Daniel John Bauer

Daniel John Bauer
University of North Carolina at Chapel Hill | UNC · Department of Psychology and Neuroscience

PhD Developmental Psychology

About

109
Publications
22,442
Reads
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15,458
Citations
Introduction
The aims of my program of research are to propose, evaluate, and apply quantitative modeling techniques to improve research on the development of negative social and health behaviors and psychopathology (see https://dbauer.web.unc.edu/). I also conduct training seminars and provide consultation on the application of advanced quantitative methods (see https://curranbauer.org/)
Education
December 2000 - August 2002
Odum Institute for Research in Social Science
Field of study
  • Post-Doctoral Fellowship in Applied Statistics
August 1996 - May 2000
University of North Carolina at Chapel Hill
Field of study
  • Developmental Psychology
August 1991 - May 1994
Trinity University
Field of study
  • B.A., Psychology; Minors: Mathematics and History; Concentration: Computer Science

Publications

Publications (109)
Article
Full-text available
Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation, though this may be a dubious assumption. Alternative model specifications have been proposed to reduce nonconve...
Preprint
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This 17-month longitudinal study on a representative sample of 4,361 Norwegian adults employs an observational ABAB design across six repeated assessments and three pandemic waves to systematically investigate the evolution of depressive symptomatology across all modifications of viral mitigation protocols (VMPs) from their onset to termination. Us...
Article
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Individual differences in the timing of developmental processes are often of interest in longitudinal studies, yet common statistical approaches to modeling change cannot directly estimate the timing of when change occurs. The time-to-criterion framework was recently developed to incorporate the timing of a prespecified criterion value; however, th...
Preprint
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We investigate novel parameter estimation and goodness-of-fit (GOF) assessment methods for large-scale confirmatory item factor analysis (IFA) with many respondents, items, and latent factors. For parameter estimation, we extend Urban and Bauer's (2021) deep learning algorithm for exploratory IFA to the confirmatory setting by showing how to handle...
Article
An important step in scale development and assessment is to evaluate differential item functioning (DIF) across segments of the population. Recent approaches use lasso regularization to simultaneously detect DIF in all items and avoid incorrect anchor item assumptions that incur inflated error rates for classical DIF evaluation methods. Although pr...
Article
Objective: To describe the clinical and psychosocial characteristics, and their hypothesized interrelations, as it pertains to risk for stimulant diversion (sharing, selling, or trading) for adolescents in pediatric primary care treatment for attention-deficit/hyperactivity disorder. Methods: Baseline data for 341 adolescents in a cluster-random...
Preprint
Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation. This approach is criticized because it introduces a dubious homoskedasticity assumption across classes. Alterna...
Article
Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator’s consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-the-art MML estimation procedures such as the Metropolis–Hastings Robbins–Monro (MH-RM) algorithm as...
Article
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Deciding which random effects to retain is a central decision in mixed effect models. Recent recommendations advise a maximal structure whereby all theoretically relevant random effects are retained. Nonetheless, including many random effects often leads to nonpositive definiteness. A typical remedy is to simplify the random effect structure by rem...
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...
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...
Preprint
Deep learning methods are the gold standard for non-linear statistical modeling in computer vision and in natural language processing but are rarely used in psychometrics. To bridge this gap, we present a novel deep learning algorithm for exploratory item factor analysis (IFA). Our approach combines a deep artificial neural network (ANN) model call...
Article
A common challenge in the behavioral sciences is evaluating measurement invariance, or whether the measurement properties of a scale are consistent for individuals from different groups. Measurement invariance fails when differential item functioning (DIF) exists, that is, when item responses relate to the latent variable differently across groups....
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
Determining whether measures are equally valid for all individuals is a core component of psychometric analysis. Traditionally, the evaluation of measurement invariance (MI) involves comparing independent groups defined by a single categorical covariate (e.g., men and women) to determine if there are any items that display differential item functio...
Article
Recent work reframes direct effects of covariates on items in mixture models as differential item functioning (DIF) and shows that, when present in the data but omitted from the fitted latent class model, DIF can lead to overextraction of classes. However, less is known about the effects of DIF on model performance—including parameter bias, classif...
Article
Objectives: To address increasing rates of stimulant misuse in college students, this study developed an evidence-based, brief clinical practice intervention for primary care providers (PCPs) to reduce stimulant medication diversion among young adults with ADHD. Methods: College students (N-114; 18-25 years; 68% attending universities; 24% atten...
Article
Objective: Distress tolerance (DT), the ability to withstand aversive internal states, represents an important risk factor for substance use relapse and a potential treatment target. Neurobiological research in substance using populations suggests that continued substance use could erode DT, whereas abstinence could bolster it. The current study c...
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
When generating scores to represent latent constructs, analysts have a choice between applying psychometric approaches that are principled but that can be complicated and time-intensive versus applying simple and fast, but less precise approaches, such as sum or mean scoring. We explain the reasons for preferring modern psychometric approaches: nam...
Article
It is common in addictions research for statistical analyses to include interaction effects to test moderation hypotheses. Far less commonly do researchers consider the possibility that a given predictor may exert a nonlinear effect on the outcome. This lack of attention to the possible nonlinear effects of individual predictors is problematic beca...
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
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 appr...
Article
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...
Article
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 analysi...
Article
This 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 parti...
Article
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 parasymp...
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
Full-text available
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
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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....
Article
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 (SUD...
Article
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...
Article
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. The MEPSUM model combines survival analysis and mixture modeling - specifically latent class analysis - to examine individual differen...
Article
Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e. g., a high-stable class vs. late-onset class vs. moderate-desisting class). In rand...
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
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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 para...
Article
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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-...
Article
Full-text available
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 r...
Article
Full-text available
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...
Article
Full-text available
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
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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 insta...
Article
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 c...
Article
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 struct...
Article
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...
Article
Full-text available
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can...
Article
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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...
Article
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. Ba...
Article
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Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005)3. Bauer , D. J. 2005. A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Model...
Article
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 ind...
Article
Better understanding individual differences in social, cognitive, and behavioral processes is a core goal of much psychological theory and research. Although great progress has been made toward this goal, I argue here that the classical design and analysis approach that dominates individual difference research, namely, the collection of single-time...
Article
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 fo...
Article
Full-text available
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 d...
Article
Abstract— It is critical to the progress of developmental science that researchers make proper use of statistical models for analyzing individual change over time. Latent curve models, hierarchical linear growth models, group-based trajectory models, and growth mixture models are increasingly important tools for longitudinal data analysis. To facil...
Article
Full-text available
This research examined the impact of perceived discrimination on ambulatory blood pressure (ABP) and daily level affect during social interaction. For 24 hrs, adult Black and White participants wore an ABP monitor and completed palm pilot diary entries about their social interactions. Mean level and time-trend trajectories of blood pressure and hea...
Article
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...
Chapter
This chapter gives an overview of structural equation models (SEMs) and describes some of the recent work that has shown how to incorporate multilevel models, mixture models, item response theory, and sample design into the SEM framework. It discusses two general approaches to multilevel SEM. The author refers to the first approach as the between-a...
Article
Full-text available
Reports a clarification to "Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models" by Daniel J. Bauer and Andrea M. Hussong (Psychological Methods, 2009[Jun], Vol 14[2], 101-125). In this article, the authors wrote, "To our knowledge, the multisample framework is the only available optio...
Article
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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 rel...
Article
Full-text available
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 r...
Article
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When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for bina...
Article
Applications of multilevel models have increased markedly during the past dec-ade. In incorporating lower-level predictors into multilevel models, a key interest is often whether or not a given predictor requires a random slope, that is, whether the effect of the predictor varies over upper-level units. If the variance of a ran-dom slope significan...
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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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 th...
Article
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)2. Nagin , D. 2005. Group-based modeling of development, Cambridge: Harvard University Press. [CrossRef]View all references suggested a semipara...
Article
This study hypothesized that several baseline client characteristics (i.e. age, symptoms, insight, social functioning) would significantly predict client-rated group alliance in out-patients with schizophrenia spectrum disorders. Hierarchical linear modeling (HLM) was used to evaluate the contributions of selected baseline individual client charact...
Article
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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 independen...
Article
Full-text available
The current study tested whether and why children of alcoholics (COAs) showed telescoped (adolescent) drinking initiation-to-disorder trajectories as compared with non-COAs. Using longitudinal data from a community-based sample, the authors confirmed through survival analyses that COAs progressed more quickly from initial adolescent alcohol use to...
Article
The relations among several alternative parameterizations of the binary factor analysis model and the 2-parameter item response theory model are discussed. It is pointed out that different parameterizations of factor analysis model parameters can be transformed into item response model theory parameters, and general formulas are provided. Illustrat...
Article
This study investigates the effects of sample size, factor overdetermination, and communality on the precision of factor loading estimates and the power of the likelihood ratio test of factorial invariance in multigroup confirmatory factor analysis. Although sample sizes are typically thought to be the primary determinant of precision and power, th...