Distinct trajectories of neuropsychiatric symptoms in the 12 months following traumatic brain injury (TBI): a TRACK-TBI study.

BACKGROUND: Neuropsychiatric symptoms are common after traumatic brain injury (TBI) and often resolve within 3 months post-injury. However, the degree to which individual patients follow this course is unknown. We characterized trajectories of neuropsychiatric symptoms over 12 months post-TBI. We hypothesized that a substantial proportion of individuals would display trajectories distinct from the group-average course, with some exhibiting less favorable courses. METHODS: Participants were level 1 trauma center patients with TBI (n = 1943), orthopedic trauma controls (n = 257), and non-injured friend controls (n = 300). Trajectories of six symptom dimensions (Depression, Anxiety, Fear, Sleep, Physical, and Pain) were identified using growth mixture modeling from 2 weeks to 12 months post-injury. RESULTS: Depression, Anxiety, Fear, and Physical symptoms displayed three trajectories: Stable-Low (86.2-88.6%), Worsening (5.6-10.9%), and Improving (2.6-6.4%). Among symptomatic trajectories (Worsening, Improving), lower-severity TBI was associated with higher prevalence of elevated symptoms at 2 weeks that steadily resolved over 12 months compared to all other groups, whereas higher-severity TBI was associated with higher prevalence of symptoms that gradually worsened from 3-12 months. Sleep and Pain displayed more variable recovery courses, and the most common trajectory entailed an average level of problems that remained stable over time (Stable-Average; 46.7-82.6%). Symptomatic Sleep and Pain trajectories (Stable-Average, Improving) were more common in traumatically injured groups. CONCLUSIONS: Findings illustrate the nature and rates of distinct neuropsychiatric symptom trajectories and their relationship to traumatic injuries. Providers may use these results as a referent for gauging typical v. atypical recovery in the first 12 months post-injury.

Evidence that intergenerational income mobility is the strongest predictor of drug overdose deaths in U. S. Midwest counties.

BACKGROUND: Our goal in this report was to quantify the degree to which opioid prescription rates and socioeconomic correlates of income inequality predicted overdose deaths in the 1055 U.S. Midwest counties. The study follows up a state-level analysis which reported that opioid prescription rates, social capital and unemployment explained much of the variance in opioid overdose death rates (Heyman, McVicar, & Brownell, 2019). METHODS: We created a data set that included drug overdose death rates, opioid prescription rates, and correlates of income inequality. Given that the variables of interest varied at the state and county level, multilevel regression was our statistical approach. RESULTS: From 2006 to 2021, Midwest overdose drug deaths increased according to an exponential equation that closely approximated the equation that describes the increases in overdose deaths for the entire U.S. from 1978 to 2016 (e.g., Jalal et al., 2018). Retail opioid prescription sales increased from 2006 to 2012, but then declined so that by 2017 they were lower than in 2006. The regression analyses revealed that intergenerational income mobility was the strongest predictor of overdose deaths. The other consistently statistically significant predictors were opioid prescription rates, social capital, and unemployment rates. Together these predictors, plus pupil teacher ratios, single parent families, and attending college accounted for approximately 47 % of the variance in overdose death rates each year. In keeping with the decline in opioid prescription rates, the explanatory power of opioid prescription rates weakened over the course of the study. CONCLUSIONS: Overdose deaths increased at a constant exponential rate for the years that it was possible to apply our regression model. This occurred even though access to legal opioids decreased. What remained invariant was the predictive strength of intergenerational income mobility; each year it was the predictor that explained the most variance in overdose deaths.

Innovative theory and methods for the next generation of diversity, equity, and inclusion sciences: Introduction to the special issue.

Diversity, equity, and inclusion sciences were invigorated by Plaut's (2010) landmark publication "Diversity science: Why and how difference makes a difference." As this field has expanded over the last decade, it is timely to reflect on its current state and future directions. The goal of this special issue is to bring together a collection of articles that advance innovative theory and methods for the next generation of diversity, equity, and inclusion sciences. The articles in this special issue cover cutting-edge themes, such as critical research methods for liberation, healing, and reconciliation; advancing intersectional theory and methods; innovative theory and methods on individual-social dynamics; promoting equity and advancing research on culture, ethnicity, and race; and new approaches to measuring and overcoming racism. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

"A Streetcar Named Instagram Desire": Evolutionary Psychological Perspectives on the Multifarious Human Desires That Shape Instagram Selfie-and-Groupfie Cultures.

Instagram not only offers an arena for the fulfillment of basic human desires but also cultivates new types of multifaceted desires and consumptions in Web 2.0 environments. This study aims to examine a wide variety of dispositional, psychological, and attitudinal predictors of Instagram consumption and selfie-and-groupfie cultures. Three cross-sectional surveys (Study 1 (N = 108); Study 2 (N = 140); Study 3 (N = 557)) were conducted, and empirical data were analyzed using structural equation modeling (SEM) with Mplus 8.0. Study 1 shows associations among appearance-related self-confidence, appearance-related actual-ideal self-discrepancy, materialism, and Instagram consumption. Study 2 confirms relationships among weight status perception, self-esteem, eating disorder, malicious envy, and Instagram consumption intensity. Study 3 further demonstrates dynamic associations among eating disorders, perceived mate value, narcissistic grandiosity, envy, social comparison, intrasexual competition for mates, and frequency of posting selfies/groupfies on Instagram. Theoretical contributions to the psychosocial and human aspects of the Web 2.0 digital culture, managerial implications for online dating cultures, and practical implications for consumption markets including social media-based health communication, cultural communication, and marketing communication are discussed.

Integrating Region- and Network-level Contributions to Episodic Recollection Using Multilevel Structural Equation Modeling.

The brain is composed of networks of interacting brain regions that support higher-order cognition. Among these, a core network of regions has been associated with recollection and other forms of episodic construction. Past research has focused largely on the roles of individual brain regions in recollection or on their mutual engagement as part of an integrated network. However, the relationship between these region- and network-level contributions remains poorly understood. Here, we applied multilevel structural equation modeling to examine the functional organization of the posterior medial (PM) network and its relationship to episodic memory outcomes. We evaluated two aspects of functional heterogeneity in the PM network: first, the organization of individual regions into subnetworks, and second, the presence of regionally specific contributions while accounting for network-level effects. Our results suggest that the PM network is composed of ventral and dorsal subnetworks, with the ventral subnetwork making a unique contribution to recollection, especially to recollection of spatial information, and that memory-related activity in individual regions is well accounted for by these network-level effects. These findings highlight the importance of considering the functions of individual brain regions within the context of their affiliated networks.

"Death and Love Amid the COVID-19 Pandemic": Effects of Pathogen Threats on Online Dating and Social Distancing From Life-History Strategy Perspectives.

Drawing from the terror management theory (TMT) and evolutionary perspectives of Life-History Strategy, a between-subject online experiment examined the interaction effects of pre-existing death anxiety, fear-inducing media content (coronavirus threat vs. gun violence threat vs. low threat mental disorder), and intrasexual competition for mates on online dating intentions and social distancing intentions during the COVID-19 pandemic. Results indicate the interaction effects of participants' pre-experimental death anxiety and different types of fear-inducing media content on perceived fear and intention to use online dating websites/apps as well as the interaction effects of pre-experimental intrasexual competitiveness and fear-inducing media content on social distancing intention in the context of online dating. Theoretical contributions to the terror management literature and practical implications for the online dating industry are discussed.

"The greedy I that gives"-The paradox of egocentrism and altruism: Terror management and system justification perspectives on the interrelationship between mortality salience and charitable donations amid the COVID-19 pandemic.

Why do people give and help others in face of their own mortality salience? The existential struggle with the awareness of death impacts the gamut of human cognition, emotion, and behavior. This multi-method research (∑N = 1,219) explains the psychosocial impact of COVID-19-related mortality salience on altruism. Drawing from terror management theory, two studies tested death-thought accessibility, mortality salience, and anxiety buffer hypotheses. Study 1 (cross-sectional survey), using structural equation modeling, confirms death anxiety and fear are predictors of powerlessness and materialism which, in turn, predict charitable donations. Study 2 (between-subjects experiment) confirms the causal effects of COVID-19-induced mortality salience on altruism. Controlling income and socioeconomic status, people in the mortality salience treatment condition indicate greater monetary donations ($), ratio of prosocial (altruistic) to proself (egocentric) spending (%), donation of time (hour), monetary valuation of time (hourly rate = $/hour), and economic value of donated time (hourly rate*hour) than the controls. These effects are mediated by powerlessness. Moderating effects of relevant individual difference factors are significant: the greedier, more selfish, narcissistic, materialistic, and system-justifying the donor is, the higher monetary donations, volunteer time, and perceived value of donated time are, only when the COVID-19-induced mortality is made salient but not in the controls. Environmental and dispositional factors jointly influence vulnerability to mortality salience. The paradox of egocentrism and altruism, as an evolutionarily adaptive protective buffer against existential insecurity for social and cultural animals, can help revitalize resilience, thus shedding some lights on the sociopsychological mechanism of consumers' subjective well-being. Implications for consumer affairs, social marketers, and policymakers are discussed.

Romance 2.0 on Instagram! "What Type of Girlfriend Would You Date?"

Instagram selfies and groupies symbolize social media users' public display of narcissism. From an evolutionary psychological perspective on the renovated hierarchy of fundamental human motives and needs, this study examined the interaction effects of Instagram photo types (selfies, group selfies, long-shot photos taken by others, and neutral photos) and Instagram peer viewers' individual difference factors (intrasexual competition [ISC] for mates, need for popularity [NfP], loneliness, and need to belong [NtB]) on intersexual attraction. A randomized between-subjects experiment ( N = 110) was executed to see how the interaction affects the intention to date the target opposite-sex person. Multiple regression analyses confirm the moderating effects of ISC for mates, NfP, loneliness, and NtB on dating desire, among heterosexual/bisexual males exposed to female Instagram photos. Viewer characteristics moderate the influence of the potential mate's photo types on perception and dating desirability. This study makes theoretical contributions to the literature on evolutionary psychology of narcissism and the renovated pyramid of primary needs. With regard to practical implications for online dating apps/social networking sites, neutral photos are more effective to appeal to men who feel lonely and have higher need for belonging and popularity, whereas group selfies are more appealing to men who have lower ISC for mate, NtB, and NfP.

It gets better with time: Enhancement of age-related positivity effect in the six months following a highly negative public event.

Age is associated with shifts toward more positive memory retrieval. The current study examined these shifts following a negative public event. Participants completed two surveys examining emotional responses to the 2013 Boston Marathon bombings, one immediately following the attack and another 6 months later. Age was associated with different effects of time on how individuals reflected on this event. Time was associated with an increased focus on negative components in young adults but a decreased focus in older adults. These findings reveal a role of time in age-related positivity effects. (PsycINFO Database Record

Comparing Indirect Effects in Different Groups in Single-Group and Multi-Group Structural Equation Models.

In this article, we evaluated the performance of statistical methods in single-group and multi-group analysis approaches for testing group difference in indirect effects and for testing simple indirect effects in each group. We also investigated whether the performance of the methods in the single-group approach was affected when the assumption of equal variance was not satisfied. The assumption was critical for the performance of the two methods in the single-group analysis: the method using a product term for testing the group difference in a single path coefficient, and the Wald test for testing the group difference in the indirect effect. Bootstrap confidence intervals in the single-group approach and all methods in the multi-group approach were not affected by the violation of the assumption. We compared the performance of the methods and provided recommendations.

Multiple Group Analysis in Multilevel Structural Equation Model Across Level 1 Groups.

This article introduces and evaluates a procedure for conducting multiple group analysis in multilevel structural equation model across Level 1 groups (MG1-MSEM; Ryu, 2014). When group membership is at Level 1, multiple group analysis raises two issues that cannot be solved by a simple extension of the standard multiple group analysis in single-level structural equation model. First, the Level 2 data are not independent between Level 1 groups. Second, the standard procedure fails to take into account the dependency between members of different Level 1 groups within the same cluster. The MG1-MSEM approach provides solutions to these problems. In MG1-MSEM, the Level 1 mean structure is necessary to represent the differences between Level 1 groups within clusters. The Level 2 model is the same regardless of Level 1 group membership. A simulation study examined the performance of MUML (Muthén's maximum likelihood) estimation in MG1-MSEM. The MG1-MSEM approach is illustrated for both a multilevel path model and a multilevel factor model using empirical data sets.

Multiple-group analysis approach to testing group difference in indirect effects.

This article introduces five methods that take a multiple-group analysis approach to testing a group difference in indirect effects. Unlike the general frameworks for testing moderated indirect effects, the five methods provide direct tests for equality of indirect effects between groups. A simulation study was conducted to examine the performance of the methods in terms of the empirical type I error rate, statistical power, and coverage of 95 % confidence intervals. The likelihood ratio test and percentile bootstrap confidence intervals are recommended. The methods are illustrated using an empirical data set.

Model fit evaluation in multilevel structural equation models.

Assessing goodness of model fit is one of the key questions in structural equation modeling (SEM). Goodness of fit is the extent to which the hypothesized model reproduces the multivariate structure underlying the set of variables. During the earlier development of multilevel structural equation models, the "standard" approach was to evaluate the goodness of fit for the entire model across all levels simultaneously. The model fit statistics produced by the standard approach have a potential problem in detecting lack of fit in the higher-level model for which the effective sample size is much smaller. Also when the standard approach results in poor model fit, it is not clear at which level the model does not fit well. This article reviews two alternative approaches that have been proposed to overcome the limitations of the standard approach. One is a two-step procedure which first produces estimates of saturated covariance matrices at each level and then performs single-level analysis at each level with the estimated covariance matrices as input (Yuan and Bentler, 2007). The other level-specific approach utilizes partially saturated models to obtain test statistics and fit indices for each level separately (Ryu and West, 2009). Simulation studies (e.g., Yuan and Bentler, 2007; Ryu and West, 2009) have consistently shown that both alternative approaches performed well in detecting lack of fit at any level, whereas the standard approach failed to detect lack of fit at the higher level. It is recommended that the alternative approaches are used to assess the model fit in multilevel structural equation model. Advantages and disadvantages of the two alternative approaches are discussed. The alternative approaches are demonstrated in an empirical example.

Factorial invariance in multilevel confirmatory factor analysis.

This paper presents a procedure to test factorial invariance in multilevel confirmatory factor analysis. When the group membership is at level 2, multilevel factorial invariance can be tested by a simple extension of the standard procedure. However level-1 group membership raises problems which cannot be appropriately handled by the standard procedure, because the dependency between members of different level-1 groups is not appropriately taken into account. The procedure presented in this article provides a solution to this problem. This paper also shows Muthén's maximum likelihood (MUML) estimation for testing multilevel factorial invariance across level-1 groups as a viable alternative to maximum likelihood estimation. Testing multilevel factorial invariance across level-2 groups and testing multilevel factorial invariance across level-1 groups are illustrated using empirical examples. SAS macro and Mplus syntax are provided.

Distinguishing between-person and within-person relationships in longitudinal health research: arthritis and quality of life.

BACKGROUND: Many health measures (e.g., blood pressure, quality of life) have meaningful fluctuation over time around a relatively stable mean level for each person. PURPOSE: This didactic paper describes two closely related statistical models for examining between-person and within-person relationships between two or more sets of measures collected over time: the latent intercept model with correlated residuals (LI) in structural equation modeling framework and the multivariate multilevel model (MVML) in multilevel modeling framework. RESULTS: We illustrated that the basic LI model and the MVML model are equivalent. We presented an illustrative example using a national arthritis data resource to examine between-person and within-person relationships of symptom status, functional health, and quality of life in arthritis patients. DISCUSSION: Additional design and modeling issues for the treatment of missing data are considered. We discuss contexts in which one of the two models may be preferred. Mplus and SAS syntax are available.

Effects of skewness and kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling.

A simulation study investigated the effects of skewness and kurtosis on level-specific maximum likelihood (ML) test statistics based on normal theory in multilevel structural equation models. The levels of skewness and kurtosis at each level were manipulated in multilevel data, and the effects of skewness and kurtosis on level-specific ML test statistics were examined. When the assumption of multivariate normality was violated, the level-specific ML test statistics were inflated, resulting in Type I error rates that were higher than the nominal level for the correctly specified model. Q-Q plots of the test statistics against a theoretical chi-square distribution showed that skewness led to a thicker upper tail and kurtosis led to a longer upper tail of the observed distribution of the level-specific ML test statistic for the correctly specified model.

Multilevel modeling: current and future applications in personality research.

Traditional statistical analyses can be compromised when data are collected from groups or multiple observations are collected from individuals. We present an introduction to multilevel models designed to address dependency in data. We review current use of multilevel modeling in 3 personality journals showing use concentrated in the 2 areas of experience sampling and longitudinal growth. Using an empirical example, we illustrate specification and interpretation of the results of series of models as predictor variables are introduced at Levels 1 and 2. Attention is given to possible trends and cycles in longitudinal data and to different forms of centering. We consider issues that may arise in estimation, model comparison, model evaluation, and data evaluation (outliers), highlighting similarities to and differences from standard regression approaches. Finally, we consider newer developments, including 3-level models, cross-classified models, nonstandard (limited) dependent variables, multilevel structural equation modeling, and nonlinear growth. Multilevel approaches both address traditional problems of dependency in data and provide personality researchers with the opportunity to ask new questions of their data.

Developmental risk and young children's regulatory strategies: predicting behavior problems at age five.

Children with early developmental delays are at heightened risk for behavior problems and comorbid psychopathology. This study examined the trajectories of regulatory capabilities and their potentially mediating role in the development of behavior problems for children with and without early developmental delays. A sample of 231 children comprised of 137 typically developing children and 94 children with developmental delays were examined during mildly frustrating laboratory tasks across the preschool period (ages 3-5). Results indicated that children with delays had greater use of maladaptive strategies (distraction, distress venting) and lower use of adaptive strategies (constructive coping) than typically developing children. For both groups, strategies had similar rates of growth across time; maladaptive strategies decreased and adaptive strategies increased. The intercept of strategy use, but not the slope, was found to mediate the relation between developmental risk and externalizing behaviors. Findings support that dysregulation, rather than the developmental risk, may be responsible for the high levels of comorbid psychopathology.

Looking again, and harder, for a link between low self-esteem and aggression.

Recent field studies have revived the hypothesis that low self-esteem causes aggression. Accordingly, we reanalyzed the data from a previous experiment and conducted a new experiment to study direct physical aggression in the form of blasting a fellow participant with aversive noise. We also conducted a field study using a measure of indirect aggression in the form of a consequential negative evaluation. High narcissists were more aggressive than others but only when provoked by insult or humiliation and only toward the source of criticism. The combination of high self-esteem and high narcissism produced the highest levels of aggression. These results support the view of aggression as stemming from threatened egotism and are inconsistent with the hypothesis that low self-esteem causes either direct or indirect aggression.