Life satisfaction across adulthood: different determinants at different ages?

It is likely that with aging and changing life circumstances, individuals’ values shift in systematic ways, and that these shifts may be accompanied by shifts in the determinants of their subjective judgments of well being. To examine this possibility, the relations among the Satisfaction With Life Scale (SWLS) and a number of personality, affect, demographic, and cognitive variables were examined in a sample of 818 participants between the ages of 18 and 94. The results indicated that although many variables had significant zero-order correlations with the SWLS, only a few variables had unique utility in predicting life satisfaction. Invariance analyses indicated that while the qualitative nature of life satisfaction remains constant across adult age, the influence of fluid intelligence on judgments of life satisfaction declines with age. In contrast, negative affect is negatively associated with life satisfaction consistently across the adult age span.     

Child Care in The Netherlands: Trends in Quality Over the Years 1995-2005

The authors assessed the quality of child care in a representative national sample of 42 child-care centers in the Netherlands and compared it with the quality of care that researchers have found using similar samples in 1995 (M. H. van IJzendoorn, L. W. C. Tavecchio, G. J. J. M. Stams, M. J. E. Verhoeven, & E. J. Reiling, 1998) and 2001 (M. J. J. M. Gevers Deynoot-Schaub & J. M. A. Riksen-Walraven, 2005). In the present study, results showed a low level of overall process quality for the 2005 sample, measured by the Infant/Toddler Environment Rating Scale-Revised (T. Harms, D. Cryer, & R. M. Clifford, 2003) and the Early Childhood Environment Rating Scale-Revised (T. Harms, R. M. Clifford, & D. Cryer, 1998). The present authors found a significant decline in process quality in comparison with the 1995 and 2001 findings. They concluded that, from an international perspective, the Netherlands has lost its leading position in child-care quality compared with that from 10 years ago.     

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.     

A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

Analysis of the factor structure of the Sociocultural Attitudes Towards Appearance Questionnaire (SATAQ-3) in Spanish secondary-school students through exploratory structural equation modeling

The aims of the present study were: (1) to assess the factor structure of the SATAQ-3 in Spanish secondary-school students by means of exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) models; and (2) to study its invariance by sex and school grade. ESEM is a technique that has been proposed for the analysis of internal structure that overcomes some of the limitations of EFA and CFA. Participants were 1559 boys and girls in grades seventh to tenth. The results support the four-factor solution of the original version, and reveal that the best fit was obtained with ESEM, excluding Item 20 and with correlated uniqueness between reverse-keyed items. Our version shows invariance by sex and grade. The differences between scores of different groups are in the expected direction, and support the validity of the questionnaire. We recommend a version excluding Item 20 and without reverse-keyed items.     

Identifying Variables Responsible for Data not Missing at Random

When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology. A survey or questionnaire may contain many items; certain items may be responsible for NMAR values in other items. The paper develops statistical procedures to identify the responsible items. By comparing ML estimates (MLE), statistics are developed to test whether the MLEs are changed when excluding items. The items that cause a significant change of the MLEs are responsible for the NMAR mechanism. Normal distribution is used for obtaining the MLEs; a sandwich-type covariance matrix is used to account for distribution violations. The class of nonnormal distributions within which the procedure is valid is provided. Both saturated and structural models are considered. Effect sizes are also defined and studied. The results indicate that more missing data in a sample does not necessarily imply more significant test statistics due to smaller effect sizes. Knowing the true population means and covariances or the parameter values in structural equation models may not make things easier either. The research was supported by NSF grant DMS04-37167, the James McKeen Cattell Fund.     

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.     

绝对音高加工的认知神经机制

绝对音高(absolute pitch,AP)是一种比较罕见的音高加工能力,具有特殊的认知和神经机制。事件相关电位研究表明AP音乐家进行音高命名时,工作记忆参与较少但涉及多个认知策略。功能神经成像研究发现左侧额叶背侧后部和左侧颞叶平面对AP音乐家非常重要,而准AP音乐家(quasi-AP)的某些右侧脑区的参与则反映其增加的音高加工负荷和难度。结构神经成像研究发现AP音乐家具有特殊的灰质结构形态及白质连接。未来研究有待将AP能力进一步分为"具有相对音高能力"与"没有相对音高能力"两类并观察相应的认知神经机制,并通过影像基因组学来探索基因多态性对AP能力的影响,以及有必要观察以声调语言为母语的音乐家进行音高加工的神经机制。     

Essential independence and likelihood-based ability estimation for polytomous items

A definition ofessential independence is proposed for sequences of polytomous items. For items satisfying the reasonable assumption that the expected amount of credit awarded increases with examinee ability, we develop a theory ofessential unidimensionality which closely parallels that of Stout. Essentially unidimensional item sequences can be shown to have a unique (up to change-of-scale) dominant underlying trait, which can be consistently estimated by a monotone transformation of the sum of the item scores. In more general polytomous-response latent trait models (with or without ordered responses), anM-estimator based upon maximum likelihood may be shown to be consistent for under essentially unidimensional violations of local independence and a variety of monotonicity/identifiability conditions. A rigorous proof of this fact is given, and the standard error of the estimator is explored. These results suggest that ability estimation methods that rely on the summation form of the log likelihood under local independence should generally be robust under essential independence, but standard errors may vary greatly from what is usually expected, depending on the degree of departure from local independence. An index of departure from local independence is also proposed.This work was supported in part by Office of Naval Research Grant N00014-87-K-0277 and National Science Foundation Grant NSF-DMS-88-02556. The author is grateful to William F. Stout for many helpful comments, and to an anonymous reviewer for raising the questions addressed in section 2. A preliminary version of section 6 appeared in the author's Ph.D. thesis.     

Comparing self-esteem and perceived control as predictors of first-year college students’ academic achievement

Due to its widespread popularity, self-esteem is continually being promoted to students despite limited empirical support for its effectiveness in improving their academic achievement. As a result, constructs that are potentially more salient to academic performance, such as perceived control, have gone relatively unnoticed. Although past research has examined the link between students’ academic achievement and either their self-esteem or perceived control, few studies have compared both constructs simultaneously to elucidate which one is more important to academic success. This longitudinal study directly contrasted the effects of self-esteem and perceived control on the academic performance of 802 first-year college students. After accounting for incoming ability (high school grades), age, and gender, a structural equation model showed perceived control positively predicted students’ GPA. In contrast, the predictive effect of self-esteem on GPA was non-existent. Findings indicate that compared to self-esteem, perceived control is a more powerful predictor of first-year college students’ GPA. Implications for utilizing educational interventions to boost perceived control among college students are discussed.