Social status and friendship in peer victimization trajectories

Background/Objective: Most studies have evaluated victimization at a single time point, making it difficult to determine the impact of the time during which an individual is victimized. This longitudinal study aims to examine the differences in the levels of social status (social preference and perceived popularity) and friendship in peer victimization trajectories, and to analyse if there were changes over time in the levels of social status and friendship in each trajectory. Method: The final sample was composed of 1,239 students (49% girls) with ages between 9 and 18 (M = 12.23, SD = 1.73), from 22 schools in southern Spain. Peer nominations were collected. Results: The General Linear Model results associated the highest levels of social preference, perceived popularity and friendship with the sporadic victimization profile and the lowest levels of these dimensions with the stable profile. Conclusions:The results are discussed based on important personal aspects of stable victimization that confirms social rejection, unpopularity, and the low social support that victimization causes. This contribution is discussed in terms of health and social welfare in adolescence.     

Immigration Enforcement Policies and the Mental Health of US Citizens: Findings from a Comparative Analysis

We examined the differential impact of having a family member, friend, or co-worker/community member detained or deported on the mental health of US citizens. In 2019, a sample of 3446 adult participants of White, Black, and Latinx racial/ethnic descent were recruited to complete an online questionnaire. Participants completed the Patient Health Questionnaire-4 to screen for anxiety, depression, and psychological distress. Analyses were restricted to US citizens (n = 3282). Multivariable logistic and linear regressions were conducted to examine the mental health of US citizens who reported personally knowing a migrant who has been detained or deported and by their relationship to the migrant, overall and among Latinxs only. Among US citizens, 32% reported personally knowing someone who has been detained or deported. In multivariable analyses, US citizens who personally knew a detained or deported migrant were more likely to report anxiety, depression, and greater psychological distress. Associations were more pronounced among those who reported having a family member detained or deported. US-citizen Latinxs with social ties to migrants who have been detained or deported were especially more likely to report poor mental health than White and Latinx participants who did not personally know a migrant who has been detained or deported. It is critical that policy makers consider the potential mental health harms on migrants and its own citizens when designing policies targeting migrant communities.     

Emotional adjustment in victims and perpetrators of cyberbullying and traditional bullying

Given the rise in cyberbullying among secondary education students and the importance of certain psychological adjustment variables for the comprehension of this type of violent behavior, both in bullies and in victims, the purpose of the present study was to analyse the emotional adjustment of those involved in cyber- and traditional bullying. The adjustment variables studied were self-concept, perceived stress, loneliness, depressive symptomatology, social anxiety, life satisfaction, and emotional intelligence. Using a sample of 1318 adolescents (47% boys), aged between 11 and 17 years, four groups were established to compare victims and cybervictims (uninvolved students, traditional victims, cybervictims, and traditional–cybervictims). The analysis of variance showed that students who performed the same role (bully or victim) in both contexts (at school and online). In particular, those who suffered traditional or cyberbullying or both conjointly presented lower scores in physical and social self-concept, life satisfaction, emotional clarity, and emotion regulation, as well as higher scores in perceived stress, loneliness, depression, and social anxiety. In bullies—traditional, cyberbullying or both simultaneously—higher scores were observed in perceived stress, loneliness, depressive symptomatology, fear of negative assessment, avoidance, and general social anxiety, and lower scores in the dimensions of academic and family self-concept, life satisfaction, emotional clarity, and emotion regulation. In general, the findings indicate that students who were involved in bullying situations, both victims and bullies, presented more damaged emotional profiles than those who are uninvolved, especially students who performed the same role (bully or victim) in both contexts (at school and online).

     

Remembering changes: repetition effects in face recollection.

Two experiments examined effects of repetition and change on states of awareness in face recognition. Participants studied repeatedly presented photographs of faces, with the second presentation following either immediately after the first presentation (massed repetition) or following six intervening items (spaced repetition). To manipulate perceptual change, each repeated face was either identical or a mirror image of the first presentation. Subsequently, when recognising a face, participants indicated whether they consciously recollected its prior occurrence ("remembering") or recognised it on the basis of familiarity ("knowing"). Changes in appearance between repeated faces enhanced remember, but not know, responses, and these effects were accentuated for spaced, rather than massed, repetition. These findings suggest that distinctiveness of encoding supports the phenomenological experience of conscious remembering.     

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.     

Concise formulas for the standard errors of component loading estimates

Concise formulas for the asymptotic standard errors of component loading estimates were derived. The formulas cover the cases of principal component analysis for unstandardized and standardized variables with orthogonal and oblique rotations. The formulas can be used under any distributions for observed variables as long as the asymptotic covariance matrix for sample covariances/correlations is available. The estimated standard errors in numerical examples were shown to be equivalent to those by the methods using information matrices.The author is indebted to anonymous reviewers for the corrections and suggestions on this study, which have led to improvements of earlier versions of this article.     

A unified approach to exploratory factor analysis with missing data,nonnormal data,and in the presence of outliers

Factor analysis is regularly used for analyzing survey data. Missing data, data with outliers and consequently nonnormal data are very common for data obtained through questionnaires. Based on covariance matrix estimates for such nonstandard samples, a unified approach for factor analysis is developed. By generalizing the approach of maximum likelihood under constraints, statistical properties of the estimates for factor loadings and error variances are obtained. A rescaled Bartlett-corrected statistic is proposed for evaluating the number of factors. Equivariance and invariance of parameter estimates and their standard errors for canonical, varimax, and normalized varimax rotations are discussed. Numerical results illustrate the sensitivity of classical methods and advantages of the proposed procedures.This project was supported by a University of North Texas Faculty Research Grant, Grant #R49/CCR610528 for Disease Control and Prevention from the National Center for Injury Prevention and Control, and Grant DA01070 from the National Institute on Drug Abuse. The results do not necessarily represent the official view of the funding agencies. The authors are grateful to three reviewers for suggestions that improved the presentation of this paper.     

Category induction from distributional cues in an artificial language

The ability to identify the grammatical category of a word (e.g., noun, verb, adjective) is a fundamental aspect of competence in a natural language. Children show evidence of categorization by as early as 18 months, and in some cases younger. However, the mechanisms that underlie this ability are not well understood. The lexical co-occurrence patterns of words in sentences could provide information about word categories--for example, words that follow the in English often belong to the same category. As a step in understanding the role distributional mechanisms might play in language learning, the present study investigated the ability of adults to categorize words on the basis of distributional information. Forty participants listened for approximately 6 min to sentences in an artificial language and were told that they would later be tested on their memory for what they had heard. Participants were next tested on an additional set of sentences and asked to report which sentences they recognized from the first 6 min. The results suggested that learners performed a distributional analysis on the initial set of sentences and recognized sentences on the basis of their memory of sequences of categories of words. Thus, mechanisms that would be useful in natural language learning were shown to be active in adults in an artificial language learning task.