“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.     

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.     

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.     

Thurstonian modeling of ranking data via mean and covariance structure analysis

Although Thurstonian models provide an attractive representation of choice behavior, they have not been extensively used in ranking applications since only recently efficient estimation methods for these models have been developed. These, however, require the use of special-purpose estimation programs, which limits their applicability. Here we introduce a formulation of Thurstonian ranking models that turns an idiosyncratic estimation problem into an estimation problem involving mean and covariance structures with dichotomous indicators. Well-known standard solutions for the latter can be readily applied to this specific problem, and as a result any Thurstonian model for ranking data can be fitted using existing general purpose software for mean and covariance structure analysis. Although the most popular programs for covariance structure analysis (e.g., LISREL and EQS) cannot be presently used to estimate Thurstonian ranking models, other programs such as MECOSA already exist that can be straightforwardly used to estimate these models.This paper is based on the author's doctoral dissertation. Ulf Böckenholt was my advisor. The author is indebted to Ulf Böckenholt for his comments on a previous version of this paper and to Gerhard Arminger for his extensive support on the use of MECOSA. The final stages of this research took place while the author was at the Department of Statistics and Econometrics, Universidad Carlos III de Madrid. Conversations with my colleague there, Adolfo Hernández, helped to greatly improve this paper.     

An incomplete data approach to the analysis of covariance structures

In this paper, linear structural equation models with latent variables are considered. It is shown how many common models arise from incomplete observation of a relatively simple system. Subclasses of models with conditional independence interpretations are also discussed. Using an incomplete data point of view, the relationships between the incomplete and complete data likelihoods, assuming normality, are highlighted. For computing maximum likelihood estimates, the EM algorithm and alternatives are surveyed. For the alternative algorithms, simplified expressions for computing function values and derivatives are given. Likelihood ratio tests based on complete and incomplete data are related, and an example on using their relationship to improve the fit of a model is given.This research forms part of the author's doctoral thesis and was supported by a Commonwealth Postgraduate Research Award. The author also wishes to acknowledge the support of CSIRO during the preparation of this paper and the referees' comments which led to substantial improvements.     

SERIOUSLY, BUT NOT LITERALLY: PRAGMATISM AND REALISM IN RELIGION AND SCIENCE

Abstract. Critical realists would have us believe that representations have a connection to the world, that of truth or reference for example, which is independent of their usefulness to us. They would have us believe further that knowledge about this connection serves to put religion and science in their proper places with respect to one another. This essay raises pragmatic objections to these belief's.     

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.     

A note on the unbiased estimation of the intraclass correlation

The intraclass correlation,, is a parameter featured in much psychological research. Two commonly used estimators of, the maximum likelihood and least squares estimators, are known to be negatively biased. Olkin and Pratt (1958) derived the minimum variance unbiased estimator of the intraclass correlation, but use of this estimator has apparently been impeded by the lack of a closed form solution. This note briefly reviews the unbiased estimator and gives a FORTRAN 77 subroutine to calculate it.The first author was supported by an All-University Fellowship from the University of Southern California.     

Easy bayes estimation for rasch-type models

A Bayes estimation procedure is introduced that allows the nature and strength of prior beliefs to be easily specified and modal posterior estimates to be obtained as easily as maximum likelihood estimates. The procedure is based on constructing posterior distributions that are formally identical to likelihoods, but are based on sampled data as well as artificial data reflecting prior information. Improvements in performance of modal Bayes procedures relative to maximum likelihood estimation are illustrated for Rasch-type models. Improvements range from modest to dramatic, depending on the model and the number of items being considered.This research was supported by ORN Contact #00014-86-K0087. We wish to thank Sheng-Hui Chu and Dzung-Ji Lii for providing intelligent and energetic programming support for this article. We also thank one of the reviewers for pointing out several interesting and useful perspectives.     

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.