PSY105 Statistics in Psychology
PSY105 Statistics in Psychology
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Psychology
Faruk Obuća
Course Lecturer
Course Objectives
Statistics in psychology provides an introduction to descriptive and inferential statistics, with the purpose of preparing students for performing and consuming quantitative research. After successful completion of the course student will be able to: Demonstrate basic statistical literacy. Recognise different types of variables and generate appropriate scales for their measurement. Perform basic inferential statistics methods and tests. Distinguish and choose inferential testing methods appropriate to real life situations related to their scientific interest. Interpret outputs from statistical software packages.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Statistic without Maths for Psychology, CHRISTINE DANCEY, JOHN REIDY, 7th Edition, Harlow, Pearson, 2017.
Additional Literature
J. (2001). SPSS Survival Manual: A Step by Step Guide to Data Analysis using IBM SPSSTeaching Methods
Class discussions with examples
Group workouts for engaged learning and continuous feedback on progress
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction, syllabus presentation, course presentation | |
| 2 | Variables and research design | Chapter 1 |
| 3 | Descriptive statistics | Chapter 3 |
| 4 | Probability, sampling and distributions | Chapter 4 |
| 5 | Quiz & Hypothesis testing and statistical significance | Chapter 5 |
| 6 | Correlational analysis: Pearson’s r | Chapter 6 |
| 7 | preparation for midterm exam | all relevant chapters |
| 8 | midterm exam | all relevant chapters |
| 9 | From Research to Practice | all relevant chapters |
| 10 | practical session 1 | all relevant chapters |
| 11 | practical session 2 | all relevant chapters |
| 12 | practical session 3 | all relevant chapters |
| 13 | group presentations | |
| 14 | group presentations | |
| 15 | preparation for final exam | all relevant chapter |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| PSY105.1 | Course | Thursday 09:00 - 11:50 | B F2.15 - Amphitheater II | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 13:00 - 16:00 | B F2.3B | |
| Tuesday | 13:00 - 15:00 | B F2.3B |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 2
Midterm exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Group presentation
AI: Not AllowedAlignment with Learning Outcomes : 4 5
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 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 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
42 hours ⏳ (14 week × 3 h)
Home Study
14 hours ⏳ (14 week × 1 h)
presentation
18 hours ⏳ (3 week × 6 h)
Final Exam
30 hours ⏳ (10 week × 3 h)
midterm exam
28 hours ⏳ (7 week × 4 h)
quiz
18 hours ⏳ (3 week × 6 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 [PSY105] 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.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
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.
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.
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 Feb 02, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| PSY105 | Statistics in Psychology | 2 | 1 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | PSY214, PSY402, PSY523, PSY531 | |||||||
| Lecturer | Faruk Obuća | Office Hours / Room / Phone | Monday: 13:00-16:00 Tuesday: 13:00-15:00 |
|||||||
| fobuca@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | Statistics in psychology provides an introduction to descriptive and inferential statistics, with the purpose of preparing students for performing and consuming quantitative research. After successful completion of the course student will be able to: Demonstrate basic statistical literacy. Recognise different types of variables and generate appropriate scales for their measurement. Perform basic inferential statistics methods and tests. Distinguish and choose inferential testing methods appropriate to real life situations related to their scientific interest. Interpret outputs from statistical software packages. |
|||||||||
| Textbook | Statistic without Maths for Psychology, CHRISTINE DANCEY, JOHN REIDY, 7th Edition, Harlow, Pearson, 2017. | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | Class discussions with examples. Group workouts for engaged learning and continuous feedback on progress. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction, syllabus presentation, course presentation | |||||||||
| Week 2 | Variables and research design | Chapter 1 | ||||||||
| Week 3 | Descriptive statistics | Chapter 3 | ||||||||
| Week 4 | Probability, sampling and distributions | Chapter 4 | ||||||||
| Week 5 | Quiz & Hypothesis testing and statistical significance | Chapter 5 | ||||||||
| Week 6 | Correlational analysis: Pearson’s r | Chapter 6 | ||||||||
| Week 7 | preparation for midterm exam | all relevant chapters | ||||||||
| Week 8 | midterm exam | all relevant chapters | ||||||||
| Week 9 | From Research to Practice | all relevant chapters | ||||||||
| Week 10 | practical session 1 | all relevant chapters | ||||||||
| Week 11 | practical session 2 | all relevant chapters | ||||||||
| Week 12 | practical session 3 | all relevant chapters | ||||||||
| Week 13 | group presentations | |||||||||
| Week 14 | group presentations | |||||||||
| Week 15 | preparation for final exam | all relevant chapter | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Quiz | 1 | 20 | 1,2 | Not Allowed | |
| Midterm exam | 1 | 30 | 1,2,3 | Not Allowed | |
| Group presentation | 1 | 20 | 4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 14 | 42 | Home Study | 1 | 14 | 14 | |||
| presentation | 6 | 3 | 18 | Final Exam | 3 | 10 | 30 | |||
| midterm exam | 4 | 7 | 28 | quiz | 6 | 3 | 18 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 26/02/2026 | |||||||||