Research Design & Conceptual Blueprints
Exploratory, Descriptive, Diagnostic & Experimental Research Designs
Comprehensive architecture for structuring research investigations: controlling extraneous variables, establishing causality, minimizing bias, and balancing internal vs. external validity.
1. Foundational Concepts & Core Principles
Meaning and components of a research design
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Key concepts: Dependent/Independent variables, Extraneous variables, Control groups
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Exploratory (Formulative) research design: experience surveys, focus groups, case studies
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Descriptive and Diagnostic research designs: cross-sectional vs. longitudinal studies
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Hypothesis-testing (Experimental) designs: randomized block designs, Latin square designs, factorial designs
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
2. Methodological Comparison & Trade-offs
Flexible, unstructured, discovery-oriented, hypothesis-generating.
Rigid, pre-planned, focused on accurate population or phenomenon profile.
Manipulative control of independent variables, randomized trials, causal attribution.
3. Practical Guidelines & Common Methodological Pitfalls
- Explicitly state operational definitions before measurement.
- Acknowledge potential confounders and data limitations in the methodology section.
- Pre-register hypothesis and statistical tests when conducting quantitative trials.
- Provide full provenance for historical, epigraphical, and archival texts.
- Confusing statistical correlation with causal determination.
- Using non-probability convenience samples to claim population generalizations.
- Failing to report negative results or unexpected anomalies.
- Relying on unverified secondary citations without reading the primary source.