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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS PSI 5085 CATEGORICAL DATA ANALYSIS ELECTIVE 3 0 0 8

Offered By

PSYCHOLOGY

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR DUYGU GÜNGÖR CULHA

Offered to

PSYCHOLOGY

Course Objective

The aim of this course is to introduce the basics of psychometrine. In the course, concepts related to classical test theory will be examined.

Learning Outcomes of the Course Unit

1   Being able to define basic concepts related to categorical data analysis. 2   Being able to explain main categorical data analysis models 3   Being able to give examples recent research on the categorical data analysis 4   Being able to use various statistical package programs related to categorical data analysis 5   Being able to use and interpret techniques for advanced categorical data analysis. 6   Being able to pursue and contribute to current research in the subject of the categorical data analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Statistical Models for Categorical Variables 2 Regression toward the mean and tendency toward the mode 3 factor analysis with categorical indicators 4 factor analysis with categorical indicators 5 factor analysis with categorical indicators 6 Logistic models 7 Midterm 8 Logistic models 9 Logistic models 10 cross tabulation 11 cross tabulation 12 mutli categorical variables 13 DINA Model 14 DINA Model

Recomended or Required Reading

Van der Ark, L.A., Croon, M.A. & Sijtsma, K. (2005). New developments in categorical data analysis for the social and behavioural sciences, Lawrence Erlbaum Associates, London. Çilan, Ç.A. (2009). Sosyal bilimlerde kategorik verilerle ilişki analizi, Pegem Akademi, Ankara.

Planned Learning Activities and Teaching Methods

Lesson Question-Answer Presentation 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 * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

Assignments, Midterm and Final Exams

Assessment Criteria

LO 1-2: They will be evaluated by questions in the midterm examination LO 3-6:They will be evaluated by questions in the final examination

Language of Instruction

Turkish

Course Policies and Rules

Attendance must be at least %70 for the lecture.

Contact Details for the Lecturer(s)

Doç. Dr. Duygu Gungör Culha e-mail: duygu.gungor@deu.edu.tr

Office Hours

Tue: 14.00-15.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 7 91 Preparation for midterm exam 1 10 10 Preparation for final exam 1 14 14 Preparing presentations 13 4 52 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 212

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 4 4 LO.2 4 5 4 4 4 LO.3 5 5 4 4 4 4 LO.4 4 5 4 4 LO.5 4 5 4 4 LO.6 4 5 5 5