Longitudinal associations between adolescent skin color satisfaction and adult health outcomes in Black women.

OBJECTIVE: Although emerging studies examine the inverse relationship between body satisfaction and disordered eating for Black women, it has not been established how racially salient aspects of body satisfaction may have implications for eating behaviors and longitudinal health outcomes. METHOD: In a longitudinal sample of 455 Black women, we examined whether skin color satisfaction across ages 10-15 was directly related to adult health outcomes at age 40 (e.g., disordered eating, self-esteem, self-reported health, depressive symptoms, and cardiovascular risk). We also investigated the indirect impact of skin color satisfaction on adult health, mediated by body satisfaction, and binge eating. RESULTS: No significant direct or indirect effects of adolescent skin color satisfaction were observed for depressive symptoms or cardiovascular health outcomes. At ages 10 and 12, skin color satisfaction had negative and positive direct effects, respectively, on self-esteem. At age 15, greater skin color satisfaction was directly associated with greater self-reported health. Post hoc analyses revealed that when additionally accounting for adolescent body satisfaction, greater skin color satisfaction was indirectly associated with greater self-esteem and self-reported health, alongside lower cardiovascular risk. CONCLUSIONS: Although previous research suggests that in adolescence, Black girls' skin color satisfaction affects both body satisfaction and disordered eating behaviors, this association does not hold into midlife. Rather, post hoc analyses suggest that the lasting effects of adolescent skin color satisfaction are mediated by the longitudinal stability of body satisfaction, which in turn, is associated with adult health outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A practice quit model to test early efficacy of medications for alcohol use disorder in a randomized clinical trial.

RATIONALE: Screening novel medications for alcohol use disorder (AUD) requires models that are both efficient and ecologically-valid. Ideally, such models would be associated with the outcomes of a given medication in clinical trials. OBJECTIVES: To test a novel human laboratory model in which individuals with intrinsic motivation to change their drinking engage in a "practice quit" attempt consisting of 6 days of complete abstinence from alcohol. METHOD: Individuals with current AUD completed a randomized, double-blind, placebo-controlled study of naltrexone (50 mg), varenicline (2 mg bid), or matched placebo. Participants were titrated onto the study medication for 1 week prior to starting the 6-day practice quit attempt. During the practice quit attempt, participants completed daily interviews with research staff. All participants completed an alcohol cue-exposure paradigm before starting the study medication and after 2 weeks of study medication. RESULTS: There were no significant medication effect on drinks per drinking day (F(2,49) = 0.66, p = 0.52) or percent days abstinent (F(2,49) = 0.14, p = 0.87) during the 6-day practice quit period. There were no medication effects on alcohol cue-reactivity (F(2,44) = 0.80, p = 0.46). Notably, participants sharply reduced their drinking during the entire 13-day medication treatment period, as compared to reducing only during the 6-day practice quit period. During the total medication period, higher levels of motivation to change was associated with higher percent days abstinent (F(1,49) = 8.12, p < 0.01). CONCLUSIONS: This study reports mostly null findings, which challenges us to decompose its nuanced design to consider model refinements. Possible changes to the model include considering the requirement for intrinsic motivation for change, including a longer practice quit period, encompassing the medication administration timeframe in the practice quit period, increasing the required sample size for signal detection, and examining a post COVID-19 pandemic cohort.

A simple Monte Carlo method for estimating power in multilevel designs.

Estimating power for multilevel models is complex because there are many moving parts, several sources of variation to consider, and unique sample sizes at Level 1 and Level 2. Monte Carlo computer simulation is a flexible tool that has received considerable attention in the literature. However, much of the work to date has focused on very simple models with one predictor at each level and one cross-level interaction effect, and approaches that do not share this limitation require users to specify a large set of population parameters. The goal of this tutorial is to describe a flexible Monte Carlo approach that accommodates a broad class of multilevel regression models with continuous outcomes. Our tutorial makes three important contributions. First, it allows any number of within-cluster effects, between-cluster effects, covariate effects at either level, cross-level interactions, and random coefficients. Moreover, we do not assume orthogonal effects, and predictors can correlate at either level. Second, our approach accommodates models with multiple interaction effects, and it does so with exact expressions for the variances and covariances of product random variables. Finally, our strategy for deriving hypothetical population parameters does not require pilot or comparable data. Instead, we use intuitive variance-explained effect size expressions to reverse-engineer solutions for the regression coefficients and variance components. We describe a new R package mlmpower that computes these solutions and automates the process of generating artificial data sets and summarizing the simulation results. The online supplemental materials provide detailed vignettes that annotate the R scripts and resulting output. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Comparing DIC and WAIC for multilevel models with missing data.

In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). We use a multilevel mediation model as an illustrative example to compare different types of DIC and WAIC. More specifically, we aim to compare the performance of conditional and marginal DICs and WAICs, and investigate their performance with missing data. We focus on two versions of DIC ([Formula: see text] and [Formula: see text]) and one version of WAIC. In addition, we explore whether it is necessary to include the nuisance models of incomplete exogenous variables in likelihood. Based on the simulation results, whether [Formula: see text] is better than [Formula: see text] and WAIC and whether we should include the nuisance models of exogenous variables in likelihood functions depend on whether we use marginal or conditional likelihoods. Overall, we find that the marginal likelihood based-[Formula: see text] that excludes the likelihood of covariate models generally had the highest true model selection rates.

Longitudinal Stability of Disordered-Eating Symptoms From Age 12 to 40 in Black and White Women.

The purpose of the current study was to test the longitudinal association between disordered eating symptoms (body dissatisfaction, drive for thinness and bulimia) in adolescence (ages 12, 14, 16, 18, 19) and adulthood (age 40) in a sample of 883 white and Black women. We also investigated moderation by race. Adolescent symptoms at each time point significantly predicted adulthood symptoms for the body dissatisfaction and drive for thinness subscales, for both Black and white women. Bulimia symptoms in adolescence predicted symptoms in adulthood; however, the effect was largely driven by white women. Although moderation was non-significant, among white women, bulimia symptoms at all adolescent time points predicted adulthood bulimia, but among Black women, only symptoms at ages 18 and 19 were predictive of adulthood bulimia. Results suggest that both Black and white women are susceptible to disordered eating and that symptoms emerging in adolescence can potentially follow women into midlife.

Multigenerational adversity impacts on human gut microbiome composition and socioemotional functioning in early childhood.

Adversity exposures in the prenatal and postnatal period are associated with an increased risk for psychopathology, which can be perpetuated across generations. Nonhuman animal research highlights the gut microbiome as a putative biological mechanism underlying such generational risks. In a sample of 450 mother-child dyads living in Singapore, we examined associations between three distinct adversity exposures experienced across two generations-maternal childhood maltreatment, maternal prenatal anxiety, and second-generation children's exposure to stressful life events-and the gut microbiome composition of second-generation children at 2 y of age. We found distinct differences in gut microbiome profiles linked to each adversity exposure, as well as some nonaffected microbiome features (e.g., beta diversity). Remarkably, some of the microbial taxa associated with concurrent and prospective child socioemotional functioning shared overlapping putative functions with those affected by adversity, suggesting that the intergenerational transmission of adversity may have a lasting impact on children's mental health via alterations to gut microbiome functions. Our findings open up a new avenue of research into the underlying mechanisms of intergenerational transmission of mental health risks and the potential of the gut microbiome as a target for intervention.

Personality predicts pre-COVID-19 to COVID-19 trajectories of transdiagnostic anxiety and depression symptoms.

This study aimed to characterize within-person pre-COVID-19 and coronavirus pandemic (COVID-19) transdiagnostic anxiety and depression symptom trajectories in emerging adults and determine the roles of neuroticism and behavioral activation in predicting these COVID-19-related changes. We recruited a sample of 342 emerging adults (aged 18-19 at baseline) who were screened on neuroticism and behavioral activation and completed symptom questionnaires on multiple occasions before and after the start of the pandemic. We examined estimates of the symptom factors of General Distress, Anhedonia-Apprehension, and Fears at each wave. The stress amplification model predicts a multiplicative neuroticism-adversity interaction with those high on neuroticism showing the greatest symptom increases to the pandemic. The stably elevated negative affect model is an additive model and predicts that persons high on neuroticism will display elevated symptoms at every wave. General Distress and Anhedonia-Apprehension showed large increases from the pre-COVID-19 to COVID-19 transition then decreased thereafter. The increase brought the average General Distress score to clinical levels at the first COVID-19 wave. There was a small decrease in Fears from the pre-COVID-19 to COVID-19 transition followed by a large increase. Thus, COVID-19 was associated with both increases in psychological symptoms and some resilience. Neuroticism positively predicted the pre-COVID-19 to COVID-19 transition change in Fears but was associated with a dampening of increases in General Distress and Anhedonia-Apprehension. The results disconfirmed the stress amplification model of neuroticism but partially supported the stably elevated negative affect model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A factored regression model for composite scores with item-level missing data.

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score applications. Many studies have investigated this issue, and the near-universal theme is that item-level missing data treatment is superior because it maximizes precision and power. However, item-level missing data handling can be challenging because missing data models become very complex and suffer from the same "curse of dimensionality" problem that plagues the estimation of psychometric models. A good deal of recent missing data literature has focused on advancing factored regression specifications that use a sequence of regression models to represent the multivariate distribution of a set of incomplete variables. The purpose of this paper is to describe and evaluate a factored specification for composite scores with incomplete item responses. We used a series of computer simulations to compare the proposed approach to gold standard multiple imputation and latent variable modeling approaches. Overall, the simulation results suggest that this new approach can be very effective, even under extreme conditions where the number of items is very large (or even exceeds) the sample size. A real data analysis illustrates the application of the method using software available on the internet. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Missing data: An update on the state of the art.

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of Psychological Methods. Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of applications that are possible with modern missing data techniques has increased dramatically, and software options are light years ahead of where they were. This article provides an update on the state of the art that catalogs important innovations from the past two decades of missing data research. The paper addresses topics described in the original paper, including developments related to missing data theory, full information maximum likelihood, Bayesian estimation, multiple imputation, and models for missing not at random processes. The paper also describes newer factored regression specifications and missing data handling for multilevel models, both of which have been a focus of recent research. The paper concludes with a summary of the current software landscape and a discussion of several practical issues. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An Investigation of Factored Regression Missing Data Methods for Multilevel Models with Cross-Level Interactions.

A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study presents a series of Monte Carlo computer simulations that investigates Bayesian and multiple imputation strategies based on factored regressions. When the model's distributional assumptions are satisfied, these methods generally produce nearly unbiased estimates and good coverage, with few exceptions. Severe misspecifications that arise from substantially non-normal distributions can introduce biased estimates and poor coverage. Follow-up simulations suggest that a Yeo-Johnson transformation can mitigate these biases. A real data example illustrates the methodology, and the paper suggests several avenues for future research.

Early-life adversity and risk for depression and anxiety: The role of interpersonal support.

Early-life adversity is a major risk factor for psychopathology, but not all who experience adversity develop psychopathology. The current study evaluated whether the links between child and adolescent adversity and depression and anxiety were described by general benefits and/or buffering effects of interpersonal support. Data from 456 adolescents oversampled on neuroticism over a 5-year period were examined in a series of discrete-time survival analyses to predict subsequent disorder onsets. Models examined linear, quadratic, and interactive effects of interpersonal support over time, as measured by chronic interpersonal stress interview ratings. Results did not support buffering effects of interpersonal support against either child or adolescent adversity in predicting depression or anxiety. However, there was support for the general benefits model of interpersonal support as evidenced by follow-up analyses of significant quadratic effects of interpersonal support, demonstrating that higher interpersonal support led to decreased likelihood of depression and anxiety onsets. Secondary analyses demonstrated that effects of interpersonal support remained after accounting for baseline depression and anxiety diagnoses. Further, quadratic effects were driven by social domains as opposed to familial domains when considering child adversity. Implications for interventions and randomized controlled prevention trials regarding interpersonal relationships are discussed.

Combination treatment with varenicline and naltrexone reduces World Health Organization risk drinking levels.

BACKGROUND: The U.S. Food and Drug Administration identifies abstinence and the absence of heavy drinking days as outcomes for pharmacotherapy trials for alcohol use disorder (AUD). However, many individuals with AUD struggle to achieve these outcomes, which may discourage them from seeking treatment. World Health Organization (WHO) risk drinking levels have garnered attention in the alcohol field as potential non-abstinent outcomes for AUD medication trials. Further, testing combination pharmacotherapy for AUD represents an important direction in the field, particularly using medications such as naltrexone and varenicline, which are approved for treating AUD and smoking, respectively. The objective of the current study was to test the utility of the WHO risk drinking levels as a drinking outcome in a randomized clinical trial of combined varenicline and naltrexone for smoking cessation and drinking reduction. These analyses provide additional tests of the efficacy of this combination treatment. METHODS: The current study is a secondary analysis of a phase 2, randomized, double-blind clinical trial, wherein participants (N = 165) who were daily smokers and heavy drinkers were randomly assigned to receive either 2 mg/day of varenicline plus 50 mg/day of naltrexone or 2 mg/day of varenicline plus placebo for 12 weeks. Medication effects on 1- and 2-level reductions in WHO risk drinking levels were assessed at 4, 8, and 12 weeks into the active medication period. RESULTS: In logistic growth curve models individuals receiving the combined treatment had greater reductions in WHO risk drinking levels than individuals taking varenicline alone when assessed at 4 weeks into the active medication period. Among individuals who were WHO high and very high risk drinkers at baseline, the largest effect sizes favoring combination treatment were at Week 4 for the WHO 2-level reduction outcome (Cohen's h = 0.202) and Week 12 for the WHO 1-level reduction outcome (Cohen's h = 0.244), although these effects did not reach statistical significance. CONCLUSIONS: These findings provide evidence that combined varenicline plus naltrexone treatment is effective at reducing WHO risk drinking levels, particularly among individuals who smoke cigarettes daily and drink heavily. These results add to a growing body of literature validating reductions in WHO risk drinking levels as outcomes of alcohol medication trials.

Examining the dimensionality of anxiety and depression: A latent profile approach to modeling transdiagnostic features.

Depression and anxiety are highly prevalent psychological disorders; our understanding of these conditions remains limited. Efforts to explain anxiety and depression have been constrained in part by binary classification systems. Dimensional approaches to understanding psychopathology may be more effective. The present study used latent profile analysis (LPA) to assess whether unique subgroups exist within a tri-level model of anxiety and depression. Participants (N=627) completed self-report questionnaires from which tri-level model factors were derived. LPA was conducted on those factors. A 4-profile model offered optimal fit to the data at baseline. This model was replicated at a second time point. Models derived included profiles labelled 'Mixed Fears,' 'Anxious Arousal,' 'Low Mood/Anhedonia,' and 'Sub-Clinical.' Profiles were validated at Time 1 using diagnostic status and clinical severity ratings associated with mood and anxiety presentations. Profiles demonstrated flexibility in accommodating breadth in clinical presentations and common comorbidities. Latent variable models may offer more ecologically valid approaches to understanding psychopathology.

Prospective relationships between skin color satisfaction, body satisfaction, and binge eating in Black girls.

Although it has been demonstrated that (a) body dissatisfaction and internalization of societal appearance standards contribute to disordered eating and (b) that internalization of societal appearance standards leads to decreased skin color satisfaction among Black women, it has not been established whether skin color dissatisfaction contributes to disordered eating among Black women or girls. The objective of the present study is to determine the influence of skin color satisfaction as a potential predictor for binge eating, and its effect through body image in Black girls during the vulnerable developmental period of adolescence. Using data from ten annual measurements in 1213 Black girls across ages 10-19, we sought to determine whether skin color satisfaction predicts Binge Eating Disorder (BED) risk and symptoms using pre-registered logistic and multilevel models. We found that lower skin color satisfaction at ages 13 and 14 significantly predicted greater odds of BED and lower skin color satisfaction at all ages predicted greater BED symptoms. Body satisfaction mediated the relationship between skin color satisfaction and BED symptoms. Our results suggest that skin color dissatisfaction is a novel component of body image for Black girls that is also related to binge eating.

A Bayesian Latent Variable Selection Model for Nonignorable Missingness.

Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-linear terms, and random slopes. We propose a Bayesian latent variable imputation approach to impute missing data due to MNAR (and other missingness mechanisms) and estimate the model of substantive interest simultaneously. In addition, even when the incomplete covariates involves interactions, non-linear terms, and random slopes, the proposed method can handle missingness appropriately. Computer simulation results suggested that the proposed Bayesian latent variable selection model (BLVSM) was quite effective when the outcome and/or covariates were MNAR. Except when the sample size was small, estimates from the proposed BLVSM tracked closely with those from the complete data analysis. With a small sample size, when the outcome was less predictable from the covariates, the missingness proportions of the covariates and the outcome were larger, and the missingness selection processes of the covariates and the outcome were more MNAR and MAR, the performance of BLVSM was less satisfactory. When the sample size was large, BLVSM always performed well. In contrast, the method with an MAR assumption provided biased estimates and undercoverage confidence intervals when the missingness was MNAR. The robustness and the implementation of BLVSM in real data were also illustrated. The proposed method is available in the Blimp software application, and the paper includes a data analysis example illustrating its use.

Visual impairment and depression in uveal melanoma: Optimism and pessimism as moderators.

OBJECTIVE: Uveal melanoma, a rare eye cancer, presents potential vision loss and life threat. This prospective, longitudinal study interrogated the predictive utility of visual impairment, as moderated by optimism/pessimism, on depressive symptoms in 299 adults undergoing diagnostic evaluation. METHOD: Depressive symptoms (Center for Epidemiologic Studies Depression Scale), subjective (Measure of Outcome in Ocular Disease vision subscale) and objective (logarithm of the minimum angle of resolution) visual impairment, and optimism/pessimism (Life Orientation Test-Revised) were assessed before diagnostic evaluation and 1 week, 3 months, and 12 months after diagnosis. Multilevel modeling, with repeated measures (Level 1) nested within individuals (Level 2) and imputation of missing data (Blimp software), was performed. RESULTS: Depressive symptoms were significantly more elevated 1 week after diagnosis in cancer patients (n = 107) versus patients not diagnosed with cancer (n = 192). Higher subjective (but not objective) visual impairment predicted greater depressive symptoms (p < .001). Across the entire sample, the two-way (Optimism/Pessimism × Subjective Visual Impairment) interactions were statistically significant (ps < .05), but not the three-way interaction (with diagnosis). The positive association between subjective visual impairment and depressive symptoms was significant at low and moderate levels of optimism (ps < .001), but not at high optimism (p > .05). The association was significant at high and moderate levels (ps < .001), but not low (p > .05) levels of pessimism. CONCLUSIONS: Elevated depressive symptoms are evident in adults who do (vs. do not) receive a diagnosis of uveal melanoma but appear to remit within 3 months. Perceived impaired vision, especially coupled with low optimism or high pessimism, predicts depressive symptoms over time, with implications for intervention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Efficacy of Combining Varenicline and Naltrexone for Smoking Cessation and Drinking Reduction: A Randomized Clinical Trial.

OBJECTIVE: Pharmacological treatments that can concomitantly address cigarette smoking and heavy drinking stand to improve health care delivery for these highly prevalent co-occurring conditions. This superiority trial compared the combination of varenicline and naltrexone against varenicline alone for smoking cessation and drinking reduction among heavy-drinking smokers. METHODS: This was a phase 2 randomized double-blind clinical trial. Participants (N=165) who were daily smokers and drank heavily received either 2 mg/day of varenicline plus 50 mg/day of naltrexone or 2 mg/day of varenicline plus matched placebo pills for 12 weeks. Primary outcomes were 7-day point prevalence of nicotine abstinence (bioverified by a breath CO reading ≤5 ppm) at the 26-week follow-up and number of drinks per drinking day during the 12-week treatment phase. RESULTS: Smoking abstinence at week 26 was significantly higher in the varenicline plus placebo condition than in the varenicline plus naltrexone condition (N=37 [45.1%] compared with N=22 [26.5%]). For drinks per drinking day, there was a medication effect favoring the combination of varenicline and naltrexone over varenicline alone across the 12-week treatment phase, although it did not meet the significance threshold. CONCLUSIONS: These findings suggest that smoking cessation and drinking reduction can be concomitantly targeted with pharmacotherapy and that while varenicline alone may be sufficient as a smoking cessation aid in heavy-drinking smokers, the combination of varenicline and naltrexone may confer benefits with regard to drinking outcomes, particularly during the 12-week period of active medication treatment.

Reciprocal effects of neuroticism and life stress in adolescence.

BACKGROUND: Stressful life experiences and personality can influence one another. Personality may contribute to the amount and type of stress individuals experience, which is referred to as a selection effect. Life stress may also impact one's personality, which is referred to as a socialization effect. It was hypothesized that neuroticism would predict increased chronic and episodic stress (selection effect) and that chronic and episodic stress would predict increased neuroticism (socialization effect). METHODS: The current study investigated selection and socialization effects of neuroticism and life stress over a three-year period in 627 adolescents. Life stress data were examined in terms of duration (chronic versus episodic) and type (interpersonal versus non-interpersonal). Episodic stress data were examined as dependent or independent. RESULTS: The results from ten cross-lagged panel models provided some evidence for significant selection and socialization effects depending on stress type. Over three years, we observed that neuroticism increases interpersonal chronic stress and non-interpersonal stressful events (selection effects) and that dependent non-interpersonal stressful events and chronic stress increase neuroticism (socialization effects). LIMITATIONS: Study limitations include a lack of a lifespan perspective and a statistical approach that does not differentiate between- from within-person variance. CONCLUSIONS: Findings suggest the value of attending to stress response as well as targeting neuroticism in prevention and intervention approaches in adolescents.

A multiple imputation score test for model modification in structural equation models.

Structural equation modeling (SEM) applications routinely employ a trilogy of significance tests that includes the likelihood ratio test, Wald test, and score test or modification index. Researchers use these tests to assess global model fit, evaluate whether individual estimates differ from zero, and identify potential sources of local misfit, respectively. This full cadre of significance testing options is not yet available for multiply imputed data sets, as methodologists have yet to develop a general score test for this context. Thus, the goal of this article is to outline a new score test for multiply imputed data. Consistent with its complete-data counterpart, this imputation-based score test provides an estimate of the familiar expected parameter change statistic. The new procedure is available in the R package semTools and naturally suited for identifying local misfit in SEM applications (i.e., a model modification index). The article uses a simulation study to assess the performance (Type I error rate, power) of the proposed score test relative to the score test produced by full information maximum likelihood (FIML) estimation. Due to the two-stage nature of multiple imputation, the score test exhibited slightly lower power than the corresponding FIML statistic in some situations but was generally well calibrated. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects, and nonlinear terms.

Despite the broad appeal of missing data handling approaches that assume a missing at random (MAR) mechanism (e.g., multiple imputation and maximum likelihood estimation), some very common analysis models in the behavioral science literature are known to cause bias-inducing problems for these approaches. Regression models with incomplete interactive or polynomial effects are a particularly important example because they are among the most common analyses in behavioral science research applications. In the context of single-level regression, fully Bayesian (model-based) imputation approaches have shown great promise with these popular analysis models. The purpose of this article is to extend model-based imputation to multilevel models with up to 3 levels, including functionality for mixtures of categorical and continuous variables. Computer simulation results suggest that this new approach can be quite effective when applied to multilevel models with random coefficients and interaction effects. In most scenarios that we examined, imputation-based parameter estimates were quite accurate and tracked closely with those of the complete data. The new procedure is available in the Blimp software application for macOS, Windows, and Linux, and the article includes a data analysis example illustrating its use. (PsycINFO Database Record (c) 2020 APA, all rights reserved).