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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS PSI 6100 COMPUTER AIDED QUANTITATIVE METHODS IN PSYCHOLOGY II* 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 computeraided 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   Be able to understand key concepts involved in psychological statistics and the use of computers (statistical package programs). 2   Be able to understand basic statistical techniques (conceptually and numerically). 3   Be able to correctly apply statistical techniques to psychological data 4   Be able to correctly interpret results of analyses of psychological data. 5   Be able to clearly convey orally and in writing the details of statistical analyses and results 6   To learn statistical package programs suitable 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 overview of the course, rules 2 Confirmatory factor analysis with Lavaan 3 Categorical data (log-lineer and chi square) 4 Logistic Regression 5 Latent Class analysis 6 Latent Markov model and measurement invariance 7 ROC and Cluster analysis 8 Comparing Groups 9 General Lineer Model 1 10 General Lineer Model 2 11 General Lineer Model 3 12 General Lineer Model 4 13 General Lineer Model 5 14 SEM

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

Tuesday 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 9 126 Preparation for midterm exam 1 25 25 Preparation for final exam 1 30 30 Preparing assignments 3 21 63 Midterm 1 2 2 Final 1 2 2 TOTAL WORKLOAD (hours) 290

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