PSY523 Applied Statistics
PSY523 Applied Statistics
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
Syllabus Quick Jump
Search and navigate to any syllabus instantly
Psychology
Course Objectives
Aquire general principles of scientific thinking Develop basic statistical concepts and skills Evaluate statistical data in research reports Visually communicate statitical results Apply statistics in psychology
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Gravetter, F. J., & Wallnau, L. B. (2034). Statistics for the behavioral sciences (9th ed.). Belmont, CA: Wadsworth. (ISNB: 1-111-83099-1) + Supplementary Readings
Additional Literature
Teaching Methods
Class lectures, discussions, and exercises will be held online
Active tutorial sessions for engaged learning and feedback on progress
Assessment of outcomes will involve assignments, six quizzes, midterm exam, and final exam
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course scope and review exercises | Ch 1, 9 |
| 2 | Introduction to the t-statistic | Ch 9 |
| 3 | t-test for independent samples (Q#1) | Ch 10 |
| 4 | t-test for dependent samples | Ch 11 |
| 5 | Introduction to Analysis of Variance (Q#2) | Ch 12 |
| 6 | Repeated measures Analysis of variance | Ch 13 |
| 7 | Two-factor Avalysis of Variance (Q#3) | Ch 14 |
| 8 | MIDTERM EXAM | Ch 15 |
| 9 | Correlation | Ch 16 |
| 10 | Introduction to regression (Q#4) | Ch 17 |
| 11 | Chi-square statistic | Ch 18 |
| 12 | Binomial test (Q#5) | Ch 19 |
| 13 | Non-parametric tests, confidence intervals, effect sizes, power, meta analysis | Ch 20 |
| 14 | Review (Q#6) | |
| 15 | Review |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
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
28 hours ⏳ (14 week × 2 h)
assignments
6 hours ⏳ (6 week × 1 h)
active tutorials
12 hours ⏳ (12 week × 1 h)
home study
28 hours ⏳ (14 week × 2 h)
ın term study
10 hours ⏳ (1 week × 10 h)
final exam
30 hours ⏳ (2 week × 15 h)
quizzes
36 hours ⏳ (6 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 [PSY523] 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 Mar 03, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| PSY523 | Applied Statistics | 3 | 0 | 6 | ||||||
| Prerequisite | PSY105 | It is a prerequisite to | - | |||||||
| Lecturer | Office Hours / Room / Phone | Currently not available |
||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | Aquire general principles of scientific thinking Develop basic statistical concepts and skills Evaluate statistical data in research reports Visually communicate statitical results Apply statistics in psychology |
|||||||||
| Textbook | Gravetter, F. J., & Wallnau, L. B. (2034). Statistics for the behavioral sciences (9th ed.). Belmont, CA: Wadsworth. (ISNB: 1-111-83099-1) + Supplementary Readings | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
| Teaching Methods | Class lectures, discussions, and exercises will be held online. Active tutorial sessions for engaged learning and feedback on progress. Assessment of outcomes will involve assignments, six quizzes, midterm exam, and final exam. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course scope and review exercises | Ch 1, 9 | ||||||||
| Week 2 | Introduction to the t-statistic | Ch 9 | ||||||||
| Week 3 | t-test for independent samples (Q#1) | Ch 10 | ||||||||
| Week 4 | t-test for dependent samples | Ch 11 | ||||||||
| Week 5 | Introduction to Analysis of Variance (Q#2) | Ch 12 | ||||||||
| Week 6 | Repeated measures Analysis of variance | Ch 13 | ||||||||
| Week 7 | Two-factor Avalysis of Variance (Q#3) | Ch 14 | ||||||||
| Week 8 | MIDTERM EXAM | Ch 15 | ||||||||
| Week 9 | Correlation | Ch 16 | ||||||||
| Week 10 | Introduction to regression (Q#4) | Ch 17 | ||||||||
| Week 11 | Chi-square statistic | Ch 18 | ||||||||
| Week 12 | Binomial test (Q#5) | Ch 19 | ||||||||
| Week 13 | Non-parametric tests, confidence intervals, effect sizes, power, meta analysis | Ch 20 | ||||||||
| Week 14 | Review (Q#6) | |||||||||
| Week 15 | Review | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | Not Allowed | ||
| Semester Evaluation Components | |||||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 2 | 14 | 28 | assignments | 1 | 6 | 6 | |||
| active tutorials | 1 | 12 | 12 | home study | 2 | 14 | 28 | |||
| ın term study | 10 | 1 | 10 | final exam | 15 | 2 | 30 | |||
| quizzes | 6 | 6 | 36 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||