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- Graphical user interface
- Efficient analysis of binary items including multiple choice or short-answer items scored right, wrong, omitted, or not-presented
- Capable of large scale production analysis, and handling of multiple groups
- Performs item analysis and scoring of any number of subtests or subscales
- Non-equivalent groups equating
- Vertical equating of test forms
- Differential item functioning (DIF)
- Detection and correction for parameter trends over time (DRIFT)
- Calibration and Scoring of tests in two-stage testing procedures
- Estimation of latent ability or proficiency distributions
- Provision for items inserted in tests to estimate item statistics, but not included in calculation of examinee scores ("variant items")
- Item fit statistics, theoretical and empirical reliability
- Information curves and reliabilities for putative test forms
- Presentation quality IRT graphics, can be imported in Word, Access, etc.
- Detailed online HELP documentation include description of interface, syntax, and examples.
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- Easy to use graphical user interface
- One, two and three-parameter logistic models
- Samejima's model for graded responses
- Bock's model for nominal (non-ordered) responses
- Steinberg's model for multiple-choice items
- Handling of multiple-alternative items, such as multiple-choice tests or Likert-type attitude questionnaires
- Scoring of items with multiple alternatives
- Differential item functioning (DIF)
- Handling of data from several populations simultaneously
- Analysis of mixtures of items types
- Testing of item parameters across groups
- Handling of equality constraints and fixed parameters
- Presentation quality IRT graphics, can be imported in Word, Access, etc.
- Detailed online HELP documentation include description of interface, syntax, and examples.
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- The flexibility and the wealth of information provided by this program have kept it in regular use by researchers around the world
- One, two, and three-parameter logistic models
- Samejima's model for graded responses
- Master's partial credit model
- Generalized partial credit model
- Analysis of rating scale items such as open-ended essay questions
- Analysis of multiple-choice items
- Differential item functioning (DIF)
- Analysis of mixtures of item types
- Rater's-effect analysis
- Multiple-group polytomous item response models
- Presentation quality IRT graphics, can be imported in Word, Access, etc.
- Detailed online HELP documentation include syntax and examples.
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- Marginal maximum likelihood (MML) exploratory factor analysis and classical item analysis of binary data
- Computes tetrachoric correlations, principal factor solution, classical item descriptive statistics, fractile tables and plots
- Handles up to 10 factors using numerical quadrature: up to 5 for non-adaptive and up to 10 for adaptive quadrature
- Handles up to 15 factors using Monte Carlo integration techniques
- Varimax (orthogonal) and PROMAX (oblique) rotation of factor loadings
- Handles an important form of confirmatory factor analysis known as "bifactor" analysis: Factor pattern consists of one main factor plus group factors
- Simulation of responses to items based on user specified parameters
- Correction for guessing and not-reached items
- Allows imposition of constraints on item parameter estimates Handles omitted and not-presented items
- Detailed online HELP documentation include syntax and annotated examples.
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