Methods of Data Collection: Primary, Secondary & Fieldwork
Interviews, Questionnaires, Field Observations & Archival Sourcing
Detailed guidelines on designing survey instruments, conducting structured in-depth interviews, executing participatory observations, and evaluating secondary archival literature for authenticity.
1. Foundational Concepts & Core Principles
Collection of primary data: Observation, In-depth Interviewing, Questionnaire, Schedule
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Guidelines for constructing effective questionnaires and avoiding leading questions
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Secondary data collection: Institutional databases, government gazettes, historical manuscripts
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Evaluating secondary sources: Reliability, Suitability, Adequacy, and Authenticity checks
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Digital data harvesting, ethical permissions, and participant consent
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
2. Methodological Comparison & Trade-offs
Standardized, scalable, anonymous, best for wide quantitative coverage.
Deep probe, nuanced follow-up, captures unquantifiable cognitive insights.
Historical grounding, primary document provenance, critical collation.
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.