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IRT Regression arrives — Latent Regression and Explanatory IRT (LLTM) bring person-level and item-level structural modeling to PACER for the first time.
What's new
Latent Regression
Model the latent ability distribution as a function of person-level covariates: θi ~ N(Xi′γ, σ²). Supply a pre-calibrated item parameter file and a wide-format response matrix with person covariate columns. PACER estimates regression weights γ and residual population SD σ using L-BFGS with numerical standard errors, Wald z-tests, and p-values for each covariate. Categorical predictors are dummy-coded automatically. Supports binary and polytomous items (1PL / 2PL / 3PL / GRM / GPCM).
Plausible Values
After a successful Latent Regression run, generate K posterior draws of θ for each person from the individual posterior P(θi | data, Xi′γ, σ²). Unlike EAP point estimates, plausible values preserve measurement uncertainty for downstream group-level analyses, matching the methodology used in large-scale assessments (NAEP, PISA). Results download as CSV with one column per draw.
Explanatory IRT (LLTM)
The Linear Logistic Test Model decomposes item difficulty as a linear function of observable item attributes: bj = Wj′β. Load long-format data with item attribute columns alongside responses, select predictors (categorical or numeric), and PACER runs the EM algorithm to jointly estimate attribute weights β and population SD σ. Polytomous items (e.g. ordered rating scales) are automatically recoded using adjacent-category coding. Results include predictor importance charts, parameter estimates with SEs and p-values, predicted item difficulties, and AIC/BIC model fit statistics. Guided tours in all 8 supported languages.
Web application
Windows desktop