Notes

Detection of small goals in the infrared: a good home look for as well as keep track of training.
Results demonstrated an overall sensitivity to the Hindi contrast in Experiment 2a. Bilingual infants tested in Experiment 2b were not sensitive to the Hindi contrast. Findings are discussed with reference to discontinuities in the growth of a phonological lexicon as well as possible mechanisms elicit nonnative sensitivity in word learning. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Black Lives Matter (BLM) has profoundly shifted public and political discourse about race in the United States and thus the broader sociopolitical landscape in which children learn about race and their own racial identities. A sample of Black, White, and Multiracial children (N = 100; Mage = 10.18 years old) were interviewed about their racial identities in 2014 and again in 2016. During these 2 years, BLM surged with the National March on Washington, widespread news coverage of multiple cases of police brutality, and a highly racialized presidential election. The current analysis examines longitudinal change in children's racial identity narratives across these two time points with attention to the role of BLM. Qualitative interview analyses show that (a) the importance of racial identity increased among Black and Multiracial (but not White) children, and (b) the content of children's race narratives shifted to include BLM-related themes and more discussions of race as interpersonal and structural (not just individual). We discuss age-related changes and how to conceptualize maturation during significant sociopolitical moments, like the current one, in relation to racial identity development. (PsycInfo Database Record (c) 2020 APA, all rights reserved).The COVID-19 crisis has compelled many organizations to implement full-time telework for their employees in a bid to prevent a transmission of the virus. At the same time, the volatile COVID-19 situation presents unique, unforeseen daily disruptive task setbacks that divert employees' attention from routinized work tasks and require them to respond adaptively and effortfully. Yet, little is known about how telework employees react to such complex demands and regulate their work behaviors while working from home. Drawing on Hobfoll's (1989) conservation of resources (COR) theory, we develop a multilevel, two-stage moderated-mediation model arguing that daily COVID-19 task setbacks are stressors that would trigger a resource loss process and will thus be positively related to the employee's end-of-day emotional exhaustion. The emotionally exhausted employee then enters a resource preservation mode that precipitates a positive relationship between end-of-day exhaustion and next-day work withdrawal behaviors. selleck chemical Based on COR, we also predict that the relation between daily COVID-19 task setbacks and exhaustion would be more positive in telework employees who have higher (vs. lower) task interdependence with coworkers, but organizations could alleviate the positive relation between end-of-day exhaustion and next-day work withdrawal behavior by providing employees with higher (vs. lower) telework task support. We collected daily experience-sampling data over 10 workdays from 120 employees (Level 1, n = 1,022) who were teleworking full-time due to the pandemic lockdown. The results generally supported our hypotheses, and their implications for scholars and managers during and beyond the pandemic are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).In order to combat the spread of the novel coronavirus, the Centers for Disease Control and Prevention (CDC) has developed a list of recommended preventative health behaviors for Americans to enact, including social distancing, frequent handwashing, and limiting nonessential trips from home. Drawing upon scarcity theory, the purpose of this study was to examine whether the economic stressors of perceived job insecurity and perceived financial insecurity are related to employee self-reports of enacting such behaviors. Moreover, we tested propositions regarding the impact of two state-level contextual variables that may moderate those relationships the generosity of unemployment insurance benefits and extensiveness of statewide COVID-19-related restrictions. Using a multilevel data set of N = 745 currently employed U.S. workers nested within 43 states, we found that both job insecurity and financial insecurity were negatively related to the enactment of the CDC-recommended guidelines. However, the state-level variables acted as cross-level moderators, such that the negative relationship between job insecurity and compliance with the CDC guidelines was attenuated within states that have a more robust unemployment system. However, working in a state with more extensive COVID-19 restrictions seemed to primarily benefit more financially secure workers. When statewide policies were more restrictive, employees reporting more financial security were more likely to enact the CDC-recommended guidelines compared to their financially insecure counterparts. We discuss these findings in light of the continuing need to develop policies to address the public health crisis while also protecting employees facing economic stressors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Psychological research often builds on between-group comparisons of (measurements of) latent variables; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same latent variable(s) are measured in exactly the same way across all groups (i.e., measurement invariance). Otherwise, one would be comparing apples and oranges. Nowadays, measurement invariance is often tested across a large number of groups by means of multigroup factor analysis. When the assumption is untenable, one may compare group-specific measurement models to pinpoint sources of noninvariance, but the number of pairwise comparisons exponentially increases with the number of groups. This makes it hard to unravel invariances from noninvariances and for which groups they apply, and it elevates the chances of falsely detecting noninvariance. An intuitive solution is clustering the groups into a few clusters based on the measurement model parameters. Therefore, we present mixture multigroup factor analysis (MMG-FA) which clusters the groups according to a specific level of measurement invariance.
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