PSY616 Advanced Statistics in Clinical Psychology

Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026

Referencing Curricula

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Psychology

Academic Year

2022 - 2023

Semester

Fall

Course Code

PSY616

Weekly Hours

3 Teaching + 0 Practice

ECTS

Prerequisites

None

Teaching Mode Delivery

Face-to-face

Prerequisite For

Teaching Mode Delivery Notes

Cycle

III Cycle

Orkun Aydin

Course Lecturer

Position

Associate Professor Dr.

oaydin@ius.edu.ba

Phone

033 957 305

Assistant(s)

Assistant E-mail

Course Objectives

An essential goal of this course is to approach data analysis from the perspective of understanding statistics and their relationship to research. The underlying theory of statistics will be presented and content will be related to research to facilitate learning.

Learning Outcomes

After successful completion of the course, the student will be able to:

Identify and test assumptions for statistical tests

Select, conduct and report appropriate statistics to test hypotheses with one independent variable and three or more levels (aka groups)

Select, conduct and report appropriate statistics to test hypotheses with one independent variable and three or more levels with confounding variable (aka covariate)

Select, conduct and report appropriate statistics to test hypotheses with one group measured repeatedly with and without covariate

Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with 2 or more groups with and without covariate

Select, conduct and report appropriate statistics to test hypotheses with Two or more independent variables with 1 group measured repeatedly with and without covariate

Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with two or more independent variables and mixed methods with and without covariate

Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with one or more independent variables and the prediction of one or more dependent variables

Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with multiple Independent and Dependent Variables

Interpret reported statistical findings

Course Materials

Required Textbook

1. John J. Shaughnessy ( 2012 ) Research Methods in Psychology. McGraw Hill.

Additional Literature

2. Cronk, B. (2013). How to Use SPSS: A Step-by-Step Guide to Analysis and Interpretation. 8th ed.

Teaching Methods

Since individuals learn in different ways, varied activities will be provided to foster learning

Repetition is often key to understanding statistics, so we will spend the time to read, re-read, do assignments, redo them as needed after feedback, and reinforce your learning rather than quickly going through material

Weekly Topics

This weekly planning is subject to change with advance notice.

Week	Topic	Readings / References
1	Introduction to advanced statistics	(2) Relevant Chapters
2	Review and t-tests	(1) 384-396
3	ANOVA	(1) 396-407
4	ANOVA	(1) 407-414
5	ANCOVA	(1) 414-416
6	Factorial ANOVA	(1) 416-420
7	Correlation	(1) 184-225
8	MIDTERM EXAM
9	Correlation	(1) 225-249
10	Multiple Regression	(1) 371-377
11	Multiple Regression	(1) 420-424
12	Logistic Regression	(1) 424-429
13	Logistic Regression	(1) 429-431
14	SEM modelling	(1) 431-433
15	Path analysis

Course Schedule (All Sections)

Course Schedules with all sections will be available here soon.

Office Hours & Room

Day	Time	Office
Monday	09:00 - 15:00	A B.1
Wednesday	09:00 - 12:00	A B.1
Thursday	09:00 - 15:00	A B.1
Friday	09:00 - 11:00	A B.1

Assessment Methods and Criteria

Assessment Components

40%x1

Final Report

AI: Not Allowed

Alignment with Learning Outcomes :

30%x1

Mid term report

AI: Not Allowed

Alignment with Learning Outcomes :

15%x2

Assignments

AI: Not Allowed

Alignment with Learning Outcomes : LO 1 2 3

IUS Grading System

Grading Scale	IUS Grading System	IUS Coeff.	Letter (B&H)	Numerical (B&H)
0 - 44	F	0	F	5
45 - 54	E	1	F	5
55 - 64	C	2	E	6
65 - 69	C+	2.3	D	7
70 -74	B-	2.7	D	7
75 - 79	B	3	C	8
80 - 84	B+	3.3	C	8
85 - 94	A-	3.7	B	9
95 - 100	A	4	A	10

Late Work Policy

Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.

ECTS Credit Calculation

📚 Student Workload

This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:

Lecture hours

45 hours ⏳ (15 week × 3 h)

Assignments

10 hours ⏳ (2 week × 5 h)

Tutorials

15 hours ⏳ (15 week × 1 h)

Home study

14 hours ⏳ (14 week × 1 h)

In-term exam study

10 hours ⏳ (1 week × 10 h)

Final exam study

40 hours ⏳ (2 week × 20 h)

Assingment/presentation

16 hours ⏳ (2 week × 8 h)

150 Total Workload Hours

6 ECTS Credits

Course Policies

Academic Integrity

All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.

Attendance Policy

Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.

Technology & AI Policy

Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.

Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.

Communication Policy

All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [PSY616] in the subject line.

Academic Quality Assurance Policy

Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.

More info

Learning Tips

Engage Actively

Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.

Read and Review Purposefully

Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.

Think Critically in Assignments

Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.

Ask Questions Early

Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.

Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo

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Referencing Curricula Print this page

Course Code	Course Title			Weekly Hours*		ECTS		Weekly Class Schedule
				T	P
PSY616	Advanced Statistics in Clinical Psychology			3	0	6		N/A
Prerequisite	None		It is a prerequisite to	-
Lecturer	Orkun Aydin		Office Hours / Room / Phone	Monday: 9:30-15:00 Wednesday: 9:30-12:00 Thursday: 9:00-15:00 Friday: 9:30-11:00 A B.1 - 033 957 305
E-mail	oaydin@ius.edu.ba
Assistant			Assistant E-mail
Course Objectives	An essential goal of this course is to approach data analysis from the perspective of understanding statistics and their relationship to research. The underlying theory of statistics will be presented and content will be related to research to facilitate learning.
Textbook	1. John J. Shaughnessy ( 2012 ) Research Methods in Psychology. McGraw Hill.
Additional Literature	2. Cronk, B. (2013). How to Use SPSS: A Step-by-Step Guide to Analysis and Interpretation. 8th ed.
Learning Outcomes	After successful completion of the course, the student will be able to:
	Identify and test assumptions for statistical tests Select, conduct and report appropriate statistics to test hypotheses with one independent variable and three or more levels (aka groups) Select, conduct and report appropriate statistics to test hypotheses with one independent variable and three or more levels with confounding variable (aka covariate) Select, conduct and report appropriate statistics to test hypotheses with one group measured repeatedly with and without covariate Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with 2 or more groups with and without covariate Select, conduct and report appropriate statistics to test hypotheses with Two or more independent variables with 1 group measured repeatedly with and without covariate Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with two or more independent variables and mixed methods with and without covariate Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with one or more independent variables and the prediction of one or more dependent variables Select, conduct and report appropriate statistics to test hypotheses with two or more independent variables with multiple Independent and Dependent Variables Interpret reported statistical findings
Teaching Methods	Since individuals learn in different ways, varied activities will be provided to foster learning. Repetition is often key to understanding statistics, so we will spend the time to read, re-read, do assignments, redo them as needed after feedback, and reinforce your learning rather than quickly going through material.
Teaching Method Delivery	Face-to-face	Teaching Method Delivery Notes
WEEK	TOPIC						REFERENCE
Week 1	Introduction to advanced statistics						(2) Relevant Chapters
Week 2	Review and t-tests						(1) 384-396
Week 3	ANOVA						(1) 396-407
Week 4	ANOVA						(1) 407-414
Week 5	ANCOVA						(1) 414-416
Week 6	Factorial ANOVA						(1) 416-420
Week 7	Correlation						(1) 184-225
Week 8	MIDTERM EXAM
Week 9	Correlation						(1) 225-249
Week 10	Multiple Regression						(1) 371-377
Week 11	Multiple Regression						(1) 420-424
Week 12	Logistic Regression						(1) 424-429
Week 13	Logistic Regression						(1) 429-431
Week 14	SEM modelling						(1) 431-433
Week 15	Path analysis

Assessment Methods and Criteria	Evaluation Tool	Quantity	Weight	Alignment with LOs	AI Usage
	Final Report	1	40	LO all	Not Allowed
	Semester Evaluation Components
	Mid term report	1	30	LO all	Not Allowed
	Assignments	2	15	LO 1, 2, 3	Not Allowed
* ECTS Credit Calculation *

Activity	Hours	Weeks	Student Workload Hours	Activity	Hours	Weeks	Student Workload Hours
Lecture hours	3	15	45	Assignments	5	2	10
Tutorials	1	15	15	Home study	1	14	14
In-term exam study	10	1	10	Final exam study	20	2	40
Assingment/presentation	8	2	16
				Total Workload Hours =			150
*T= Teaching, P= Practice				ECTS Credit =			6
Course Academic Quality Assurance: Semester Student Survey						Last Update Date: 27/03/2026

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PSY616 Advanced Statistics in Clinical Psychology

PSY616 Advanced Statistics in Clinical Psychology

Psychology

Orkun Aydin

Course Objectives

Learning Outcomes

Course Materials

Required Textbook

Additional Literature

Teaching Methods

Since individuals learn in different ways, varied activities will be provided to foster learning

Repetition is often key to understanding statistics, so we will spend the time to read, re-read, do assignments, redo them as needed after feedback, and reinforce your learning rather than quickly going through material

Weekly Topics Expand All

Course Schedule (All Sections)

Office Hours & Room

Assessment Methods and Criteria

Assessment Components

Final Report

Mid term report

Assignments

IUS Grading System

Late Work Policy

ECTS Credit Calculation

📚 Student Workload

Lecture hours

Assignments

Tutorials

Home study

In-term exam study

Final exam study

Assingment/presentation

Course Policies

Academic Integrity

Attendance Policy

Technology & AI Policy

Communication Policy

Academic Quality Assurance Policy

Learning Tips

Weekly Topics