Patrick J. Curran's research while affiliated with University of North Carolina at Charlotte and other places

Publications (124)

Article
Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychomet...
Article
Goal: Perceived organizational support (POS) may promote healthcare worker mental health, but organizational factors that foster POS during the COVID-19 pandemic are unknown. The goals of this study were to identify actions and policies regarding COVID-19 that healthcare organizations can implement to promote POS and to evaluate the impact of POS...
Article
Objective: The aim of this naturalistic process study was to investigate the relationship between emotional clarity and tolerance of emotional distress and depressive symptoms over the course of short-term psychodynamic psychotherapy for chronically depressed patients. Method: Weekly self-reports of emotional clarity, tolerance of emotional dist...
Article
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Background The burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May–June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries...
Article
One of the most vexing challenges facing developmental researchers today is the statistical modeling of two or more behaviors as they unfold jointly over time. Although quantitative methodologists have studied these issues for more than half a century, no widely agreed‐upon principled strategy exists to empirically analyze codevelopmental processes...
Preprint
Full-text available
Background The burden of COVID-19 in low-income and conflict-affected countries is still unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May-June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeterie...
Article
In the current study, we used an analogue integrative data analysis (IDA) design to test optimal scoring strategies for harmonizing alcohol- and drug-use consequence measures with varying degrees of alteration across four study conditions. We evaluated performance of mean, confirmatory factor analysis (CFA), and moderated nonlinear factor analysis...
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Although there is empirical evidence supporting associations between exposure to violence and engaging in physically aggressive behavior during adolescence, there is limited longitudinal research to determine the extent to which exposure to violence is a cause or a consequence of physical aggression, and most studies have not addressed the influenc...
Chapter
In this chapter, we introduce Integrative Data Analysis (IDA) for use in the field of Global Health. IDA is a novel framework for simultaneous analysis of individual-level data pooled from multiple studies. This framework has been applied to address questions about substance use, cancer, HIV, and rare diseases from studies around the world. Advanta...
Article
Conducting valid and reliable empirical research in the prevention sciences is an inherently difficult and challenging task. Chief among these is the need to obtain numerical scores of underlying theoretical constructs for use in subsequent analysis. This challenge is further exacerbated by the increasingly common need to consider multiple reporter...
Article
Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with greater heavy alcohol use and depressive symptoms in adulthood. Yet, few studies have investigated whether childhood ADHD predicts an increased association between heavy drinking and depression in adulthood when this co-occurrence becomes more common. We examined associ...
Article
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-...
Article
The structure of adolescents’ families, and thus parental forms, in the United States, have become more heterogeneous and fluid over the past several decades. These changes are due to increases in never‐married, single parents, divorce, cohabitation, same‐sex parenting, multipartnered fertility, and co‐residence with grandparents. We document curre...
Article
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 ex...
Article
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...
Article
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 modeling. 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...
Article
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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...
Article
Adolescent alcohol use is a serious public health concern. Despite advances in the theoretical conceptualization of pathways to alcohol use, researchers are limited by the statistical techniques currently available. Researchers often fit linear models and restrictive categorical models (e.g., proportional odds models) to ordinal data with many resp...
Article
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Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis is becoming an increasingly valuable tool. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item level data across...
Article
Social and behavioral scientists often measure constructs that are truly discrete counts by collapsing (or binning) the counts into a smaller number of ordinal responses. While prior quantitative research has identified a series of concerns with similar binning procedures, there has been a lack of study on the consequences of multiplying these ordi...
Article
Full-text available
Integrative data analysis (IDA) is a methodological framework that allows for the fitting of models to data that have been pooled across 2 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 developm...
Article
A few years back I became friends with a research scientist who was a member of a molecular physics lab on campus. We mostly spent our time arguing about baseball (the pitcher should always bat), but occasionally we would talk about work. He was researching the development of a space elevator that consisted of a satellite placed in geosynchronous o...
Article
Objective: Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic meth...
Article
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The last 25 years have seen significant advances in our conceptualization of alcohol use and alcohol use disorders within a developmental framework, along with advances in our empirical understanding that have been potentiated by advances in quantitative methods. These include advances in understanding the heterogeneity of trajectories of alcohol o...
Article
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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 exami...
Article
Full-text available
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...
Article
Startle habituation is present in all startle studies, whether as a dependent variable, discarded habituation block, or ignored nuisance. However, there is still much that remains unknown about startle habituation, including: (1) what is the nature of the startle habituation curve?; (2) at what point does startle habituation approach an asymptote?;...
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The current study examined the distal, proximal, and time-varying effects of parents' alcohol-related consequences on adolescents' substance use. Previous studies show that having a parent with a lifetime diagnosis of alcoholism is a clear risk factor for adolescents' own substance use. Less clear is whether the timing of a parent's alcohol-related...
Article
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Research on the relation between childhood attention-deficit/hyperactivity disorder (ADHD) and adolescent alcohol use has found mixed results. Studies are needed that operationalize alcohol use in developmentally appropriate ways and that test theoretically plausible moderators and mediators in a longitudinal framework. The current study tested chi...
Article
Longitudinal data analysis has long played a significant role in empirical research within the developmental sciences. The past decade has given rise to a host of new and exciting analytic methods for studying between-person differences in within-person change. These methods are broadly organized under the term growth curve models. The historical l...
Article
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Although previous studies show that children of alcoholic parents have higher rates of externalizing symptoms compared to their peers, it remains unclear whether the timing of children's externalizing symptoms is linked to that of their parent's alcohol-related symptoms. Using a multilevel modeling approach, we tested whether children aged 2 throug...
Article
The goal of any empirical science is to pursue the construction of a cumulative base of knowledge upon which the future of the science may be built. However, there is mixed evidence that the science of psychology can accurately be characterized by such a cumulative progression. Indeed, some argue that the development of a truly cumulative psycholog...
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There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. Howev...
Article
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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 reg...
Article
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Patterns of smoking behavior over time exhibit substantial variation, and these patterns, in turn, hold the potential to inform possible phenotypes of tobacco use and dependence. This chapter examines the literature concerning developmental trajectories of cigarette smoking between adolescence and adulthood. It also presents an empirical example th...
Article
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To investigate the relation between developmental phenotypes of parental smoking (trajectories of smoking from adolescence to adulthood) and the intergenerational transmission of smoking to their adolescent children. A longitudinal, multigenerational study of a midwestern community sample followed individuals from adolescence into adulthood and was...
Article
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This longitudinal study was designed to (a) examine changes in children's deliberate memory across the 1st grade; (b) characterize the memory-relevant aspects of their classrooms; and (c) explore linkages between the children's performance and the language their teachers use in instruction. To explore contextual factors that may facilitate the deve...
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A large literature emphasizes the importance of testing for measurement equivalence in scales that may be used as observed variables in structural equation modeling applications. When the same construct is measured across more than one developmental period, as in a longitudinal study, it can be especially critical to establish measurement equivalen...
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This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and th...
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We tested whether children show greater internalizing symptoms when their parents are actively abusing alcohol. In an integrative data analysis, we combined observations over ages 2 through 17 from two longitudinal studies of children of alcoholic parents and matched controls recruited from the community. Using a mixed modeling approach, we tested...
Article
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There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies th...
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Adopting a developmental epidemiology perspective, the current study examines sources of risk heterogeneity for internalizing symptomatology among children of alcoholic parents (COAs). Parent-based factors, including comorbid diagnoses and the number of alcoholic parents in the family, as well as child-based factors, namely child gender, formed the...
Article
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 und...
Article
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The authors examined heterogeneity in risk for externalizing symptoms in children of alcoholic parents, as it may inform the search for entry points into an antisocial pathway to alcoholism. That is, they tested whether the number of alcoholic parents in a family, the comorbid subtype of parental alcoholism, and the gender of the child predicted tr...
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This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of in...
Article
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the condition...
Chapter
Dichotomous and Ordinal Repeated MeasuresRepeated Latent Variables with Multiple IndicatorsLatent CovariatesConclusions
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Conditional Model and AssumptionsIdentificationStructural Equation Modeling ApproachInterpretation of Conditional Model EstimatesEmpirical ExampleConclusions
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Half TitleWiley Series PageTitleCopyrightContentsPreface
Chapter
Modeling Nonlinear Functions of TimeNonlinear Curve Fitting: Estimated Factor LoadingsPiecewise Linear Trajectory ModelsAlternative Parametric FunctionsLinear Transformations of the Metric of TimeConclusions Appendix 4A: Identification of Quadratic and Piecewise Latent Curve Models
Chapter
Conceptualization and Analysis of TrajectoriesThree Initial Questions About TrajectoriesBrief History of Latent Curve ModelsOrganization of the Remainder of the Book
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Dummy Variable ApproachMultiple-Group AnalysisUnknown Group MembershipConclusions Appendix 6A: Case-by-Case Approach to Analysis of Various Groups
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Repeated MeasuresGeneral Model and AssumptionsIdentificationCase-By-Case ApproachStructural Equation Model ApproachAlternative Approaches to the SEMConclusions Appendix 2A: Test Statistics, Nonnormality, and Statistical Power
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Time-Invariant CovariatesTime-Varying CovariatesSimultaneous Inclusion of Time-Invariant and Time-Varying CovariatesMultivariate Latent Curve ModelsAutoregressive Latent Trajectory ModelGeneral Equation for All ModelsImplied Moment MatricesConclusions
Chapter
Missing DataMissing Data and Alternative Metrics of TimeConclusions
Article
An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts a...
Article
Many important research hypotheses concern conditional relations in which the ef- fect of one predictor varies with the value of another. Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed- and ran- dom-effects regression. Often, these interactive effects must be further probed to fully explicate th...
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Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent proces...
Article
We are honored to have the opportunity to offer a few words in response to Molenaar's manifesto (this issue) calling for the return of the individual to scientific psychology. His arguments are clearly articulated, strongly supported, and quite timely, particularly given the recent surge in popularity of longitudinal studies of individual developme...
Article
A key strength of latent curve analysis (LCA) is the ability to model individual variability in rates of change as a function of 1 or more explanatory variables. The measurement of time plays a critical role because the explanatory variables multiplicatively interact with time in the prediction of the repeated measures. However, this interaction is...
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While past research has suggested possible seasonal trends in crime rates, this study employs a novel methodology that directly models these changes and predicts them with explanatory variables. Using a nonlinear latent curve model, seasonal fluctuations in crime rates are modeled for a large number of communities in the U.S. over a three-year peri...