Two ideal-point probabilistic choice models from the external analysis of preferences are presented that allow for more flexible distributions of ideal-points. The first extends the probit model of W. Kamakura and R. Srivastava. The second is based on simplifying assumptions that lead to a multidimensional histogram of ideal-points. (SLD)

This paper considers the following questions: (1) what is the relationship between the method of paired comparisons and Rasch measurement theory? (2) what is the relationship between the method of paired comparisons and graph theory? and (3) what can graph theory contribute to the understanding of Rasch measurement theory? It is specifically shown…

Previous research has demonstrated particular inadequacies in conventional methods used to identify outlier cases in bivariate regression models. Only through a combination of methods can one detect all of the deviant points potentially overinfluencing a regression model's parameters. This paper investigates whether a range of data points might…

The extent to which properties of the ability scale and the form of the latent trait distribution influence the estimated item parameters of item response theory (IRT) was investigated using real and simulated data. Simulated data included 5,000 ability values randomly drawn from the standard normal distribution. Real data included the results for…

The Markov random graphs of O. Frank and D. Strauss (1986) and the estimation strategy for these models developed by Strauss and M. Ikeda (1990) are promising contributions. This paper describes a large class of models that can be used to investigate structure in social networks and illustrates their use. (SLD)

Kernel smoothing methods for nonparametric item characteristic curve estimation are reviewed. A simulation with 500 examinees and real data from 3,000 records of the Graduate Record Examination illustrate the rapidity of kernel smoothing. Even when population curves are three-parameter logistic, simulation suggests no loss of efficiency. (SLD)

A generalized variance criterion is used for sequential sampling in the two-parameter item response theory model. Some principles are offered to enable the researcher to select the best sampling design for efficient estimation of item parameters. Topics include the choice of an optimality criterion, two-stage designs, and sequential designs. (SLD)

Estimation/smoothing methods that are flexible enough to fit a wide variety of test score distributions are reviewed: kernel method, strong true-score model-based method, and method that uses polynomial log-linear models. Applications of these methods include describing/comparing test score distributions, estimating norms, and estimating…

F. Samejima's approximation for the bias function for the maximum likelihood estimate of the latent trait in the general case where item responses are discrete is explored. Observations are made about the behavior of this bias function for the dichotomous response level in general. Empirical examples are given. (SLD)

A probabilistic choice model, based on L. L. Thurstone's Law of Comparative Judgment Case V, is developed for paired comparisons data about psychological stimuli. The model assumes that each stimulus is measured on a small number of physical variables. An algorithm for estimating parameters is illustrated with real data. (SLD)

The form of item log-likelihood surface was investigated under two-parameter and three-parameter logistic models. Results confirm that the LOGIST program procedures used to locate the maximum of the likelihood functions are consistent with the form of the item log-likelihood surface. (SLD)

A method is presented for estimating the item characteristic curve (ICC) using polynomial regression splines. Estimation of spline ICCs is described by maximizing the marginal likelihood formed by integrating ability over a beta prior distribution. Simulation results compare this approach with the joint estimation of ability and item parameters.…

A model of category effects found in reports of episodic memory is proposed. The model holds that stimuli are presented at a fine-grain level of detail and a category. Four experiments involving a total of 131 University of Chicago (Illinois) students and faculty are reported in support of the model. (SLD)

A metrics and mapping framework is proposed to account for how heuristics, domain-specific reasoning, and intuitive statistical induction processes are integrated to generate estimates. Results of 4 experiments involving 188 undergraduates illustrate framework usefulness and suggest when people use heuristics and when they emphasize…

This Monte-Carlo study compared modified Newton (NW), expectation-maximization algorithm (EM), and minimum Cramer-von Mises distance (MD), used to estimate parameters of univariate mixtures of two components. Data sets were fixed at size 160 and manipulated by mean separation, variance ratio, component proportion, and non-normality. Results…