Network models for social influence processes

This paper generalizes thep* class of models for social network data to predict individual-level attributes from network ties. Thep* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so constructp* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.This research was supported by grants from the Australian Research Council. The authors would like to acknowledge the help of Stanley Wasserman, Janice Langan-Fox and Larry Hubert, and would like to thank four anonymous reviewers for their helpful comments. Earlier versions of this article were presented at the North American Conference of the Psychometric Society, Lawrence, Kansas, June, 1999, and at the Australasian Mathematical Psychology Conference, Brisbane, Australia, December 1999.     

Bayesian modeling of measurement error in predictor variables using item response theory

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between the latent variables and dichotomous observed variables, which may be responses to tests or questionnaires. It will be shown that the multilevel model with measurement error in the observed predictor variables can be estimated in a Bayesian framework using Gibbs sampling. In this article, handling measurement error via the normal ogive model is compared with alternative approaches using the classical true score model. Examples using real data are given.This paper is part of the dissertation by Fox (2001) that won the 2002 Psychometric Society Dissertation Award.     

A hierarchical bayesian statistical framework for response time distributions

This paper provides a statistical framework for estimating higher-order characteristics of the response time distribution, such as the scale (variability) and shape. Consideration of these higher order characteristics often provides for more rigorous theory development in cognitive and perceptual psychology (e.g., Luce, 1986). RT distribution for a single participant depends on certain participant characteristics, which in turn can be thought of as arising from a distribution of latent variables. The present work focuses on the three-parameter Weibull distribution, with parameters for shape, scale, and shift (initial value). Bayesian estimation in a hierarchical framework is conceptually straightforward. Parameter estimates, both for participant quantities and population parameters, are obtained through Markov Chain Monte Carlo methods. The methods are illustrated with an application to response time data in an absolute identification task. The behavior of the Bayes estimates are compared to maximum likelihood (ML) estimates through Monte Carlo simulations. For small sample size, there is an occasional tendency for the ML estimates to be unreasonably extreme. In contrast, by borrowing strength across participants, Bayes estimation shrinks extreme estimates. The results are that the Bayes estimators are more accurate than the corresponding ML estimators.We are grateful to Michael Stadler who allowed us use of his data. This research is supported by (a) National Science Foundation Grant SES-0095919 to J. Rouder, D. Sun, and P. Speckman, (b) University of Missouri Research Board Grant 00-77 to J. Rouder, (c) National Science Foundation grant DMS-9972598 to Sun and Speckman, and (d) a grant from the Missouri Department of Conservation to D. Sun.     

Models of causation and the semantics of causal verbs

This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation (; ), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.     

Developmental continuity in the processes that underlie spatial recall

This study investigated whether children's spatial recall performance shows three separable characteristics: (1) biases away from symmetry axes (geometric effects); (2) systematic drift over delays; and (3) biases toward the exemplar distribution experienced in the task (experience-dependent effects). In Experiment 1, the location of one target within each geometric category was varied. Children's responses showed biases away from a midline axis that increased over delays. In Experiment 2, multiple targets were placed within each category at the same locations used in Experiment 1. After removing geometric effects, 6-year-olds'--but not 11-year-olds'--responses were biased toward the average remembered location over learning. In Experiment 3, children responded to one target more frequently than the others. Both 6- and 11-year-olds showed biases toward the most frequent target over learning. These results provide a bridge between the performance of younger children and adults, demonstrating continuity in the processes that underlie spatial memory abilities across development.     

Bayesian hierarchical response time modelling—A tutorial

Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques facilitate estimation of response time models. Implementations of these models in standard statistical software, however, are still sparse. In this accessible tutorial, we discuss one of the most common response time models—the lognormal response time model—embedded in the hierarchical framework by van der Linden (2007). We provide detailed guidance on how to specify and estimate this model in a Bayesian hierarchical context. One of the strengths of the presented model is its flexibility, which makes it possible to adapt and extend the model according to researchers' needs and hypotheses on response behaviour. We illustrate this based on three recent model extensions: (a) application to non-cognitive data incorporating the distance-difficulty hypothesis, (b) modelling conditional dependencies between response times and responses, and (c) identifying differences in response behaviour via mixture modelling. This tutorial aims to provide a better understanding of the use and utility of response time models, showcases how these models can easily be adapted and extended, and contributes to a growing need for these models to answer novel substantive research questions in both non-cognitive and cognitive contexts.     

“Oilfield Trash or Oilfield Treasure? A Pastoral Response to Living in the Extraction Economy of the Permian Basin” by Rev. Dr. Dawn Darwin Weaks

The climate crisis is witnessed on a global scale and it is also experienced in the local communities that work in extractive industries. In this article, a pastor in the Permian Basin explores the term “oilfield trash” as it is used for oilfield workers in the Permian Basin, connecting the epithet with the negative conditions for quality of life there, and comparing it to treatment of oilfield workers in Norway. Treatment of workers is identified as essential to esteem of communities and land. Renaming workers “treasure” in keeping with the tradition of Isaiah 62:4 is identified as part of the healing needed for extractive communities to transition away from fossil fuels. Four avenues of congregational ministry within mining economies are identified, with the way of “partnership” with the workers in the industry recommended as offering hope for churches to help relieve the climate crisis.     

Further evaluation of treatment duration on the resurgence of destructive behavior

Translation of promising procedures for mitigating treatment relapse has received considerable attention recently from researchers across the basic–applied continuum. One procedure that has demonstrated mixed support involves increasing the duration of treatment as a strategy for blunting resurgence. In a recent translational study, Greer et al. (2020) failed to detect a mitigation effect of increased treatment duration on the resurgence of destructive behavior. However, design limitations may have been responsible. The present study corrected these limitations by (a) employing a sequential design to decrease the possibility of multiple-treatment interference, (b) evaluating more treatment durations, (c) arranging treatments of fixed durations, and (d) conducting treatments of more extreme duration in a different clinical sample. Despite these improvements in experimental rigor and the testing of more extreme boundary conditions, the present study also failed to detect a mitigation effect of increased treatment duration. Likely explanations are discussed.     

An irt-based model for dichotomous longitudinal data

The LLRA (linear logistic model with relaxed assumptions; Fischer, 1974, 1977a, 1977b, 1983a) was developed, within the framework of generalized Rasch models, for assessing change in dichotomous item score matrices between two points in time; it allows to quantify change on latent trait dimensions and to explain change in terms of treatment effects, treatment interactions, and a trend effect. A remarkable feature of the model is that unidimensionality of the item set is not required. The present paper extends this model to designs with any number of time points and even with different sets of items presented on different occasions, provided that one unidimensional subscale is available per latent trait. Thus unidimensionality assumptions within subscales are combined with multidimensionality of the item set. Conditional maximum likelihood methods for parameter estimation and hypothesis testing are developed, and a necessary and sufficient condition for unique identification of the model, given the data, is derived. Finally, a sample application is presented.To my friend Josef Roppert who has taught me how to apply statistical reasoning to substantive problems.This research was supported in part by Österreichische Forschungsgemeinschaft under grant No. 01/0054. The author wishes to thank B. Wild for the numerical computation of the sample application in section 5.     

Comment on “Correspondence analysis used complementary to loglinear analysis”

In van der Heijden and de Leeuw (1985) it was proposed to use loglinear analysis to detect interactions in a multiway contingency table, and to explore the form of these interactions with correspondence analysis. After performing the exploratory phase of the analysis, we will show here how the results found in this phase can be used for confirmation.This research was conducted while the authors were visiting the Laboratoire de Statistique et Probabilité, Universite Paul Sabatier, Toulouse. This visit was partly made possible by a joint grant of the Netherlands Organisation for the Advancement of Pure Research (Z.W.O.) and the French National Center for Scientific Research (C.N.R.S.). For helpful comments, the authors are indebted to H. Caussinus, J. de Leeuw, and two anonymous reviewers.