SPS311 Quantitative Research Methods
SPS311 Quantitative Research Methods
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Political Science and International Relations
Aliaksandr Novikau
Course Lecturer
Course Objectives
This course provides a comprehensive introduction to the principles of quantitative reasoning and statistical analysis within the social sciences. Students will examine the fundamental logic of empirical research, ranging from the conceptualization and measurement of variables to the execution of rigorous hypothesis testing. The curriculum addresses both descriptive statistics, focusing on measures of central tendency, dispersion, and data visualization, and inferential techniques, including sampling theory, correlation, and regression analysis. Additionally, the course emphasizes the practical application of these methods through the use of statistical software, enabling students to conduct independent data analysis and to critically evaluate quantitative information presented in academic and public discourse.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2000). Social statistics for a diverse society. Thousand Oaks, Calif: Pine Forge Press (6th edition is fine).
Additional Literature
N/ATeaching Methods
Lectures and class discussions
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introudction | |
| 2 | What is empirical research; What is the role of data in research? Concepts of statistics, data, variables, cause, effect, levels of measurements, types of variables, hypothesis | Chapter 1 |
| 3 | Constructing and interpreting a pie chart, bar graph, histogram, line graph, and time-series chart; Analyzing and interpreting charts and graphs in the literature | Chapter 2 |
| 4 | Defining all measures of central tendency, explaining their differences, relative strengths and weaknesses; (the mode, the median, the mean and percentiles; Determining the shape of a distribution | Chapter 3 |
| 5 | Understanding the importance of measuring variability ( index of qualitative variation (IQV), range, interquartile range, the variance, and the standard deviation) | Chapter 4 |
| 6 | Recognizing the importance and the use of the normal distribution in statistics (properties, (Z) score, the standard normal table) | Chapter 5 |
| 7 | Understanding the aims of sampling and basic principles of probability; Understanding and applying the concept of the sampling distribution; Understanding the nature of the central limit theorem | Chapter 6 |
| 8 | Midterm | All above |
| 9 | Understanding the concept of estimation and the reasons for it; Estimating confidence intervals for means; Understanding the concept of risk and how to reduce it; Estimating confidence intervals for proportions. | Chapter 7 |
| 10 | Understanding the assumptions of statistical hypothesis testing; Defining and applying the components in hypothesis testing (the research and null hypotheses, test statistic) | Chapter 8 |
| 11 | Constructing a bivariate table and determining the properties of a bivariate relationship | Chapter 9 |
| 12 | Understanding hypothesis testing with chi-square; Defining statistical independence, sample size and statistical significance | Chapter 10 |
| 13 | Understanding linear relations and prediction rules (straight-line graphs and finding the bestfitting line, calculating and interpreting intercept and slope) | Chapter 11 |
| 14 | Understanding the meaning of prediction errors; Calculating and interpreting the coefficient of determination (r2) and Pearson’s correlation coefficient (r); Understanding multiple regression | Chapter 12 |
| 15 | Conclusion. | N/A |
Course Schedule (All Sections)
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 14:00 - 15:00 | B F1.5 | |
| Wednesday | 11:00 - 14:00 | B F1.5 | |
| Thursday | 11:00 - 12:00 | B F1.5 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 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
45 hours ⏳ (15 week × 3 h)
Preparation for midterm exam
7 hours ⏳ (1 week × 7 h)
Home Study
84 hours ⏳ (14 week × 6 h)
Preparation for final exam
14 hours ⏳ (2 week × 7 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 [SPS311] 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
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| SPS311 | Quantitative Research Methods | 3 | 2 | 6 | ||||||
| Prerequisite | Junior Standing | It is a prerequisite to | - | |||||||
| Lecturer | Aliaksandr Novikau | Office Hours / Room / Phone | Monday: 14:00-15:00 Wednesday: 11:00-14:00 Thursday: 11:00-12:00 |
|||||||
| anovikau@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course provides a comprehensive introduction to the principles of quantitative reasoning and statistical analysis within the social sciences. Students will examine the fundamental logic of empirical research, ranging from the conceptualization and measurement of variables to the execution of rigorous hypothesis testing. The curriculum addresses both descriptive statistics, focusing on measures of central tendency, dispersion, and data visualization, and inferential techniques, including sampling theory, correlation, and regression analysis. Additionally, the course emphasizes the practical application of these methods through the use of statistical software, enabling students to conduct independent data analysis and to critically evaluate quantitative information presented in academic and public discourse. | |||||||||
| Textbook | Frankfort-Nachmias, C., & Leon-Guerrero, A. (2000). Social statistics for a diverse society. Thousand Oaks, Calif: Pine Forge Press (6th edition is fine). | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lectures and class discussions.. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introudction | |||||||||
| Week 2 | What is empirical research; What is the role of data in research? Concepts of statistics, data, variables, cause, effect, levels of measurements, types of variables, hypothesis | Chapter 1 | ||||||||
| Week 3 | Constructing and interpreting a pie chart, bar graph, histogram, line graph, and time-series chart; Analyzing and interpreting charts and graphs in the literature | Chapter 2 | ||||||||
| Week 4 | Defining all measures of central tendency, explaining their differences, relative strengths and weaknesses; (the mode, the median, the mean and percentiles; Determining the shape of a distribution | Chapter 3 | ||||||||
| Week 5 | Understanding the importance of measuring variability ( index of qualitative variation (IQV), range, interquartile range, the variance, and the standard deviation) | Chapter 4 | ||||||||
| Week 6 | Recognizing the importance and the use of the normal distribution in statistics (properties, (Z) score, the standard normal table) | Chapter 5 | ||||||||
| Week 7 | Understanding the aims of sampling and basic principles of probability; Understanding and applying the concept of the sampling distribution; Understanding the nature of the central limit theorem | Chapter 6 | ||||||||
| Week 8 | Midterm | All above | ||||||||
| Week 9 | Understanding the concept of estimation and the reasons for it; Estimating confidence intervals for means; Understanding the concept of risk and how to reduce it; Estimating confidence intervals for proportions. | Chapter 7 | ||||||||
| Week 10 | Understanding the assumptions of statistical hypothesis testing; Defining and applying the components in hypothesis testing (the research and null hypotheses, test statistic) | Chapter 8 | ||||||||
| Week 11 | Constructing a bivariate table and determining the properties of a bivariate relationship | Chapter 9 | ||||||||
| Week 12 | Understanding hypothesis testing with chi-square; Defining statistical independence, sample size and statistical significance | Chapter 10 | ||||||||
| Week 13 | Understanding linear relations and prediction rules (straight-line graphs and finding the bestfitting line, calculating and interpreting intercept and slope) | Chapter 11 | ||||||||
| Week 14 | Understanding the meaning of prediction errors; Calculating and interpreting the coefficient of determination (r2) and Pearson’s correlation coefficient (r); Understanding multiple regression | Chapter 12 | ||||||||
| Week 15 | Conclusion. | N/A | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm exam | 1 | 30 | 1,2,3,4,5 | Not Allowed | |
| Quizzes | 2 | 30 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Preparation for midterm exam | 7 | 1 | 7 | |||
| Home Study | 6 | 14 | 84 | Preparation for final exam | 7 | 2 | 14 | |||
| 0 | 0 | |||||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 31/03/2026 | |||||||||