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Cross-cultural research in social and behavioral sciences has expanded hugely over the past 50 years, but progress is currently hampered by a lack of appreciation of the profoundly differing principles and goals of two distinct traditions. The first is the main variant of cross-cultural psychology (CCP), focusing on how culture shapes individual psychological functioning. The second was pioneered by Hofstede. It studies societal differences, and we name it "comparative culturology" (CC). We explain how these two paradigms differ. CCP is grounded in psychology and typically looks for unobservable individual-level constructs, which supposedly exist independently of their measurement, to provide understanding of individual differences as affected by culture. CC is an interdisciplinary field whose roots and impact span sociology, anthropology, political science, economics, management studies, psychology, and beyond. CC measures cultural dimensions as group-level constructs created by researchers, which are best understood as ecological manifolds : conglomerates of conceptually and statistically associated variables (not necessarily held together by a single underlying factor) that collectively explain national (and other group) differences. Given these paradigmatic distinctions, the two fields need not, and cannot, use the same validation methods. They should co-exist and collaborate based on mutual appreciation of their differences, without attempts by either field to impose its idiosyncrasies on the other. [ABSTRACT FROM AUTHOR]

This study considers a time-varying coefficient additive hazards model with latent variables to examine potential observed and latent risk factors for survival of interest. The model consists of two parts: confirmatory factor analysis to measure each latent factor through multiple observable variables and a varying coefficient additive hazards model to examine the time-varying effects of the observed and latent risk factors on the hazard function. A hybrid estimation procedure that combines the expectation-maximum algorithm and corrected estimating equation method is developed to estimate the unknown parameters and nonparametric cumulative hazard functions. The consistency and asymptotic normality of the proposed estimators are established, and the pointwise confidence intervals and general confidence bands of the nonparametric functions are constructed accordingly. A satisfactory performance of the proposed method is demonstrated through simulation studies. An application to a study of chronic kidney disease for Chinese type 2 diabetes patients is presented. [ABSTRACT FROM AUTHOR]

Moderation testing through latent factor models is relatively underutilized in hospitality and tourism research. The purpose of this research is to highlight the differences in the treatment of measurements of reflective constructs as composite indices versus latent factors in moderating effect tests in hospitality research. For this research, we build our primer on the investigation of the differences in customer satisfaction with the perceived entertainment experience at a hospitality/tourism attraction, contingent on customers' personality trait extraversion, borrowed from the Big-Five mini marker inventory. Our findings illustrate the consequences of the measurement conceptualization and the representation of constructs in statistical models with interaction effects. While using composites simplifies the estimation of the regression paths and provides a reasonable sense of the direction of the effect and its statistical significance, it is not always aligned with the theoretical and conceptual underpinning of the employed constructs. A statistical model with composites may underestimate an interaction effect, whereas a model with a dichotomized moderator may overestimate the interaction effect. The findings of this research draw the attention of the hospitality and tourism research community on different representations of reflective constructs in their measurement and statistical models. [ABSTRACT FROM AUTHOR]

Tools for assessing decoding skill in students attending elementary grades are of fundamental importance for guaranteeing an early identification of reading disabled students and reducing both the primary negative effects (on learning) and the secondary negative effects (on the development of the personality) of this disability. This article presents results obtained by administering existing standardized tests of reading and a new screening procedure to about 1,500 students in the elementary grades in Italy. It is found that variables measuring speed and accuracy in all administered reading tests are not Gaussian, and therefore the threshold values used for classifying a student as a normal decoder or as an impaired decoder must be estimated on the basis of the empirical distribution of these variables rather than by using the percentiles of the normal distribution. It is also found that the decoding speed and the decoding accuracy can be measured in either a 1-minute procedure or in much longer standardized tests. The screening procedure and the tests administered are found to be equivalent insofar as they carry the same information. Finally, it is found that speed and accuracy act as complementary effects in the measurement of decoding ability. On the basis of this last finding, the study introduces a new composite indicator aimed at determining the student’s performance, which combines speed and accuracy in the measurement of decoding ability. [ABSTRACT FROM PUBLISHER]

The network perspective represents a novel contribution to personality theory by conceptualising personality traits as emerging from the mutual dependencies between fundamental and causal affective, behavioural, and cognitive components. We argue that incorporating a more nuanced biological and developmental perspective to causality and a more precise approach to affective, behavioural, cognitive and motivational components may serve to enrich the network perspective. Although the graphical approach to modelling personality is aesthetically pleasing, analytic techniques are not yet available to put network models to the (quantitative) test. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We suggest that the description by Cramer et al. (2012) of traditional models of personality structure does not perfectly reflect the models actually endorsed by researchers. Personality researchers assume that many variables will have considerable secondary loadings and that the major personality factors will not account for all of the covariation among those variables. A model that includes common factors provides a more parsimonious explanation of covariation among personality variables than does a model consisting of network links only. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]