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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS PSI 6001 COMPUTER AIDED QUANTITATIVE METHODS IN PSYCHOLOGY I* COMPULSORY 3 0 0 12

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 the course is to teach computer aided statistical methods for psychological research focuses on advanced data analysis using computers. Students will consider experimental design in psychology, extending analysis of variance into several advanced topics such as planned and unplanned comparisons, multiple random factors, power analysis, regression (multiple, loglinear and logistic), analysis of covariance, and metaanalysis.

Learning Outcomes of the Course Unit

1   Being able to understand key concepts involved in psychological statistics 2   Being able to understand basic statistical techniques (conceptually and numerically). 3   Being able to correctly apply statistical techniques to psychological data 4   Being able to correctly interpret results of analyses of psychological data 5   Being able to clearly convey orally and in writing the details of statistical analyses and results. 6   Learning the use of statistical package programs for psychological data analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Review of basic statistical concepts I 2 Review of basic statistical concepts II 3 Getting to know SPSS I 4 SPSS 5 Exploring Assumptions 6 Correlation 7 Regression 8 Multiple regression 9 Midterm Exam 10 Comparing Two Means 11 Comparing Several Means 12 Repeated Measures ANOVA 13 Factorial ANOVA 14 Mixed ANOVA

Recomended or Required Reading

Field, A. (2009). Discovering Statistics Using SPSS. Dubai: Sage Publications

Planned Learning Activities and Teaching Methods

Lecture Presentation Homework

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FCG FINAL COURSE GRADE 3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

1.Midterm 2.Final

Assessment Criteria

LO 1-2: Assessed with Midterm exam LO 3-5: Assessed with homework/report, presentation and final exam

Language of Instruction

English

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

Wednesday 13:00-14:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 14 8 112 Preparation for midterm exam 1 20 20 Preparation for final exam 1 30 30 Preparing assignments 4 20 80 Midterm 1 2 2 Final 1 2 2 TOTAL WORKLOAD (hours) 288

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