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, Psychological Methods, "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: report both, 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.

Rules this gate enforces:

1. Alpha alone is insufficient. Ask for omega; if only alpha is available, state the tau-equivalence caveat explicitly.
2. Reliability presupposes dimensionality. A high alpha on a multidimensional set is meaningless — check the factor structure (CFA, alterlab-sem-psychometrics) first.
3. Item diagnostics. Item-total correlations and (for two halves) Spearman-Brown; drop or revise items that do not cohere — but pre-specify such decisions, do not fish.

Validity: four kinds, none optional

Validity	Question	Evidence
Content	Do items cover the construct's domain?	expert review, blueprint against the definition
Criterion	Does it predict/correlate with a gold standard?	concurrent & predictive correlations
Convergent	Does it correlate with related measures?	AVE, correlations with kindred scales
Discriminant	Is it distinct from other constructs?	heterotrait-monotrait ratio, cross-loadings

Construct validity is established with a confirmatory factor analysis — route the fit to alterlab-sem-psychometrics (CFI/TLI, RMSEA, SRMR against Hu & Bentler cutoffs).

Measurement invariance before group comparison

Comparing a latent construct across groups (countries, waves, conditions) is only valid if the measure means the same thing in each group. Require a multi-group CFA invariance sequence — configural → metric → scalar — before interpreting group differences in means. Without at least metric/scalar invariance, a "difference" may be a measurement artifact, not a real effect. Route the test to alterlab-sem-psychometrics.

The Design Passport (hand-off)

Append to each construct in the Passport: reliability (alpha + omega + caveat), validity_evidence (which of the four kinds are shown), dimensionality (CFA fit), and invariance (level established) before any group comparison downstream.

Self-Check Before Advancing

• Is each construct explicitly defined and operationalized to its items?
• Is reliability shown with omega (or alpha with the tau-equivalence caveat stated)?
• Is at least one form of validity evidence present — not just reliability?
• Was dimensionality confirmed (CFA) before trusting a single reliability coefficient?
• Is measurement invariance established before comparing the construct across groups?

References

• references/reliability_and_validity.md — alpha vs omega math, the four validities, the invariance sequence, and reporting templates. Loaded on demand.

Part of the AlterLab Academic Skills suite.