Psychometrics
📐 IRT Model Selection Guide
Visual guide for choosing among 1PL (Rasch), 2PL, and 3PL IRT models. Load binary response data to see item p-values.
MODEL COMPARISON
Feature1PL Rasch2PL3PL
ParametersDifficulty (β)Difficulty (β), Discrimination (α)Difficulty (β), Discrimination (α), Guessing (γ)
AssumptionsEqual discrimination, no guessingVarying discrimination, no guessingVarying discrimination, guessing allowed
ICC ShapeParallel logistic curvesVariable-slope logistic curvesAsymptotic lower bound > 0
Sample SizeN ≥ 100N ≥ 250N ≥ 500
EstimationJML / CMLMML (EM)MML (EM + priors)
Best ForMeasurement-focused, Rasch philosophyExploratory psychometricsMultiple-choice tests
Fit IndicesInfit/Outfit MSQ-2LL, AIC, BIC-2LL, AIC, BIC
DECISION FLOWCHART
Start: Binary item response data
Are items multiple-choice with plausible distractors?
Yes
🔴 3PL Model
Accounts for guessing (N≥500)
No
Do items vary in discrimination?
Yes
🔸 2PL Model
Free discrimination (N≥250)
No / Rasch
🔵 1PL Rasch
Equal discrimination (N≥100)
QUICK CHECK: ITEM P-VALUES
📋Paste binary response matrix (persons x items, 0/1 coded, first row = item names) to see item difficulty (p-values) and point-biserial correlations.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
🔵 1PL Rasch Model
Joint Maximum Likelihood (JML) estimation of the Rasch model. P(X=1|θ,β) = 1/(1+exp(-(θ-β))). Estimates item difficulties and person abilities with Newton-Raphson iteration.
DATA & OPTIONS
📋Binary matrix: Persons (rows) x Items (columns), coded 0/1. First row = item names. One person per row.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
📊 Statistical Interpretation
🔸 2PL Model
Marginal Maximum Likelihood (MML) estimation via EM algorithm with Gauss-Hermite quadrature (Q=21 nodes). Estimates item discrimination (α) and difficulty (β), plus EAP ability scores.
DATA & OPTIONS
📋Binary matrix: Same format as 1PL. The 2PL model frees the discrimination parameter for each item.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
📊 Statistical Interpretation
🔴 3PL Model
Three-parameter logistic model via EM with Bayesian priors on discrimination and guessing. P(X=1|θ) = γ + (1-γ)/(1+exp(-α(θ-β))). Requires larger samples (N≥500).
DATA & OPTIONS
⚠️Warning: 3PL requires N ≥ 500 for stable estimation. For smaller samples, use 1PL or 2PL.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
📊 Statistical Interpretation
⚖️ Differential Item Functioning
Detect measurement bias using Mantel-Haenszel and Logistic Regression methods. Tests whether items function differently across groups (e.g., gender, ethnicity).
DATA & OPTIONS
📋Format: Binary response matrix with an additional first column for group membership (0=reference, 1=focal). First row = headers.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
📊 Statistical Interpretation
🧩 Latent Class Analysis — Model Fit
EM algorithm for categorical latent class models with multiple random starts. Compare models with 1-K classes using AIC, BIC, SABIC, entropy, and bootstrap LRT.
DATA & OPTIONS
📋Binary response matrix: Persons (rows) x indicator variables (columns), coded 0/1. First row = variable names.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
📊 Statistical Interpretation
↔️ TOST Equivalence Testing
Two One-Sided Tests procedure (Schuirmann, 1987). Demonstrates equivalence by rejecting both H01: δ≤-Δ and H02: δ≥Δ. Supports independent means, paired means, proportions, and correlation tests.
TEST CONFIGURATION
Configuration
📊 Statistical Interpretation
🔢 McDonald's ω Reliability
Factor-analytic reliability: ωtotal = (Σλ)² / [(Σλ)² + Σθ]. Extracts a single factor via ULS, computes Cronbach's α for comparison, with bootstrap 95% CI.
DATA & OPTIONS
📋Continuous data matrix: Persons (rows) x Items (columns). First row = item names. Values can be Likert-scale or continuous.
📥 Data
Data Grid
Edit cells directly. Click 'Apply to CSV' to transfer data.
📊 Statistical Interpretation