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 Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
|in Spanish:||Análisis de Rasch para todos, Agustín Tristán||Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez|
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