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|Title:||A Model That Combines IRT and Latent Class Models|
|Doctoral Committee Chair(s):||Tatsuoka, Kikumi K.|
|Department / Program:||Education|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Subject(s):||Education, Tests and Measurements|
|Abstract:||A measurement model called Hybrid model is developed to overcome the weaknesses of the widely used Item Response Theory (IRT) and Latent Class models by combining them both. The weakness of IRT is that it is unable to express cognitive structure whether that is rule of operations or particular misunderstanding, or to give descriptive information of knowledge possessed by examinees. The Latent Class models require severe constraints, namely possession of complete knowledge of latent classes, and has the indeterminacy of number of ordered classes. The Hybrid model is most applicable in the so-called achievement testing, diagnostic testing, and mastery testing environment, and it is applicable even in some "ability tests" as long as a unique understanding or misunderstanding of the examinee can be characterized by a particular response pattern.
Within the field of educational testing and evaluation, item response models can be classified into two types, one is "continuous" because of the propensity to answer in a certain way on an item changes accordingly as continuous ability value changes, and the other is "discrete" because a set of conditional probabilities are assigned to a limited number of identifiable ability states which may be ordered or not ordered. The hybrid model combines the two continuous and discrete measurement models, namely the two-parameter Logistic model of IRT and the Latent Class models respectively. The marginal maximum likelihood estimation method was used to develop the algorithm for a computer program which estimates parameters of IRT, mixture proportions and vectors of conditional probabilities of correct responses for the latent classes. Several types of latent class models are considered, including both more traditional models as well as the model that uses person characteristic curves. To evaluate the accuracy of the algorithm and also the computer program itself, several simulation data sets were generated. The comparison of estimated parameter values and the original values shows that the model parameters are accurately estimated, in particular, IRT item parameters estimation was found to be robust to the wrong specification of the latent class type.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.
|Date Available in IDEALS:||2014-12-15|