Ocean warming undermines the recovery resilience of New England kelp forests following a fishery-induced trophic cascade

Ecological theory predicts that kelp forests structured by trophic cascades should experience recovery and persistence of their foundation species when herbivores become rare. Yet, climate change may be altering the outcomes of top-down forcing in kelp forests, especially those located in regions that have rapidly warmed in recent decades, such as the Gulf of Maine. Here, using data collected annually from 30+ sites spanning >350 km of coastline, we explored the dynamics of Maine's kelp forests in the ~20 years after a fishery-induced elimination of sea urchin herbivores. Although forests (Saccharina latissima and Laminaria digitata) had broadly returned to Maine in the late 20th century, we found that forests in northeast Maine have since experienced slow but significant declines in kelp, and forest persistence in the northeast was juxtaposed by a rapid, widespread collapse in the southwest. Forests collapsed in the southwest apparently because ocean warming has-directly and indirectly-made this area inhospitable to kelp. Indeed, when modeling drivers of change using causal techniques from econometrics, we discovered that unusually high summer seawater temperatures the year prior, unusually high spring seawater temperatures, and high sea urchin densities each negatively impacted kelp abundance. Furthermore, the relative power and absolute impact of these drivers varied geographically. Our findings reveal that ocean warming is redefining the outcomes of top-down forcing in this system, whereby herbivore removal no longer predictably leads to a sustained dominance of foundational kelps but instead has led to a waning dominance (northeast) or the rise of a novel phase state defined by "turf" algae (southwest). Such findings indicate that limiting climate change and managing for low herbivore abundances will be essential for preventing further loss of the vast forests that still exist in northeast Maine. They also more broadly highlight that climate change is "rewriting the rules" of nature, and thus that ecological theory and practice must be revised to account for shifting species and processes.

The nature of status: Navigating the varied approaches to conceptualizing and measuring status

Members of small groups fundamentally desire status as status underpins members' self-concept and dictates behavior in groups. Moreover, group members readily orient and update status perceptions that index the social standing of themselves and other members. Yet, our understanding is obscured by variability in how researchers study status. In the current review, we crystallize knowledge regarding the nature of status by characterizing variability in definitions, measures, and analytic frameworks. We advocate a definition of status that draws together attributes of respect, admiration, and voluntary deference. We also distinguish reputational and relational status operationalizations and address implications pertaining to measurement along with downstream decisions involving data management and analysis. We encourage a deliberate approach to ensure congruency in how status is defined, measured, and analyzed within a research program. This review also guides theory and hypothesis generation regarding how status-related processes may vary based on different forms of status or differing contexts.

Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.

Peer Victimization and Adolescent Mental Health: School-level Victimization as a Moderator

As a global issue, peer victimization is closely associated with adolescent mental health. Although previous research has explored the relationship between peer victimization and mental health in some contexts, the school context, particularly within the Chinese cultural context, has not received sufficient attention. Based on the Healthy Context Paradox, this study aimed to explore the moderating role of school-level victimization in the relationship between individual-level peer victimization and mental health. This study tested two hypotheses by using a multilevel design: higher individual-level and school-level peer victimization are associated with higher depressive symptoms and lower life satisfaction (Hypothesis 1); school-level victimization moderates the association between individual-level peer victimization and mental health (Hypothesis 2). Participants were 39,720 adolescents (50.41% females; Mage = 13.68, SD = 2.39) across 292 Chinese schools. They completed a set of questionnaires, including the Center for Epidemiologic Studies Depression Scale, the single-item Life Satisfaction Questionnaire, the Peer Victimization Scale, and demographics. The multilevel model indicated that both individual-level and school-level peer victimization were positively correlated with depressive symptoms and negatively correlated with life satisfaction. In schools with lower levels of victimization, there was a stronger association between individual peer victimization and adolescent mental health. A potential explanation for these results might be the victimization visibility and perceived severity in different contexts. These findings extended the discussion of the Healthy Context Paradox within the Chinese school context and provided valuable insights for developing school support strategies for victimized adolescents. School management might play a significant role in affecting the mental health of victimized adolescents.

Social anxiety and emotion regulation flexibility: a daily diary approach

Previous research suggests that social anxiety symptoms are maintained and intensified by inflexible emotion regulation (ER). Therefore, we examined whether trait-level social anxiety moderates ER flexibility operationalised at both between-person (covariation between variability in emotional intensity and variability in strategy use across occasions) and within-person (associations between emotional intensity and strategy use on a given day) levels. In a sample of healthy college-aged adults (N = 185, Mage = 21.89), we examined overall and emotion-specific intensities (shame, guilt, anxiety, anger, sadness) and regulatory strategies (i.e. experiential avoidance, expressive suppression, and rumination) in response to each day's most emotionally intense event over 6 days. During the study period, we found a positive association between variability in emotional intensity and variability of experiential avoidance in individuals with lower, rather than higher, levels of trait social anxiety after controlling for key covariates (i.e. gender, personality traits, and stress exposure). However, we did not find evidence for the moderating role of trait social anxiety in ER flexibility assessed at within-person levels. Our findings highlight the need to delineate dynamic ER flexibility across everyday events.

Size of government and willingness-to-pay for environmental policy: Evidence from a cross-country survey

Citizen support is an important precursor to climate change mitigation polices. Public opinion can shape public policy and vice versa. This paper uses the 2010 International Social Survey Programme (ISSP) Environment Module to investigate cross-national differences in support for climate policy. We introduce size of government, measured by government revenues as a share of GDP, as a new country-level factor. Our sample includes 31,511 responses from 33 countries. We use multilevel models to estimate the relationship between country-level factors and environmental policy support, conditional on a series of individual factors. Increasing the size of government by one standard deviation reduces support for environmental policy by 0.13 points on a 5-point scale. For comparison, a one standard deviation increase in GDP per capita leads to a 0.24 increase in support and a one standard deviation increase in air pollution leads to a 0.13 point increase. The implication for environmental policy is that high tax countries have an aversion to price and tax increases aimed at protecting the environment. We conclude that use of taxes for environmental policy must include clear expectations for how revenues will be recycled or how other taxes will be lowered if they are to gain widespread support.

Political context and immigrants' work-related performance errors: Insights from the National Basketball Association

In numerous countries, both international migration and regional support for far-right political parties are on the rise. This is important considering that a frequent aim of far-right political parties is to aggressively limit the inflow of immigrants. Understanding how regional far-right political support affects the immigrants working in these regions is therefore vital for executives and organizations as a whole. Integrating political science research at the macro-level with stereotype threat theory at the individual level, we argue that regional far-right political support makes negative immigrant stereotypes salient, increasing the number of work-related performance errors conducted by immigrants while reducing those by natives. Using objective field data from a professional sports context, we demonstrate how subordinates' immigrant status interacts with the political context in which they reside to predict their frequency of performance errors.

On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models

For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random component (termed random conflation). The latter is rarely recognized and not well understood. Here we explain that a model commonly used to disaggregate the fixed component-the contextual effect model with random slopes-troublingly still yields a conflated random component. Negative consequences of such random conflation have not been demonstrated. Here we show that they include erroneous interpretation and inferences about the substantively important extent of between-cluster differences in slopes, including either underestimating or overestimating such slope heterogeneity. Furthermore, we show that this random conflation can yield inappropriate standard errors for fixed effects. To aid researchers in practice, we delineate which types of random slope specifications yield an unconflated random component. We demonstrate the advantages of these unconflated models in terms of estimating and testing random slope variance (i.e., improved power, Type I error, and bias) and in terms of standard error estimation for fixed effects (i.e., more accurate standard errors), and make recommendations for which specifications to use for particular research purposes.

Combating COVID-19 with charisma: Evidence on governor speeches in the United States

Using field and laboratory data, we show that leader charisma can affect COVID-related mitigating behaviors. We coded a panel of U.S. governor speeches for charisma signaling using a deep neural network algorithm. The model explains variation in stay-at-home behavior of citizens based on their smart phone data movements, showing a robust effect of charisma signaling: stay-at-home behavior increased irrespective of state-level citizen political ideology or governor party allegiance. Republican governors with a particularly high charisma signaling score impacted the outcome more relative to Democratic governors in comparable conditions. Our results also suggest that one standard deviation higher charisma signaling in governor speeches could potentially have saved 5,350 lives during the study period (02/28/2020-05/14/2020). Next, in an incentivized laboratory experiment we found that politically conservative individuals are particularly prone to believe that their co-citizens will follow governor appeals to distance or stay at home when exposed to a speech that is high in charisma; these beliefs in turn drive their preference to engage in those behaviors. These results suggest that political leaders should consider additional "soft-power" levers like charisma-which can be learned-to complement policy interventions for pandemics or other public heath crises, especially with certain populations who may need a "nudge."

Growing Up in the Great Recession: The Effects of Three Dimensions of Economic Well-being on Child Behavioral Difficulties from Ages 3 to 17

Empirical research into the relationship between economic well-being and child outcomes has been limited by its cross-sectional nature, or its narrow focus on predominantly financial aspects of economic well-being. This article attempts to overcome these shortcomings by using data from the Growing Up in Ireland Cohort98 (age: 9-17; N = 5,748; female: 51.4%) and Cohort08 studies (age: 3-9 years; N = 7,208; female: 49.8%), which cover a period of large macroeconomic fluctuation (2007-2017). This fluctuation makes a robust fixed effects analysis feasible, allowing for economic well-being effects to be isolated by controlling for all time-invariant confounders. The article uses three different measures of economic well-being (subjective financial strain, material deprivation, income) to explore how distinct forms of economic well-being affect child behavior. The results suggest that household income is not related to behavioral difficulties, whereas subjective financial strain is predictive of externalized behavioral difficulties in adolescent boys. Material deprivation is predictive of externalized behavioral difficulties in adolescent boys and internalized behavioral difficulties in younger boys, but has no effect on girls' behavioral outcomes. The findings indicate that the relationship between economic well-being and child behavioral outcomes is complex, and requires multi-dimensional measures of economic well-being to accurately ascertain the different effects.

X matters too: How the blended slope problem manifests differently in unilevel vs. multilevel models

Aside from multilevel models (MLMs), several analytic approaches are available for handling cluster-induced dependencies. Nevertheless, the literature on MLM alternatives has called less explicit attention to the potential bias in level-1 (L1) slope coefficients resulting from the “blended slope” problem—a problem that arises when dependencies in predictors (Xs) exist and when L1 predictor-outcome (X-Y) relations differ from those at level-2 (L2). As such, applied researchers may be drawing incorrect inferences about their L1 predictor effects when they specify models without considering clustering in Xs. The present paper reviews this “blended slope” problem and uses Monte Carlo simulation to illustrate how the problem manifests more for unilevel models compared with MLMs. In short, analyses of clustered data should always: 1) report outcome and predictor ICCs, 2) cluster-mean center L1 predictors or incorporate L2 aggregate predictors, and 3) employ a model that takes clustered residuals into account.

Teachers' Self-Efficacy and Perceptions of School Climate are Uniquely Associated with Students' Externalizing and Internalizing Behavior Problems

Early externalizing and internalizing problems undermine children's school success and long-term well-being. Leveraging a large, U.S.-representative dataset (N ≈ 14,810), we examined how kindergarten teachers' self-efficacy and school climate perceptions were linked to students' behavior problems in kindergarten and first grade. Teachers' self-efficacy and school climate perceptions were uniquely linked to kindergarteners' externalizing and internalizing behaviors, after controlling for demographic covariates and children's executive functions. Kindergarten teachers' higher self-efficacy predicted lower levels of children's externalizing problems in first grade, while teachers' positive school climate perceptions predicted children's lower internalizing problems in first grade. Longitudinal models demonstrated that teachers' perceived school climate and self-efficacy were uniquely associated with decreases in children's externalizing and internalizing behaviors from kindergarten to first grade. Findings highlight the importance of teachers' self-efficacy and school climate for children's socio-emotional development and underscore an urgent need to determine how best to support teacher well-being to optimize children's outcomes.

Front- and back-end employee satisfaction during service transition

Purpose

Scholars studying servitization argue that manufacturers moving into services need to develop new job roles or modify existing ones, which must be enacted by employees with the right mentality, skill sets, attitudes and capabilities. However, there is a paucity of empirical research on how such changes affect employee-level outcomes.

Design/methodology/approach

The authors theorize that job enrichment and role stress act as countervailing forces during the manufacturer's service transition, with implications for employee satisfaction. The authors test the hypotheses using a sample of 21,869 employees from 201 American manufacturers that declared revenues from services over a 10-year period.

Findings

The authors find an inverted U-shaped relationship between the firm's level of service infusion and individual employee satisfaction, which is flatter for front-end staff. This relationship differs in shape and/or magnitude between firms, highlighting the role of unobserved firm-level idiosyncratic factors.

Practical implications

Servitized manufacturers, especially those in the later stage of their transition (i.e. when services start to account for more than 50% of annual revenues), should try to ameliorate their employees' role-induced stress to counter a drop in satisfaction.

Originality/value

This is one of the first studies to examine systematically the relationship between servitization and individual employee satisfaction. It shows that back-end employees in manufacturing firms are considerably affected by an increasing emphasis on services, while past literature has almost exclusively been concerned with front-end staff.

Evaluating an Abbreviated Version of the Circumplex Team Scan Inventory of Within-Team Interpersonal Norms

Abstract. The Circumplex Team Scan (CTS) assesses the degree to which a team’s interaction/communication norms reflect each segment (16th) of the interpersonal circle/circumplex. We developed and evaluated an abbreviated 16-item CTS-16 that uses one CTS item to measure each segment. Undergraduates ( n = 446) completing engineering course projects in 139 teams completed the CTS-16. CTS-16 items showed a good fit to confirmatory structural models (e.g., that expect greater positive covariation between items theoretically closer to the circumplex). Individuals’ ratings sufficiently reflected team-level norms to justify averaging team members’ ratings. However, individual items’ marginal reliabilities suggest using the CTS-16 to assess general circumplex-wide patterns rather than specific segments. CTS-16 ratings correlated with respondents’ and their teammates’ ratings of team climate (inclusion, justice, psychological safety). Teams with more extraverted (introverted) members were perceived as having more confident/engaged (timid/hesitant) cultures. Members predisposed to social alienation perceived their team’s culture as relatively disrespectful/unengaged, but their teammates did not corroborate those perceptions. The results overall support the validity and utility of the CTS-16 and of an interpersonal circumplex model of team culture more generally.

Have a good day! An experience-sampling study of daily meaningful and pleasant activities

We organize our daily lives with a relatively high degree of freedom. Some things must be done; others are optional. Some we find meaningful, some pleasant, some both, and some neither. The present study looks at such evaluations of daily activities and how they relate to perceived meaning in life. Sixty-two students from an Austrian university first completed the meaningfulness scale from the Sources of Meaning and Meaning in Life Questionnaire (SoMe). They then participated in a 1-week experience-sampling assessment, wherein they completed a short questionnaire at five random time-points per day. They indicated their current activity and then reported, on a 6-point Likert scale, how pleasant and meaningful they perceived it to be. Activities could thus be categorized as meaningful, pleasant, both, or neither. Results reflected that activities grouped under culture/music, communication, intimacy, and sports are experienced as both highly meaningful and pleasant. A two-level hierarchical linear regression suggested that people with high trait meaningfulness experience their daily activities as more meaningful than people with lower trait meaningfulness if they also enjoy what they are doing. People with low trait meaningfulness, however, tended to experience their daily activities as rather meaningless, even if they enjoyed them very much. Thus, when looking for advice on how to have a good day, clarifying one’s meaning in life seems to represent the best starting point.

Work Stressors and Intention to Leave the Current Workplace and Profession: The Mediating Role of Negative Affect at Work

The first aim of this study was to determine whether organisational constraints, interpersonal conflicts at work, workload and negative affect at work have a positive relationship with intention to leave a current job or profession. The second aim was to investigate whether negative affect at work mediates the relationship between work stressors and intention to leave a current job or profession. The study was a quantitative cross-sectional design in nature. Data were collected between March and April 2022. The sample consisted of 306 midwives working in the Polish public health service. The theoretical model was tested using structural equation modelling. The results confirmed positive direct relationships between workload and negative affect with intention to leave the current workplace and intention to leave the midwifery profession in general. The relationships between organisational constraints and interpersonal conflicts at work and intention to leave a job or profession were found to be completely mediated by negative affect at work. Our study revealed that workload is the strongest direct predictor of intention to leave the current job or profession. Organisational constraints and interpersonal conflicts at work lead to an intention to leave a job or profession by inducing negative affect at work. Interpersonal conflicts at work are the strongest predictor of negative affect at work.

The pandemic that shocked managers across the world: the impact of the COVID-19 crisis on leadership behavior

In March 2020, the COVID-19 virus turned into a pandemic that hit organizations globally. This pandemic qualifies as an exogenous shock. Based on the threat-rigidity hypothesis, we hypothesize that this shock led to an increase in directive leadership behavior. We also argue that this relationship depends on the magnitude of the crisis and on well-learned responses of managers. In our empirical analysis we employ a differences-in-differences design with treatment intensity and focus on the period of the first lockdown, March until June 2020. Using a dataset covering monthly data for almost 27,000 managers across 48 countries and 32 sectors for January 2019 to December 2020, we find support for the threat-rigidity hypothesis. During the first lockdown, directive leadership increased significantly. We also find that this relationship is moderated by COVID-19 deaths per country, the sectoral working from home potential, and the organizational level of management. Our findings provide new evidence how large exogenous shocks like COVID-19 can impact leadership behavior.

Stressful life events, neighbourhood characteristics, and systolic blood pressure in South Africa

The relationship between negative events, neighbourhood characteristics, and systolic blood pressure in developing countries is not well-documented, particularly using longitudinal data. To explore this relationship, we analysed panel data from the first three waves of the South African National Income Dynamics Study using a correlated random effects model adjusted for confounding risk factors. Our sample comprised of 15,631 respondents in 2008, 14,443 respondents in 2010/2011, and 14,418 respondents in 2012, all aged above 15 years. The prevalence of at least one negative household event across the three waves was approximately 30%. In any of the three waves, the adjusted prevalence of hypertension was 23.84%. This share was 21.75% in 2008 (95% CI 18.06-25.44), 23.16% in 2010/11 (95% CI 19.18-27.14), and 18.39% in 2012 (95% CI 16.03-20.75). In our adjusted correlated random effects model, we found that systolic blood pressure was significantly higher among respondents from households that reported death of a household member (0.85 mmHg; p =  0.02) and a reduction in grant income and remittances (2.14 mm Hg; p = 0.01). We also found no significant association between systolic blood pressure and neighbourhood income level. In a country with social and economic challenges, our results indicate that grief and negative financial events are adversely associated with blood pressure, which may explain in part the significant burden of hypertension in low- and middle-income countries.

Bridging the gap between multilevel modeling and economic methods

Many datasets used in the social sciences have a hierarchical structure, where lower units of aggregation are 'nested' in higher units. In many disciplines, such data are analyzed using multilevel modeling (MLM, also known as hierarchical linear modeling). However, MLM as a framework is relatively unknown in economics. Instead, economists use a range of separate econometric methods, including cluster-robust standard errors, fixed effects models, models with cross-level interactions, and estimated dependent variable models. Relying on an extensive literature review, this paper describes this methodological divide and provides a detailed comparison between MLM and 'economic methods' in their abilities to deal with three methodological challenges inherent in multilevel data ‒ clustering, omitted variables, and coefficients' heterogeneity across groups. We unfold the comparative advantages of these two methodological approaches and provide practical recommendations about which of them should be used, why, and in what settings.

Employees' fear at work, job crafting, and work engagement on a daily basis: The case for fear of COVID-19

In the present study, we seek to understand how public sector employees that go to work to perform essential duties for the society and the economy cope with the fear of COVID-19 and maintain their motivation, energy, and enthusiasm for their work. We hypothesized that because employees are motivated to protect their health, an increase in daily fear of COVID-19 would be related to a daily increase in coping behaviors in the form of job crafting, which would consequently be related to employees' daily motivation. Data were based on 64 tenured employees working in public service organizations during the third wave of the COVID-19 pandemic (March 2021), who completed a quantitative diary for five consecutive workdays (N = 320 occasions). Results from multilevel analysis indicated that fear of COVID-19 had an indirect effect on work engagement through only one dimension of job crafting, seeking job resources. The study contributes to the ongoing theoretical extension of the beneficial role of job crafting by suggesting seeking social resources as an effective coping strategy for fear of COVID-19.

A Cross-Cultural Examination of the Role of Youth Emotion Regulation in Mentoring Relationships

Youth mentoring programs have grown in popularity, both within the United States (U.S.) and abroad, as an intervention to support youth with common behavioral and emotional difficulties. However, it is unclear whether certain dimensions of youth risk may diminish the positive impact of formalized mentoring relationships. The current study therefore examined whether youth emotion regulation, a transdiagnostic risk factor for both externalizing and internalizing behavioral difficulties, predicted mentoring relationship quality and the likelihood of early match closure. Participants included 1,298 randomized mentor-youth dyads from two nationwide mentoring programs, one with chapters across the U.S. (youth: 56% female; 37% White), and another with chapters across Mexico (youth: 49% female; 100% non-Indigenous). At baseline, youth completed the Emotion Regulation Questionnaire for Children and Adolescents (ERQ-CA). At program completion, youth and mentors completed measures of mentoring relationship quality. Multigroup structural equation models of youth outcomes revealed that greater youth use of cognitive reappraisal predicted better mentoring relationship quality in both countries when co-varying for sex, and that this relationship was stronger for mentor-youth pairs in the U.S. compared to those in Mexico. These findings have important implications for understanding the ways in which youth characteristics might shape the quality and impact of mentoring relationships across different cultural settings.

Conducting and Evaluating Multilevel Studies: Recommendations, Resources, and a Checklist

Multilevel methods allow researchers to investigate relationships that expand across levels (e.g., individuals, teams, and organizations). The popularity of these methods for studying organizational phenomena has increased in recent decades. Methodologists have examined how these methods work under different conditions, providing an empirical base for making sound decisions when using these methods. In this article, we provide recommendations, tools, resources, and a checklist that can be useful for scholars involved in conducting or assessing multilevel studies. The focus of our article is on two-level designs, in which Level-1 entities are neatly nested within Level-2 entities, and top-down effects are estimated. However, some of our recommendations are also applicable to more complex multilevel designs.

Catching Up on Multilevel Modeling

This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions—mixed-effects location–scale models—designed for predicting differential amounts of variability.

Utility of Linear Mixed Effects Models for Event-Related Potential Research with Infants and Children

Event-related potentials (ERPs) are advantageous for investigating cognitive development. However, their application in infants/children is challenging given children’s difficulty in sitting through the multiple trials required in an ERP task. Thus, a large problem in developmental ERP research is high subject exclusion due to too few analyzable trials. Common analytic approaches (that involve averaging trials within subjects and excluding subjects with too few trials, as in ANOVA and linear regression) work around this problem, but do not mitigate it. Moreover, these practices can lead to inaccuracies in measuring neural signals. The greater the subject exclusion, the more problematic inaccuracies can be. We review recent developmental ERP studies to illustrate the prevalence of these issues. Critically, we demonstrate an alternative approach to ERP analysis—linear mixed effects (LME) modeling—which offers unique utility in developmental ERP research. We demonstrate with simulated and real ERP data from preschool children that commonly employed ANOVAs yield biased results that become more biased as subject exclusion increases. In contrast, LME models yield accurate, unbiased results even when subjects have low trial-counts, and are better able to detect real condition differences. We include tutorials and example code to facilitate LME analyses in future ERP research.

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Linear Mixed Effects models (LMEs) have advantages for event-related potential analyses.

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For infant/child ERPs especially, LME is superior to traditional ANOVA models.

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In simulated ERP data, ANOVAs returned biased results, but LME was unbiased.

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In real, child ERP data, LME detected condition differences where ANOVA did not.

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Tutorial and sample codes given to guide use of LMEs in future research.

When Does the Leader's Positive Expression Get a Positive Response From Members? The Three-Way Interaction Effects of Perceived Deep/Surface Acting, Positive Affect, and Quality of Leader-Member Exchange on Work Engagement

Although researchers have argued that a leader's positive affective display effectively induces work motivation among members, it has not always resulted in desirable outcomes. This research addresses these critical issues and explains why individuals react differently, by considering the three-way interaction of the characteristics of expression, the positive affect of the members, and quality of leader-member exchange (LMX). To verify our hypotheses, 698days from 47 leaders and 146 members were collected through the Experience Sampling Method. The analysis was conducted using HLM, and the results showed that, for members with high quality LMX, the positive effect of perceived deep acting on work engagement was strengthened when positive affect was high, and the negative effect of perceived surface acting was weakened when positive affect was high. On the other hand, members with low-quality LMX showed a stronger positive effect of perceived deep acting on work engagement when positive affect was high, and the negative effect of perceived deep acting was mitigated when positive affect was low. These results demonstrate that quality of LMX serves as a context of the affective display between leaders and members, and the effect of displaying positive affect relies on members' perception of the characteristics of the expression and the affective state.

Understanding how family demands impair health behaviors in working sole mothers: The role of perceived control over leisure time

Working sole mothers (i.e., nonpartnered women who work) may experience elevated family demands that impose barriers to pursuing health behaviors during their daily leisure time. We aimed to map the process through which evening family demands influence leisure-time health behaviors in this priority population of employees, in an effort to identify targets for intervention development and health disparity reduction. Conducting a 7-day daily survey study in a sample of 102 working sole mothers, we supported perceptions of control over leisure time as a key mechanism linking evening family demands to leisure-time exercise. Furthermore, we identified the individual difference of present focus (i.e., a tendency to focus on current experiences) as a key factor that alters how evening family demands affect control over leisure time, which ultimately mitigates the detrimental influence of these demands on evening exercise engagement. In contrast, we did not find evidence to support relationships of evening family demands with the health behaviors of leisure time consumption of alcohol or high sugar, high fat foods via control over leisure time. We discuss how our findings advance theory regarding how family demands influence health and inform practical efforts to reduce health disparities that working sole mothers face.

The Language of Inequality: Evidence Economic Inequality Increases Wealth Category Salience

There is evidence that in more economically unequal societies, social relations are more strained. We argue that this may reflect the tendency for wealth to become a more fitting lens for seeing the world, so that in economically more unequal circumstances, people more readily divide the world into “the haves” and “have nots.” Our argument is supported by archival and experimental evidence. Two archival analyses reveal that at times of greater inequality, books in the United Kingdom and the United States and news media in English-speaking countries were more likely to mention the rich and poor. Three experiments, two preregistered, provided evidence for the causal role of economic inequality in people’s use of wealth categories when describing life in a fictional society; effects were weaker when examining real economic contexts. Thus, one way in which inequality changes the world may be by changing how we see it.

THE INTERPLAY BETWEEN THE SOCIAL AND ECONOMIC HUMAN RESOURCE MANAGEMENT SYSTEMS ON INNOVATION CAPABILITY AND PERFORMANCE

This study established the social and economic human resource management (HRM) systems and analysed their respective impacts and interplay on the innovation capability and product innovation. With 1,940 longitudinal samples over five waves, the results show that the effects of the social HRM system on the capability and performance of innovation are much stronger than the effect of the economic HRM system on innovation capability and product innovation. The findings also show that no congruence effects between the social and economic HRM systems on the innovation capability and product innovation were found in that the combined effects of the two HRM systems are not maximised when the levels of the two HRM systems are in agreement. Further, findings suggest that the main effects of the social HRM system can be supplemented with the economic HRM system in that the effects of the social HRM system tend to increase as the level of the economic HRM system increases. This study contributes to the understanding of the HRM system that has traditionally pursued one best bundle of HRM practices regardless of underlying principles and assumptions on human nature and society.

Multilevel Methods and Statistics: The Next Frontier

The purpose of this article is to take stock of extant multilevel methodological and statistical work and highlight needed areas for future research. A basic overview of the history and progression of multilevel methods and statistics in the organizational sciences is provided, as well as a discussion of recent developments to summarize the current state of the science. The eight articles in the current feature topic are also summarized and integrated to depict several themes and directions for the next wave of multilevel methods and statistics. Last, to highlight what still needs to be accomplished in the field, several unresolved issues and future research topics are noted and an agenda related to future multilevel work is discussed.