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Rijmen, Frank; Vansteelandt, Kristof; De Boeck, Paul – Psychometrika, 2008
The increasing use of diary methods calls for the development of appropriate statistical methods. For the resulting panel data, latent Markov models can be used to model both individual differences and temporal dynamics. The computational burden associated with these models can be overcome by exploiting the conditional independence relations…
Descriptors: Markov Processes, Patients, Regression (Statistics), Probability
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Maydeu-Olivares, Albert – Psychometrika, 2001
Relates Thurstonian models for paired comparisons data to Thurstonian models for ranking data and proposes an intermediate model for paired comparisons data that assigns nonzero probabilities to all transitive patterns and to some, but not all, intransitive patterns. Conducted a simulation study to investigate the performance of three alternative…
Descriptors: Estimation (Mathematics), Probability, Sampling, Simulation
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Smith, Robert A. – Psychometrika, 1971
Descriptors: Data Analysis, Data Collection, Groups, Probability
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Geisser, Seymour; Kappenman, Russell F. – Psychometrika, 1971
Descriptors: Bayesian Statistics, Mathematics, Probability, Profiles
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Chen, Yuguo; Small, Dylan – Psychometrika, 2005
Rasch proposed an exact conditional inference approach to testing his model but never implemented it because it involves the calculation of a complicated probability. This paper furthers Rasch's approach by (1) providing an efficient Monte Carlo methodology for accurately approximating the required probability and (2) illustrating the usefulness…
Descriptors: Testing Problems, Probability, Methods, Testing
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Kearns, Jack; Meredith, William – Psychometrika, 1975
Examines the question of how large a sample must be in order to produce empirical Bayes estimates which are preferable to other commonly used estimates, such as proportion correct observed score. (Author/RC)
Descriptors: Bayesian Statistics, Item Analysis, Probability, Sampling
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Haberman, Shelby J. – Psychometrika, 2006
When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the…
Descriptors: Bias, Computation, Sample Size, Classification
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Lewis, Charles; And Others – Psychometrika, 1975
A Bayesian Model II approach to the estimation of proportions in m groups is extended to obtain posterior marginal distributions for the proportions. The approach is extended to allow greater use of prior information than previously and the specification of this prior information is discussed. (Author/RC)
Descriptors: Bayesian Statistics, Data Analysis, Individualized Instruction, Models
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Hamdan, M. A.; And Others – Psychometrika, 1975
Four different extensions to McNemar's problem concerning the hypothesis of equal probabilities for the unlike pairs of correlated binary variables are considered, each for testing simultaneous equality of proportions of unlike pairs in c independent populations of correlated binary variables, but each under different assumptions and/or additional…
Descriptors: Comparative Analysis, Criteria, Goodness of Fit, Hypothesis Testing
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Wilcox, Rand R. – Psychometrika, 1978
Several Bayesian approaches to the simultaneous estimation of the means of k binomial populations are discussed. This has particular applicability to criterion-referenced or mastery testing. (Author/JKS)
Descriptors: Bayesian Statistics, Criterion Referenced Tests, Mastery Tests, Probability
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Swain, A. J. – Psychometrika, 1975
Considers a class of estimation procedures for the factor model. The procedures are shown to yield estimates possessing the same asymptotic sampling properties as those from estimation by maximum likelihood or generalized last squares, both special members of the class. General expressions for the derivatives needed for Newton-Raphson…
Descriptors: Factor Analysis, Least Squares Statistics, Matrices, Maximum Likelihood Statistics
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Gross, Alan L. – Psychometrika, 1973
Expressions for the expected value, density, and distribution function (DF) of GS (gain from selection) are derived and studied in terms of sample size, number of predictors, and the prior distribution assigned to the population multiple correlation. (Author/RK)
Descriptors: Academic Achievement, College Admission, Item Sampling, Predictive Measurement