Some Rasch-capable Computer Software

BILOG is a program for one-group analysis of binary data with the 1-, 2-, or 3-parameter logistic model. Item parameter estimation is performed by marginal maximum likelihood MMLE, with provision for concurrent estimation of the latent distribution of the person parameters. Estimation of person parameters is by maximum likelihood, or Bayes estimation methods (EAP/MAP). BILOG includes options for the analysis of multiple form tests, multiple subtests in one pass, group-level educational assessment data, case weighted probability samples, and test and item information.

LPCM-WIN applies the Rasch Model (RM), the Multifactorial (Multifacet) RM, the Linear Logistic Test Model (LLTM), the Rating Scale Model (RSM), the Partial Credit Model (PCM), and a family of extensions of these models resulting from imposing a linear structure on the item parameters of the PCM. It also applies the models to multidimensional items in the measurement of change and the assessment of treatment effects. The data may be dichotomous or polytomous items, ratings, or symptoms.

MULTILOG employs item response theory to perform analysis and test scoring for multiple category items. It provides item parameter estimation and subject scoring under the Samejima logistic model for graded responses, the Bock multinomial logit model for multiple nominal categories, the Bock-Samejima-Thissen model for multiple choice items with guessing, and Masters partial-credit model. These models may be fit to a latent ability continuum by marginal maximum likelihood, or to a manifest ability criterion by maximum likelihood. The program has the capacity to impose equality constraints on selected subsets of item parameters, making it possible to analyze models intermediate between conventional 1-, 2-, and 3-parameter logistic models. MULTILOG also permits (quasi-)continuous measured variables to be mixed with the multiple category responses.

PARSCALE implements Samejima's model for graded categories, and also extends it to Likert-type data in which all items are rated with the same categories. In this case, a common set of category boundaries is estimated for all items, and conventional difficulty and discriminating power parameters are provided for each item. Alternatively, the user may choose the Masters-Andrich partial credit model, including extensions that generalize the model to items with differing discriminating powers and to binary scored, multiple choice items for which guessing effects are estimated. Multiple subtests may be analyzed, while scale scores are estimated for each subtest. Scores for subscales or subtests may be combined into a weighted overall score for each subject.

RASCAL analyzes test item responses to estimate the item difficulty and person (ability) parameters based on the one-parameter (Rasch) logistic IRT models for dichotomous data. RASCAL can center the scale of the parameter estimates on difficulty (i.e., a true Rasch scale) or on ability (a three-parameter IRT model with fixed discrimination and zero guessing). RASCAL can fix certain item parameters to specified values and automatically calibrate the remaining items onto that scale. RASCAL also generates a table for converting number-correct (raw) scores into IRT (ability) scores.

RUMMFOLDss and RUMMFOLDpp estimate the person trait levels and item location parameters of the one-parameter logistic Rasch unfolding measurement model (RUMM). This model assumes a symmetric single-peaked item response function for the items, in which the probability of a correct response decreases as the distance between the person's trait level and the item's location increases in either direction. Unfolding models can arise from two data collection designs - the direct-response single-stimulus (SS) design and the pair-comparison or pairwise preference (PP) design.

T-Rasch carries out exact or non-parametric tests for the Rasch model against a host of alternative hypotheses. It also tests the applicability of the Rasch model to small samples.

WINMIRA 32 is used for analyses with the Latent Class Analysis (LCA), the Rasch model (RM), and the Mixed Rasch model (MRM) and Hybrid models (HYBRID). For polytomous data, WINMIRA 32 is capable of estimating the partial credit model, the rating scale model, the equidistance model, and the dispersion model. The software can handle both dichotomous and polytomous variables.

ProGamma Some Rasch-capable Computer Software … Rasch Measurement Transactions, 1999, 13:3 p. 709

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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