal work: ExercisesAssignments - individualProject-based work - individual
5. Assessment method
Exam: Oral, with written preparation
6. Compulsory reading – study material
Mortelmans, D., Dehertogh, B. (2006) Kennismaken met SPSS en SAS. Leuven, Acco
Mortelmans, D., Dehertogh, B. (2006) Databeheer met SPSS en SAS. Leuven, Acco
Course material (reader)
7. Recommended reading - study material
Mortelmans, D., Dehertogh, B. (2007) Regressieanalyse. Leuven, Acco.
Mortelmans, D., Dehertogh, B. (2008) Factoranalyse. Leuven, Acco.
8. Tutoring
After class and by appointment.