Measurement, Scaling & Instrument Reliability
Nominal, Ordinal, Interval, Ratio Scales & Psychometric Validation
Examines the four fundamental levels of measurement, construction of attitude scales (Likert, Thurstone, Semantic Differential), and validation testing for reliability and construct validity.
1. Foundational Concepts & Core Principles
Four levels of measurement: Nominal, Ordinal, Interval, and Ratio
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Sources of error in measurement: Respondent, Situational, Measurer, and Instrument errors
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Tests of sound measurement: Content validity, Criterion-related validity, Construct validity
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Reliability testing: Test-retest, Split-half method, and Cronbach's alpha coefficients
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
Scale construction techniques: Likert summated scales, Semantic differential, Guttman scalogram
Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.
2. Methodological Comparison & Trade-offs
Categorical classification without intrinsic order (e.g., discipline, region).
Rank-ordered metrics without uniform interval distance (e.g., Likert ratings).
Precise measurable units with meaningful zero (e.g., age, citations, test scores).
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.