Can Robert Adams Survive Moral Twin Earth?

Richard Boyd and Robert Adams have both developed semantic accounts of moral terms based on Hilary Putnam's causal regulation theory for natural kind terms, according to which the terms in question refer to the properties which predominantly causally regulated the terms. However, Terence Horgan and Mark Timmons have mounted an objection to Boyd's semantics—their Moral Twin Earth argument. If this argument is successful against Boyd then it might be thought that it should also be successful against Adams, given the similarity between their semantic accounts. I will argue in this essay that Adams's semantics is sufficiently different from Boyd's to enable him to survive Moral Twin Earth, but that he is vulnerable to a modified version of Moral Twin Earth that I describe.     

Psychological Variables Associated with Pain Perceptions Among Individuals with Chronic Spinal Cord Injury Pain

Structural equation modeling was used to examine the relationships between selected psychological variables and pain perceptions in 103 individuals experiencing chronic pain following traumatic spinal cord injury (SCI). Previous studies have suggested strong relationships between psychological variables and chronic SCI pain, but further delineation of such relationships is needed in order ultimately to develop more effective pain management strategies for individuals afflicted with such pain. Anger was found to be significantly related to perceptions of pain (p < .05), but neither guilt nor anger suppression was significantly associated with perceived pain. Internal health locus of control was associated with decreased pain perceptions (p < .05), but there was no significant relationship between internal health locus of control and anger. Punishing responses from significant others to pain complaints were related to feelings of guilt (p < .05) and perceived pain (p < .05), but this relationship was not mediated by guilt.     

What Religion Affords Grassroots NGOs: Frames,Networks, Modes of Action

The international relief and development sector has swelled in the last two decades thanks to American grassroots NGOs: groups that rely on volunteer labor and individual contributions, often on budgets of less than $25,000 a year. Most of these organizations reject the label of “faith‐based organization,” yet they find the symbolic and material resources of religion indispensable. Religion affords these NGOs three kinds of resources to meet their distinct organizational needs. First, it provides frames, or ways of thinking and speaking about relief and development work that imbue it with legitimacy. Next, religion offers networks that provide money, volunteers, and entrée into aid‐receiving communities. Finally, religion affords familiar modes of action that link the NGO, supporters, and local aid recipients. I support these claims with LDA topic modeling (a computerized method of text analysis), content analysis of websites, and in‐depth interviews with 43 informants.     

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.     

A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness,Kurtosis, and Covariance Matrix

We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empirically that popular test statistics in covariance analysis tend to reject true models more often under the IG transform than under the VM transform. This implies that overly optimistic evaluations of estimators and fit statistics in covariance structure analysis may be tempered by including the IG transform for nonnormal data generation. We provide an implementation of the IG transform in the R environment.     

Accounting for Parcel-Allocation Variability in Practice: Combining Sources of Uncertainty and Choosing the Number of Allocations

Item parceling remains widely used under conditions that can lead to parcel-allocation variability in results. Hence, researchers may be interested in quantifying and accounting for parcel-allocation variability within sample. To do so in practice, three key issues need to be addressed. First, how can we combine sources of uncertainty arising from sampling variability and parcel-allocation variability when drawing inferences about parameters in structural equation models? Second, on what basis can we choose the number of repeated item-to-parcel allocations within sample? Third, how can we diagnose and report proportions of total variability per estimate arising due to parcel-allocation variability versus sampling variability? This article addresses these three methodological issues. Developments are illustrated using simulated and empirical examples, and software for implementing them is provided.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.