STATISTICS
- Course
- STAT502 - STATISTICS
- Department
- Basic Sciences and Humanities
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
This course is designed to introduce basic statistical techniques necessary for carrying out a scientific research. It covers descriptive statistics and inferential statistics; variables and levels of measurement; measures of central tendency; display data by using graphs, charts, histograms, tables, etc.; a population and samples drawn from it; measures of variability of given data; the normal curve; the concept of probability in making statistical decisions; distributions of sample means; intervals for making statistical inference, hypothesis testing; correlation to detect relationships between and among phenomena; regression analysis; hypothesis testing to make statistical inference; tests (t-test) for the difference between the population means; the null hypothesis; analysis of variance (ANOVA), and analysis of covariance (ANCOVA); non-parametric techniques (chi-square test) for nominal data; other non-parametric techniques.
STATISTICS
Evaluation Tools (Active Term)
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Course outcomes
- 01 They analyse the descriptive statistics of psychological data sets in SPSS. 4
- 02 They summarize the data sets by using graphs in SPSS 4
- 03 They can hypothesize the research problems 5
- 04 They can apply proper t tests for comparing two population means based on given research problems in SPSS. 4
- 05 They can appraise and apply the variance analysis for comparing group means more than two in SPSS 4
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to the course; basic concepts and terms related to descriptive and inferential statistics. |
| Week 2 | Entering data collected to the computer, organizing and getting data ready for statistical analyses |
| Week 3 | normal distribution and sampling distribution of the mean; establishing confidence intervals |
| Week 4 | Graphs in SPSS |
| Week 5 | Descriptive statistics: Measures of central tendency and position measurements (arithmetic mean, median, mode, percentile and quartile) |
| Week 6 | Descriptive statistics: measures of variability (range, variance, standard deviation); display of data. |
| Week 7 | Standard error and confidence interval |
| Week 8 | writing research questions, research hypotheses, null hypotheses; hypothesis testing. |
| Week 9 | Inferential statistics: t-test applications (one-sample t-test, independent samples t-test, dependent samples t-test) |
| Week 10 | Inferential statistics: t-test applications (one-sample t-test, independent samples t-test, dependent samples t-test) |
| Week 11 | Midterm Exams |
| Week 12 | Inferential statistics: t-test applications (one-sample t-test) |
| Week 13 | Analysis of variance applications (one-way ANOVA) |
| Week 14 | Analysis of variance applications |
| Week 15 | Final Exams |
Reference Books & Course Materials
- 01 Green, S. B., Salkind, N. J. (2014). Using SPSS For Windows And Macintosh -7TH Edition. Boston: Pearson.
- 02 Elzey, F. F. (1985). Elementary Statistical Techniques. California: Brooks/Cole.
- 03 Coursepack prepared by the instructor.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
- Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
- Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
- Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
- Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
- Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
- Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
- Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
- Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
- Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
- Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
- Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.
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