Data Processing, Coding & Statistical Analysis
Editing, Tabulation, Exploratory Data Analysis & Thematic Coding
From raw fieldwork and text transcripts to rigorous analytical insights: editing responses, building categorical coding frameworks, bivariate tabulation, and qualitative thematic extraction.
1. Foundational Concepts & Core Principles
Data editing: field editing vs. central editing, handling missing responses
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Coding rules and categorization of open-ended vs. closed-ended data
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Tabulation techniques: Unidimensional, Bivariate, and Multidimensional cross-tables
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Exploratory Data Analysis (EDA): central tendencies, dispersion, skewness, kurtosis
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Qualitative data processing: Grounded theory coding (open, axial, selective coding)
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
2. Methodological Comparison & Trade-offs
Mean, median, standard deviation, interquartile ranges.
Confidence intervals, regression models, significance testing.
Extracting recurring motifs, discursive patterns, and conceptual themes.
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.