Lesson Specification: Module 0.2

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Learning Objectives

By the end of this lesson, students will be able to:

1. Define the differences between quantitative, qualitative, and mixed-methods research paradigms.
2. Identify appropriate methodologies for different types of research questions.
3. Evaluate the strengths and limitations of each major methodology.
4. Explain key concepts of validity, reliability, and bias in research design.

Key Concepts

Concept 1: The Research Paradigms

• What it is: The foundational approaches to research.
  • Quantitative: Focuses on numbers, statistics, and objective measurement (e.g., surveys, experiments). Deductive approach (testing theories).
  • Qualitative: Focuses on words, meanings, and subjective experiences (e.g., interviews, ethnography). Inductive approach (building theories).
  • Mixed Methods: Combining both to provide a more complete picture.
• Why it matters: Choosing the wrong paradigm for a research question guarantees a flawed study. You cannot measure human subjective grief with a Likert scale, and you cannot prove a drug's efficacy with an anecdote.
• Analogy: Quantitative research is looking at a forest with a satellite to measure total biomass. Qualitative research is walking into the forest to study a specific tree up close. Mixed methods is doing both to understand the ecosystem.

Concept 2: Matching Methodology to the Question

• What it is: The methodology must flow directly from the research question.
  • "How many?" or "Does X cause Y?" $\rightarrow$ Quantitative (Experimental, Correlational).
  • "How do people experience X?" or "Why does Y happen?" $\rightarrow$ Qualitative (Phenomenology, Grounded Theory).
• Why it matters: A common mistake for junior researchers is picking a method they are comfortable with (like an online survey) before clearly defining what they need to know.

Concept 3: Validity and Reliability

• What it is:
  • Reliability: Consistency. If you repeat the study, do you get the same result?
  • Validity: Accuracy. Are you actually measuring what you claim to be measuring?
• Why it matters: A broken scale can be highly reliable (always says you weigh 150 lbs) but completely invalid (you actually weigh 170 lbs). Scientific research requires both.

Verified Technical Facts

(Conceptual lesson - Academic publishing facts)

• The gold standard for quantitative causal claims is the Randomized Controlled Trial (RCT).
• Qualitative sample sizes are driven by "data saturation" (when new interviews yield no new information), not statistical power calculations.
• Triangulation (using multiple methods or data sources) is a key strategy in mixed methods to increase validity.

Suggested Hooks & Motivation

• Pain Point: "Have you ever designed a survey, collected 200 responses, and then realized your data doesn't actually answer your core research question?"
• Wow Moment: "Today, we're going to learn how to reverse-engineer your research design. I'll show you how starting with the correct question automatically selects the correct methodology."
• Real-World Scenario: "Imagine you are researching a new educational app. A quantitative approach will tell you that test scores went up by 15%. But only a qualitative approach will tell you why the students actually enjoyed using it. To build a great product, you need both."

Cross-References

• Builds on: Module 0.1 (We are now looking specifically at how the 'Methods' section is constructed).
• Leads to: Module 0.3 (The Publishing Ecosystem - where different methodological papers get published).
• Related tools: scholar-search-kit (When searching for literature, you often filter by methodology, e.g., "systematic review" vs "randomized controlled trial").

Sources & Further Reading

1. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
2. Bryman, A. (2016). Social research methods. Oxford university press.