Longitudinal processes among humility, social justice activism, transcendence, and well-being.

Introduction: Existing research shows positive associations between humility and well-being, and between civic engagement and well-being. Rarely have humility, civic engagement, and well-being been examined together. We build off of previous cross-sectional findings and a prior longitudinal study that used three waves of data and found significant positive bivariate correlations between humility and the presence of life purpose across time points. Methods: Extending these previous findings, we used six waves of data obtained from graduate students at 18 seminaries across North America (N = 574; Mage = 31.54; 46.7% female; 65.3% White) to explore the dynamic associations among humility and life purpose, along with horizontal transcendence (an indicator of the attitudinal dimension of civic engagement) and social justice activism (an indicator for the behavioral dimension). We explored reciprocal short-run processes and dynamic long-run effects using a general cross-lagged panel model. Results and discussion: We found robust evidence for a reciprocal influence between the presence of life purpose and horizontal transcendence, and long-run effects for initial levels of life purpose to influence later levels of horizontal transcendence. We also found long-run effects for the influence of initial levels of life purpose on later levels of humility, and initial levels of social justice activism on later levels of horizontal transcendence. Implications center on the use of the findings for planning future one-time life purpose and social justice interventions to affect changes in humility and horizontal transcendence.

A randomized controlled trial of moderated online social therapy for family carers of first-episode psychosis patients in a specialist treatment setting.

BACKGROUND: Family members, who provide the majority of informal care during the recovery period from first-episode psychosis (FEP), experience high levels of psychological distress. However, there is a lack of effective and accessible interventions for FEP carers. OBJECTIVE: To determine the effectiveness of an online intervention ("Altitudes") in relation to the primary outcome of FEP-carer stress at 6 months follow-up. METHODS: We conducted a cluster randomized controlled trial in which FEP carers were randomized to Altitudes combined with specialized treatment as usual (STAU) or STAU alone. In addition to questionnaires, we included multiple waves of intensive ecological momentary assessment (EMA) to measure carer stress and family outcomes in 164 carers of young (15-27 years) FEP patients. RESULTS: Both groups improved over time on stress and a range of secondary outcomes, including mental health symptoms, self-efficacy, and expressed emotion with no group by time interactions. At 12 months there were significantly fewer visits to emergency departments by FEP patients in the Altitudes group (p = 0.022). Modelling of multiple EMA waves revealed that more time spent by carers with FEP patients predicted greater worry, expressed emotion, and adaptive coping. CONCLUSIONS: Engagement and usability findings for Altitudes were positive. Further refinements to our online carer interventions may be needed to engage carers in purposeful skill development for improved management of stress and communication with the young person compared with existing specialist family interventions. TRIAL REGISTRATION: ACTRN12616000968471.

Beyond Experiments.

It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.

Diversity climate, trust, and turnover intentions: A multilevel dynamic system.

Past research on employee trust and diversity climate is cross-sectional and often overlooks the distinction between overall unit climate and individual perceptions of climate. The current article addresses the complex relationship between trust and diversity climate, including directionality, evolution over time, multilevel characteristics, and influence on the critical outcome of turnover intentions. Using a novel, a multilevel analysis of cross-lagged panel data with latent interactions, we examined 6 years of data from 3,218 faculty members across 294 departments in a large U.S. university. We then (a) separated within-department and between-department diversity climate perceptions, (b) examined the directionality and durability of the relationships among trust and diversity climate perceptions, and (c) examined employee turnover intentions as an outcome of the trust/diversity climate perception feedback process that we identified. Results revealed a reciprocal relationship between within-department diversity climate and trust. These relationships continued over the full 6-year period and, as hypothesized, were stronger in departments with more unsupportive diversity climates. When all three variables were modeled at both levels of analysis, an influence on future turnover intentions was observed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The cultural dynamics of concept creep.

Emerging methods for studying cultural dynamics allow researchers to investigate cultural change with newfound rigor. One change that has recently attracted the attention of social commentators is "concept creep," the semantic inflation of harm-related concepts such as trauma, bullying, and prejudice. In theory, concept creep is driven distally by several recent cultural and societal trends, but psychology also plays a proximal role in developing and disseminating expansionary concepts of harm. However, there have been few systematic attempts to document concept creep and none to explore factors that influence it. The present work reviews concept creep from the perspective of cultural dynamics and lays out a conceptual framework for exploring processes implicated in it. Illustrative analyses are presented that apply computational linguistic methods to very large text corpora, including a new corpus of psychology article abstracts. They demonstrate that harm has risen steeply in prominence both in psychology and in the wider culture in recent decades, and that harm-related concepts have inflated their meanings over this period. The analyses also provide evidence of dynamic relationships between the prominence and semantic breadth of harm-related concepts, and between psychology and the culture at large. Implications are drawn for theory and research on concept creep. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM).

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.

Observing Change Over Time in Strength-Based Parenting and Subjective Wellbeing for Pre-teens and Teens.

The focus of this study was on adolescent mental health. More specifically, the relationship between strength-based parenting (SBP) and subjective wellbeing (SWB) during adolescence was examined at three time points over 14 months (N = 202, M age = 12.97, SD age = 0.91, 48% female). SBP was positively related to life satisfaction and positive affect at each of the three time points, and was negatively related to negative affect. SBP and SWB both declined significantly over time. When examining the causal relationships between SBP and SWB, two different statistical models were applied: latent growth-curve models (LGM) and random-intercept cross-lagged panel models (RI-CLPM). The LGM revealed a strong positive relationship between changes in SBP and SWB. Specifically, this model showed that SBP at one time point predicted adolescent SWB at future time points. However, when the more stringent statistical test was completed through RI-CLPMs, no cross-lagged paths reached significance. Thus, while parenting is a significant predictor of wellbeing for pre-teens and teens in real time, it is not predictive of wellbeing at future time points. Parents, thus, cannot assume that their current levels of SBP are 'banked' by their children to support future wellbeing. Instead, SBP needs to be an ongoing, contemporary parenting practice. Furthermore, the fact that perceptions of SBP decline in this age bracket suggest that SBP interventions may be helpful in supporting adolescent mental health.

Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

To model data from multi-item scales, many researchers default to a confirmatory factor analysis (CFA) approach that restricts cross-loadings and residual correlations to zero. This often leads to problems of measurement-model misfit while also ignoring theoretically relevant alternatives. Existing research mostly offers solutions by relaxing assumptions about cross-loadings and allowing residual correlations. However, such approaches are critiqued as being weak on theory and/or indicative of problematic measurement scales. We offer a theoretically-grounded alternative to modeling survey data called an autoregressive confirmatory factor analysis (AR-CFA), which is motivated by recognizing that responding to survey items is a sequential process that may create temporal dependencies among scale items. We compare an AR-CFA to other common approaches using a sample of 8,569 people measured along five common personality factors, showing how the AR-CFA can improve model fit and offer evidence of increased construct validity. We then introduce methods for testing AR-CFA hypotheses, including cross-level moderation effects using latent interactions among stable factors and time-varying residuals. We recommend considering the AR-CFA as a useful complement to other existing approaches and treat AR-CFA limitations.

How does it feel to be treated like an object? Direct and indirect effects of exposure to sexual objectification on women's emotions in daily life.

Exposure to sexual objectification is an everyday experience for many women, yet little is known about its emotional consequences. Fredrickson and Roberts' (1997) objectification theory proposed a within-person process, wherein exposure to sexual objectification causes women to adopt a third-person perspective on their bodies, labeled self-objectification, which has harmful downstream consequences for their emotional well-being. However, previous studies have only tested this model at the between-person level, making them unreliable sources of inference about the proposed intraindividual psychological consequences of objectification. Here, we report the results of Bayesian multilevel structural equation models that simultaneously tested Fredrickson and Roberts' (1997) predictions both within and between persons, using data from 3 ecological momentary assessment (EMA) studies of women's (N = 268) experiences of sexual objectification in daily life. Our findings support the predicted within-person indirect effect of exposure to sexual objectification on increases in negative and self-conscious emotions via self-objectification. However, lagged analyses suggest that the within-person indirect emotional consequences of exposure to sexual objectification may be relatively fleeting. Our findings advance research on sexual objectification by providing the first comprehensive test of the within-person process proposed by Fredrickson and Roberts' (1997) objectification theory. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Income Inequality and White-on-Black Racial Bias in the United States: Evidence From Project Implicit and Google Trends.

Several theories predict that income inequality may produce increased racial bias, but robust tests of this hypothesis are lacking. We examined this relationship at the U.S. state level from 2004 to 2015 using Internal Revenue Service-based income-inequality statistics and two large-scale racial-bias data sources: Project Implicit ( N = 1,554,109) and Google Trends. Using a multimethod approach, we found evidence of a significant positive within-state association between income inequality and Whites' explicit racial bias. However, the effect was small, with income inequality accounting for 0.4% to 0.7% of within-state variation in racial bias, and was also contingent on model specification, with results dependent on the measure of income inequality used. We found no conclusive evidence linking income inequality to implicit racial bias or racially offensive Google searches. Overall, our findings admit multiple interpretations, but we discuss why statistically small effects of income inequality on explicit racial bias may nonetheless be socially meaningful.

Loneliness over time: The crucial role of social anxiety.

Loneliness is known to be associated with multiple adverse physical outcomes, including higher mortality and morbidity risk. However, the impact of loneliness on mental health is less well researched, with most studies assessing how loneliness relates to depressive symptoms alone. We hypothesized that 2 mental health symptoms that relate to the fear of others-social anxiety and paranoia-would contribute to loneliness. We examined how loneliness relates to social anxiety, paranoia, and depression symptoms in a general community sample aged 18-87 years old (N = 1,010). We administered online measures over 3 time points across a 6-month period. In a cross-lagged structural equation model controlling for trait levels and prior states, our results indicated that earlier loneliness positively predicted future states of social anxiety, paranoia, and depression. However, in the same model, earlier social anxiety was the only predictor of future loneliness. These results suggest that loneliness may be a potential antecedent to emerging mental health symptoms and that identifying and treating co-occurring social anxiety symptoms may reduce the severity of loneliness. (PsycINFO Database Record

Multilevel structural equation models for assessing moderation within and across levels of analysis.

Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects. (PsycINFO Database Record

Income, personality, and subjective financial well-being: the role of gender in their genetic and environmental relationships.

Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.

Reliability estimation in a multilevel confirmatory factor analysis framework.

Scales with varying degrees of measurement reliability are often used in the context of multistage sampling, where variance exists at multiple levels of analysis (e.g., individual and group). Because methodological guidance on assessing and reporting reliability at multiple levels of analysis is currently lacking, we discuss the importance of examining level-specific reliability. We present a simulation study and an applied example showing different methods for estimating multilevel reliability using multilevel confirmatory factor analysis and provide supporting Mplus program code. We conclude that (a) single-level estimates will not reflect a scale's actual reliability unless reliability is identical at each level of analysis, (b) 2-level alpha and composite reliability (omega) perform relatively well in most settings, (c) estimates of maximal reliability (H) were more biased when estimated using multilevel data than either alpha or omega, and (d) small cluster size can lead to overestimates of reliability at the between level of analysis. We also show that Monte Carlo confidence intervals and Bayesian credible intervals closely reflect the sampling distribution of reliability estimates under most conditions. We discuss the estimation of credible intervals using Mplus and provide R code for computing Monte Carlo confidence intervals.

Taking a look behind the wheel: an investigation into the personality predictors of aggressive driving.

The present study evaluated a theoretical model of the relationships among six aspects of driver personality (i.e., driving anger and the Big Five personality factors), aggressive driving, and two outcomes of aggressive driving: motor vehicle crashes and moving violations. Data from 308 drivers recruited from two vehicle licensing offices were analyzed using structural equation modeling. As expected, aggressive driving predicted crashes and moving violations. Based on the zero-order correlations, emotional stability, agreeableness, and conscientiousness were related to aggressive driving in the expected directions; however, the picture changed when the joint effects of all variables were examined via structural equation modeling. A model in which driver personality predicted aggressive driving, which in turn predicted crashes and moving violations was supported. Drivers who were high on driving anger and low on agreeableness reported driving more aggressively. Implications for traffic safety professionals and researchers are discussed.

A general multilevel SEM framework for assessing multilevel mediation.

Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM.

Job performance over time is a function of latent trajectories and previous performance.

Previous literature addressing job performance over time notes that past performance can affect future performance and that individuals often have distinct latent performance trajectories. However, no research to date has modeled these 2 aspects of job performance in tandem. Drawing on previous literature, the authors note that current performance may act as performance feedback, influencing future performance directly (i.e., autoregression), and that individuals differ in their performance trajectories due to individual-difference factors (i.e., latent trajectories). The authors demonstrate an autoregressive latent trajectory (ALT) model to show how both autoregressive and latent trajectory parameters may be incorporated in modeling job performance over time. Also discussed are the implications of the ALT model for future studies examining job performance longitudinally.

Conducting multilevel analyses in medical education.

A significant body of education literature has begun using multilevel statistical models to examine data that reside at multiple levels of analysis. In order to provide a primer for medical education researchers, the current work gives a brief overview of some issues associated with multilevel statistical modeling. To provide an example of this technique, we then present a multilevel analysis examining the relationship between two individual-level variables and the "cross-level" interaction between this relationship and a school-level variable. In offering this discussion and example of multilevel modeling, we hope to provide medical educators with a basic introduction to multilevel statistics, including the advantages of utilizing these techniques.