THE BEHAVIORAL THEORY OF TIMING: TRANSITION ANALYSES

Gibbon and Church (1990, 1992) have recently confirmed an important, parameter-free prediction of the behavioral theory of timing (Killeen & Fetterman, 1988): The times of exiting from a bout of activity are positively correlated with the times of entrance to it. The correlations were slightly less than predicted, however, and the correlations between the start of an activity and the time spent engaged in that activity were negative, rather than zero. We adapted their serial model as an augmented (one-parameter) version of the behavioral theory, positing a lag between the receipt of a pulse from the pacemaker and transition into the next class of responses. The augmented version of the behavioral theory further improved the correspondence between the theory and the correlational data reported by Gibbon and Church. It also accounts for previously unpublished data from our laboratory derived from a new timing technique, the “peak choice” procedure. We show that the measured variance of movement times from one key to another closely approximates the estimated variance of transition times recovered from fits of the augmented model to the data. Such correspondence both attests to the correct identification of this source of variance and suggests ways to remove it, both from behavior and from our models of behavior.     

D-optimal design for the Rasch counts model with multiple binary predictors

In this paper we derive optimal designs for the Rasch Poisson counts model and its extended version of the (generalized) negative binomial counts model incorporating several binary predictors for the difficulty parameter. To efficiently estimate the regression coefficients of the predictors, locally D-optimal designs are developed. After an introduction to the Rasch Poisson counts model and its extension, we will specify these models as particular generalized linear models. Based on this embedding, optimal designs for both models including several binary explanatory variables will be presented. Therefore, we will derive conditions on the effect sizes for certain designs to be locally D-optimal. Finally, it is pointed out that the results derived for the Rasch Poisson models can be applied for more general Poisson regression models which should receive more attention in future psychological research.     

An INAR(1) negative multinomial regression model for longitudinal count data

This paper discusses a regression model for the analysis of longitudinal count data observed in a panel study. An integer-valued first-order autoregressive [INAR(1)] Poisson process is adapted to represent time-dependent correlations among the counts. By combining the INAR(1)-representation with a random effects approach, a new negative multinomial distribution is derived that includes the bivariate negative binomial distribution proposed by Edwards and Gurland (1961) and Subrahmaniam (1966) as a special case. A detailed analysis of the relationship between personality factors and daily emotion experiences illustrates the approach.This research was partially supported by NSF grant SBR-9409531. The author is grateful to Ulrich Schimmack and Ed Diener for providing the data set used in the application section and for helpful comments on this research.     

Counselor Education Doctoral Program Faculty Members’ Refereed Article Publications

This study investigated articles published in refereed journals by counselor educators (N = 378) employed by 55 doctoral programs accredited by the Council for Accreditation of Counseling and Related Educational Programs. The counselor educators published in 321 different journals, and the variables that predicted publications rates included (a) year earned doctoral degree, (b) academic rank, (c) Carnegie category classification of current university, and (d) gender. Implications for counselor education and scholarly productivity are discussed.