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Course module: 424805-M-6
424805-M-6
RM: Categorical Data Analysis
Course info
Course module424805-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Social and Behavioral Sciences; TSB: Methodology and Statistics; Methodology and Statistics;
Is part of
M Social and Behavioural Sciences (research)
Contact personprof. dr. J.K. Vermunt
Lecturer(s)
Lecturer
prof. dr. J.K. Vermunt
Other course modules lecturer
Coordinator course
prof. dr. J.K. Vermunt
Other course modules lecturer
Academic year2019
Starting block
BLOK 1
Course mode
Full-time
RemarksCourse is not offered in 19/20
Registration openfrom 13/08/2019 up to and including 18/10/2019
Aims
The aim of the course is to gain practical and theoretical knowledge of the most relevant techniques for the analysis of categorical data.
Specifics
The course consists of 14 two-hour lectures/seminars and 7 two-hour computer practicals. In the interactive lecture, the lecturer explains the subject matter, asks questions, and invites students to discuss the subject matter. In the practicals, the students apply the acquired methods and techniques to real-data sets from the Social Sciences and Social Psychology using SPSS, R, Latent GOLD, and Excel.
Paper, oral exam, and computer assignments. The assignments consist of analyzing data and reporting the data analysis and results. Students should attend lab sessions. Students who fail to to attend more than one lab session should do an additional assignment.
The 168 hours in the course consist of 28 (14 × 2) hours attending lectures/seminars and 14 (7 × 2) hours attending computer practicals, 28 (7 × 4) hours working on exercises and assignments, and 42 hours self-tuition, and 56 hours for the final paper and the exam preparation.
Recommended Prerequisites
Research Master course Mathematical Methods
Content
The most important theme in this course is regression analysis with non-continuous dependent variables using binary, multinomial and ordinal logistic, probit, and Poisson regression models; that is, regression models belonging to the generalized linear modelling family. In addition, we pay attention to technical aspects such as the principle of and algorithm for maximum likelihood estimation and statistical inference (Wald, score, and likelihood testing; delta method). The third topic is latent class analysis, which is a method for cluster analysis, scaling, and the random effects modeling. The relevant software packages in this course are SPSS, R, Latent GOLD, and Excel.
Type of instructions
Seminars and lab sessions
Type of exams
Oral exam and paper

Compulsory Reading
  1. Agresti, A., Categorical data analysis, Hoboken, NJ: Wiley, 2002 (second edition), ISBN 0-4713-6093-1.
  2. Research papers to be handed out during the course.
Timetable information
RM: Categorical Data Analysis
Required materials
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Recommended materials
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Tests
Paper

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Kies de Nederlandse taal