Course Code : | 2300PSWMVA |

Study domain: | Statistics |

Academic year: | 2019-2020 |

Semester: | 1st semester |

Contact hours: | 45 |

Credits: | 6 |

Study load (hours): | 168 |

Contract restrictions: | No contract restriction |

Language of instruction: | Dutch |

Exam period: | exam in the 1st semester |

Lecturer(s) | Dimitri Mortelmans |

At the start of this course the student should have acquired the following competences:

an active knowledge of

an active knowledge of

- Dutch

- English

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.

- Using SAS for data entry, data management and data analysis.
- Building structural equation models (SEM), estimating the models in SAS and interpreting the output.
- Building multilevel models (linear mixed models), estimating the models in SAS and interpreting the output.
- Have a basic notion of the concepts of longitudinal analysis.

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).

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.

The course has an international dimension.

Class contact teachingLectures Seminars/Tutorials

Personal workExercises Assignments Individually

**5.3 Facilities for working students ***

Classroom activities

Personal work

Classroom activities

- Lectures: recording available via video link on Blackboard
- Exercise sessions: free to choose the group division

ExaminationOral with written preparation Closed book Open-question

Continuous assessmentAssignments

Continuous assessment

- Mortelmans, D. (2016) SAS in Onderzoek. Leuven, Acco.

- Cost handbook, see: www.acco.be (reduction price for shareholders).

- Course material

- Cost course, see www.cursusdienst.be.

Mortelmans, D., Dehertogh, B. (2007) Regressieanalyse. Leuven, Acco.

Mortelmans, D., Dehertogh, B. (2008) Factoranalyse. Leuven, Acco.

Mortelmans, D. (2009) Logistische regressie. Leuven, Acco.

For useful tips & tricks on paraphrasing, citing and referencing, we strongly advise students to consult the folder **‘Citing and referencing academic sources’ **in the English Blackboard environment: https://blackboard.uantwerpen.be/webapps/blackboard/content/listContentEditable.jsp?content_id=_2144769_1&course_id=_74421_1.

Dutch speaking students can consult the Blackboard course **‘Zelfstudiepakket Academische Bronverwerking’ **under ‘Mijn Studie’.

Lecturer: prof. dr. Dimitri Mortelmans (dimitri.mortelmans@uantwerpen.be)

Assistant: Elke Claessens (elke.claessens@uantwerpen.be)