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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS PSI 6107 ADVANCED APPLICATIONS IN CATEGORICAL DATA METHODS ELECTIVE 3 0 0 5

Offered By

PSYCHOLOGY

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR DUYGU GÜNGÖR CULHA

Offered to

PSYCHOLOGY

Course Objective

The aim of this course is to introduce complex categorical data methods. The course will examine categorical methods for observed and latent variables.

Learning Outcomes of the Course Unit

1   Define types of categorical variables 2   Use categorical analysis models appropriate to the data 3   Interpret advanced categorical models 4   To be able to plan research with categorical variables 5   To be able to perform Monte Carlo simulation studies with categorical variables 6   Ability to compare different models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to the course and its requirements 2 Distributional characteristics of categorical data 3 Defining crosstables 4 Linear models for categorical data 5 Logistic regression applications 6 Logistic regression for multicategorical variables 7 Midterm Exam 8 Models for paired data 9 Logistic regression applications in repeated measures 10 Cluster analysis 11 Cluster analysis Applications 12 Applications of discriminant analysis 13 Presentations 14 Review of the course

Recomended or Required Reading

Agresti, A. (2002). Categorical data analysis, Wiley Series, Canada. Van der Ark., L.A., , Croon, M.A. & Sijtsma, K. (2005). New developments in categorical data analysis fort he social and behavioural sciences, Lawrence Erlbaum Assoc., London.

Planned Learning Activities and Teaching Methods

Lesson Presentation Question and Answer Homework Discussion

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.20 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + RST* 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

LO 1-6: It will be evaluated by midterm exam, homework/presentation and final exam.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to 70% of the courses is compulsory.

Contact Details for the Lecturer(s)

duygu.gungor@deu.edu.tr

Office Hours

Tuesday 14:00-15:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 14 4 56 Preparation for midterm exam 1 6 6 Preparation for final exam 1 5 5 Preparation for quiz etc. 1 5 5 Preparing assignments 1 4 4 Final 1 1 1 Midterm 1 1 1 TOTAL WORKLOAD (hours) 120

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 LO.1 5 5 5 LO.2 5 5 5 LO.3 5 5 5 LO.4 5 5 5 LO.5 5 5 5 LO.6 5 5 5