Review of Evnine,Simon J., Epistemic Dimensions of Personhood,New York: Oxford University Press, 2008, pp. viii + 176, £32.50 (cloth)

The different occurrences of a word in a sentence cannot be identified with the one word type, nor with its many tokens. What then are occurrences of a word? How can one type occur more than once in another type? Is the conception of ‘structural universals’ that leads to these questions incoherent, as Lewis maintained? I argue against the answer Wetzel suggested, which identifies sentences with functions from numbers to expressions, and propose instead that occurrences of one type in another are subtypes with different relative positions in the whole. Types have subtypes as well as tokens. Occurrences in non-symmetrical types like sequences are individuated by their relative positions in the type. Occurrences in a symmetrical whole have different positions but stand in the same relations to the other components. They are numerically different subtypes that do not differ from each other qualitatively.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

公平感对社会认知的影响:经济因素的调节作用

目的及方法:基于中国综合社会调查2010年数据,采用多层结构方程模型,探究中国民众公平感对社会认知的作用,及经济因素在其中的调节效应。研究结果:第一,公平感越高,则社会冲突感越低,外在政治效能感越高,越倾向于对社会不平等进行内部归因,即对于社会的整体认知更加积极; 第二,经济因素能调节公平感与社会不平等内归因的关系,具体而言,经济水平越低,则公平感对于社会不平等的内部归因正向预测作用越大。     

On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

The Process Through Which an Advocacy Intervention Resulted in Positive Change for Battered Women Over Time

A prior experimental evaluation of a community-based advocacy program for women with abusive partners demonstrated positive change in the lives of women even 2 years postintervention (C. M. Sullivan & D. I. Bybee, 1999). The current study explored the complex mediational process through which this change occurred, using longitudinal structural equation modeling and formal tests of mediation. As hypothesized, the advocacy intervention first resulted in women successfully obtaining desired community resources and increasing their social support, which enhanced their overall quality of life. This improvement in well-being appeared to serve as a protective factor from subsequent abuse, as women who received the intervention were significantly less likely to be abused at 2-year follow-up compared with women in the control condition. Increased quality of life completely mediated the impact of the advocacy intervention on later reabuse. Discussion places advocacy for women in the context of other efforts that are needed to build an effective community response to preventing intimate violence against women.     

Beyond bruises and broken bones: the joint effects of stress and injuries on battered women's health

We investigated the joint mediating effects of injuries and stress on the relationship between abuse and women's health. A community sample of 397 women, half of whom had been assaulted by an intimate partner within the prior 6 months, was interviewed about their experience of intimate partner violence, injuries, stress, income, depression, and physical health problems. Structural equation modeling techniques confirmed the complex model of hypothesized indirect effects of abuse on women's physical health problems through injuries, stress, and depression. Stress accounted for 80% of the indirect effect of abuse on women's physical health. Its direct effect on physical health was somewhat larger than its indirect effect through depression, but both processes played a key role in determining the effect of abuse on women's physical health problems. Furthermore, abuse was a stronger predictor of women's stress than was poverty. Implication and future research are discussed.     

Model comparison of nonlinear structural equation models with fixed covariates

Recently, it has been recognized that the commonly used linear structural equation model is inadequate to deal with some complicated substantive theory. A new nonlinear structural equation model with fixed covariates is proposed in this article. A procedure, which utilizes the powerful path sampling for computing the Bayes factor, is developed for model comparison. In the implementation, the required random observations are simulated via a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm. It is shown that the proposed procedure is efficient and flexible; and it produces Bayesian estimates of the parameters, latent variables, and their highest posterior density intervals as by-products. Empirical performances of the proposed procedure such as sensitivity to prior inputs are illustrated by a simulation study and a real example.This research is fully supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 4346/01H). The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and grateful to ICPSR and the relevant funding agency for allowing use of the data in the example. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Cognitive processing in literate and illiterate subjects: a review of some recent behavioral and functional neuroimaging data

The study of illiterate subjects, which for specific socio-cultural reasons did not have the opportunity to acquire basic reading and writing skills, represents one approach to study the interaction between neurobiological and cultural factors in cognitive development and the functional organization of the human brain. In addition the naturally occurring illiteracy may serve as a model for studying the influence of alphabetic orthography on auditory-verbal language. In this paper we have reviewed some recent behavioral and functional neuroimaging data indicating that learning an alphabetic written language modulates the auditory-verbal language system in a non-trivial way and provided support for the hypothesis that the functional architecture of the brain is modulated by literacy. We have also indicated that the effects of literacy and formal schooling is not limited to language related skills but appears to affect also other cognitive domains. In particular, we indicate that formal schooling influences 2D but not 3D visual naming skills. We have also pointed to the importance of using ecologically relevant tasks when comparing literate and illiterate subjects. We also demonstrate the applicability of a network approach in elucidating differences in the functional organization of the brain between groups. The strength of such an approach is the ability to study patterns of interactions between functionally specialized brain regions and the possibility to compare such patterns of brain interactions between groups or functional states. This complements the more commonly used activation approach to functional neuroimaging data, which characterize functionally specialized regions, and provides important data characterizing the functional interactions between these regions.