Expected A Posteriori (EAP) Measures

Under Rasch model conditions, there is some probability that a person will succeed or fail on any item, no matter how easy or hard. This means that there is some probability that any person could produce any response string. Even the most able person could fail on every item.

The measure estimated for a person is usually that for which the observed response string is most likely, or that for which the response string best fits a Rasch model. We may, however, have some rough idea about a person's ability measure (or an item's difficulty) prior to the current data collection and wish to incorporate this idea into the newly estimated measure. To do this, we calibrate the test items in the usual way. Then we combine the item calibrations, our prior rough idea, and the observed responses to obtain an improved, a posteriori, person measure. Mislevy and Stocking (1989) recommend this approach for IRT models. John Uebersax (1993 and on his website) outlines a general procedure for this.

The technique capitalizes on an insight of Thomas Bayes:
Prior Probability x Data Probability => Posterior Probability

which implies that
Prob (B' given {X}) =
Prob (B' ) x Prob ({X} given B' ) / Sum over all B [ Prob (B) x Prob ({X} given B) ]

where B' is a particular value of the person measure, and the sum is over all possible values of our rough idea, B. {X} is the person's response string. The EAP estimate of the person measure is the expected value of this:
EAP estimate = Sum over all B [B x Prob (B given {X})].

Thus, suppose that our rough idea, the prior distribution of B, φ(B), is a convenient distribution, such as N(μ,σ²). The test consists i=1,L items. P_Xni is the probability of person n of ability B scoring X_ni on item i.

EAP estimates may be more central or more diverse than MLE estimates depending on the choice of prior distribution.

Then

This can be evaluated using numeric quadrature to approximate the integrals.

John M. Linacre

Mislevy RJ & Stocking ML (1989) A consumer's guide to LOGIST and BILOG. Applied Psychological Measurement, 13, 57-75.

Uebersax JS (1993) Statistical modeling of expert ratings on medical treatment appropriateness. Journal of the American Statistical Association, 88, 421-427.

Expected A Posteriori (EAP) Measures. Uebersax JS. … 16:3 p.891

Expected A Posteriori (EAP) Measures. Uebersax JS. … Rasch Measurement Transactions, 2002, 16:3 p.891

Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free	An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse	Rasch Measurement Theory Analysis in R, Wind, Hua	Applying the Rasch Model in Social Sciences Using R, Lamprianou	El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar	Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch	Rasch Models for Measurement, David Andrich	Constructing Measures, Mark Wilson	Best Test Design - free, Wright & Stone Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias	Diseño de Mejores Pruebas - free, Spanish Best Test Design	A Course in Rasch Measurement Theory, Andrich, Marais	Rasch Models in Health, Christensen, Kreiner, Mesba	Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
Rasch Books and Publications: Winsteps and Facets
Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene	Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver	Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone	Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale	Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes	Rasch Models for Solving Measurement Problems (Facets), George Engelhard, Jr. & Jue Wang	Statistical Analyses for Language Testers (Facets), Rita Green	Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind	Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind	Rasch Measurement: Applications, Khine	Winsteps Tutorials - free Facets Tutorials - free	Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre	Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan

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