Longitudinal trajectories of bicultural identity integration in recently immigrated Hispanic adolescents: Links with mental health and family functioning

This study examined, in a sample of recently immigrated Hispanic adolescents in Miami and Los Angeles, the extent to which bicultural identity integration (BII; involving the ability to synthesise one's heritage and receiving cultural streams and to identify as a member of both cultures) is best understood as a developmental construct that changes over time or as an individual‐difference construct that is largely stable over time. We were also interested in the extent to which these trajectories predicted mental health and family functioning. Recent‐immigrant 9th graders (N = 302) were assessed 6 times from 9th to 12th grade. Latent class growth analyses using the first 5 timepoints identified 2 trajectory classes—one with lower BII scores over time and another with higher BII scores over time. Higher heritage and US identity at baseline predicted membership in the higher BII class. At the 6th study timepoint, lower BII adolescents reported significantly poorer self‐esteem, optimism, prosocial behaviour and family relationships compared with their higher BII counterparts. These findings are discussed in terms of further research on the over‐time trajectory of biculturalism, and on the need to develop interventions to promote BII as a way of facilitating well‐being and positive family functioning.     

The (Non) Religion of Mechanical Turk Workers

Social science researchers have increasingly come to utilize Amazon's Mechanical Turk (MTurk) to obtain adult, opt‐in samples for use with experiments. Based on the demographic characteristics of MTurk samples, studies have provided some support for the representativeness of MTurk. Others have warranted caution based on demographic characteristics and comparisons of reliability. Yet, what is missing is an examination of the most glaring demographic difference in MTurk—religion. We compare five MTurk samples with a student convenience sample and the 2012 General Social Survey, finding that MTurk samples have a consistent bias toward nonreligion. MTurk surveys significantly overrepresent seculars and underrepresent Catholics and evangelical Protestants. We then compare the religiosity of religious identifiers across samples as well as relationships between religiosity and partisanship, finding many similarities and a few important differences from the general population.     

Group‐based discrimination,national identification,and British Muslims’ attitudes toward non‐Muslims: the mediating role of perceived identity incompatibility

In two studies, we tested a model in which the perceived (in)compatibility of being British and Muslim (identity incompatibility) was expected to mediate between group‐based discrimination and the identifications and attitudes of British Muslims. In Study 1 (N = 76), anti‐Muslim discrimination was associated with lower national identification and more negative attitudes toward non‐Muslims, and these relationships were mediated by perceived identity incompatibility. In Study 2 (N = 70), we additionally found that anti‐Muslim discrimination predicted stronger endorsement of Islamic group rights, and this relationship was also mediated by perceived identity incompatibility. The studies highlight the importance of group‐based discrimination in shaping the perceived (in)compatibility of being British and Muslim, and, in turn, the identifications and attitudes of British Muslims.     

Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure

This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple‐group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy‐to‐use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large.     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.