alterlab-ssci-measurement-gate

name	alterlab-ssci-measurement-gate
description	Gates measurement quality before a scale or instrument is trusted — checks that each construct is defined and operationalized, that reliability is evidenced with McDonald omega (not Cronbach alpha alone, which assumes tau-equivalence and is only a lower bound), that reliability is not confused with validity (content, criterion, convergent/discriminant construct validity), and that measurement invariance is tested before comparing groups. Use when asking whether a scale or survey instrument is valid, reporting a Cronbach alpha, building or adopting a multi-item measure, or comparing a latent construct across groups. For designing the questionnaire items prefer alterlab-survey-design; for running the confirmatory factor analysis prefer alterlab-sem-psychometrics; to execute basic statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Bash(python:*)
compatibility	No API key required. A discipline-enforcing measurement-quality skill; reliability/CFA execution is handed to sibling skills that run locally via `uv run python`.
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-survey-design (item design), alterlab-sem-psychometrics (CFA/invariance), alterlab-statistical-analysis"}

Measurement Gate — Reliability Is Not Validity

Skill type: DISCIPLINE-ENFORCING. Before any hypothesis test consumes a scale, this gate checks that the scale measures what it claims, reliably, and comparably across groups. It does not fit factor models itself — it requires the right evidence and routes execution to the psychometrics skill.

The Core Rule

A RELIABLE MEASURE OF THE WRONG CONSTRUCT IS STILL WRONG. RELIABILITY ≠ VALIDITY.

High internal consistency only means the items move together; it says nothing about whether they capture the intended construct. Evidence for validity (content, criterion, construct) is separate and mandatory. And the usual reliability number — Cronbach alpha — rests on an assumption (tau-equivalence: equal item loadings) that real scales rarely meet, so alpha is a lower bound that can under- or (with correlated errors) over-state reliability.

When to Use This Skill

• "Is my scale / survey instrument valid?"
• "My Cronbach alpha is 0.78 — is the scale good to use?" (← alpha alone is not enough)
• "I built a new 6-item measure of burnout; how do I show it works?"
• "I want to compare a latent trait (e.g. trust) across countries/groups."

Does NOT Trigger

The request is really about…	Route to	Why not this skill
Writing / wording the questionnaire items, response scales	alterlab-survey-design	Instrument construction, upstream of validation.
Running the CFA / SEM / IRT / invariance model	alterlab-sem-psychometrics	This gate requires CFA; that skill fits it.
Basic descriptive/inferential stats execution	alterlab-statistical-analysis	Computation, not measurement discipline.
Which statistical test to run on a measured outcome	alterlab-test-selection-guard	Test choice, not measurement quality.
Sampling frame / how many respondents	alterlab-ssci-sampling-gate	Sampling, not measurement.

Reliability: report alpha AND omega

Cronbach's alpha assumes tau-equivalence (all items load equally on one factor) and is a lower bound on reliability under that model. McDonald's omega relaxes tau-equivalence by weighting items by their factor loadings and should be reported alongside alpha (McNeish, 2018, , "Thanks coefficient alpha, we'll take it from here"). In practice the alpha-vs-omega gap for well-constructed unidimensional scales is often small (Warne, 2025 — around 4.5% underestimate), so the discipline is: , plus item-total correlations and a dimensionality check — not to fetishize a single number, but to show the estimate is not resting on an unexamined assumption.