Departure from normal assumptions: A promise for future psychometrics with substantive mathematical modeling

DOI10.1007/BF02294639zbMath0898.62134MaRDI QIDQ1387694

Fumiko Samejima

Publication date: 8 June 1998

Published in: Psychometrika (Search for Journal in Brave)

zbMATH Keywords

item response theorynormalitynon-normalitydichotomous responseslatent trait modelslogistic positive exponent family of modelssubstantive mathematical modelingtask complexity

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (8)

A new skew logistic distribution: properties and applications ⋮ Multidimensional multiple group IRT models with skew normal latent trait distributions ⋮ Heteroscedastic latent trait models for dichotomous data ⋮ Revisiting the Samejima-Bolfarine-Bazán IRT models: new features and extensions ⋮ The heteroscedastic graded response model with a skewed latent trait: testing statistical and substantive hypotheses related to skewed item category functions ⋮ Bayesian inference for a skew-normal IRT model under the centred parameterization ⋮ A multiple group item response theory model with centered skew-normal latent trait distributions under a Bayesian framework ⋮ Extensions of the skew-normal ogive item response model

Cites Work

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• Polytomous IRT models and monotone likelihood ratio of the total score
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• A Rasch model for partial credit scoring
• Kernel smoothing approaches to nonparametric item characteristic curve estimation
• Efficient nonparametric approaches for estimating the operating characteristics of discrete item responses
• A comment on Birnbaum's three-parameter logistic model in the latent trait theory
• A new proof for monotone likelihood ratio for the sum of independent Bernoulli random variables
• Logistic positive exponent family of models: virtue of asymmetric item characteristic curves
• An approximation for the bias function of the maximum likelihood estimate of a latent variable for the general case where the item responses are discrete
• Estimating item parameters and latent ability when responses are scored in two or more nominal categories

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