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This paper considers a regression approach to estimating signal detection parameters for rating data. The methodology is based on the statistical modeling of ordinal data and requires only standard statistical software such as SAS (SAS/STAT User's Guide, 1999) for computation. The approach is more efficient than the current practice of extracting the parameter estimates with the use of specialized software and analyzing the estimates with the use of a standard statistical package. It greatly facilitates exploration of the effects of covariates on model parameters. The method is illustrated using a published data set from a single factor multiple-alternative perceptual task, and data from a more complex factorial design examining recognition memory rating data.

The purpose of this paper is to describe and illustrate a regression approach to the analysis of correlated binary outcomes (Liang & Zeger, 1986). Ignoring the correlations between repeated observations can lead to invalid inferences. This approach extends logistic regression to account for repeated observations in each of a series of individuals. In this paper, I present a nontechnical introduction to the generalized estimating equations (GEE) approach. A fictitious example is used to demonstrate that GEE regression correctly adjusts for the correlations between repeated binary observations. The approach is illustrated with an analysis of safer sex practices among high-risk teenagers.

We illustrate the application of BUGS, a Bayesian computer program, with two examples. The algorithm used in the program is a popular Markov chain Monte Carlo procedure called Gibbs sampling. Bayesian analysis based on simulation has been applied to a wide range of complex problems (Gilks, Richardson, & Spiegelhalter, 1996). The availability of a general purpose program like BUGS should facilitate important applications of Bayesian inference in psychological research.

In this paper, we discuss the use of Java, a programming language for the Internet, in introductory statistics courses. Monte Carlo estimation and simple linear regression are used as tutorial examples of the versatility of Java. Advantages over traditional instructional methods and other programming languages are examined.

Multivariate nonparametric statistical methods have not been widely used by psychologists. One reason for this may have been that the usual general-purpose packages do not provide easy implementation of these methods. In this article, we briefly describe the multivariate extensions of the sign, signed-rank, and rank-sum tests and use S, a programming environment for data analysis, to implement these statistical procedures. Three numerical examples are used to illustrate the flexibility and efficiency of these computations in S.