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Psychometric & Survey Validation (Rasch & Aiken)

ParameterAnalytical Specification
Content Validity MetricAiken’s V Content Validity Coefficient (Multi-Expert Panel Judgment)
Measurement FrameworkRasch Rating Scale Model (Winsteps / Python Psychometrics)
Target OutputsItem Reliability, Person Reliability, Infit/Outfit MNSQ, Wright Person-Item Separation Maps
Pillar4. Data Analysis

1. Classical Test Theory (CTT) Limitations vs Rasch Measurement

Commercial customer feedback surveys and behavioral research instruments frequently rely on simplistic Pearson bivariate correlations and Cronbach’s Alpha. Classical Test Theory harbors fundamental mathematical flaws:

  • It treats non-linear ordinal Likert rating categories (Strongly Disagree to Strongly Agree) as linear interval scales.
  • Instrument calibration remains sample-dependent: test difficulty fluctuations cannot be decoupled from the specific respondent cohort.

2. Rasch Measurement Model Framework

The Rasch Rating Scale Model mathematically maps ordinal discrete responses onto an equal-interval linear logit scale:

ln(P_ni / (1 - P_ni)) = beta_n - delta_i

Where beta_n represents the latent ability or endorsement level of respondent n, and delta_i defines the calibration threshold difficulty of item i.

Deterministic Item Fit Criteria:

  1. Outfit Mean Square (MNSQ): 0.5 to 1.5 (productive for measurement).
  2. Standardized Z (ZSTD): -2.0 to +2.0.
  3. Point-Measure Correlation (Pt-Measure Corr): Positive (0.40 to 0.85).

3. Measurable Outcomes & Wright Maps

Through Rasch calibration, leadership teams and academic review boards obtain an objective, visual Wright Map (Person-Item Map). This diagnostic identifies which survey dimensions are excessively demanding or redundant, and pinpoints uncalibrated respondent rating biases.

4. Contact Psychometrics Specialist

  • Email Direct: devapenseo@gmail.com
  • Consultation Portal: Available via the homepage project initiation form.