DATA ANALYSIS AND COMPUTER APPLICATIONS IN BUSINESS

Course Description

This course covers a description of the research process in general and each of the steps involved in more detail. The course focuses on alternative ways of carrying out each step and guidelines for selecting among these alternatives according to the needs of a specific research problem. The course also describes using software packages like SPSS and interpretation of the results of data analysis. Course is designed to enable student to apply commonly used statistical techniques to different datasets. For this purpose, the course will focus on descriptive analysis, correlation, t-test, ANOVA, regression and mediation and moderation analysis. The students will use SPSS software to work on the datasets.

DATA ANALYSIS AND COMPUTER APPLICATIONS IN BUSINESS

Evaluation Tools (Active Term)

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Course outcomes

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Course Syllabus

Reference Books & Course Materials

1. 01 Field, A. 2024 . Discovering Statistics Using IBM SPSS Statistics, 6th ed., Sage, London.
2. 02 Pituch, K.A. & Stevens, J.P. 2016. Applied multivariate statistics for the social sciences, Taylor& Francis: New York.
3. 03 Te Grotenhuis, M. & Matthijssen, A. 2016. Basic SPSS Tutorial, Sage: UK.

Learning Outcomes

1. L01 Listing basic analysis techniques SOLO 3
2. L02 Formulate research problems SOLO 4
3. L03 Creates quantitative models SOLO 5
4. L04 Evaluate approperiate statistical methods SOLO 5
5. L05 Formulates research hypotheses SOLO 4

Program Outcomes

1. 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.
2. 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.
3. 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.
4. Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
5. Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
6. 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.
7. 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.
8. Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
9. 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.
10. Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
11. Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
12. 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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