A general model for the analysis of multilevel data

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).     

Forgiveness, personality and gratitude

Recent evidence indicates that personality plays a role in the study of forgiveness, but the literature has been limited. This study sought to determine whether gratitude accounted for a significant portion of the variance beyond that of personality in the study of dispositional forgiveness (enduring resentment, sensitivity to circumstances, and overall propensity to forgive). One hundred and fifty-two Portuguese college students (51% females and 49% males, mean = 21.10 years) participated in the study. The results confirmed that personality, particularly agreeableness and neuroticism correlate with enduring resentment and overall tendency to forgive. Additionally, results using multiple regression models indicated that gratitude explained a significant amount of variance of overall propensity to forgive.     

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.     

The many null distributions of person fit indices

This paper deals with the situation of an investigator who has collected the scores ofn persons to a set ofk dichotomous items, and wants to investigate whether the answers of all respondents are compatible with the one parameter logistic test model of Rasch. Contrary to the standard analysis of the Rasch model, where all persons are kept in the analysis and badly fittingitems may be removed, this paper studies the alternative model in which a small minority ofpersons has an answer strategy not described by the Rasch model. Such persons are called anomalous or aberrant. From the response vectors consisting ofk symbols each equal to 0 or 1, it is desired to classify each respondent as either anomalous or as conforming to the model. As this model is probabilistic, such a classification will possibly involve false positives and false negatives. Both for the Rasch model and for other item response models, the literature contains several proposals for a person fit index, which expresses for each individual the plausibility that his/her behavior follows the model. The present paper argues that such indices can only provide a satisfactory solution to the classification problem if their statistical distribution is known under the null hypothesis that all persons answer according to the model. This distribution, however, turns out to be rather different for different values of the person's latent trait value. This value will be called ability parameter, although our results are equally valid for Rasch scales measuring other attributes.As the true ability parameter is unknown, one can only use its estimate in order to obtain an estimated person fit value and an estimated null hypothesis distribution. The paper describes three specifications for the latter: assuming that the true ability equals its estimate, integrating across the ability distribution assumed for the population, and conditioning on the total score, which is in the Rasch model the sufficient statistic for the ability parameter.Classification rules for aberrance will be worked out for each of the three specifications. Depending on test length, item parameters and desired accuracy, they are based on the exact distribution, its Monte Carlo estimate and a new and promising approximation based on the moments of the person fit statistic. Results for the likelihood person fit statistic are given in detail, the methods could also be applied to other fit statistics. A comparison of the three specifications results in the recommendation to condition on the total score, as this avoids some problems of interpretation that affect the other two specifications.The authors express their gratitude to the reviewers and to many colleagues for comments on an earlier version.     

Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task

The purpose of the popular Iowa gambling task is to study decision making deficits in clinical populations by mimicking real-life decision making in an experimental context. Busemeyer and Stout [Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253-262] proposed an “Expectancy Valence” reinforcement learning model that estimates three latent components which are assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, and we finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence.     

Applying memories of reinforcement outcomes mainly to Pavlovian conditioning

A theory devised initially on the basis of instrumental reward schedule data, such as the PREE, was extended to deal with various Pavlovian findings. These Pavlovian findings include blocking, unblocking, relative validity, positive and negative patterning, renewal, reinstatement, reacquisition, and inhibition. In addition, the sequential model was discussed in relation to the rule-learning view of Hulse and Dorsky (1977) employing data from a report by Bloom and Capaldi (1961).     

Examining the Relationships Between the Justice Facets and Turnover Intent: The Mediating Roles of Overall Justice and Psychological Strain

We examined how interpersonal justice from coworkers, the traditional justice facets (i.e., distributive, procedural, interpersonal, informational), and overall justice perceptions relate to employee psychological strain and turnover intentions in Canadian Armed Forces personnel. Specifically, we hypothesized that overall justice would mediate the relationships between the justice facets (including coworker justice) and strain, and strain would mediate the relationship between overall justice and turnover intent. We used a cross-sectional correlational design with personnel from 2 military units (total N = 218) as our sample. A 2-step structural equation modeling technique was used to evaluate our hypotheses. Support for our hypotheses was obtained with 1 exception: informational justice did not predict overall justice judgments. Our results suggest that employees’ reactions to individual justice events and different sources of justice exert their effects on strain and turnover intent indirectly through their impact on a global evaluation of justice in their work environment.     

The response dynamics of preferential choice

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one “online” preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.     

Young children's interpersonal trust consistency as a predictor of future school adjustment

Young children's interpersonal trust consistency was examined as a predictor of future school adjustment. One hundred and ninety two (95 male and 97 female, M_age = 6 years 2 months, SD_age = 6 months) children from school years 1 and 2 in the United Kingdom were tested twice over one year. Children completed measures of peer trust and school adjustment and teachers completed the Short-Form Teacher Rating Scale of School Adjustment. Longitudinal quadratic relationships emerged between consistency of children's peer trust beliefs and peer-reported trustworthiness and school adjustment and these varied according to social group, facet of trust, and indicator of school adjustment. The findings support the conclusion that interpersonal trust consistency, especially for secret-keeping, predicts aspects of young children's school adjustment.