A house divided? The psychology of red and blue America

Recently it has become commonplace in America for commentators and the public to use the terms "red" and "blue" to refer to perceived cultural differences in America and American politics. Although a political divide may exist in America today, these particular terms are inaccurate and reductive. This article presents research from social psychology demonstrating that the increased use of these terms is likely to increase the conflict between political groups in America by making political conflict salient in nonpolitical contexts, reducing the ability of Americans to form multifaceted complex identities, pushing Americans to misperceive political in-groups and out-groups, and contributing to a "spiral of silence." An alternative model for discussing cultural differences is proposed.     

A dynamical model of general intelligence: the positive manifold of intelligence by mutualism

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.     

Representation of lexical form: evidence from studies of sublexical ambiguity

The authors examined the role of intermediate, sublexical representations in spoken word perception. In particular, they tested whether flaps, which are neutralized allophones of intervocalic /t/s and /d/s, map onto their underlying phonemic counterparts. In 2 shadowing tasks, the authors found that flaps primed their carefully articulated counterparts, and vice versa. Because none of the flapped stimuli were lexically ambiguous (e.g., between rater and raider), these results provide evidence that such priming is sublexically mediated. Therefore, the current study provides further insights into when underlying form-based representations are activated during spoken word processing. In particular, the authors argue that phonological ambiguity, inherent in their flapped stimuli, is one of the conditions leading to the activation of underlying representations.     

A cross-national perspective on school bullying in Northern Ireland: a supplement to Smith, et al. (1999)

There is great value in exploring the prevalence of school bullying from a cross-national perspective. Smith, Morita, Junger-Tas, Olweus, Catalano, and Slee in 1999 presented a cross-national perspective on the nature, prevalence, and correlates of school bullying that encompassed a wide range of countries. However, Northern Ireland was not included, despite potentially being an important country to include, given its volatile social, ethnic, and religious history--leading to the concern that the population has become somewhat habituated to low level aggression. Thus, the present paper provides a review of the current literature on school bullying in the Northern Ireland school system. Evidence presented suggests that the incidence of school bullying in Northern Ireland may be higher than that in the rest of Ireland and the United Kingdom.     

On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Investigating sources of within- and between-group differences and measurement invariance (MI) across groups is fundamental to any meaningful group comparison based on observed test scores. It is shown that by placing certain restrictions on the multigroup confirmatory factor model, it is possible to investigate the hypothesis that within- and between-group differences are due to the same factors. Moreover, the modeling approach clarifies that absence of measurement bias implies common sources of within- and between-group variation. It is shown how the influence of background variables can be incorporated in the model. The advantages of the modeling approach as compared with other commonly used methods for group comparisons is discussed and illustrated by means of an analysis of empirical data.     

Using Two-Level Factor Analysis to Test for Cluster Bias in Ordinal Data

The test for cluster bias is a test of measurement invariance across clusters in 2-level data. This article examines the true positive rates (empirical power) and false positive rates of the test for cluster bias using the likelihood ratio test (LRT) and the Wald test with ordinal data. A simulation study indicates that the scaled version of the LRT that accounts for nonnormality of the data gives untrustworthy results, whereas the unscaled LRT and the Wald test have acceptable false positive rates and perform well in terms of empirical power rate if the amount of cluster bias is large. The test for cluster bias is illustrated with data from research on teacher-student relations.     

Review of the theoretical underpinnings of loyalty programs

A review of the extant literature reveals that the theoretical underpinnings of the majority of loyalty program research rest on psychological mechanisms from three specific domains—status, habit, and relational. We propose that to understand how loyalty programs actually work, a broader, more holistic research perspective is needed to account for the simultaneous effects across these three theoretical domains as well as both cross-customer and temporal effects. The contribution of this approach is a fresh research agenda advanced in 15 research propositions.     

Fitting multivariage normal finite mixtures subject to structural equation modeling

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.