ADVANCED STATISTICS IN PSYCHOLOGY

PSY315

Course Code: PSY315
Semester: Fifth Term (Fall)
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Compulsory Course

Course Level

Bachelor's Degree

Year of Study

Semester

Fifth Term (Fall)

ECTS

Name of Lecturer(s)

Assistant Professor Ayşegül Özkan

Learning Outcomes

At the end of this course, the students;

Can use the programming language learned in the course as a statistical computing environment.
Can organize, transform, and prepare datasets for statistical analysis using a programming language.
Develop the foundational statistical analysis and programming skills necessary to transfer their knowledge to other programming languages and computing environments based on similar principles.
Can analyze applied examples discussed within an advanced statistical context.
Develop the computational foundation required to work with advanced statistical methods in psychological research.

Mode of Delivery

Face to face

Prerequisites

Yes(PSY212)

Recommended Optional Programme Components

PSY212

Course Description

The main objective of this course is to build on what has been learned in Statistics in Behavioural Sciences II. Multivariate analysis methods in advanced statistics and their applications in psychological science will be discussed. Advanced variance analysis, and regression models will be examined. It is assumed that students have completed the Statistics in Behavioural Sciences II course before taking this course.

Course Contents

WEEK	TOPICS
1^st Week	Course overview and why R?
2^nd Week	R and R Studio
3^rd Week	Installing packages and importing data
4^th Week	R Markdown
5^th Week	Mid-Term Exam
6^th Week	Different types of data and creating new variables/columns
7^th Week	Data manipulation and exporting data
8^th Week	Mid-Term Exam
9^th Week	Introduction to meta-analyses
10^th Week	Effect sizes
11^th Week	Between-study heterogeneity
12^th Week	Forest plots
13^th Week	Subgroup analyses
14^th Week	Meta-regression
15^th Week	Overview

Recommended or Required Reading

Dauber, D. (2025). R for non-programmers. URL: https://r4np.com/
Harrer, M., Cuijpers, P., Furukawa, T.A., & Ebert, D.D. (2021). Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL and London: Chapman & Hall/CRC Press. URL: https://doing-meta.guide/

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Practice

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	2	40
Total(%)		40
Contribution of In-term Studies to Overall Grade(%)		40
Contribution of Final Examination to Overall Grade(%)		60
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	2	1	2
Preparation for Quiz
Individual or group work	14	6	84
Preparation for Final exam	1	10	10
Course hours	15	3	45
Preparation for Midterm exam	1	10	10
Laboratory (including preparation)
Final exam	1	1	1
Homework
Total Work Load			152
Total Work Load / 30 (h)			5.06
ECTS Credit of the Course			5

Language of Instruction

English

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5
K1	X	X
K2
K3
K4					X
K5	X	X	X	X	X
K6				X
K7
K8
K9		X		X
K10
K11
K12
K13