Predictors of Complicated Grief During the COVID-19 Pandemic: A Cross-Classified Analysis.

The COVID-19 pandemic left many people grieving multiple deaths and at risk for developing symptoms of complicated grief (CG). The present study is a prospective examination of the role of neuroticism and social support in the development of CG symptoms. Findings from cross-classified multilevel models pointed to neuroticism as a risk factor for subsequent CG symptoms. Social support had a stress-buffering effect, emerging as a protective factor following the loss of a first degree relative. More recent loss and younger age of the deceased were both independently associated with heightened CG symptoms. Results from the present study provide insight into heterogeneity in CG symptom development at the between-person level, and variability in CG symptoms within individuals in response to different deaths. Findings could therefore aid in the identification of those at risk for the development of CG symptoms.

R-squared Measures for Multilevel Models with Three or More Levels.

Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and level-specific versions of MLM R-squared measures have not been clarified, despite such relationships becoming increasingly important to understand when there are more levels. Furthermore, the impact of predictor centering strategy on R-squared computation and interpretation has not been explicated for MLMs with any number of levels. To fill these gaps, we extend the Rights and Sterba two-level MLM R-squared framework to three or more levels, providing a general set of measures that includes preexisting three-level measures as special cases and yields additional results not obtainable from existing measures. We mathematically and pedagogically relate total and level-specific R-squareds, and show how all total and level-specific R-squared measures in our framework can be computed under any centering strategy. Finally, we provide and empirically demonstrate software (available in the r2mlm R package) to compute measures and graphically depict results.

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.

Disaggregating level-specific effects in cross-classified multilevel models.

In psychology and other fields, data often have a cross-classified structure, whereby observations are nested within multiple types of non-hierarchical clusters (e.g., repeated measures cross-classified by persons and stimuli). This paper discusses ways that, in cross-classified multilevel models, slopes of lower-level predictors can implicitly reflect an ambiguous blend of multiple effects (for instance, a purely observation-level effect as well as a unique between-cluster effect for each type of cluster). The possibility of conflating multiple effects of lower-level predictors is well recognized for non-cross-classified multilevel models, but has not been fully discussed or clarified for cross-classified contexts. Consequently, in published cross-classified modeling applications, this possibility is almost always ignored, and researchers routinely specify models that conflate multiple effects. In this paper, we show why this common practice can be problematic, and show how to disaggregate level-specific effects in cross-classified models. We provide a novel suite of options that include fully cluster-mean-centered, partially cluster-mean-centered, and contextual effect models, each of which provides a unique interpretation of model parameters. We further clarify how to avoid both fixed and random conflation, the latter of which is widely misunderstood even in non-cross-classified models. We provide simulation results showing the possible deleterious impact of such conflation in cross-classified models, and walk through pedagogical examples to illustrate the disaggregation of level-specific effects. We conclude by considering additional model complexities that can arise with cross-classification, providing guidance for researchers in choosing among model specifications, and describing newly available software to aid researchers who wish to disaggregate effects in practice.

r2mlm: An R package calculating R-squared measures for multilevel models.

Multilevel models are used ubiquitously in the social and behavioral sciences and effect sizes are critical for contextualizing results. A general framework of R-squared effect size measures for multilevel models has only recently been developed. Rights and Sterba (2019) distinguished each source of explained variance for each possible kind of outcome variance. Though researchers have long desired a comprehensive and coherent approach to computing R-squared measures for multilevel models, the use of this framework has a steep learning curve. The purpose of this tutorial is to introduce and demonstrate using a new R package - r2mlm - that automates the intensive computations involved in implementing the framework and provides accompanying graphics to visualize all multilevel R-squared measures together. We use accessible illustrations with open data and code to demonstrate how to use and interpret the R package output.

Assessing the role of adolescent hormonal contraceptive use on risk for depression: a 3-year longitudinal study protocol.

BACKGROUND: The incidence of depression in human females rises steadily throughout adolescence, a critical period of pubertal maturation marked by increasing levels of gonadal hormones including estrogens and progesterone. These gonadal hormones play a central role in social and emotional development and may also contribute to the increased occurrence of depression in females that begins in early adolescence. In this study, we examine whether and how introducing synthetic estrogen and progestin derivatives through the use of combined hormonal contraceptives (CHC), affects adolescent females' risk for developing depression. We further assess potential links between CHC use and alterations in stress responses and social-emotional functioning. METHODS: Using a longitudinal cohort design, we will follow a sample of adolescent females over the span of three years. Participants will be assessed at three time points: once when they are between 13 and 15 years of age, and at approximately 18 and 36 months after their initial assessment. Each time point will consist of two online sessions during which participants will complete a clinical interview that screens for key symptoms of mental health disorders, along with a series of questionnaires assessing their level of depressive symptoms and history of contraceptive use. They will also complete a standardized social-evaluative stress test and an emotion recognition task, as well as provide saliva samples to allow for assessment of their circulating free cortisol levels. DISCUSSION: In this study we will assess the effect of CHC use during adolescence on development of Major Depressive Disorder (MDD). We will control for variables previously found to or proposed to partially account for the observed relationship between CHC use and MDD, including socioeconomic status, age of sexual debut, and CHC-related variables including age of first use, reasons for use, and its duration. In particular, we will discover whether CHC use increases depressive symptoms and/or MDD, whether elevated depressive symptoms and/or MDD predict a higher likelihood of starting CHC, or both. Furthermore, this study will allow us to clarify whether alterations in stress reactivity and social-emotional functioning serve as pathways through which CHC use may result in increased risk of depressive symptoms and/or MDD.

Pandemic stressors and depressive symptoms: Examining within- and between-person effects of neuroticism.

Experiencing stressors related to the COVID-19 pandemic such as health-related concern, social isolation, occupational disruption, financial insecurity, and resource scarcity can adversely impact mental health; however, the extent of the impact varies greatly between individuals. In this study, we examined the role of neuroticism as an individual-level risk factor that exacerbates the association between pandemic stressors and depressive symptoms. With repeated assessments of pandemic stressors and depressive symptoms collected from 3181 participants over the course of the pandemic, we used multilevel modeling to test if neuroticism moderated the association between pandemic stressors and depressive symptoms at both between- and within-person levels. At the between-person level, we found that participants who reported more pandemic stressors on average had higher levels of depressive symptoms and that this association was stronger among those high in neuroticism. At the within-person level, reporting more pandemic stressors relative to one's average on any given occasion was also associated with heightened depressive symptoms and this effect was similarly exacerbated by neuroticism. The findings point to pandemic stressor exposure and neuroticism as risk factors for depressive symptoms and, in demonstrating their synergistic impact, may help identify individuals at greatest risk for adverse psychological responses to the COVID-19 pandemic.

Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R-squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies.

Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds (R2 ) that convey variance explained by terms in the model. An integrative framework for computing R-squareds in MLMs with random intercepts and/or slopes was recently introduced by Rights and Sterba and it subsumed pre-existing MLM R-squareds as special cases. However, this work focused on cross-sectional applications, and hence did not address how the computation and interpretation of MLM R-squareds are affected by modeling considerations typically arising in longitudinal settings: (a) alternative centering choices for time (e.g., centering-at-a-constant vs. person-mean-centering), (b) nonlinear effects of predictors such as time, (c) heteroscedastic level-1 errors and/or (d) autocorrelated level-1 errors. This paper addresses these gaps by extending the Rights and Sterba R-squared framework to longitudinal contexts. We: (a) provide a full framework of total and level-specific R-squared measures for MLMs that utilize any type of centering, and contrast these with Rights and Sterba's measures assuming cluster-mean-centering, (b) explain and derive which measures are applicable for MLMs with nonlinear terms, and extend the R-squared computation to accommodate (c) heteroscedastic and/or (d) autocorrelated errors. Additionally, we show how to use differences in R-squared (ΔR2 ) measures between growth models (adding, for instance, time-varying covariates as level-1 predictors or time-invariant covariates as level-2 predictors) to obtain effects sizes for individual terms. We provide R software (r2MLMlong) and a running pedagogical example analyzing growth in adolescent self-efficacy to illustrate these methodological developments. With these developments, researchers will have greater ability to consider effect size when analyzing and predicting change using MLMs.

New Recommendations on the Use of R-Squared Differences in Multilevel Model Comparisons.

When comparing multilevel models (MLMs) differing in fixed and/or random effects, researchers have had continuing interest in using R-squared differences to communicate effect size and importance of included terms. However, there has been longstanding confusion regarding which R-squared difference measures should be used for which kind of MLM comparisons. Furthermore, several limitations of recent studies on R-squared differences in MLM have led to misleading or incomplete recommendations for practice. These limitations include computing measures that are by definition incapable of detecting a particular type of added term, considering only a subset of the broader class of available R-squared difference measures, and incorrectly defining what a given R-squared difference measure quantifies. The purpose of this paper is to elucidate and resolve these issues. To do so, we define a more general set of total, within-cluster, and between-cluster R-squared difference measures than previously considered in MLM comparisons and give researchers concrete step-by-step procedures for identifying which measure is relevant to which model comparison. We supply simulated and analytic demonstrations of limitations of previous MLM studies on R-squared differences and show how application of our step-by-step procedures and general set of measures overcomes each. Additionally, we provide and illustrate graphical tools and software allowing researchers to automatically compute and visualize our set of measures in an integrated manner. We conclude with recommendations, as well as extensions involving (a) how our framework relates to and can be used to obtain pseudo-R-squareds, and (b) how our framework can accommodate both simultaneous and hierarchical model-building approaches.

Simultaneously examining negative appraisals, emotion reactivity, and cognitive reactivity in relation to depressive symptoms in children.

Prior theory and research have linked negative appraisals (NA), emotion reactivity (ER), and cognitive reactivity (CR) to depression; however, few studies have examined whether even two of these constructs simultaneously, but none have done so in child or adolescent populations. A total of 571 youths (ages 9-13) completed a novel procedure in which all three constructs were assessed in response to the same personally relevant, hypothetical, peer victimization events. Multilevel modeling enabled the extraction of dynamic, within-person, latent-variable measures of NA, ER, and CR. All three constructs were related to children's depressive symptoms in ways that were commensurate with most (but not all) theoretical frameworks. Gender and age differences also emerged. Support for an NA-predicts-ER-predicts-CR model suggests ways that these constructs can be integrated into a more complete, transtheoretical understanding of the cognitive-emotional substrate of depression in children.

Maternal Communication in Childhood Cancer: Factor Analysis and Relation to Maternal Distress.

Objective: This study aimed to characterize mothers' communication with their children in a sample of families with a new or newly relapsed pediatric cancer diagnosis, first using factor analysis and second using structural equation modeling to examine relations between self-reported maternal distress (anxiety, depression, and posttraumatic stress) and maternal communication in prospective analyses. A hierarchical model of communication was proposed, based on a theoretical framework of warmth and control. Methods: The sample included 115 children (age 5-17 years) with new or newly relapsed cancer (41% leukemia, 18% lymphoma, 6% brain tumor, and 35% other) and their mothers. Mothers reported distress (Beck Anxiety Inventory, Beck Depression Inventory-II, and Impact of Events Scale-Revised) 2 months after diagnosis (Time 1). Three months later (Time 2), mother-child dyads were video-recorded discussing cancer. Maternal communication was coded with the Iowa Family Interaction Ratings Scales. Results: Confirmatory factor analysis demonstrated poor fit. Exploratory factor analysis suggested a six-factor model (root mean square error of approximation = .04) with one factor reflecting Positive Communication, four factors reflecting Negative Communication (Hostile/Intrusive, Lecturing, Withdrawn, and Inconsistent), and one factor reflecting Expression of Negative Affect. Maternal distress symptoms at Time 1 were all significantly, negatively related to Positive Communication and differentially related to Negative Communication factors at Time 2. Maternal posttraumatic stress and depressive symptoms each predicted Expression of Negative Affect. Conclusions: Findings provide a nuanced understanding of maternal communication in pediatric cancer and identify prospective pathways of risk between maternal distress and communication that can be targeted in intervention.

Effect Size Measures for Multilevel Models in Clinical Child and Adolescent Research: New R-Squared Methods and Recommendations.

Clinical psychologists studying child and adolescent populations commonly analyze hierarchically structured data via multilevel modeling (MLM). In clinical child and adolescent psychology, and in psychology more broadly, increasing emphasis is being placed on the reporting of effect size, such as R-squared (R2) measures of explained variance. In MLM, however, the literature on R2 had, until recently, suffered from several shortcomings: (a) the relations among existing measures were unknown, (b) methods for quantifying some types of explained variance were unavailable, (c) which (if any) measures should be used for model comparison was unclear, (d) most measures did not generalize to models with more than two levels, and (e) software to compute measures was unavailable. The purpose of this article is to summarize recent methodological developments that resolved these issues and encourage the use of MLM R2 in practice. We provide a nontechnical discussion of how the issues have been resolved and demonstrate how the new measures and methods can be implemented, highlighting their utility with an empirical example. We first consider a two-level MLM for a single hypothesized model in which we examine emotional response to social situations as a predictor of maladaptive self-cognitions, demonstrating the various ways we can quantify explained variance. We then discuss and demonstrate the use of R2 for model comparison, and discuss the extension to models with more than two levels. Last, we discuss new free software that researchers can use to compute measures and produce associated graphics.

Accounting for Parcel-Allocation Variability in Practice: Combining Sources of Uncertainty and Choosing the Number of Allocations.

Item parceling remains widely used under conditions that can lead to parcel-allocation variability in results. Hence, researchers may be interested in quantifying and accounting for parcel-allocation variability within sample. To do so in practice, three key issues need to be addressed. First, how can we combine sources of uncertainty arising from sampling variability and parcel-allocation variability when drawing inferences about parameters in structural equation models? Second, on what basis can we choose the number of repeated item-to-parcel allocations within sample? Third, how can we diagnose and report proportions of total variability per estimate arising due to parcel-allocation variability versus sampling variability? This article addresses these three methodological issues. Developments are illustrated using simulated and empirical examples, and software for implementing them is provided.

Within-session chasing of losses and wins in an online eCasino.

Chasing refers to the escalation of betting behaviour. It is conventionally seen when losing but can also be seen after wins. Diagnostic and screening items for gambling problems describe chasing as returning 'another day' to gamble. However, gamblers may also chase within sessions, and this is particularly relevant in online gambling. This study focused on two expressions of within-session chasing: (1) increasing the bet amount, or (2) a reduced probability of quitting the session, as a function of prior losses or wins. These expressions were examined across five online gambling products: slot machines, probability games, blackjack, video poker, and roulette. Our results showed that gamblers bet more and played longer sessions after immediate losses, but they bet less and played shorter sessions when losing cumulatively. The reversed pattern in the cumulative model may be due to financial constraints. For wins, gamblers bet more after both immediate and cumulative wins, but they also played shorter sessions. Chasing patterns were qualitatively similar by game type-with limited evidence for our hypothesis that chasing would be greatest for slot machines as an established high-risk category. Overall, chasing is multi-faceted, varying across the behavioural expressions, by the immediate or cumulative timeframe of prior outcomes, and by game type.

Between-session chasing of losses and wins in an online eCasino.

Background and aims: This study characterized chasing behaviour as the time to return to an online gambling website after a losing or a winning visit. Methods: We analyzed a naturalistic dataset from an eCasino (PlayNow.com, the provincial platform for British Columbia, Canada), comprising 1,909,681 sessions from 15,544 individuals. Analyses distinguished sessions on slot machines, blackjack, roulette, video poker, probability games, or mixed-category sessions. Results: Overall, gamblers on most games returned more slowly as a function of the prior loss, and more quickly as a function of the prior win. Loss chasing intensities in blackjack, probability, video poker, and mixed sessions did not differ significantly from slot machines, but roulette was associated with shorter intervals to return (b = -0.13, p < 0.001). Similarly, win chasing did not vary across slot machines, blackjack, probability games, and video poker, but roulette (b = -0.08, p < 0.001) and mixed (b = -0.02, p = 0.009) sessions were associated with shorter intervals. Discussion and conclusions: The average behavioural patterns provide limited evidence for loss chasing but clearly indicate win chasing. Although slot machines are commonly considered a high-risk product, roulette in our analyses was associated with the greatest chasing intensities.

Are autistic traits associated with a social-emotional memory bias?

Autistic traits are associated with differential processing of emotional and social cues. By contrast little is known about the relationship of autistic traits to socio-emotional memory, though research suggests an integral relationship between episodic memory processes and psychosocial well-being. Using an experimental paradigm, we tested if autistic traits moderate the effects of negative emotion and social cues on episodic memory (i.e. memory for past events). Young adults (N = 706) with varied levels of self-reported autistic traits (24% in clinical range) encoded images stratified by emotion (negative, neutral) and social cues (social, non-social) alongside a neutral object. After 24 h, item memory for images and associative memory for objects was tested. For item memory, after controlling for anxiety, a small effect emerged whereby a memory-enhancing effect of social cues was reduced as autistic traits increased. For associative memory, memory for pairings between neutral, but not negative, images reduced as autistic traits increased. Results suggest autistic traits are associated with reduced ability to bind neutral items together in memory, potentially impeding nuanced appraisals of past experience. This bias toward more negative, less nuanced memories of past experience may represent a cognitive vulnerability to social and mental health challenges commonly associated with autistic traits and a potential intervention target.

Aberrant distortion of variance components in multilevel models under conflation of level-specific effects.

Methodologists have often acknowledged that, in multilevel contexts, level-1 variables may have distinct within-cluster and between-cluster effects. However, a prevailing notion in the literature is that separately estimating these effects is primarily important when there is specific interest in doing so. Consequently, in practice, researchers uninterested in disaggregating these effects (or unaware of their difference) routinely fit models that conflate them. Furthermore, even researchers who properly disaggregate the fixed components in a model (avoid fixed conflation) may still inadvertently and unknowingly conflate the random effects (fail to avoid random conflation). The purpose of this article is to elucidate an unappreciated consequence of such fixed or random conflation, namely, that it can cause systematic distortion in all variance components, yielding uninterpretable variances that adversely affect the entire model. In this article, I provide novel mathematical derivations, simulations, and pedagogical illustrations of such variance distortion, showing how it leads to several aberrant consequences: (1) error variances at level-1 and level-2 can systematically increase (in the population) with the addition of predictors; (2) there can be a large apparent degree of between-cluster random-effect variability in cases in which there is actually no between-cluster outcome variability; (3) R-squared measures of explained variance can be severely biased, uninterpretable, and well below the logical bound of 0; and (4) inference for all fixed components of the model-not just the conflated slopes themselves-can be compromised. I conclude with recommendations for practice, including cautionary notes on interpreting results from prior research that had specified conflated slopes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects.

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.

Daily Peer Victimization Experiences of Adolescents With and Without Chronic Pain: Associations With Mood, Sleep, Pain, and Activity Limitations.

This study aims to 1) examine the temporal influence of peer victimization on mood, sleep quality, pain, and activity limitations in clinical and community samples of youth, and 2) test mood and sleep as mediators of peer victimization-pain pathways. One hundred fifty-six adolescents (n = 74 chronic pain group) completed a week of online diary monitoring assessing their daily peer victimization experiences, negative mood, sleep quality, pain intensity, and pain-related activity limitations. In multilevel models controlling for group status, person-mean peer victimization (averaged across days) significantly predicted worse mood, pain, and activity limitations (all Ps < .01) while daily victimization predicted worse mood (P < .05). Results from within-person mediation indicated a significant indirect effect of daily peer victimization on next-day activity limitations, through daily negative mood. Results from between-person mediation indicated that negative mood significantly mediated the relation between peer victimization and pain and the relation between peer victimization and activity limitations. Peer victimization is associated with negative health indicators in clinical and community samples of youth and may exert its influence on pain and pain-related activity limitations through negative mood. PERSPECTIVE: This article examines the temporal influence of peer victimization on pain in adolescents with and without chronic pain, and examines mood and sleep quality as mechanisms linking victimization to pain. This information may be useful for pain prevention researchers as well as providers who assess and treat pain in childhood.