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Background: With increasing data archives comprised of studies with similar measurement, optimal methods for data harmonization and measurement scoring are a pressing need. We compare three methods for harmonizing and scoring the AUDIT as administered with minimal variation across 11 samples from eight study sites within the STTR (Seek-Test-Treat-Retain) Research Harmonization Initiative. Descriptive statistics and predictive validity results for cut-scores, sum scores, and Moderated Nonlinear Factor Analysis scores (MNLFA; a psychometric harmonization method) are presented., Methods: Across the eight study sites, sample sizes ranged from 50 to 2405 and target populations varied based on sampling frame, location, and inclusion/exclusion criteria. The pooled sample included 4667 participants (82% male, 52% Black, 24% White, 13% Hispanic, and 8% Asian/ Pacific Islander; mean age of 38.9 years). Participants completed the AUDIT at baseline in all studies., Results: After logical harmonization of items, we scored the AUDIT using three methods: published cut-scores, sum scores, and MNLFA. We found greater variation, fewer floor effects, and the ability to directly address missing data in MNLFA scores as compared to cut-scores and sum scores. MNLFA scores showed stronger associations with binge drinking and clearer study differences than did other scores., Conclusions: MNLFA scores are a promising tool for data harmonization and scoring in pooled data analysis. Model complexity with large multi-study applications, however, may require new statistical advances to fully realize the benefits of this approach., (Copyright © 2018 Elsevier B.V. All rights reserved.)

A wealth of information is currently known about the epidemiology, etiology, and evaluation of drug and alcohol use across the life span. Despite this corpus of knowledge, much has yet to be learned. Many factors conspire to slow the pace of future advances in the field of substance use including the need for long-term longitudinal studies of often hard-to-reach subjects who are reporting rare and episodic behaviors. One promising option that might help move the field forward is integrative data analysis (IDA). IDA is a principled set of methodologies and statistical techniques that allow for the fitting of statistical models to data that have been pooled across multiple, independent samples. IDA offers a myriad of potential advantages including increased power, greater coverage of rare behaviors, more rigorous psychometric assessment of theoretical constructs, accelerated developmental time period under study, and enhanced reproducibility. However, IDA is not without limitations and may not be useful in a given application for a variety of reasons. The goal of this article is to describe the advantages and limitations of IDA in the study of individual development over time, particularly as it relates to trajectories of substance use. An empirical example of the measurement of polysubstance use is presented and this article concludes with recommendations for practice.

Although it is currently best-practice to directly model latent factors whenever feasible, there remain many situations in which this approach is not tractable. Recent advances in covariate-informed factor score estimation can be used to provide manifest scores that are used in second-stage analysis, but these are currently understudied. Here we extend our prior work on factor score recovery to examine the use of factor score estimates as predictors both in the presence and absence of the same covariates that were used in score estimation. Results show that whereas the relation between the factor score estimates and the criterion are typically well recovered, substantial bias and increased variability is evident in the covariate effects themselves. Importantly, using covariate-informed factor score estimates substantially, and often wholly, mitigates these biases. We conclude with implications for future research and recommendations for the use of factor score estimates in practice.

Poor physiological self-regulation has been proposed as a potential biological vulnerability for adolescent suicidality. This study tested this hypothesis by examining the effect of parasympathetic stress responses on future suicide ideation. In addition, drawing from multilevel developmental psychopathology theories, the interplay between parasympathetic regulation and friendship support, conceptualized as an external source of regulation, was examined. At baseline, 132 adolescent females (M age = 14.59, SD = 1.39) with a history of mental health concerns participated in an in vivo interpersonal stressor (a laboratory speech task) and completed self-report measures of depressive symptoms and perceived support within a close same-age female friendship. Respiratory sinus arrhythmia (RSA) was measured before and during the speech task. Suicide ideation was assessed at baseline and at 3, 6, and 9 months follow-up. The results revealed that females with greater relative RSA decreases to the laboratory stressor were at higher risk for reporting suicide ideation over the subsequent 9 months. Moreover, parasympathetic responses moderated the effect of friendship support on suicide ideation; among females with mild changes or higher relative increases in RSA, but not more pronounced RSA decreases, friendship support reduced risk for future suicide ideation. Findings highlight the crucial role of physiological and external regulation sources as protective factors for youth suicidality.

The current study demonstrates the application of an analytic approach for incorporating multiple time trends in order to examine the impact of cohort effects on individual trajectories of eight drugs of abuse. Parallel analysis of two independent, longitudinal studies of high-risk youth that span ages 10 to 40 across 23 birth cohorts between 1968 and 1991 was conducted. The two studies include the Michigan Longitudinal Study (current analytic sample of n=579 over 12 cohorts between 1980-1991 and ages 10-27) and the Adolescent/Adult and Family Development Project (current analytic sample of n=849 over 11 cohorts between 1968-1978 and ages 10-40). A series of nonlinear, multi-level growth models controlled simultaneously for cohort and age trends in substance use trajectories. Evidence was found for both age and cohort effects across most outcomes as well as several significant age-by-cohort interactions. Findings suggest cohort trends in developmental trajectories of substance use are sample and drug-specific in the adolescent and early to mid-adult years. Thus, studies that do not control for both trends may confound cohort and developmental trends in substance use. For this reason, demonstration of one analytic approach that can be used to examine both time trends simultaneously is informative for future multi-cohort longitudinal studies where change over time is of interest., Competing Interests: There are no conflicts of interest to report.

The evaluation of measurement invariance is an important step in establishing the validity and comparability of measurements across individuals. Most commonly, measurement invariance has been examined using 1 of 2 primary latent variable modeling approaches: the multiple groups model or the multiple-indicator multiple-cause (MIMIC) model. Both approaches offer opportunities to detect differential item functioning within multi-item scales, and thereby to test measurement invariance, but both approaches also have significant limitations. The multiple groups model allows 1 to examine the invariance of all model parameters but only across levels of a single categorical individual difference variable (e.g., ethnicity). In contrast, the MIMIC model permits both categorical and continuous individual difference variables (e.g., sex and age) but permits only a subset of the model parameters to vary as a function of these characteristics. The current article argues that moderated nonlinear factor analysis (MNLFA) constitutes an alternative, more flexible model for evaluating measurement invariance and differential item functioning. We show that the MNLFA subsumes and combines the strengths of the multiple group and MIMIC models, allowing for a full and simultaneous assessment of measurement invariance and differential item functioning across multiple categorical and/or continuous individual difference variables. The relationships between the MNLFA model and the multiple groups and MIMIC models are shown mathematically and via an empirical demonstration. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Objective: This study examined perceived support for autonomy-the extent to which individuals feel empowered and supported to make informed choices-among participants in the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE ETP). The aims of this study were to evaluate whether NAVIGATE, the active treatment studied in RAISE ETP, was associated with greater improvements in perceived autonomy support over the two-year intervention, compared with community care, and to examine associations between perceived autonomy support and quality of life and symptoms over time and across treatment groups., Methods: This study examined perceived autonomy support among the 404 individuals with first-episode psychosis who participated in the RAISE ETP trial (NAVIGATE, N=223; community care, N=181). Three-level conditional linear growth modeling was used given the nested data structure., Results: The results indicated that perceived autonomy support increased significantly over time for those in NAVIGATE but not in community care. Once treatment began, higher perceived autonomy support was related to higher quality of life at six, 12, and 18 months in NAVIGATE and at 12, 18, and 24 months in community care. Higher perceived autonomy support was related to improved scores on total symptoms and on excited symptoms regardless of treatment group and time., Conclusions: Overall, perceived autonomy support increased in NAVIGATE but not for those in community care and was related to improved quality of life and symptoms across both treatment groups. Future research should examine the impact of perceived autonomy support on a wider array of outcomes, including engagement, medication adherence, and functioning.

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common-as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed.

The current study explored the extent to which variations in self-report measures across studies can produce differences in the results obtained from mixture models. Data ( N = 854) come from a laboratory analogue study of methods for creating commensurate scores of alcohol- and substance-use-related constructs when items differ systematically across participants for any given measure. Items were manipulated according to four conditions, corresponding to increasing levels of alteration to item stems, response options, or both. In Study 1, results from latent class analyses (LCA) of alcohol consequences were compared across the four conditions, revealing differences in class enumeration and configuration. In Study 2, results from factor mixture models (FMM) of alcohol expectancies were compared across two of the conditions, revealing differences in patterns and magnitude of the factor loadings and thresholds. The results suggest that even subtle differences in measurement can have substantively meaningful effects on mixture model results.

Learned habitual responses to environmental stimuli allow efficient interaction with the environment, freeing cognitive resources for more demanding tasks. However, when the outcome of such actions is no longer a desired goal, established stimulus-response (S-R) associations or habits must be overcome. Among people with substance use disorders (SUDs), difficulty in overcoming habitual responses to stimuli associated with their addiction in favor of new, goal-directed behaviors contributes to relapse. Animal models of habit learning demonstrate that chronic self-administration of drugs of abuse promotes habitual responding beyond the domain of compulsive drug seeking. However, whether a similar propensity toward domain-general habitual responding occurs in humans with SUDs has remained unclear. To address this question, we used a visuomotor S-R learning and relearning task, the Hidden Association between Images Task, which employs abstract visual stimuli and manual responses. This task allows us to measure new S-R association learning and well-learned S-R association execution and includes a response contingency change manipulation to quantify the degree to which responding is habit-based, rather than goal-directed. We find that people with SUDs learn new S-R associations as well as healthy control participants do. Moreover, people with an SUD history slightly outperform controls in S-R execution. In contrast, people with SUDs are specifically impaired in overcoming well-learned S-R associations; those with SUDs make a significantly greater proportion of perseverative errors during well-learned S-R replacement, indicating the more habitual nature of their responses. Thus, with equivalent training and practice, people with SUDs appear to show enhanced domain-general habit formation.

Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or sub-populations, rather than individual cases. The current article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model.

A challenge facing nearly all studies in the psychological sciences is how to best combine multiple items into a valid and reliable score to be used in subsequent modelling. The most ubiquitous method is to compute a mean of items, but more contemporary approaches use various forms of latent score estimation. Regardless of approach, outside of large-scale testing applications, scoring models rarely include background characteristics to improve score quality. The current paper used a Monte Carlo simulation design to study score quality for different psychometric models that did and did not include covariates across levels of sample size, number of items, and degree of measurement invariance. The inclusion of covariates improved score quality for nearly all design factors, and in no case did the covariates degrade score quality relative to not considering the influences at all. Results suggest that the inclusion of observed covariates can improve factor score estimation.

Introduction: Theoretic models suggest that associations between substance use and dating violence perpetration may vary in different social contexts, but few studies have examined this proposition. The current study examined whether social control and violence in the neighborhood, peer, and family contexts moderate the associations between substance use (heavy alcohol use, marijuana, and hard drug use) and adolescent physical dating violence perpetration., Methods: Adolescents in the eighth, ninth, and tenth grades completed questionnaires in 2004 and again four more times until 2007 when they were in the tenth, 11th, and 12th grades. Multilevel analysis was used to examine interactions between each substance and measures of neighborhood, peer, and family social control and violence as within-person (time-varying) predictors of physical dating violence perpetration across eighth through 12th grade (N=2,455). Analyses were conducted in 2014., Results: Physical dating violence perpetration increased at time points when heavy alcohol and hard drug use were elevated; these associations were weaker when neighborhood social control was higher and stronger when family violence was higher. Also, the association between heavy alcohol use and physical dating violence perpetration was weaker when teens had more-prosocial peer networks and stronger when teens' peers reported more physical dating violence., Conclusions: Linkages between substance use and physical dating violence perpetration depend on substance use type and levels of contextual violence and social control. Prevention programs that address substance use-related dating violence should consider the role of social contextual variables that may condition risk by influencing adolescents' aggression propensity., (Copyright © 2015 American Journal of Preventive Medicine. All rights reserved.)

Aims: The purpose of this paper is to discover patterns of drug use initiations over time through a multiple event process survival mixture model (MEPSUM model), a novel approach for substance use and prevention research., Design: The MEPSUM model combines survival analysis and mixture modeling-specifically latent class analysis-to examine individual differences in the timing of initiation and cumulative risk of substance use over time, and is applied to cross-sectional survey data on drug initiations., Setting: Data are drawn from the 2009 National Survey on Drug Use and Health., Participants: The survey includes responses from 55 772 individuals (52.05% female)., Measurements: The age of first use of nine different types of substances are examined, including alcohol, tobacco, cocaine and non-medical use of prescription drugs., Findings: It is argued that six patterns parsimoniously describe the population's risk of initiating different substances over time, described colloquially as general abstainers; early, late and progressive soft drug users; and early and late hard drug users. Both gender and ethnicity significantly predict the patterns, with Caucasians and males having a higher risk for the hard drug-using patterns. The MEPSUM model produced stable results in this application, as the patterns are validated in a split-sample design., Conclusions: The MEPSUM model provides a statistical framework from which to evaluate patterns of risk for drug initiations over time and predict substance use trajectories relevant to public health interventions. The patterns that result from the model can be used as outcomes for subsequent investigations of etiological and mediating mechanisms., (© 2014 Society for the Study of Addiction.)

Objective: This study demonstrates how to use a shared parameter mixture model (SPMM) in longitudinal psychotherapy studies to accommodate missingness that is due to a correlation between rate of improvement and termination of therapy. Traditional growth models assume that such a relationship does not exist (i.e., assume that data are missing at random) and produce biased results if this assumption is incorrect., Method: We used longitudinal data from 4,676 patients enrolled in a naturalistic study of psychotherapy to compare results from a latent growth model and an SPMM., Results: In this data set, estimates of the rate of improvement during therapy differed by 6.50%-6.66% across the two models, indicating that participants with steeper trajectories left psychotherapy earliest, thereby potentially biasing inference for the slope in the latent growth model., Conclusion: We conclude that reported estimates of change during therapy may be underestimated in naturalistic studies of therapy in which participants and their therapists determine the end of treatment. Because non-randomly missing data can also occur in randomized controlled trials or in observational studies of development, the utility of the SPMM extends beyond naturalistic psychotherapy data., (PsycINFO Database Record (c) 2014 APA, all rights reserved.)

Integrative data analysis (IDA) is a methodological framework that allows for the fitting of models to data that have been pooled across two or more independent sources. IDA offers many potential advantages including increased statistical power, greater subject heterogeneity, higher observed frequencies of low base-rate behaviors, and longer developmental periods of study. However, a core challenge is the estimation of valid and reliable psychometric scores that are based on potentially different items with different response options drawn from different studies. In Bauer and Hussong (2009) we proposed a method for obtaining scores within an IDA called moderated nonlinear factor analysis (MNLFA). Here we move significantly beyond this work in the development of a general framework for estimating MNLFA models and obtaining scale scores across a variety of settings. We propose a five step procedure and demonstrate this approach using data drawn from n =1972 individuals ranging in age from 11 to 34 years pooled across three independent studies to examine the factor structure of 17 binary items assessing depressive symptomatology. We offer substantive conclusions about the factor structure of depression, use this structure to compute individual-specific scale scores, and make recommendations for the use of these methods in practice.

Traditional survival analysis was developed to investigate the occurrence and timing of a single event, but researchers have recently begun to ask questions about the order and timing of multiple events. A multiple event process survival mixture model is developed here to analyze nonrepeatable events measured in discrete-time that may occur at the same point in time. Building on both traditional univariate survival analysis and univariate survival mixture analysis, the model approximates the underlying multivariate distribution of hazard functions via a discrete-point finite mixture in which the mixing components represent prototypical patterns of event occurrence. The model is applied in an empirical analysis concerning transitions to adulthood, where the events under study include parenthood, marriage, beginning full-time work, and obtaining a college degree. Promising opportunities, as well as possible limitations of the model and future directions for research, are discussed., (PsycINFO Database Record (c) 2014 APA, all rights reserved.)

Although numerous studies have established a link between substance use and adult partner violence, little research has examined the relationship during adolescence and most extant research has not examined multiple substance use types. The current study used hierarchical growth modeling to simultaneously examine proximal (between-person) and time-varying (within-person) relations between cigarette, alcohol, marijuana and hard drug use and physical dating aggression across grades 8 through 12 while controlling for demographic covariates and shared risk factors. Proximal effects of marijuana use on dating aggression were found for girls and proximal effects of hard drug use on dating aggression were found for boys. Time-varying effects were found for alcohol for both boys and girls and for hard drug use for boys only. Overall, findings suggest that alcohol, marijuana and hard drug use predict whether and when adolescents engage in dating aggression and should be targeted by prevention interventions., (Published by Elsevier Ltd.)

Anatomical tracing studies in primates have revealed neural tracts from the cerebellar dentate nuclei to prefrontal cortex, implicating a cerebellar role in nonmotor processes. Experiments in rats examining the functional role of this cerebellothalamocortical pathway have demonstrated the development of visuospatial and motivational deficits following lesions of the dentate nuclei, in the absence of motor impairment. These behavioral deficits possibly occur due to structural modifications of the cerebral cortex secondary to loss of cerebellar input. The current study characterized morphological alterations in prefrontal cortex important for visuospatial and motivational processes following bilateral cerebellar dentate nuclei lesions. Rats received either bilateral electrolytic cerebellar dentate nuclei lesions or sham surgery followed by a 30-day recovery. Randomly selected Golgi-impregnated neurons in prefrontal cortex were examined for analysis. Measures of branch length and spine density revealed no differences between lesioned and sham rats in either apical or basilar arbors; however, the proportion of immature to mature spines significantly decreased in lesioned rats as compared to sham controls, with reductions of 33% in the basilar arbor and 28% in the apical arbor. Although expected pruning of branches and spines did not occur, the results are consistent with the hypothesis that cerebellar lesions influence prefrontal morphology and support the possibility that functional deficits following cerebellar dentate nuclei lesions are related to prefrontal morphological alteration., (© 2013 Published by Elsevier B.V.)

In longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed random coefficient-dependent missingness , is statistically non-ignorable and can bias parameter estimates obtained from conventional growth models that assume missing data are missing at random. This paper describes a shared-parameter mixture model (SPMM) for testing the sensitivity of growth model parameter estimates to a random coefficient-dependent missingness mechanism. Simulations show that the SPMM recovers trajectory estimates as well as or better than a standard growth model across a range of missing data conditions. The paper concludes with practical advice for longitudinal data analysts.

Psychologists often obtain ratings for target individuals from multiple informants such as parents or peers. In this article we propose a trifactor model for multiple informant data that separates target-level variability from informant-level variability and item-level variability. By leveraging item-level data, the trifactor model allows for examination of a single trait rated on a single target. In contrast to many psychometric models developed for multitrait-multimethod data, the trifactor model is predominantly a measurement model. It is used to evaluate item quality in scale development, test hypotheses about sources of target variability (e.g., sources of trait differences) versus informant variability (e.g., sources of rater bias), and generate integrative scores that are purged of the subjective biases of single informants., (PsycINFO Database Record (c) 2014 APA, all rights reserved.)

Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression., (PsycINFO Database Record (c) 2013 APA, all rights reserved.)

Integrative data analysis (IDA), a novel framework for conducting the simultaneous analysis of raw data pooled from multiple studies, offers many advantages including economy (i.e., reuse of extant data), power (i.e., large combined sample sizes), the potential to address new questions not answerable by a single contributing study (e.g., combining longitudinal studies to cover a broader swath of the lifespan), and the opportunity to build a more cumulative science (i.e., examining the similarity of effects across studies and potential reasons for dissimilarities). There are also methodological challenges associated with IDA, including the need to account for sampling heterogeneity across studies, to develop commensurate measures across studies, and to account for multiple sources of study differences as they impact hypothesis testing. In this review, we outline potential solutions to these challenges and describe future avenues for developing IDA as a framework for studies in clinical psychology.

Drawing on a large, nationally representative sample of young adults (the National Longitudinal Study of Adolescent Health; N = 15,701; M age = 29.10), we evaluated the psychometric properties of the Mini-IPIP, a 20-item inventory designed to concisely assess the 5 factors of personality. The results suggest that the Mini-IPIP has a 5-factor structure; most of the scales have acceptable reliability; all the scales have partial or full metric invariance; and the scales exhibit some degree of criterion validity. However, the absence of scalar invariance for many of the scales suggests caution when comparing personality scores among groups defined by sex or race and ethnicity. We offer practical considerations for researchers interested in using this inventory with this sample, and also suggestions for modification of the Mini-IPIP.

While numerous studies have established a link between alcohol use and partner violence in adulthood, little research has examined this relation during adolescence. The current study used multivariate growth models to examine relations between alcohol use and dating aggression across grades 8 through 12 controlling for shared risk factors (common causes) that predict both behaviors. Associations between trajectories of alcohol use and dating aggression were reduced substantially when common causes were controlled. Concurrent associations between the two behaviors were significant across nearly all grades but no evidence was found for prospective connections from prior alcohol use to subsequent dating aggression or vice versa. Findings suggest that prevention efforts should target common causes of alcohol use and dating aggression.

We examined the hypothesis that family, peer and neighborhood violence would moderate relations between heavy alcohol use and adolescent dating violence perpetration such that relations would be stronger for teens in violent contexts. Random coefficients growth models were used to examine the main and interaction effects of heavy alcohol use and four measures of violence (family violence, friend dating violence, friend peer violence and neighborhood violence) on levels of physical dating violence perpetration across grades 8 through 12. The effects of heavy alcohol use on dating violence tended to diminish over time and were stronger in the spring than in the fall semesters. Consistent with hypotheses, across all grades, relations between heavy alcohol use and dating violence were stronger for teens exposed to higher levels of family violence and friend dating violence. However, neither friend peer violence nor neighborhood violence moderated relations between alcohol use and dating violence. Taken together, findings suggest that as adolescents grow older, individual and contextual moderators may play an increasingly important role in explaining individual differences in relations between alcohol use and dating violence. Implications for the design and evaluation of dating abuse prevention programs are discussed.

Psychologists have long been interested in characterizing individual differences in change over time. It is often plausible to assume that the distribution of these individual differences is continuous in nature, yet theory is seldom so specific as to designate its parametric form (e.g., normal). Semiparametric groups-based trajectory models (SPGMs) were thus developed to provide a discrete approximation for continuously distributed growth of unknown form. Previous research has demonstrated the adequacy of the approximation provided by SPGM but only under relatively narrow, theoretically optimal conditions. Under alternative conditions, which may be more common in practice (e.g., higher dimension random effects, smaller sample sizes), this study shows that approximation adequacy can suffer. Furthermore, this study also evaluates whether SPGM's discrete approximation is preferable to a parametric trajectory model that assumes normally distributed random effects when in fact the distribution is modestly nonnormal. The answer is shown to depend on distributional characteristics of both repeated measures (binary or continuous) and random effects (bimodal or skewed). Implications for practice are discussed in light of empirical examples on externalizing behavior.

There is growing evidence supporting the role of inflammation in early brain injury and cerebral vasospasm following subarachnoid hemorrhage (SAH). Matrix metalloproteinases (MMPs) are released by inflammatory cells and can mediate early brain injury via disruption of the extracellular matrix and mediate vasospasm by cleaving endothelin-1 into vasoactive fragments. We hypothesize that inflammation marked by neutrophil elevation and MMP-9 release in human SAH is associated with vasospasm and with poor clinical outcome. We enrolled consecutive SAH subjects (N = 55), banked serial blood and cerebrospinal fluid (CSF) samples, and evaluated their 3-month modified Rankin scores (mRS). Vasospasm was defined as >50% vessel caliber reduction on angiography 6-8 days post-SAH. A poor outcome was defined as mRS > 2. We compared blood leukocyte and neutrophil counts during post-SAH days 0-14 with respect to vasospasm and 3-month outcome. In a subset of SAH subjects (N = 35), we compared blood and CSF MMP-9 by enzyme-linked immunosorbent assay (ELISA) on post-SAH days 0-1, 2-3, 4-5, 6-8, and 10-14 with respect to vasospasm and to 3-month outcome. Persistent elevation of blood leukocyte (p = 0.0003) and neutrophil (p = 0.0002) counts during post-SAH days 0-14 are independently associated with vasospasm after adjustment for major confounders. In the same time period, blood neutrophil count (post-SAH days 2-3, p = 0.018), blood MMP-9 (post-SAH days 4-5, p = 0.045), and CSF MMP-9 (post-SAH days 2-3, p = 0.05) are associated with poor 3-month SAH clinical outcome. Neutrophil count correlates with blood MMP-9 (post-SAH days 6-8, R = 0.39; p = 0.055; post-SAH days 10-14, R = 0.79; p < 0.0001), and blood MMP-9 correlates with CSF MMP-9 (post-SAH days 4-5, R = 0.72; p = 0.0002). Elevation of CSF MMP-9 during post-SAH days 0-14 is associated with poor 3-month outcome (p = 0.0078). Neither CSF nor blood MMP-9 correlates with vasospasm. Early rise in blood neutrophil count and blood and CSF MMP-9 are associated with poor 3-month SAH clinical outcome. In blood, neutrophil count correlates with MMP-9 levels, suggesting that neutrophils may be an important source of blood MMP-9 early in SAH. Similarly, CSF and blood MMP-9 correlate positively early in the course of SAH, suggesting that blood may be an important source of CSF MMP-9. Blood and CSF MMP-9 are associated with clinical outcome but not with vasospasm, suggesting that MMP-9 may mediate brain injury independent of vasospasm in SAH. Future in vitro studies are needed to investigate the role of MMP-9 in SAH-related brain injury. Larger clinical studies are needed to validate blood and CSF MMP-9 as potential biomarkers for SAH outcome.

Previous research has compared methods of estimation for fitting multilevel models to binary data, but there are reasons to believe that the results will not always generalize to the ordinal case. This article thus evaluates (a) whether and when fitting multilevel linear models to ordinal outcome data is justified and (b) which estimator to employ when instead fitting multilevel cumulative logit models to ordinal data, maximum likelihood (ML), or penalized quasi-likelihood (PQL). ML and PQL are compared across variations in sample size, magnitude of variance components, number of outcome categories, and distribution shape. Fitting a multilevel linear model to ordinal outcomes is shown to be inferior in virtually all circumstances. PQL performance improves markedly with the number of ordinal categories, regardless of distribution shape. In contrast to binary data, PQL often performs as well as ML when used with ordinal data. Further, the performance of PQL is typically superior to ML when the data include a small to moderate number of clusters (i.e., ≤ 50 clusters).

Many approaches have been proposed to estimate interactions among latent variables. These methods often assume a specific functional form for the interaction, such as a bilinear interaction. Theory is seldom specific enough to provide a functional form for an interaction, however, so a more exploratory, diagnostic approach may often be required. Bauer (2005) proposed a semiparametric approach that allows for the estimation of interaction effects of unknown functional form among latent variables. A structural equation mixture model (SEMM) is first fit to the data. Then an approximation of the interaction is obtained by aggregating over the mixing components. A simulation study is used to examine the performance of this semiparametric approach to two parametric approaches: the latent moderated structures approach (Klein & Moosbrugger, 2000) and the unconstrained product-indicator approach (Marsh, Wen, & Hau, 2004). Data were generated from four functional forms: main effects only, quadratic trend, bilinear interaction, and exponential interaction. Estimates of bias and root mean squared error of approximation were calculated by comparing the surface used to generate the data and the model-implied surface constructed from each approach. As expected, the parametric approaches were more efficient than the SEMM. For the main effects model, bias was similar for both the SEMM and parametric approaches. For the bilinear interaction, the parametric approaches provided nearly identical results, although the SEMM approach was slightly more biased. When the parametric approaches assumed a bilinear interaction and the data were generated from a quadratic trend or an exponential interaction, the parametric approaches generated biased estimates of the true surface. The SEMM approach approximated the true data generation surface with a similarly low level of bias for all the nonlinear surfaces. For example, Figure 1 shows the true surface for the bilinear interaction along with the SEMM estimated average surface. The results suggest that the SEMM approach can provide a relatively unbiased approximation to variety of nonlinear relationships among latent variables.

Partially clustered designs, where clustering occurs in some conditions and not others, are common in psychology, particularly in prevention and intervention trials. This article reports results from a simulation comparing 5 approaches to analyzing partially clustered data, including Type I errors, parameter bias, efficiency, and power. Results indicate that multilevel models adapted for partially clustered data are relatively unbiased and efficient and consistently maintain the nominal Type I error rate when using appropriate degrees of freedom. To attain sufficient power in partially clustered designs, researchers should attend primarily to the number of clusters in the study. An illustration using data from a partially clustered eating disorder prevention trial is provided., (© 2011 American Psychological Association)

We describe the draft genome of the microcrustacean Daphnia pulex, which is only 200 megabases and contains at least 30,907 genes. The high gene count is a consequence of an elevated rate of gene duplication resulting in tandem gene clusters. More than a third of Daphnia's genes have no detectable homologs in any other available proteome, and the most amplified gene families are specific to the Daphnia lineage. The coexpansion of gene families interacting within metabolic pathways suggests that the maintenance of duplicated genes is not random, and the analysis of gene expression under different environmental conditions reveals that numerous paralogs acquire divergent expression patterns soon after duplication. Daphnia-specific genes, including many additional loci within sequenced regions that are otherwise devoid of annotations, are the most responsive genes to ecological challenges.

The current study examined the role of heavy alcohol use in the developmental process of desistance in physical dating aggression during adolescence. Using longitudinal data spanning grades 8 through 12 we tested the hypotheses that (a) higher levels of early heavy alcohol use would be associated with decreased deceleration from dating aggression during late adolescence and (b) higher levels of heavy alcohol use during time-points in late adolescence would be contemporaneously associated with elevated levels of dating aggression at those same time points. Contrary to expectations, findings indicate that the effects of both early and continuing heavy alcohol use on dating aggression were strong during early adolescence but tended to diminish over time. Unexpectedly, the contemporaneous effects of alcohol use on dating aggression were stronger in the spring than in the fall semesters. Implications for prevention and for understanding developmental relations between the two behaviors are discussed.

Recently identified pathways from the dentate nuclei of the cerebellum to the rostral cerebral cortex via the thalamus suggest a cerebellar role in frontal and prefrontal non-motor functioning. Disturbance of cerebellar morphology and connectivity, particularly involving these cerebellothalamocortical (CTC) projections, has been implicated in motivational and cognitive deficits. The current study explored the effects of CTC disruption on motivation in male Long Evans rats. The results of two experiments demonstrate that electrolytic lesions of the cerebellar dentate nuclei lower breaking points on an operant conditioning progressive ratio schedule and decrease open field exploration compared to sham controls. Changes occurred in the absence of motor impairment, assessed via lever pressing frequency and rotarod performance. Similar elevated plus maze performances between lesioned and sham animals indicated that anxiety did not influence task performance. Our results demonstrate hedonic and purposive motivational reduction and suggest a CTC role in global motivational processes. These implications are discussed in terms of psychiatric disorders such as schizophrenia and autism, in which cerebellar damage and motivational deficits often present concomitantly., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Longitudinal models are becoming increasingly prevalent in the behavioral sciences, with key advantages including increased power, more comprehensive measurement, and establishment of temporal precedence. One particularly salient strength offered by longitudinal data is the ability to disaggregate between-person and within-person effects in the regression of an outcome on a time-varying covariate. However, the ability to disaggregate these effects has not been fully capitalized upon in many social science research applications. Two likely reasons for this omission are the general lack of discussion of disaggregating effects in the substantive literature and the need to overcome several remaining analytic challenges that limit existing quantitative methods used to isolate these effects in practice. This review explores both substantive and quantitative issues related to the disaggregation of effects over time, with a particular emphasis placed on the multilevel model. Existing analytic methods are reviewed, a general approach to the problem is proposed, and both the existing and proposed methods are demonstrated using several artificial data sets. Potential limitations and directions for future research are discussed, and recommendations for the disaggregation of effects in practice are offered.

The proper use of statistical models for analyzing individual change over time is critical for the progress of developmental science. Latent curve models, hierarchical linear growth models, group-based trajectory models, and growth mixture models constitute increasingly important tools for longitudinal data analysis. To facilitate their understanding and use, this paper clarifies similarities and differences between these models, with particular attention to the assumptions they make about individual development. An example shows how the results and interpretation vary across model types. Discussion centers on reviewing the strengths and limitations of each approach for developmental research.

Objective: This research examined the impact of perceived discrimination on ambulatory blood pressure (ABP) and daily level affect during social interaction., Design: For 24 hrs, adult Black and White participants wore an ABP monitor and completed palm pilot diary entries about their social interactions., Main Outcome Measures: Mean level and time-trend trajectories of blood pressure and heart rate were examined as well as mean level measures of positive and negative affect after stressful and nonstressful social interactions., Results: Analyses showed that, after controlling for important covariates, perceived discrimination predicted the slopes of both wake and nocturnal ABP responses, with those who reported more discrimination having steeper daytime trajectories for systolic and diastolic blood pressure and less nighttime dipping in heart rate over time as compared to those who had reported relatively infrequent discrimination. High levels of perceived discrimination were also related to positive and negative affective responses after stressful encounters., Conclusions: These results suggest that, regardless of race, perceived discrimination is related to cardiovascular and affective responses that may increase vulnerability to pathogenic processes., (PsycINFO Database Record (c) 2010 APA, all rights reserved)

The person-oriented approach seeks to match theories and methods that portray development as a holistic, highly interactional, and individualized process. Over the past decade, this approach has gained popularity in developmental psychopathology research, particularly as model-based varieties of person-oriented methods have emerged. Although these methods allow some principles of person-oriented theory to be tested, little attention has been paid to the fact that these methods cannot test other principles, and may actually be inconsistent with certain principles. Lacking clarification regarding which aspects of person-oriented theory are testable under which person-oriented methods, assumptions of the methods have sometimes been presented as testable hypotheses or interpreted as affirming the theory. This general blurring of the line between person-oriented theory and method has even led to the occasional perception that the method is the theory and vice versa. We review assumptions, strengths, and limitations of model-based person-oriented methods, clarifying which theoretical principles they can test and the compromises and trade-offs required to do so.

The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixtures of linear structural equations. Second, it provides several examples showing how the method works and how it is implemented and interpreted in practical applications. In particular, this article examines the potentially nonlinear relationships between positive and negative affect and cognitive processing. Third, a recommended display format for illustrating latent bivariate relationships is demonstrated. Finally, the article describes an R package and an online utility that generate these displays automatically.

When conducting an integrative analysis of data obtained from multiple independent studies, a fundamental problem is to establish commensurate measures for the constructs of interest. Fortunately, procedures for evaluating and establishing measurement equivalence across samples are well developed for the linear factor model and commonly used item response theory models. A newly proposed moderated nonlinear factor analysis model generalizes these models and procedures, allowing for items of different scale types (continuous or discrete) and differential item functioning across levels of categorical and/or continuous variables. The potential of this new model to resolve the problem of measurement in integrative data analysis is shown via an empirical example examining changes in alcohol involvement from ages 10 to 22 years across 2 longitudinal studies., ((PsycINFO Database Record (c) 2009 APA, all rights reserved).)

The current study examined differences between children of alcoholic (COAs) and nonalcoholic parents in their experience of negative life events across 3 longitudinal studies together spanning the first 3 decades of life. The authors posited that COAs would differ from their peers in the life domains in which they are vulnerable to stressors, in the recurrence of stressors, and in the severity of stressors. Scale- and item-level analyses of adjusted odds ratios based on stressors across 7 life domains showed that COAs consistently reported greater risk for stressors in the family domain. COAs were also more likely to experience stressors repetitively and to rate their stressors as more severe (in adulthood). Implications for prevention and intervention programs targeting this risk group are discussed., (Copyright 2008 APA, all rights reserved.)

Conventional growth models assume that the random effects describing individual trajectories are conditionally normal. In practice, this assumption may often be unrealistic. As an alternative, Nagin (2005) suggested a semiparametric group-based approach (SPGA) which approximates an unknown, continuous distribution of individual trajectories with a mixture of group trajectories. Prior simulations ( Brame, Nagin, & Wasserman, 2006 ; Nagin, 2005 ) indicated that SPGA could generate nearly-unbiased estimates of means and variances of a nonnormal distribution of individual trajectories, as functions of group-trajectory estimates. However, these studies used few random effects-usually only a random intercept. Based on the analytical relationship between SPGA and adaptive quadrature, we hypothesized that SPGA's ability to approximate (a) random effect variances/covariances and (b) effects of time-invariant predictors of growth should deteriorate as the dimensionality of the random effects distribution increases. We expected this problem to be mitigated by correlations among the random effects (highly correlated random effects functioning as fewer dimensions) and sample size (larger N supporting more groups). We tested these hypotheses via simulation, varying the number of random effects (1, 2, or 3), correlation among the random effects (0 or .6), and N (250, 500). Results indicated that, as the number of random effects increased, SPGA approximations remained acceptable for fixed effects, but became increasingly negatively biased for random effect variances. Whereas correlated random effects and larger N reduced this underestimation, correlated random effects sometimes distorted recovery of predictor effects. To illustrate this underestimation, Figure 1 depicts SPGA's approximation of the intercept variance from a three correlated random effect generating model (N < eqid1 > 500). These results suggest SPGA approximations are inadequate for the nonnormal, high-dimensional distributions of individual trajectories often seen in practice.[Figure: see text].

Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as independent, given that these individuals are not placed into groups. Although this kind of design is common in intervention research, the statistical models applied to evaluate the treatment effects are usually inconsistent with the resulting data structure, potentially leading to biased inferences. This article presents an alternative model that explicitly accounts for the fact that only treated participants are grouped. In addition to providing a useful test of the overall treatment effect, this approach also permits one to formally determine the extent to which treatment effects vary over treatment groups and whether there is evidence that individuals within treatment groups become similar to one another. This strategy is demonstrated with data from the Reconnecting Youth program for high school students at risk of school failure and behavioral disorders.

Multilevel models have come to play an increasingly important role in many areas of social science research. However, in contrast to other modeling strategies, there is currently no widely used approach for graphically diagramming multilevel models. Ideally, such diagrams would serve two functions: to provide a formal structure for deriving the underlying equations and to provide a mechanism for clearly and efficiently communicating the model structure, assumptions, and empirical results. Here the authors propose a path diagramming approach for multilevel models that seeks to meet these goals. The authors begin with a description of the core components of their proposed diagramming system and establish tracing rules for the direct derivation of model equations. They then demonstrate their approach using several published empirical multilevel applications and conclude with potential limitations and directions for future developments., ((PsycINFO Database Record (c) 2007 APA, all rights reserved).)

Background: Functional and comparative studies of insect genomes have shed light on the complement of genes, which in part, account for shared morphologies, developmental programs and life-histories. Contrasting the gene inventories of insects to those of the nematodes provides insight into the genomic changes responsible for their diversification. However, nematodes have weak relationships to insects, as each belongs to separate animal phyla. A better outgroup to distinguish lineage specific novelties would include other members of Arthropoda. For example, crustaceans are close allies to the insects (together forming Pancrustacea) and their fascinating aquatic lifestyle provides an important comparison for understanding the genetic basis of adaptations to life on land versus life in water., Results: This study reports on the first characterization of cDNA libraries and sequences for the model crustacean Daphnia pulex. We analyzed 1,546 ESTs of which 1,414 represent approximately 787 nuclear genes, by measuring their sequence similarities with insect and nematode proteomes. The provisional annotation of genes is supported by expression data from microarray studies described in companion papers. Loci expected to be shared between crustaceans and insects because of their mutual biological features are identified, including genes for reproduction, regulation and cellular processes. We identify genes that are likely derived within Pancrustacea or lost within the nematodes. Moreover, lineage specific gene family expansions are identified, which suggest certain biological demands associated with their ecological setting. In particular, up to seven distinct ferritin loci are found in Daphnia compared to three in most insects. Finally, a substantial fraction of the sampled gene transcripts shares no sequence similarity with those from other arthropods. Genes functioning during development and reproduction are comparatively well conserved between crustaceans and insects. By contrast, genes that were responsive to environmental conditions (metal stress) and not sex-biased included the greatest proportion of genes with no matches to insect proteomes., Conclusion: This study along with associated microarray experiments are the initial steps in a coordinated effort by the Daphnia Genomics Consortium to build the necessary genomic platform needed to discover genes that account for the phenotypic diversity within the genus and to gain new insights into crustacean biology. This effort will soon include the first crustacean genome sequence.

Aims: To examine the prevalence and correlates of crystal methamphetamine use among young adults in the USA., Design: Cross-sectional analyses of nationally representative data of young adults from the National Longitudinal Study of Adolescent Health (Add Health)., Setting: In-home interviews conducted in 2001-02., Participants: A total of 14,322 respondents aged 18-26 years., Measurements: Past year and 30-day crystal methamphetamine use; crime/violence (ever arrested, past year drug selling, past year violent behavior) and sexual risk behaviors (multiple partners, poor condom use, regretted sex, sex for money)., Findings: Prevalence of past year crystal methamphetamine use was 2.8%; past month was 1.3%. White or Native American race, residence in the west or south, having an ever-incarcerated father, marijuana, cocaine, intravenous drug use and high novelty seeking were associated with greater likelihood of past year use in multivariate analyses. Compared to marijuana and cocaine users, crystal methamphetamine users were more likely to be male, unemployed, reside in the west or south, have an ever-incarcerated father and less likely to be black or Hispanic. Frequent users were no different from occasional users, except being more likely to have dropped out of school. Although crystal methamphetamine use was associated with crime and risky sex, controlling for covariates greatly diminished this relationship., Conclusions: Most users are occasional users, but any past year use is associated with risky and antisocial behaviors, including other illicit drug use. Further research is needed to examine how other drug addiction is associated with methamphetamine use, and to identify longitudinal antecedents for prevention.

This paper contributes new evidence on the association between immigrant status and health by describing and attempting to explain patterns of co-occurring sex and drug use behaviors among Asian and Latino adolescents in the United States. Nine patterns of sex and drug use behaviors were identified from a cluster analysis of data from 3,924 Asian and Latino youth (grades 7-12) who participated in the National Longitudinal Study of Adolescent Health (Add Health). The relationship between immigrant status and risk cluster membership was evaluated with multinomial logistic regression. Compared to foreign-born youth, U.S. born Asian and Latino adolescents were more likely to engage in sex and drug risk behaviors. Family and residential characteristics associated with immigrant status partly accounted for this finding. The results indicate that among Asian and Latino adolescents, assimilation to U.S. risk behavior norms occurs rapidly and is evident by the second generation.

Toxicogenomics has provided innovative approaches to chemical screening, risk assessment, and predictive toxicology. If applied to ecotoxicology, genomics tools could greatly enhance the ability to understand the modes of toxicity in environmentally relevant organisms. Daphnia magna, a small aquatic crustacean, is considered a "keystone" species in ecological food webs and is an indicator species for toxicant exposure. Our objective was to demonstrate the potential utility of gene expression profiling in ecotoxicology by identifying novel biomarkers and uncovering potential modes of action in D. magna. Using a custom D. magna cDNA microarray, we identified distinct expression profiles in response to sublethal copper, cadmium, and zinc exposures and discovered specific biomarkers of exposure including two probable metallothioneins, and a ferritin mRNA with a functional IRE. The gene expression patterns support known mechanisms of metal toxicity and reveal novel modes of action including zinc inhibition of chitinase activity. By integrating gene expression profiling into an environmentally important organism, this study provides experimental support for the utility of ecotoxicogenomics.

Objectives: We used nationally representative data to examine whether individuals' sexual and drug behavior patterns account for racial disparities in sexually transmitted disease (STD) and HIV prevalence., Methods: Data were derived from wave III of the National Longitudinal Study of Adolescent Health. Participants were aged 18 to 26 years old; analyses were limited to non-Hispanic Blacks and Whites. Theory and cluster analyses yielded 16 unique behavior patterns. Bivariate analyses compared STD and HIV prevalences for each behavior pattern, by race. Logistic regression analyses examined within-pattern race effects before and after control for covariates., Results: Unadjusted odds of STD and HIV infection were significantly higher among Blacks than among Whites for 11 of the risk behavior patterns assessed. Across behavior patterns, covariates had little effect on reducing race odds ratios., Conclusions: White young adults in the United States are at elevated STD and HIV risk when they engage in high-risk behaviors. Black young adults, however, are at high risk even when their behaviors are normative. Factors other than individual risk behaviors and covariates appear to account for racial disparities, indicating the need for population-level interventions.