Advanced Research Methods Revision

Master of Science in Information Systems / Data Science / Data Analytics

1. Research Frameworks & Definitions

Q: Differentiate between Research Methodology and Research Design. [cite: 75, 168]
Research Methodology: The broad philosophical approach and theoretical underpinnings that guide the research process (e.g., Qualitative vs. Quantitative)[cite: 78, 175].
Research Design: The specific blueprint or plan of action used to collect and analyze data to answer the research question[cite: 78].
Q: Define Concepts, Variables, and Attributes with examples. [cite: 39, 121, 223]

2. Variables in a Conceptual Framework [cite: 41, 123, 202]

Describe the roles of different variable types.

3. Literature Review & Scholarly Writing

Q: What are the purposes of a Literature Review? [cite: 143, 177]
Identifies gaps in existing knowledge, provides theoretical background, justifies the research problem, helps refine research questions, and avoids duplication of effort[cite: 30, 177, 187].
Q: Define Systematic Review and Scoping Review. [cite: 27, 146, 225]
Systematic Review: A structured, rigorous method to identify, evaluate, and synthesize all relevant research on a specific topic to minimize bias[cite: 29, 149].
Scoping Review: A technique used to map the existing literature in a broad field to identify key concepts and types of evidence available[cite: 148].
Q: Why is Scholarly Writing and publication necessary? [cite: 71, 153, 172]
It allows for peer review to ensure quality, contributes to the global body of knowledge, enables other researchers to replicate or build on findings, and establishes the researcher's credibility[cite: 12, 71, 153].

4. Statistical Methods & Error Testing

Q: Compare Type I and Type II Errors. [cite: 170, 245, 279]
Type I Error: Rejecting a true null hypothesis (a "false positive")[cite: 279].
Type II Error: Failing to reject a false null hypothesis (a "false negative")[cite: 170, 245].
Q: When should the following statistical models be used? [cite: 259]

5. Measurement Quality & Sampling

Q: Reliability vs. Validity. [cite: 37, 119, 221]
Reliability: The consistency of a measurement tool (getting the same result repeatedly)[cite: 37, 221].
Validity: The accuracy of a measurement tool (actually measuring what it is supposed to measure)[cite: 37, 221, 279].
Q: Define Triangulation. [cite: 273]
The use of multiple methods, data sources, or researchers to study the same phenomenon to enhance the credibility and validity of the findings[cite: 273, 274].