How do I install the Alterlab Sem Psychometrics skill?

Run npx skillmds@latest add alterlab-ieu/alterlab-sem-psychometrics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Alterlab Sem Psychometrics free to use?

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Who published Alterlab Sem Psychometrics?

AlterLab-IEU (@alterlab-ieu) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Alterlab Sem Psychometrics by alterlab-ieu

Name: Alterlab Sem Psychometrics
Rating: 4.666666666666667 (3 reviews)
Author: AlterLab-IEU

Alterlab Sem Psychometrics

Fits and evaluates measurement models — confirmatory factor analysis, full structural equation models, exploratory factor analysis, item response theory, and multi-group measurement invariance — using the verified Python stack: semopy (model syntax =~ / ~ / ~~, Model.fit, inspect(std_est=True), calc_stats for CFI/TLI/RMSEA), factor_analyzer (EFA, KMO, Bartlett, ConfirmatoryFactorAnalyzer), and pingouin/girth, computing McDonald's omega from standardized loadings and judging fit against Hu & Bentler cutoffs. Use when the request mentions confirmatory factor analysis, structural equation modeling, a latent variable or construct model, factor loadings, IRT, or measurement invariance across groups. For deciding whether a scale is trustworthy at all prefer alterlab-ssci-measurement-gate; for plain regression prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

AlterLab-IEU Updated Sep 8, 2026 60 repo stars

File contents

SEM & Psychometrics — Fit the Measurement Model, Judge It Honestly

Skill type: ANALYSIS MODULE. Fits CFA / SEM / EFA / IRT and tests measurement invariance. The discipline: report fit against real cutoffs, report reliability with omega (not alpha alone), and establish invariance before comparing groups. Whether the scale should be trusted at all is the upstream call of alterlab-ssci-measurement-gate.

Core Mission

A MEASUREMENT MODEL IS ONLY DONE WHEN FIT, RELIABILITY (OMEGA), AND INVARIANCE ARE ALL REPORTED.

When to Use This Skill

"Run a confirmatory factor analysis on my scale."
"Fit a structural equation model / a latent-variable path model."
"Do an exploratory factor analysis — how many factors?"
"Fit an IRT model / get item difficulty and discrimination."
"Test measurement invariance across countries before I compare latent means."

Does NOT Trigger

The request is really about…	Route to	Why not this skill
Whether the scale is valid/trustworthy at all (reliability≠validity discipline)	`alterlab-ssci-measurement-gate`	Gate decision, upstream of fitting.
Writing / wording the questionnaire items	`alterlab-survey-design`	Instrument construction.
Plain regression / descriptive stats	`alterlab-statistical-analysis`	No latent structure.
A causal treatment-effect estimate	`alterlab-causal-inference`	Different problem.

Verified calls (pinned)

CFA / SEM — semopy (v2.3):

from semopy import Model, calc_stats
desc = """
# measurement model
Trust =~ q1 + q2 + q3
Efficacy =~ q4 + q5 + q6
# structural path
Trust ~ Efficacy
"""
m = Model(desc); m.fit(data)          # obj='MLW' default; 'FIML' for missing
est = m.inspect(std_est=True)          # standardized loadings for omega
stats = calc_stats(m)                  # DataFrame: chi2, CFI, TLI, RMSEA, AIC, BIC ...

Note: calc_stats returns CFI/TLI/RMSEA but not SRMR — report the indices it gives and compute SRMR separately if required.

EFA — factor_analyzer (v0.5):

from factor_analyzer import FactorAnalyzer, calculate_kmo, calculate_bartlett_sphericity
kmo_all, kmo_model = calculate_kmo(X)          # sampling adequacy > .60
chi2, p = calculate_bartlett_sphericity(X)      # sphericity
fa = FactorAnalyzer(n_factors=3, rotation="promax"); fa.fit(X)
fa.loadings_; fa.get_factor_variance()          # (variance, proportional, cumulative)

ConfirmatoryFactorAnalyzer + ModelSpecificationParser.parse_model_specification_from_dict give a CFA alternative when semopy is unavailable.

Reliability — omega (not alpha alone): pingouin gives cronbach_alpha but no omega, and semopy does not return omega. Compute McDonald's omega from the standardized loadings with scripts/omega.py (ω = (Σλ)² / [(Σλ)² + Σ(1−λ²)]). Report alpha + omega together.

IRT: girth (twopl_mml, rasch_mml) for dichotomous 2PL/Rasch; py-irt for Bayesian/ scalable. For polytomous/multidimensional IRT with full fit statistics, R mirt is the practical standard — say so rather than forcing a thin Python path.

Fit cutoffs (Hu & Bentler 1999, as guidelines not gates)

CFI/TLI ≥ .95 · RMSEA ≤ .06 · SRMR ≤ .08. Use a two-index combination; do not chase a single number. Report the χ² and df alongside (χ² is sample-size sensitive, so not decisive).

Measurement invariance (before any group comparison)

Multi-group CFA sequence — configural → metric → scalar → strict — judged by ΔCFI ≤ .01 / ΔRMSEA ≤ .015, not only Δχ². Scalar invariance is the prerequisite for comparing latent means; without it a group "difference" may be a measurement artifact. Fit each nested model and compare.

Output Template

MODEL:        <CFA/SEM/EFA/IRT + the semopy/factor_analyzer spec>
FIT:          CFI= TLI= RMSEA= (chi2/df=)   vs Hu & Bentler cutoffs
RELIABILITY:  omega= (alpha= )              per factor, from standardized loadings
VALIDITY:     <AVE, HTMT if convergent/discriminant claimed>
INVARIANCE:   <configural|metric|scalar|strict established; ΔCFI reported>

References

references/fit_and_invariance.md — omega math, fit-index nuance, the invariance sequence, IRT notes.
scripts/omega.py — stdlib McDonald's omega from standardized loadings.

Part of the AlterLab Academic Skills suite.

---name: alterlab-sem-psychometricsdescription: Fits and evaluates measurement models — confirmatory factor analysis, full structural equation models, exploratory factor analysis, item response theory, and multi-group measurement invariance — using the verified Python stack: semopy (model syntax =~ / ~ / ~~, Model.fit, inspect(std_est=True), calc_stats for CFI/TLI/RMSEA), factor_analyzer (EFA, KMO, Bartlett, ConfirmatoryFactorAnalyzer), and pingouin/girth, computing McDonald's omega from standardized loadings and judging fit against Hu & Bentler cutoffs. Use when the request mentions confirmatory factor analysis, structural equation modeling, a latent variable or construct model, factor loadings, IRT, or measurement invariance across groups. For deciding whether a scale is trustworthy at all prefer alterlab-ssci-measurement-gate; for plain regression prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.license: MIT---# SEM & Psychometrics — Fit the Measurement Model, Judge It Honestly**Skill type: ANALYSIS MODULE.** Fits CFA / SEM / EFA / IRT and tests measurement invariance.The discipline: report fit against real cutoffs, report reliability with **omega** (not alphaalone), and establish invariance before comparing groups. Whether the scale should be trusted atall is the upstream call of `alterlab-ssci-measurement-gate`.## Core Mission```A MEASUREMENT MODEL IS ONLY DONE WHEN FIT, RELIABILITY (OMEGA), AND INVARIANCE ARE ALL REPORTED.```## When to Use This Skill- "Run a confirmatory factor analysis on my scale."- "Fit a structural equation model / a latent-variable path model."- "Do an exploratory factor analysis — how many factors?"- "Fit an IRT model / get item difficulty and discrimination."- "Test measurement invariance across countries before I compare latent means."### Does NOT Trigger| The request is really about… | Route to | Why not this skill ||---|---|---|| Whether the scale is valid/trustworthy at all (reliability≠validity discipline) | `alterlab-ssci-measurement-gate` | Gate decision, upstream of fitting. || Writing / wording the questionnaire items | `alterlab-survey-design` | Instrument construction. || Plain regression / descriptive stats | `alterlab-statistical-analysis` | No latent structure. || A causal treatment-effect estimate | `alterlab-causal-inference` | Different problem. |## Verified calls (pinned)**CFA / SEM — semopy (v2.3):**```pythonfrom semopy import Model, calc_statsdesc = """# measurement modelTrust =~ q1 + q2 + q3Efficacy =~ q4 + q5 + q6# structural pathTrust ~ Efficacy"""m = Model(desc); m.fit(data) # obj='MLW' default; 'FIML' for missingest = m.inspect(std_est=True) # standardized loadings for omegastats = calc_stats(m) # DataFrame: chi2, CFI, TLI, RMSEA, AIC, BIC ...```Note: `calc_stats` returns CFI/TLI/RMSEA but **not SRMR** — report the indices it gives andcompute SRMR separately if required.**EFA — factor_analyzer (v0.5):**```pythonfrom factor_analyzer import FactorAnalyzer, calculate_kmo, calculate_bartlett_sphericitykmo_all, kmo_model = calculate_kmo(X) # sampling adequacy > .60chi2, p = calculate_bartlett_sphericity(X) # sphericityfa = FactorAnalyzer(n_factors=3, rotation="promax"); fa.fit(X)fa.loadings_; fa.get_factor_variance() # (variance, proportional, cumulative)````ConfirmatoryFactorAnalyzer` + `ModelSpecificationParser.parse_model_specification_from_dict`give a CFA alternative when semopy is unavailable.**Reliability — omega (not alpha alone):** pingouin gives `cronbach_alpha` but **no omega**, andsemopy does not return omega. Compute McDonald's omega from the standardized loadings with`scripts/omega.py` (ω = (Σλ)² / [(Σλ)² + Σ(1−λ²)]). Report alpha + omega together.**IRT:** `girth` (`twopl_mml`, `rasch_mml`) for dichotomous 2PL/Rasch; `py-irt` for Bayesian/scalable. For polytomous/multidimensional IRT with full fit statistics, R `mirt` is the practicalstandard — say so rather than forcing a thin Python path.## Fit cutoffs (Hu & Bentler 1999, as guidelines not gates)CFI/TLI ≥ .95 · RMSEA ≤ .06 · SRMR ≤ .08. Use a two-index combination; do not chase a singlenumber. Report the χ² and df alongside (χ² is sample-size sensitive, so not decisive).## Measurement invariance (before any group comparison)Multi-group CFA sequence — **configural → metric → scalar → strict** — judged by ΔCFI ≤ .01 /ΔRMSEA ≤ .015, not only Δχ². Scalar invariance is the prerequisite for comparing **latent means**;without it a group "difference" may be a measurement artifact. Fit each nested model and compare.## Output Template```MODEL: <CFA/SEM/EFA/IRT + the semopy/factor_analyzer spec>FIT: CFI= TLI= RMSEA= (chi2/df=) vs Hu & Bentler cutoffsRELIABILITY: omega= (alpha= ) per factor, from standardized loadingsVALIDITY: <AVE, HTMT if convergent/discriminant claimed>INVARIANCE: <configural|metric|scalar|strict established; ΔCFI reported>```## References- `references/fit_and_invariance.md` — omega math, fit-index nuance, the invariance sequence, IRT notes.- `scripts/omega.py` — stdlib McDonald's omega from standardized loadings.Part of the AlterLab Academic Skills suite.

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