Assessing differential effects: applying regression mixture models to identify variations in the influence of family resources on academic achievement

Building a framework to understand and address vulnerability to reading difficulties among children in schools in the United States

This paper presents a vulnerability framework as a means to contextualize inequities in reading achievement among children who are vulnerable to poor reading outcomes. Models to understand vulnerability have been applied in the social sciences and public health to identify population disparities and design interventions to improve outcomes. Vulnerability is multifaceted and governed by context. Using a vulnerability framework for the science of reading provides an innovative approach for acknowledging multilevel factors contributing to disparities. The ecological considerations of both individual differences in learners and conditions within and outside of schools ensures that scientific advances are realized for learners who are more vulnerable to experiencing reading difficulty in school.

The Six Components of Social Interactions: Actor, Partner, Relation, Activities, Context, and Evaluation

Social interactions are essential aspects of social relationships. Despite their centrality, there is a lack of a standardized approach to systematize social interactions. The present research developed (Study 1) and tested (Study 2) a taxonomy of social interactions. In Study 1 (5,676 descriptions of social interactions from N = 708 participants, age range 18–83 years), we combined a bottom-up approach based on the grounded theory with a top-down approach integrating existing empirical and theoretical literature to develop the taxonomy. The resulting taxonomy (APRACE) comprises the components Actor, Partner, Relation, Activities, Context, and Evaluation, each specified by features on three levels of abstraction. A social situation can be described by a combination of the components and their features on the respective abstraction level. Study 2 tested the APRACE using another dataset (N = 303, age range 18–88 years) with 1,899 descriptions of social interactions. The index scores of the six components, the frequencies of the features on the most abstract level, and their correlations were largely consistent across both studies, which supports the generalizability of the APRACE. The APRACE offers a generalizable tool for the comprehensive, parsimonious, and systematic description of social interactions and, thus, enables networked research on social interactions and application in a number of practical fields.

Patterns of comorbidities in hospitalised cancer survivors for palliative care and associated in-hospital mortality risk: A latent class analysis of a statewide all-inclusive inpatient data

BACKGROUND

At the end of life, cancer survivors often experience exacerbations of complex comorbidities requiring acute hospital care. Few studies consider comorbidity patterns in cancer survivors receiving palliative care.

AIM

To identify patterns of comorbidities in cancer patients receiving palliative care and factors associated with in-hospital mortality risk.

DESIGN, SETTING/PARTICIPANTS

New South Wales Admitted Patient Data Collection data were used for this retrospective cohort study with 47,265 cancer patients receiving palliative care during the period financial year 2001-2013. A latent class analysis was used to identify complex comorbidity patterns. A regression mixture model was used to identify risk factors in relation to in-hospital mortality in different latent classes.

RESULTS

Five comorbidity patterns were identified: 'multiple comorbidities and symptoms' (comprising 9.1% of the study population), 'more symptoms' (27.1%), 'few comorbidities' (39.4%), 'genitourinary and infection' (8.7%), and 'circulatory and endocrine' (15.6%). In-hospital mortality was the highest for 'few comorbidities' group and the lowest for 'more symptoms' group. Severe comorbidities were associated with elevated mortality in patients from 'multiple comorbidities and symptoms', 'more symptoms', and 'genitourinary and infection' groups. Intensive care was associated with a 37% increased risk of in-hospital deaths in those presenting with more 'multiple comorbidities and symptoms', but with a 22% risk reduction in those presenting with 'more symptoms'.

CONCLUSION

Identification of comorbidity patterns and risk factors for in-hospital deaths in cancer patients provides an avenue to further develop appropriate palliative care strategies aimed at improving outcomes in cancer survivors.

The Effects of Sample Size on the Estimation of Regression Mixture Models

Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture's ability to produce "stable" results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem.

Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance

This simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to account for non-normal outcomes. This flexible function covers heavy tails and may reduce the risk of identifying spurious classes, e.g., in case of outliers. Sample size, number of occasions and skewness of the trait variable were varied. Generally, parameter estimation accuracy increases with increasing numbers of observations and occasions. Larger bias compared to other parameters occurs for parameters referring to the skew t-distribution and variances of the latent trait variables. Standard error estimation accuracy shows diffuse patterns across conditions and parameters. Overall model performance is acceptable for large conditions, even though none of the models is free from bias. The application of the skew t mixture model in case of large numbers of occasions and observations may be possible, but results should be treated with caution. Moreover, the skew t approach may be useful for other mixture models.

A DIAGNOSTIC TOOL FOR CHECKING ASSUMPTIONS OF REGRESSION MIXTURE MODELS

Regression mixture models are becoming more widely used in applied research. It has been recognized that these models are quite sensitive to underlying assumptions, yet many of these assumptions are not directly testable. We discuss a diagnostic tool based on reconstructed residuals that can help uncover violations of model assumptions. These residuals are found by using the posterior probability of class membership to assign, based on a multinomial distribution, a class to each observation. Standard residual checks can be applied to these posterior draw residuals to explore violations of the model assumptions. We present several illustrations of the diagnostic tool.

Percepções Infantis: Relações entre Motivação Escolar e Suporte Familiar

Resumo Esta pesquisa teve por objetivo analisar a percepção infantil da motivação escolar e do suporte familiar dos alunos, de diferentes anos escolares, e verificar as relações entre as variáveis. Participaram 342 crianças, de ambos os sexos, com idades de sete a 13 anos, de três escolas municipais do interior do estado de São Paulo. Foram investigadas as orientações motivacionais e a percepção infantil do suporte familiar. Os resultados revelam que as orientações motivacionais e o suporte educativo familiar apresentaram declínio conforme o avanço da escolaridade, houve correlação positiva, significante e baixa entre a motivação intrínseca e o suporte familiar afetivo, educativo e material, e correlação negativa, significante e baixa entre a motivação extrínseca e o aspecto afetivo do suporte familiar. Conclui-se pela importância do aprofundamento dos conhecimentos do suporte familiar na motivação escolar, visto que este se mostra relacionado à motivação intrínseca dos alunos com o progredir da escolaridade.

Foundations for screening adverse childhood experiences: Exploring patterns of exposure through infancy and toddlerhood

Adverse childhood experiences (ACEs) have lifetime consequences for health and development. Identification of ACEs early in childhood provides the potential to intervene before health and development are impaired. This study examined the timing and duration of exposure to ACEs experienced by children from low-income families from ages one to three years to identify whether there were patterns of exposure when infants and toddlers were most vulnerable. We were able to confirm the early negative consequences on cognitive, health, and behavior outcomes previously reported in young children using a national, longitudinal data set of parents and children from low-income households (N=2250). Using Finite Mixture Models, five classes of exposure were identified for children, Consistently Low (63.8%), Decreasing (10.3%), High at Age 2 (11.4%), Increasing (10.4%), and Consistently High (4%). The Consistently Low and Consistently High classes had the most and least optimal development across all domains, respectively. When examining child development outcomes among children with variable exposures to adversities, we found that for cognitive, language, and physical development, the most proximal ACEs were more robust for predicting child outcomes. For socioemotional health, exposure at any time from one to three to ACEs had negative consequences. As a whole, findings from this study highlight the need to consider ACEs screening tools that are both time-sensitive and permit a lifetime report.

An evaluation of the bootstrap for model validation in mixture models

Bootstrapping has been used as a diagnostic tool for validating model results for a wide array of statistical models. Here we evaluate the use of the non-parametric bootstrap for model validation in mixture models. We show that the bootstrap is problematic for validating the results of class enumeration and demonstrating the stability of parameter estimates in both finite mixture and regression mixture models. In only 44% of simulations did bootstrapping detect the correct number of classes in at least 90% of the bootstrap samples for a finite mixture model without any model violations. For regression mixture models and cases with violated model assumptions, the performance was even worse. Consequently, we cannot recommend the non-parametric bootstrap for validating mixture models. The cause of the problem is that when resampling is used influential individual observations have a high likelihood of being sampled many times. The presence of multiple replications of even moderately extreme observations is shown to lead to additional latent classes being extracted. To verify that these replications cause the problems we show that leave-k-out cross-validation where sub-samples taken without replacement does not suffer from the same problem.

Children exposed to intimate partner violence: Identifying differential effects of family environment on children's trauma and psychopathology symptoms through regression mixture models

The majority of analytic approaches aimed at understanding the influence of environmental context on children's socioemotional adjustment assume comparable effects of contextual risk and protective factors for all children. Using self-reported data from 289 maternal caregiver-child dyads, we examined the degree to which there are differential effects of severity of intimate partner violence (IPV) exposure, yearly household income, and number of children in the family on posttraumatic stress symptoms (PTS) and psychopathology symptoms (i.e., internalizing and externalizing problems) among school-age children between the ages of 7-12 years. A regression mixture model identified three latent classes that were primarily distinguished by differential effects of IPV exposure severity on PTS and psychopathology symptoms: (1) asymptomatic with low sensitivity to environmental factors (66% of children), (2) maladjusted with moderate sensitivity (24%), and (3) highly maladjusted with high sensitivity (10%). Children with mothers who had higher levels of education were more likely to be in the maladjusted with moderate sensitivity group than the asymptomatic with low sensitivity group. Latino children were less likely to be in both maladjusted groups compared to the asymptomatic group. Overall, the findings suggest differential effects of family environmental factors on PTS and psychopathology symptoms among children exposed to IPV. Implications for research and practice are discussed.

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

Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.

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.

Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.

Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects

This paper proposes a novel exploratory approach for assessing how the effects of level-2 predictors differ across level-1 units. Multilevel regression mixture models are used to identify latent classes at level-1 that differ in the effect of one or more level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to zero. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions which can be answered with this method and the issues faced when estimating multilevel regression mixtures.

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.

Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions

Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions. Although psychologists often have missing covariate data, conditional mixtures are currently fit with a conditional likelihood, treating covariates as fixed and fully observed. Under this exogenous-x approach, missing covariates are handled primarily via listwise deletion. This sacrifices efficiency and does not allow missingness to depend on observed outcomes. Here we describe a modified joint likelihood approach that (a) allows inference about parameters of the exogenous-x conditional mixture even with nonnormal covariates, unlike a conventional multivariate mixture; (b) retains all cases under missing at random assumptions; (c) yields lower bias and higher efficiency than the exogenous-x approach under a variety of conditions with missing covariates; and (d) is straightforward to implement in available commercial software. The proposed approach is illustrated with an empirical analysis predicting membership in latent classes of conduct problems. Recommendations for practice are discussed.

Longitudinal Dynamic Analyses of Depression and Academic Achievement in the Hawaiian High Schools Health Survey using Contemporary Latent Variable Change Models

The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this paper we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai'i (following McArdle, 2009; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply three recently available forms of longitudinal data analysis: (1) Dealing with Incomplete Data -- We apply these methods to cohort-sequential data with relatively large blocks of data which are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (2) Ordinal Measurement Models (Muthén & Muthén, 2006) -- We use a variety of statistical and psychometric measurement models, including ordinal measurement models to help clarify the strongest patterns of influence. (3) Dynamic Structural Equation Models (DSEMs; McArdle, 2009). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study.

Improved random-starting method for the EM algorithm for finite mixtures of regressions

Two methods for generating random starting values for the expectation maximization (EM) algorithm are compared in terms of yielding maximum likelihood parameter estimates in finite mixtures of regressions. One of these methods is ubiquitous in applications of finite mixture regression, whereas the other method is an alternative that appears not to have been used so far. The two methods are compared in two simulation studies and on an illustrative data set. The results show that the alternative method yields solutions with likelihood values at least as high as, and often higher than, those returned by the standard method. Moreover, analyses of the illustrative data set show that the results obtained by the two methods may differ considerably with regard to some of the substantive conclusions. The results reported in this article indicate that in applications of finite mixture regression, consideration should be given to the type of mechanism chosen to generate random starting values for the EM algorithm. In order to facilitate the use of the proposed alternative method, an R function implementing the approach is provided in the Appendix of the article.

Effects of the SAFE Children preventive intervention on developmental trajectories of attention-deficit/hyperactivity disorder symptoms

This study examined whether a family-based preventive intervention for inner-city children entering the first grade could alter the developmental course of attention-deficit/hyperactivity disorder (ADHD) symptoms. Participants were 424 families randomly selected and randomly assigned to a control condition (n = 192) or Schools and Families Educating Children (SAFE) Children (n = 232). SAFE Children combined family-focused prevention with academic tutoring to address multiple developmental-ecological needs. A booster intervention provided in the 4th grade to randomly assigned children in the initial intervention (n =101) evaluated the potential of increasing preventive effects. Follow-up occurred over 5 years with parents and teachers reporting on attention problems. Growth mixture models identified multiple developmental trajectories of ADHD symptoms. The initial phase of intervention placed children on more positive developmental trajectories for impulsivity and hyperactivity, demonstrating the potential for ADHD prevention in at-risk youth, but the SAFE Children booster had no additional effect on trajectory or change in ADHD indicators.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency

PURPOSE

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors.

METHODS

We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered.

RESULTS

Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents.

CONCLUSIONS

Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed.

Finite Mixtures for Simultaneously Modelling Differential Effects and Non-Normal Distributions

Regression mixture models have been increasingly applied in the social and behavioral sciences as a method for identifying differential effects of predictors on outcomes. While the typical specification of this approach is sensitive to violations of distributional assumptions, alternative methods for capturing the number of differential effects have been shown to be robust. Yet, there is still a need to better describe differential effects that exist when using regression mixture models. The current study tests a new approach that uses sets of classes (called differential effects sets) to simultaneously model differential effects and account for non-normal error distributions. Monte Carlo simulations are used to examine the performance of the approach. The number of classes needed to represent departures from normality is shown to be dependent on the degree of skew. The use of differential effects sets reduced bias in parameter estimates. Applied analyses demonstrated the implementation of the approach for describing differential effects of parental health problems on adolescent body mass index using differential effects sets approach. Findings support the usefulness of the approach which overcomes the limitations of previous approaches for handling non-normal errors.

Differential susceptibility to the effects of child temperament on maternal warmth and responsiveness

A child's difficult temperament can elicit negative parenting and inhibit positive parenting behavior. However, mothers appear to be differentially susceptible to child temperament. The author examined the differential susceptibility to the effects of a child's temperament on the mother-child interaction style (i.e., maternal warmth and responsiveness) as well as plausible reasons for these differences. With 2,130 mothers of 14-month-old infants (51% male) as subjects, a regression mixture analysis identified three latent classes with varying associations between the child's temperament and mother-child interactions: nonsusceptible class, susceptible-high class, and susceptible-low class. Mother-reported depression was most predictive of class membership. Latent class differences in the maternal self-efficacy, marital conflict, and coparenting alliance were also found. On the other hand, family income, maternal employment, and the child's gender were not significant predictors of class membership when individual and contextual resources were considered. Overall, mothers with abundant individual and family resources (i.e., less depressed, highly self-efficacious, few marital conflicts, and high coparenting alliance with their spouse) showed that their interaction style with a child would vary according to the child's temperament, whereas mothers with slender resources interacted with their children in a less warm and responsive manner, regardless of the child's temperament. The implications of these findings are also discussed.

Differential Effects of Parental Controls on Adolescent Substance Use: For Whom Is the Family Most Important?

OBJECTIVE

Social control theory assumes that the ability of social constraints to deter juvenile delinquency will be invariant across individuals. This paper tests this hypothesis and examines the degree to which there are differential effects of parental controls on adolescent substance use.

METHODS

Analyses are based on self-reported data from 7,349 10th-grade students and rely on regression mixture models to identify latent classes of individuals who may vary in the effects of parental controls on drug use.

RESULTS

All parental controls were significantly related to adolescent drug use, with higher levels of control associated with less drug use. The effects of instrumental parental controls (e.g., parental management strategies) on drug use were shown to vary across individuals, while expressive controls (e.g., parent/child attachment) had uniform effects in reducing drug use. Specifically, poor family management and more favorable parental attitudes regarding children's drug use and delinquency had stronger effects on drug use for students who reported greater attachment to their neighborhoods, less acceptance of adolescent drug use by neighborhood residents, and fewer delinquent peers, compared to those with greater community and peer risk exposure. Parental influences were also stronger for Caucasian students versus those from other racial/ethnic groups, but no differences in effects were found based on students' gender or commitment to school.

CONCLUSIONS

The findings demonstrate support for social control theory, and also help to refine and add precision to this perspective by identifying groups of individuals for whom parental controls are most influential. Further, they offer an innovative methodology that can be applied to any criminological theory to examine the complex forces that result in illegal behavior.

Methods for synthesizing findings on moderation effects across multiple randomized trials

This paper presents new methods for synthesizing results from subgroup and moderation analyses across different randomized trials. We demonstrate that such a synthesis generally results in additional power to detect significant moderation findings above what one would find in a single trial. Three general methods for conducting synthesis analyses are discussed, with two methods, integrative data analysis and parallel analyses, sharing a large advantage over traditional methods available in meta-analysis. We present a broad class of analytic models to examine moderation effects across trials that can be used to assess their overall effect and explain sources of heterogeneity, and present ways to disentangle differences across trials due to individual differences, contextual level differences, intervention, and trial design.

Using regression mixture models with non-normal data: Examining an ordered polytomous approach

Mild to moderate skew in errors can substantially impact regression mixture model results; one approach for overcoming this includes transforming the outcome into an ordered categorical variable and using a polytomous regression mixture model. This is effective for retaining differential effects in the population; however, bias in parameter estimates and model fit warrant further examination of this approach at higher levels of skew. The current study used Monte Carlo simulations; three thousand observations were drawn from each of two subpopulations differing in the effect of X on Y. Five hundred simulations were performed in each of the ten scenarios varying in levels of skew in one or both classes. Model comparison criteria supported the accurate two class model, preserving the differential effects, while parameter estimates were notably biased. The appropriate number of effects can be captured with this approach but we suggest caution when interpreting the magnitude of the effects.

Latino Immigrant Children's Health: Effects of Sociodemographic Variables and of a Preventive Intervention Program

The number of Latino immigrant children is expanding rapidly, and the factors that affect their health are multiple and interlinked. We therefore propose to describe the sociodemographic characteristics of a mostly Dominican immigrant population, to examine to what extent immigrant status and other factors play a role in determining measures of their children's health and well-being, and finally to investigate whether a home visiting intervention modified any of these factors. The data were collected as part of an evaluation of a primary prevention home visitation program for high-risk mothers and their children. Bivariate and multivariate models were constructed to investigate the factors that affected the outcome variables. We found that numerous factors, especially a composite for overall stress, affected the health and well-being of participant children. We also demonstrated that the visitation program had a positive effect on many of these outcomes. Future program planners will need to understand the strengths and weaknesses of the specific population they serve.

Chinese high school students' academic stress and depressive symptoms: gender and school climate as moderators

In a sample of 368 Chinese high school students, the present study examined the different effects of Chinese high school students' academic stress on their depressive symptoms and the moderating effects of gender and students' perceptions of school climate on the relationships between their academic stress and depressive symptoms. Regression mixture model identified two different kinds of subgroups in the effects of students' academic stress on their depressive symptoms. One subgroup contained 90% of the students. In this subgroup, the students' perceptions of academic stress from lack of achievement positively predicted their depressive symptoms. For the other 10% of the students, academic stress did not significantly predict their depressive symptoms. Next, multinomial regression analysis revealed that girls or students who had high levels of achievement orientation were more likely to be in the first subgroup. The findings suggested that gender and students' perceptions of school climate could moderate the relationships between Chinese high school students' academic stress and their depressive symptoms.

Potential causal relationship between depressive symptoms and academic achievement in the Hawaiian high schools health survey using contemporary longitudinal latent variable change models

There is a relatively consistent negative relationship between adolescent depressive symptoms and educational achievement (e.g., grade point average [GPA]). However, the causal direction for this association is less certain due to the lack of longitudinal data with both indicators measured across at least 2 time periods and due to the lack of application of more sophisticated contemporary statistical techniques. We present multivariate results from a large longitudinal cohort-sequential study of high school students (N = 7,317) with measures of self-reported depressive symptoms and self-reported GPAs across multiple time points (following McArdle, 2009, and McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001) using an ethnically diverse sample from Hawai'i. Contemporary statistical techniques included bivariate dynamic structural equation modeling (DSEM), multigroup ethnic and gender DSEMs, ordinal scale measurement of key outcomes, and imputation for incomplete longitudinal data. The findings suggest that depressive symptoms affect subsequent academic achievement and not the other way around, especially for Native Hawaiians compared with female non-Hawaiians. We further discuss the scientific, applied, and methodological-statistical implications of the results, including the need for further theorizing and research on mediating variables. We also discuss the need for increased prevention, early intervention, screening, identification, and treatment of depressive symptoms and disorders. Finally, we argue for utilization of more contemporary methodological-statistical techniques, especially when violating parametric test assumptions.

Not quite normal: Consequences of violating the assumption of normality in regression mixture models

Regression mixture models are a new approach for finding differential effects which have only recently begun to be used in applied research. This approach comes at the cost of the assumption that error terms are normally distributed within classes. The current study uses Monte Carlo simulations to explore the effects of relatively minor violations of this assumption, the use of an ordered polytomous outcome is then examined as an alternative which makes somewhat weaker assumptions, and finally both approaches are demonstrated with an applied example looking at differences in the effects of family management on the highly skewed outcome of drug use. Results show that violating the assumption of normal errors results in systematic bias in both latent class enumeration and parameter estimates. Additional classes which reflect violations of distributional assumptions are found. Under some conditions it is possible to come to conclusions that are consistent with the effects in the population, but when errors are skewed in both classes the results typically no longer reflect even the pattern of effects in the population. The polytomous regression model performs better under all scenarios examined and comes to reasonable results with the highly skewed outcome in the applied example. We recommend that careful evaluation of model sensitivity to distributional assumptions be the norm when conducting regression mixture models.

Maternal smoking during pregnancy and risk of alcohol use disorders among adult offspring

OBJECTIVE

The aim of this study was to evaluate the association between maternal smoking during pregnancy (MSP) and lifetime risk for alcohol use disorder (AUD) and to explore possible mechanisms through which MSP may be related to neurobehavioral conditions during infancy and childhood, which could, in turn, lead to increased risk for AUD.

METHOD

A sample of 1,625 individuals was followed from pregnancy for more than 40 years. Capitalizing on the long follow-up time, we used survival analysis to examine lifetime risks of AUD (diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) in relation to levels of MSP (none, <20 cigarettes/day, and ≥20 cigarettes/day). We then used structural equation modeling to test hypotheses regarding potential mechanisms, including lower birth weight, neurological abnormalities, poorer academic functioning, and behavioral dysregulation.

RESULTS

Relative to unexposed offspring, offspring of mothers who smoked 20 cigarettes per day or more exhibited greater risks for AUD (hazard ratio = 1.31, 95% CI [1.08, 1.59]). However, no differences were observed among offspring exposed to fewer than 20 cigarettes per day. In structural equation models, MSP was associated with neurobehavioral problems during infancy and childhood, which, in turn, were associated with an increased risk for adult AUD.

CONCLUSIONS

MSP was associated with an increased lifetime risk for AUD. Adverse consequences were evident from birth to adulthood. A two-pronged remedial intervention targeted at both the mother (to reduce smoking during pregnancy) and child (to improve academic functioning) may reduce the risk for subsequent AUD.

Predictors of Asian American adolescents' suicide attempts: a latent class regression analysis

Although suicide-related outcomes among Asian American adolescents are a serious public health problem in the United States, research in this area has been relatively sparse. To address this gap in the empirical literature, this study examined subgroups of Asian American adolescents for whom family, school, and peer relationships exerted differential effects on suicide attempts. Data were drawn from Waves 1 and 2 of the National Longitudinal Study of Adolescent Health dataset and included responses from a national sample of 959 Asian American adolescents (48.0% girls; average age at Wave 2 = 16.43). A latent class regression was used to assess the optimal number of latent classes (i.e., subgroups of participants) that explained the associations between family, school, and peer relationships and subsequent suicide attempts. Three latent classes were identified. Most participants belonged to a latent class in which family, school, and peer relationships were protective factors. However, stronger school relationships and peer relationships were found to be risk factors in two other latent classes. The three latent classes also differed significantly in terms of suicide attempts, gender, and acculturation. The practical implications of this study, particularly for educators and mental health professionals, are discussed.