A maximum likelihood procedure is developed for multidimensional scaling where similarity or dissimilarity measures are taken by such ranking procedures as the method of conditional rank orders or the method of triadic combinations. An example is given. (Author/JKS)

A single-step maximum likelihood estimation procedure is developed for multidimensional scaling of dissimilarity data measured on rating scales. The procedure can fit the euclidian distance model to the data under various assumptions about category widths and under two distributional assumptions. Practical uses of the method are demonstrated.…

A maximum likelihood estimation procedure is developed for the simple and the weighted additive models. The data are assumed to be taken by either one of the following methods: (1) categorical ratings--the subject is asked to rate a set of stimuli with respect to an attribute of the stimuli on rating scales with a relatively few observation…

Uncorrectable skew and heteroscedasticity are among the "lemons" of psychological data, yet many important variables naturally exhibit these properties. For scales with a lower and upper bound, a suitable candidate for models is the beta distribution, which is very flexible and models skew quite well. The authors present…

The properties of nonmetric multidimensional scaling one explored by specifying statistical models, proving statistical consistency, and devloping hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. (Author/LMO)

After introducing some extensions of a recently proposed probabilistic vector model for representing paired comparisons choice data, an iterative procedure for obtaining maximum likelihood estimates of the model parameters is developed. The possibility of testing various hypotheses is discussed and the algorithm is applied to some existing data…

A stochastic multidimensional scaling procedure is presented for analysis of three-mode, three-way pick any/"J" data. The procedure fits both vector and ideal-point models and characterizes the effect of situations by a set of dimension weights. An application in the area of consumer psychology is discussed. (SLD)

A new method of multiple discriminant analysis allows a mixture of continuous and discrete predictors. It handles conditional, joint, or separate sampling. Subjects and criterion groups are represented as points in a multidimensional Euclidean space. Advantages of the method, deriving from Akaike Information Criterion model evaluation, are…

Y. Takane and J. Sergent developed a model (MAXRT) for scaling same/different judgments and response times (RTs) simultaneously. The adequacy of the assumption that RTs are distributed log-normally is considered, and the effect of a violation of this assumption is investigated via a computer simulation. (SLD)

Data are summarized in Scheuneman's Score x Group x Response frequency table in order to investigate item bias. The data can arise from two different sampling models: (1) multinomial sampling in which a fixed sample size is used and the responses are cross-classified according to score, group, and response; and (2) product-multinomial sampling in…

A stochastic multidimensional unfolding (MDU) procedure is presented to represent individual differences in phased or sequential decision processes spatially. A Monte Carlo analysis demonstrates estimation proficiency and the appropriateness of the proposed model selection heuristic, and an application to capture awareness, consideration, and…

A loglinear item response theory (IRT) model is proposed that relates polytomously scored item responses to a multidimensional latent space. Each item may have a different response function where each item response may be explained by one or more latent traits. Item response functions may follow a partial credit model (D. Andrich, 1978; and G. N.…