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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS PSI 5083 INTRODUCTION TO R PROGRAMMING ELECTIVE 3 0 0 8

Offered By

PSYCHOLOGY

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR ABBAS TÜRNÜKLÜ

Offered to

PSYCHOLOGY

Course Objective

The goal of this course is to introduce students the statistical programming R.

Learning Outcomes of the Course Unit

1   Describing the syntax of the R programming language 2   Using different data types properly 3   Using functions for data visualization and graphics 4   Using control structures 5   Doing fundamental psychometric analysis using the R packages 6   Doing a Monte Carlo simulation study using R

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to R environment 2 Introduction to R environment 3 Data structures 4 Data Import and export, data manipulations 5 Data Import and export, data manipulations 6 Built-in functions 7 Midterm Examination 8 Graphic functions 9 Psychometrics packages 10 Psychometrics packages 11 Psychometrics packages 12 Monte Carlo simulation studies 13 Monte Carlo simulation studies 14 Monte Carlo simulation studies

Recomended or Required Reading

1. Braun W.J., Murdoch D.J., A First Course in Statistical Programming with R, Cambridge, 2009. 2. Matloff N., The Art of R programming, 2011.

Planned Learning Activities and Teaching Methods

Lecture Answer-Question Presentations 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

None

Assessment Criteria

1. LO 1-2: They will be evaluated by questions in the midterm examination. 2. LO 3-5: They will be evaluated by questions in the final examination.

Language of Instruction

Turkish

Course Policies and Rules

1. Attendance must be at least 70% for the lectures.

Contact Details for the Lecturer(s)

duygu.gungor@deu.edu.tr

Office Hours

To be announced.

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 Final 1 3 3 Midterm 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 PO.7 PO.8 PO.9 PO.10 LO.1 5 5 5 4 5 5 4 4 4 LO.2 5 5 5 5 5 4 5 4 LO.3 5 5 5 5 5 4 4 4 LO.4 5 5 5 5 4 5 5 4 4 LO.5 5 5 5 5 5 5 5 4 LO.6 5 5 5 5 4 5 5 4 3