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Bayesian Semiparametric Item Response Modelling for Person-Specific Latent Traits

Theory, Identification, and the Literature Behind the Estimators

JoonHo Lee · The University of Alabama · jlee296@ua.edu

A web monograph on what it takes to report a person's latent trait honestly: the measurement models and their identification, information and the reliability coefficients that summarise it, hierarchical shrinkage and the empirical-Bayes tradition it comes from, Dirichlet process mixture priors on the latent-trait distribution, and the decision-theoretic case that three inferential goals — an individual's score, a ranking, and the distribution itself — imply three different posterior summaries rather than one set of numbers.

📖 Read it: https://joonho112.github.io/dpmirt-theory-book/

What is here

The book runs to 29 chapters in nine parts, plus seven appendices.

Part What it does
I–II The estimation problem; the Rasch and 2PL models, identification, and item/person estimation
III Information, measurement error, and the reliability coefficients — including why several defensible coefficients disagree
IV The Bayesian hierarchical model, the conditional posterior, shrinkage, and its empirical-Bayes lineage
V What happens when normality is relaxed: flexible and semiparametric alternatives, the Dirichlet process, and DPM priors
VI Three goals, three losses: posterior means, constrained Bayes, and triple-goal estimation
VII Positioning against the nearest neighbour in the literature, scope, and open problems
VIII What changes under the 2PL — information, reliability, and identification with free discriminations
IX The realized evidence: what a companion simulation settled, what real tests look like, and what remains open

The title is a Rasch-centred umbrella, not a claim of parallel depth across the two models: Parts I–VII form the theory spine and Part VIII is a focused two-parameter extension of the mechanisms for which free discriminations materially change the argument.

How the book is put together

Two disciplines shape the repository, and both are auditable.

Provenance. Every numbered result carries a tag — restated, adapted, or derived here — together with a source locator, and manifest/ holds the registers that make this checkable: the result register, the notation register, the corrections to the manuscript this book supports, the reviewer claim map, and the source-reading receipts. A derived-here tag asserts no priority.

Generated artifacts. Chapters read frozen tables under tables/ and figures under book/figures/; they compute nothing. Where the book quotes a companion volume, manifest/evidence-claim-register.csv binds each imported claim to its source edition, snapshot locator, and field. Appendix G states exactly what that machinery guarantees and what it does not.

Path Contents
book/ Quarto sources, figures, and bibliography. The rendered site is published from the gh-pages branch
code/R/ The build pipeline: table and figure generators, the bibliography harvester, and the verifiers
manifest/ Provenance registers
tables/ Frozen .rds artifacts with CSV mirrors under tables/supplement/
refs/ The bibliography and its acquisition ledger
verification/ Verifier outputs from the release build

Reproducing

Rendering the book needs only this repository:

quarto render book

The rendered site is served from the gh-pages branch; main carries the sources that produce it.

The full verification pipeline does not. Rscript code/R/14-build-all.R regenerates every artifact and runs the verifiers, but it reads inputs that are deliberately not published: nine reference libraries of licensed third-party PDFs that back the citation audit, the companion simulation and case-study repositories, and two Item Response Warehouse corpus studies. Point DPMIRT_PROJECT_ROOT at a directory holding those alongside this repository and the pipeline runs; without them it will stop at the first missing input, by design. The verifier outputs from the release build are included under verification/ so the result of that run can be inspected without re-running it.

Environment: R 4.6.0, Quarto 1.9.x.

The three volumes

This project is published as three linked volumes. Each stands on its own; read together they separate what is true of a model, what is true in a study, and what changes on a real test.

Volume What it owns
Theory (this repository) The mathematics and its sources: the measurement models, identification, reliability, shrinkage, DP mixture priors, and why three inferential goals imply three posterior summaries
Simulation study Realized outcomes under known truth: the preregistered design grid, the confirmatory record, Monte Carlo error, and the gate verdicts
Case studies What changes on real tests: thirteen Item Response Warehouse cases under both item models, reported as consequence rather than correctness

Citing

See CITATION.cff, or:

Lee, JoonHo (2026). Bayesian Semiparametric Item Response Modelling for Person-Specific Latent Traits: Theory, Identification, and the Literature Behind the Estimators.

Licence

Text, figures, and tables under book/ are CC BY 4.0; code under code/ is MIT. Cited sources are the copyright of their publishers and are not distributed here. See LICENSE.

About

Theory and literature companion for Bayesian semiparametric item response modelling: identification, reliability, Dirichlet process mixture priors, and goal-specific posterior summaries. Quarto web monograph.

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