Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study

Longitudinal Profiles of Recovery-Enhancing Processes: Job-Related Antecedents and Well-Being Outcomes

The present study aimed to examine longitudinal recovery profiles based on three recovery-enhancing processes, i.e., psychological detachment from work, physical exercise, and sleep. In addition, we examined whether job-related demands and resources predict profile membership and whether profile membership predicts well-being outcomes. The participants were Finnish employees (N = 664) who filled in an electronic questionnaire in three successive years. Latent profile analysis (LPA) revealed five stable profiles of recovery-enhancing processes across time: (1) physically inactive, highly detaching (15%), (2) impaired recovery processes (19%), (3) enhanced recovery processes (25%), (4) physically active, poorly detaching and sleeping (19%), and (5) physically active (29%). In addition, job-related antecedents and well-being outcomes showed unique differences between the five profiles identified. Altogether, our study takes recovery research a step forward in helping to understand how recovery-enhancing processes function simultaneously over the long-term and suggests that, from the perspective of well-being, detachment from work and good sleep are more crucial recovery processes than physical activity.

The Impact of Imposing Equality Constraints on Residual Variances Across Classes in Regression Mixture Models

The purpose of this study is to explore the impact of constraining class-specific residual variances to be equal by examining and comparing the parameter estimation of a free model and a constrained model under various conditions. A Monte Carlo simulation study was conducted under several conditions, including the number of predictors, class-specific intercepts, sample size, class-specific regression weights, and class proportion to evaluate the results for parameter estimation of the free model and the restricted model. The free model yielded a more accurate estimation than the restricted model for most of the conditions, but the accuracy of the free model estimation was impacted by the number of predictors, sample size, the disparity in the magnitude of class-specific slopes and intercepts, and class proportion. When equality constraints were imposed in residual variance discrepant conditions, the parameter estimates showed substantial inaccuracy for slopes, intercepts, and residual variances, especially for those in Class 2 (with a lower class-specific slope). When the residual variances were equal between the classes, the restricted model showed better performance under some conditions.

Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling

OBJECTIVE

This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models.

METHODS

Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software.

RESULTS

The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models.

CONCLUSIONS

Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or "classes") of individuals with shared trajectories that differ from the average trajectory.

Latent Profiles of Alcohol Consumption Among College Students Exposed to Trauma

BACKGROUND

Alcohol use/misuse is a costly public health problem, particularly among college students in the United States. Alcohol use tends to increase during adolescence and peaks in the early/mid-20s; however, there is significant heterogeneity among alcohol use during the college years. Several studies applying a mixture modeling framework to extract latent profiles of alcohol consumption have been conducted. However, none to our knowledge has included only those exposed to trauma, a group known to be at risk for alcohol misuse. The aim of this longitudinal study (n = 1,186) was to identify profiles of alcohol consumption and their associations with demographic and trauma-related constructs.

METHOD

Data were collected from a larger study of college students attending a large public university. Participants in the current study were, on average, 18.46 years old at study entry, primarily female (69.6%), and of diverse racial/ethnic backgrounds (e.g., 48.8% White, 20.4% Black, 16.8% Asian).

RESULTS

Results suggest evidence for four latent profiles. These classes include an initially high increasing, an initially high decreasing, an initially low decreasing, and an initially low increasing, the last of which had not been found. Using analyses of variance, profile membership was associated with number of traumas, probable posttraumatic stress disorder, broad drinking motives, and trauma-specific drinking-to-cope motives.

CONCLUSIONS

Results suggest that drinking motives and trauma-related factors are important correlates of these latent alcohol profiles. Work clarifying the longitudinal interrelations between profile membership and these factors is needed to help inform more effective prevention and intervention efforts.

Joining the pieces in childhood irritability: Distinct typologies predict conduct, depressive, and anxiety symptoms

This study utilised a person-centered approach to understand childhood irritability as a transdiagnostic feature of psychopathology. Latent profile analysis was employed within a community sample (n = 93) of 9-11 year olds to identify typologies of self-regulation capabilities, positive wellbeing characteristics of hope and flourishing, and social functioning that cluster with children's irritability to mitigate risk for psychopathology symptoms. Three distinct profiles of youth were derived, High Irritability/Low Self-Regulation of Negative Emotion (9%), Moderate Irritability/Low Behavioural Control (34%) and Low Irritability/High Positive Wellbeing Characteristics (57%). Profiles were empirically validated and differentially related to symptoms of anxiety, depression and conduct problems. Notably, High Irritability/Low Self-Regulation of Negative Emotion children were characterised by the highest levels of irritability and peer problems and the lowest self-regulation of negative emotion, prosocial behaviours, hope and flourishing relative to children within the other profiles, pointing to the potential utility of future targeted, transdiagnostic interventions. Within our community-based sample, a protective profile of Low Irritability/High Positive Wellbeing Characteristics children were also described by the lowest levels of irritability and peer problems and the highest positive and negative emotion self-regulation, behavioural control, prosocial behaviours, hope and flourishing. Findings demonstrate that different levels of irritability severity cluster with different self-regulation capabilities and wellbeing characteristics and predict risk for different types of psychopathology. Targeted interventions should seek to address children's irritability alongside self-regulation and positive wellbeing characteristics to further mitigate risks of psychopathology and associated problems.

Using baseline target moderation to guide decisions on adapting prevention programs

Tom Dishion, a pioneer in prevention science, was one of the first to recognize the importance of adapting interventions to the needs of individual families. Building towards this goal, we suggest that prevention trials be used to assess baseline target moderated mediation (BTMM), where preventive intervention effects are mediated through change in specific targets, and the resulting effect varies across baseline levels of the target. Four forms of BTMM found in recent trials are discussed including compensatory, rich-get-richer, crossover, and differential iatrogenic effects. A strategy for evaluating meaningful preventive effects is presented based on preventive thresholds for diagnostic conditions, midpoint targets and proximal risk or protective mechanisms. Methods are described for using the results from BTMM analyses of these thresholds to estimate indices of intervention risk reduction or increase as they vary over baseline target levels, and potential cut points are presented for identifying subgroups that would benefit from program adaptation because of weak or potentially iatrogenic program effects. Simulated data are used to illustrate curves for the four forms of BTMM effects and how implications for adaptation change when untreated control group outcomes also vary over baseline target levels.

Repeated measures regression mixture models

Regression mixture models are one increasingly utilized approach for developing theories about and exploring the heterogeneity of effects. In this study we aimed to extend the current use of regression mixtures to a repeated regression mixture method when repeated measures, such as diary-type and experience-sampling method, data are available. We hypothesized that additional information borrowed from the repeated measures would improve the model performance, in terms of class enumeration and accuracy of the parameter estimates. We specifically compared three types of model specifications in regression mixtures: (a) traditional single-outcome model; (b) repeated measures models with three, five, and seven measures; and (c) a single-outcome model with the average of seven repeated measures. The results showed that the repeated measures regression mixture models substantially outperformed the traditional and average single-outcome models in class enumeration, with less bias in the parameter estimates. For sample size, whereas prior recommendations have suggested that regression mixtures require samples of well over 1,000 participants, even for classes at a large distance from each other (classes with regression weights of .20 vs. .70), the present repeated measures regression mixture models allow for samples as low as 200 participants with an increased number (i.e., seven) of repeated measures. We also demonstrate an application of the proposed repeated measures approach using data from the Sleep Research Project. Implications and limitations of the study are discussed.

The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data

Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis. Prior research suggests that misspecifying skewed distributions of continuous indicators in mixture analysis introduces bias, though the consequences of this practice when applied to count indicators has not been studied. The present work describes results from a simulation study examining bias in mixture recovery when count indicators are dichotomized (median split; presence vs. absence), ordinalized, or the distribution is misspecified (continuous normal; incorrect count distribution). All distributional misspecifications and methods of categorizing resulted in greater bias in parameter estimates and recovery of class membership relative to specifying the true distribution, though dichotomization appeared to improve class enumeration accuracy relative to all other specifications. Overall, results demonstrate the importance of accurately modeling count indicators in mixture analysis, as misspecification and categorizing data can distort study outcomes.

Multilevel Latent Profile Analysis With Covariates

Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of the data affect the probability of a membership of a particular profile. We demonstrate the use of MLPA by investigating job characteristics profiles based on the job-demand-control-support (JDCS) model using data from 1,958 university employees clustered in 78 work departments. The implications of the results for organizational research are discussed, together with several issues related to the potential of MLPA for wider application.

Modeling predictors of latent classes in regression mixture models

The purpose of the current study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that the step-1 of the three-step approach shows adequate results in class enumeration, we suggest using an alternative approach: 1) decide the number of latent classes without predictors of latent classes and 2) bring the latent class predictors into the model with the inclusion of hypothesized direct covariates effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students' academic achievement outcome. Implications of the study are discussed.

Evaluating differential effects using regression interactions and regression mixture models

Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.