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alterlab-sem-psychometrics

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更新时间2026年7月2日 13:45

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.

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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.

同仓库更多 Skills

同仓库

alterlab-skill-finder

AlterLab-IEU/AlterLab-Academic-Skills

The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-meta-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-missing-data

AlterLab-IEU/AlterLab-Academic-Skills

Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum likelihood (FIML) where a likelihood/SEM model applies. Uses statsmodels MICE / MICEData in Python or the field-standard R mice via Rscript, and warns that single (mean/regression) imputation and scikit-learn's IterativeImputer return one completed dataset without Rubin's-rules pooling, so they understate standard errors if used as multiple imputation. Use when a dataset has missing values, when choosing an imputation strategy, or when reporting how missingness was handled. For general modeling on complete data prefer alterlab-statistical-analysis; for latent-variable models with FIML prefer alterlab-sem-psychometrics. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-multilevel-models

AlterLab-IEU/AlterLab-Academic-Skills

Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using statsmodels MixedLM and bambi (Bayesian on PyMC) in Python, or the field-standard R lme4 / glmmTMB / brms via Rscript. It enforces the reporting items reviews find under-reported: full fixed + random specification, centering, variance components + ICC, estimation method, assumption checks, model comparisons, and effect sizes. Use when data are grouped/nested (students in schools, repeated measures, panel/longitudinal) and the question concerns within- vs between-cluster variation. For general single-level regression prefer alterlab-statsmodels; for panel fixed effects used for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-qualitative-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Analyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal calculator with bootstrap CIs; Cohen's / Fleiss' kappa via statsmodels) with 95% CIs and thresholds (alpha >= .80 reliable, .667-.80 tentative). It BRANCHES by design: coefficient-based ICR for codebook / content-analytic coding, versus consensus-and-reflexivity for reflexive thematic analysis where a statistic is not the right criterion. Supports human-vs-LLM double-coding with an alpha check against a human gold standard. Use when coding interviews or open-ended text, building a codebook, or reporting intercoder reliability. For topic modeling / embeddings / supervised text classification prefer alterlab-text-as-data; for the reflexivity gate prefer alterlab-ssci-reflexivity-gate. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-ssci-orchestrator

AlterLab-IEU/AlterLab-Academic-Skills

Coordinates a social-science study through the stage-gated methods pipeline — question then design-gate then measurement-gate then sampling-gate then the right analysis module then inference-gate then reporting — holding a single YAML Design Passport that each gate reads and appends, and enforcing gate order with PASS / WARN / BLOCK semantics (design-gate and inference-gate are fail-closed). It is thin: it routes and holds the artifact, it does not run analysis itself. Use when a researcher describes a whole social-science study end to end, asks to run the methods pipeline or a full methodology review, or wants the design-to-inference workflow coordinated rather than a single step. For a single stage trigger that gate directly (alterlab-ssci-design-gate, -measurement-gate, -sampling-gate, -inference-gate); for multi-agent execution mechanics see alterlab-workflow-orchestration. Part of the AlterLab Academic Skills suite.

2026-07-0246

name	alterlab-sem-psychometrics
description	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
allowed-tools	Read Bash(python:*)
compatibility	Requires (declare in-session, no runtime install on Anthropic API): semopy>=2.3, factor_analyzer>=0.5, pingouin>=0.5, optionally girth (IRT) (pip). SRMR and omega are not returned by semopy — compute omega with the bundled script; IRT beyond 2PL is often better in R mirt. Runs locally via `uv run python`; no API key.
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-ssci-measurement-gate (gates whether to trust the scale), alterlab-statistical-analysis; audited by alterlab-ssci-inference-gate"}

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

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.

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)

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.