What do people think about changes in health behaviors? Differential perceptions of consequences of increases and decreases in health behaviors

Most health decision-making models posit that cost-benefit analyses underlie decisions to make changes in health-related behavioral practices. In a series of studies, participants imagined either increasing or decreasing the frequency of a variety of health behaviors and estimated the consequences of those changes. In Studies 1 and 2, individuals consistently estimated that increasing a health behavior produced greater consequences than did decreasing the behavior by an equivalent amount. The results of Study 3 demonstrated that this effect is due to differences in how individuals judge the impact of health behavior changes which involve not engaging in the behavior at all versus other types of changes. Taken together, these findings suggest that perceptions of the outcomes afforded by health behavior changes depend on both the behavioral frequency and direction of changes in behavior an individual is considering. This asymmetry has the potential to help explain patterns of behavior in a range of important health domains and may impact the effectiveness of behavior change interventions.     

Beta-MPT: Multinomial processing tree models for addressing individual differences

Traditionally, multinomial processing tree (MPT) models are applied to groups of homogeneous participants, where all participants within a group are assumed to have identical MPT model parameter values. This assumption is unreasonable when MPT models are used for clinical assessment, and it often may be suspect for applications to ordinary psychological experiments. One method for dealing with parameter variability is to incorporate random effects assumptions into a model. This is achieved by assuming that participants’ parameters are drawn independently from some specified multivariate hyperdistribution. In this paper we explore the assumption that the hyperdistribution consists of independent beta distributions, one for each MPT model parameter. These beta-MPT models are ‘hierarchical models’, and their statistical inference is different from the usual approaches based on data aggregated over participants. The paper provides both classical (frequentist) and hierarchical Bayesian approaches to statistical inference for beta-MPT models. In simple cases the likelihood function can be obtained analytically; however, for more complex cases, Markov Chain Monte Carlo algorithms are constructed to assist both approaches to inference. Examples based on clinical assessment studies are provided to demonstrate the advantages of hierarchical MPT models over aggregate analysis in the presence of individual differences.     

A Minister's Information-Handling

ABSTRACT

One of the roles which ministers perform is that of “caregiver.” Caregiving functions include such activities as visiting parishioners, hearing confessions, and counselling people. These functions are carried out within a legal context. In this paper, two aspects of the parish minister's information handling in respect to his/her role as caregiver are examined. Specific attention is given to the pastor's vulnerability, while carrying out pastoral duties, to civil lawsuits for malpractice and to her/his expectation of privilege against testifying in criminal proceedings. It is observed that in North America, considerable confusion exists over who actually owns and who can control the information a minister acquires and disseminates.