SOW-SCS110
Categorical data analysis
Course infoSchedule
Course moduleSOW-SCS110
Credits (ECTS)3
Category-
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Social and Cultural Sciences;
Lecturer(s)
Examiner
prof. dr. R.N. Eisinga
Other course modules lecturer
Lecturer
prof. dr. R.N. Eisinga
Other course modules lecturer
Contactperson for the course
prof. dr. R.N. Eisinga
Other course modules lecturer
Academic year2017
Period
PER4  (16/04/2018 to 13/07/2018)
Starting block
PER4
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesNo
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims

Knowledge: Students obtain up-to-date knowledge and understanding of important - in the sense of most frequently used - models for the analysis of categorical dependent variables.
Skills: Students are able to apply categorical data models and to interpret the results of their analyses. Students are able to present the results of the analysis orally in class and by reporting them in papers.
Attitudes: Students obtain a healthy critical attitude toward the application of these models, the assumptions underlying them and the obtained results.

Content
This course gives an introduction to methods and models for the analysis of categorical dependent variables and their application in comparative social science research. It first considers the binomial logistic model for the analysis of binary responses. It then discusses two models for analysing unordered categorical dependent variables, i.e., multinomial logistic models and conditional logistic (or discrete-choice) models. Next to these issues, a smash board of topics directly related to categorical data modelling will be discussed. Students complete exercises in categorical data modelling and write papers on their findings.
Levels
Master

Test information
Papers and oral presentations

Prerequisites
It is assumed that students have prior knowledge such as that covered in a typical applied linear regression course at the BA-level.

Required materials
Reader
Course reader: Categorical data analysis A collection of publications applying the models is available at the beginning of the course.

Instructional modes
Computer exercises
Attendance MandatoryYes

Lecture
Attendance MandatoryYes

Student presentations
Attendance MandatoryYes

Tests
Papers and oral presentations
Test weight1
OpportunitiesBlock HERT, Block PER4