Journal ArticleDOI
A probabilistic model for validation of behavioral hierarchies
TLDR
In this article, a probabilistic model for the validation of behavioral hierarchies is presented, which is by means of iterative convergence to maximum likelihood estimates, and two approaches to assess the fit of the model to sample data are discussed.Abstract:
A probabilistic model for the validation of behavioral hierarchies is presented. Estimation is by means of iterative convergence to maximum likelihood estimates, and two approaches to assessing the fit of the model to sample data are discussed. The relation of this general probabilistic model to other more restricted models which have been presented previously is explored and three cases of the general model are applied to exemplary data.read more
Citations
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Journal ArticleDOI
Concomitant-Variable Latent-Class Models
TL;DR: In this article, the probability of latent class membership is functionally related to concomitant variables with known distribution, and a general procedure for imposing linear constraints on the parameter estimates is introduced.
Journal ArticleDOI
Latent Class Models for Stage-Sequential Dynamic Latent Variables
TL;DR: In this article, a simulation study was conducted to determine whether model parameters are recovered adequately by Latent Transition Analysis (LTA), and whether additional indicators result in better measurement or in impossibly sparse tables.
Journal ArticleDOI
Model Selection Information Criteria for Non-Nested Latent Class Models
TL;DR: This article investigated the use of three model selection information criteria—Akaike AIC, Schwarz SIC, and Bozdogan CAIC—for non-nested models and found that SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low for such models.
Journal ArticleDOI
The Use of Probabilistic Models in the Assessment of Mastery
TL;DR: In this article, two related probabilistic models that can be used for making classification decisions with respect to mastery of specific concepts or skills are presented, and procedures for assessing the adequacy of the models, identifying optimal decision rules for mastery classification, and identifying minimally sufficient numbers of items necessary to obtain acceptable levels of misclassification.
Journal ArticleDOI
Latent Structure Models with Direct Effects between Indicators Local Dependence Models
TL;DR: A basic assumption of latent structure models is that of local independence as mentioned in this paper, where given the score on the latent variable, the scores on the manifest variables are independent of each other.
References
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Book
Linear statistical inference and its applications
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Journal ArticleDOI
Linear Statistical Inference and its Applications
P. G. Moore,C. Radhakrishna Rao +1 more
TL;DR: The theory of least squares and analysis of variance has been studied in the literature for a long time, see as mentioned in this paper for a review of some of the most relevant works. But the main focus of this paper is on the analysis of variance.
Journal ArticleDOI
The Use of Probabilistic Models in the Assessment of Mastery
TL;DR: In this article, two related probabilistic models that can be used for making classification decisions with respect to mastery of specific concepts or skills are presented, and procedures for assessing the adequacy of the models, identifying optimal decision rules for mastery classification, and identifying minimally sufficient numbers of items necessary to obtain acceptable levels of misclassification.
Journal ArticleDOI
A probabilistic formulation and statistical analysis of guttman scaling
TL;DR: In this paper, the latent or true nature of subjects is identified with a limited number of response patterns (the Guttman scale patterns), and the probability of an observed response pattern can be written as the sum of products of the true type multiplied by the chance of sufficient response error to cause the observed pattern to appear.