Computational behavior dynamics: an alternative description of Nevin (1969).

A computational processing behavior-dynamic model was instantiated in the form of a computer program that "behaved" on the task developed by Nevin (1969). In this classic discrete-trials experiment, the relative frequency of choosing a response alternative matched the relative frequency of reinforcement for that alternative, the local structure of responding was opposite that predicted by momentary maximizing (i.e., the probability of a changeover decreased with run length), and absolute and relative response rates varied independently. The behavior-dynamic model developed here qualitatively reproduced these three results (but not in quantitative and specific detail) and also generated some interesting, as-yet-untested predictions about performance in Nevin's task. The model was discussed as an example of a stochastic behavior-dynamic alternative to algebraic behavior theory.     

Constructing the exact significance level for a person fit statistic

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.     

Examining differential item functioning due to item difficulty and alternative attractiveness

A method for analyzing test item responses is proposed to examine differential item functioning (DIF) in multiple-choice items through a combination of the usual notion of DIF, for correct/incorrect responses and information about DIF contained in each of the alternatives. The proposed method uses incomplete latent class models to examine whether DIF is caused by the attractiveness of the alternatives, difficulty of the item, or both. DIF with respect to either known or unknown subgroups can be tested by a likelihood ratio test that is asymptotically distributed as a chi-square random variable.     

Beyond principal component analysis: A trilinear decomposition model and least squares estimation

The paper derives sufficient conditions for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis.     

Provision of step-level public goods with continuous contribution

Twelve groups of five subjects each participated in a nonco-operative game in which each member of a group receives the same endowment and must then decide independently and anonymously how much of it to contribute to the group benefit. Regardless of the size of his or her contribution, each member receives the same reward if, and only if, the sum of contributions is equal to or larger than a prespecified provision threshold. The results show that the level of contribution depends on the provision threshold, and that it increases when contributions are not restricted to be all-or-none. We present, discuss, and competitively test two models for this class of social dilemmas, one postulating maximization of expected utility and the other yielding an equitable solution.     

Theory change in immunology part II: The clonal selection theory

This two-part article examines the competition between the clonal selection theory and the instructive theory of the immune response from 1957–1967. In Part I the concept of a temporally extended theory is introduced, which requires attention to the hitherto largely ignored issue of theory individuation. Factors which influence the acceptability of such an extended theory at different temporal points are also embedded in a Bayesian framework, which is shown to provide a rational account of belief change in science. In Part II these factors, as elaborated in the Bayesian framework, are applied to the case of the success of the clonal selection theory and the failure of the instructive theory.     

Influential observations in principal factor analysis

We propose a method for detecting influential observations in iterative principal factor analysis. For this purpose we derive the influence functionsI(x; LL ^T ) andI(x; ) for the common variance matrixT =LL ^T and the unique variance matrix , respectively, in the common factor decomposition =LL ^T + . A numerical example is given for illustration.The authors are grateful to Tomoyuki Tarumi and Atsuhiro Hayashi for their kind permission to use their software Seto/B for drawing Figures 1 and 2 and to anonymous reviewers for comments on the paper.     

Incorporating prior theory in covariance structure analysis: A bayesian approach

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.     

The social phobia and anxiety inventory: Construct validity

The Social Phobia and Anxiety Inventory (SPAI) is a new instrument composed of social phobia and agoraphobia subscales. The latter scale is used to detect social anxiety that may result from agoraphobia. The SPAI's construct validity was assessed through several procedures. First, confirmatory factor analyses were conducted to validate the existence of the two subscales. Second, exploratory factor analyses examined the underlying structure of the social phobia subscale. Third, a Q factor procedure determined if different anxiety diagnostic groups could be differentiated by their SPAI response pattern. The results confirmed the utility of the two SPAI subscales and identified a number of dimensions contained within the social phobia subscale which differed depending upon the specific subject sample. In addition, the complaints of social phobies appeared more homogeneous than those of an agoraphobic comparison group. The results are discussed in terms of construct validity and the sensitivity of the SPAI to various dimensions of social phobia fears.This study was supported in part by NIMH Grants 41852, 30915, 18269, and 16884.