COMPUTER AIDED DATA ANALYSIS

Course Description

Following a general introduction, the qualitative and quantitative data, their systems of collection and analysis are given to the students. Regression and correlation analysis concepts. Case studies and solutions by using relevant software such as SPSS. Quality control concepts with respect to data analysis. Students will be able to manage qualitative data in an efficient and accessible manner, develop a qualitative data analysis plan, choose and apply different inductive and deductive approaches to coding appropriate to the data type and the context in which results will be used, employ and write analytical memos to aid in interpretation of qualitative data, understand the basic functions of computer-aided qualitative data analysis software, present qualitative findings in different settings using formats appropriate for different audiences.

COMPUTER AIDED DATA ANALYSIS

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Course outcomes

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Course Syllabus

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Learning Outcomes

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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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