# Kenneth A BollenUniversity of North Carolina at Chapel Hill | UNC · Department of Sociology

Kenneth A Bollen

PhD, MA, BA

## About

250

Publications

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## Publications

Publications (250)

Importance
Differences in neighborhood socioeconomic characteristics are important considerations in understanding differences in risk vs resilience in mental health. Neighborhood disadvantage is associated with alterations in the function and structure of threat neurocircuitry.
Objective
To investigate associations of neighborhood disadvantage wi...

Resilience is a dynamic process of recovery after trauma, but in most studies it is conceptualized as the absence of specific psychopathology following trauma. Using the large emergency department AURORA study (n=1,865, 63% women), we took a longitudinal, dynamic and transdiagnostic approach to define a static resilience (r) factor, that could expl...

Childhood trauma is a known risk factor for trauma and stress-related disorders in adulthood. However, limited research has investigated the impact of childhood trauma on brain structure linked to later posttraumatic dysfunction. We investigated the effect of childhood trauma on white matter microstructure after recent trauma and its relationship w...

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, two limitations ex...

Measurement error is ubiquitous in many variables - from blood pressure recordings in physiology to intelligence measures in psychology. Structural equation models (SEMs) account for the process of measurement by explicitly distinguishing between latent variables and their measurement indicators. Users often fit entire SEMs to data, but this can fa...

Methodological questions related to research design, measurement, and analysis have been intertangled with how we know, learn, and predict social phenomena. They are questions shared by sociology and all the sciences. The methodological articles published in Social Forces (SF) have been driven by and have driven trends in sociology and sociological...

Importance:
Adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure are common and have higher incidence among socioeconomically disadvantaged populations. Pain, depression, avoidance of trauma reminders, reexperiencing trauma, anxiety, hyperarousal, sleep disruption, and nightmares have been reported. Wrist-wearable device...

Aims:
Childhood adversities (CAs) predict heightened risks of posttraumatic stress disorder (PTSD) and major depressive episode (MDE) among people exposed to adult traumatic events. Identifying which CAs put individuals at greatest risk for these adverse posttraumatic neuropsychiatric sequelae (APNS) is important for targeting prevention intervent...

The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors....

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations...

Study objective
To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision.
Methods
Derivation (n=1,282, 19 ED sites) and validation (n=282, 11 separate ED sites) data were obtaine...

It is common practice for psychologists to specify models with latent variables to represent concepts that are difficult to directly measure. Each latent variable needs a scale, and the most popular method of scaling as well as the default in most structural equation modeling (SEM) software uses a scaling or reference indicator. Much of the time, t...

Objective
Whether short-term, low-potency opioid prescriptions for acute pain lead to future at-risk opioid use remains controversial and inadequately characterized. Our objective was to measure the association between emergency department (ED) opioid analgesic exposure after a physical, trauma-related event and subsequent opioid use. We hypothesiz...

Anxiety sensitivity, or fear of anxious arousal, is cross-sectionally associated with a wide array of adverse posttraumatic neuropsychiatric sequelae, including symptoms of posttraumatic stress disorder, depression, anxiety, sleep disturbance, pain, and somatization. The current study utilizes a large-scale, multi-site, prospective study of trauma...

Visual components of trauma memories are often vividly re-experienced by survivors with deleterious consequences for normal function. Neuroimaging research on trauma has primarily focused on threat-processing circuitry as core to trauma-related dysfunction. Conversely, limited attention has been given to visual circuitry which may be particularly r...

Hippocampal impairments are reliably associated with post-traumatic stress disorder (PTSD); however, little research has characterized how increased threat-sensitivity may interact with arousal responses to alter hippocampal reactivity, and further how these interactions relate to the sequelae of trauma-related symptoms. In a sample of individuals...

This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996 Bollen, K....

Background
A better understanding of the extent to which prior occurrences of posttraumatic stress disorder (PTSD) and major depressive episode (MDE) predict psychopathological reactions to subsequent traumas might be useful in targeting posttraumatic preventive interventions.
Methods
Data come from 1306 patients presenting to 29 U.S. emergency de...

Background
Cross-sectional studies have found that individuals with posttraumatic stress disorder (PTSD) exhibit deficits in autonomic functioning. While PTSD rates are twice as high in women compared to men, sex differences in autonomic functioning are relatively unknown among trauma-exposed populations. The current study used a prospective design...

Importance
A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions.
Objecti...

Substantive theory rarely provides specific enough information to guide our selection of the optimal model for longitudinal data. Instead, researchers are more likely to rely on models common to their field, even if they are not appropriate. The purpose of our study is to assess whether researchers can use overall goodness-of-fit measures from stru...

Structural equation models (SEMs) are widely used to handle multiequation systems that involve latent variables, multiple indicators, and measurement error. Maximum likelihood (ML) and diagonally weighted least squares (DWLS) dominate the estimation of SEMs with continuous or categorical endogenous variables, respectively. When a model is correctly...

Self-rated health (SRH) is ubiquitous in population health research. It is one of the few consistent health measures in longitudinal studies. Yet, extant research offers little guidance on its longitudinal trajectory. The literature on SRH suggests several possibilities, including SRH as (1) a more fixed, longer-term view of past, present, and anti...

Objective
Emergency caregivers provide initial care to women sexual assault (SA) survivors. An improved understanding of the issues facing this population can aide emergency care practitioners in providing high quality care. The goal of this study was to share the experiences of women SA survivors with the emergency care practitioners that care for...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Clinically significant new or worsening pain (CSNWP) is a common, yet often overlooked, sequelae of sexual assault. Little is known regarding factors influencing the development of CSNWP in sexual assault survivors. The current study used data from a recently completed prospective study to evaluate whether posttraumatic alterations in arousal and r...

The model-implied instrumental variable (MIIV) estimator is an equation-by-equation estimator of structural equation models that is more robust to structural misspecifications than full information estimators. Previous studies have concentrated on endogenous variables that are all continuous (MIIV-2SLS) or all ordinal . We develop a unified MIIV ap...

Prior studies highlight how threat-related arousal may impair hippocampal function. Hippocampal impairments are reliably associated with post-traumatic stress disorder (PTSD); however, little research has characterized how increased threat-sensitivity may drive arousal responses to alter hippocampal reactivity, and further how these alterations rel...

Self-rated health (SRH) is one of the most important social science measures of health. Yet its measurement properties remain poorly understood. Most studies ignore the measurement error in SRH despite the bias resulting from even random measurement error. Our goal is to estimate the measurement reliability of SRH in contemporaneous, retrospective,...

Introduction
This study examined the perspectives of female patients who had been sexually assaulted regarding the quality of care provided by sexual assault nurse examiners, including whether the patients’ perspectives varied by their demographic characteristics and health status before the assault.
Methods
A total of 695 female patients who rece...

Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes.
Approach:
1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three stan...

Neurobiological markers of future susceptibility to posttraumatic stress disorder (PTSD) may facilitate identification of vulnerable individuals in the early aftermath of trauma. Variability in resting-state networks (RSNs), patterns of intrinsic functional connectivity across the brain, has previously been linked to PTSD, and may thus be informati...

Background:
Approximately, 100,000 US women receive emergency care after sexual assault each year, but no large-scale study has examined the incidence of posttraumatic sequelae, receipt of health care, and frequency of assault disclosure to providers. The current study evaluated health outcomes and service utilization among women in the 6 weeks af...

Methodological development of the model-implied instrumental variable (MIIV) estimation framework has proved fruitful over the last three decades. Major milestones include Bollen's (Psychometrika 61(1):109-121, 1996) original development of the MIIV estimator and its robustness properties for continuous endogenous variable SEMs, the extension of th...

Structural misspecifications in factor analysis include using the wrong number of factors and omitting cross loadings or correlated errors. The impact of these errors on factor loading estimates is understudied. Factor loadings underlie our assessments of the validity and reliability of indicators. Thus knowing how structural misspecifications affe...

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artific...

Introduction
Worldwide, an estimated 10%–27% of women are sexually assaulted during their lifetime. Despite the enormity of sexual assault as a public health problem, to our knowledge, no large-scale prospective studies of experiences and recovery over time among women presenting for emergency care after sexual assault have been performed.
Methods...

Estimation methods for structural equation models with interactions of latent variables were compared in several studies. Yet none of these studies examined models that were structurally misspecified. Here, the model-implied instrumental variable 2-stage least square estimator (MIIV-2SLS; Bollen, 1995; Bollen & Paxton, 1998), the 2-stage method of...

Methodological development of the Model-implied Instrumental Variable (MIIV) estimation framework has proved fruitful over the last three decades. Major milestones include Bollen's (1996) original development of the MIIV estimator and its robustness properties for continuous endogenous variable SEMs, the extension of the MIIV estimator to ordered c...

Researchers in the medical, health, and social sciences routinely encounter ordinal variables such as self‐reports of health or happiness. When modelling ordinal outcome variables, it is common to have covariates, for example, attitudes, family income, retrospective variables, measured with error. As is well known, ignoring even random error in cov...

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals’ processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically...

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically...

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article...

Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs fr...

Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. We propose an extension to the existing MIIV-2SLS estimator that utilizes Bayesian model ave...

Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT mo...

Students’ causal attributions about the reasons underlying their academic successes are important because of the influence of those attributions on academic motivation. We investigated whether students’ success attributions tend to be similar across academic subjects versus specific to academic domain, and whether domain-generality or specificity c...

Most researchers acknowledge that virtually all structural equation models (SEMs) are approximations due to violating distributional assumptions and structural misspecifications. There is a large literature on the unmet distributional assumptions, but much less on structural misspecifications. In this paper, we examine the robustness to structural...

In recent years, longitudinal data have become increasingly relevant in many applications, heightening interest in selecting the best longitudinal model to analyze them. Too often, traditional practice rather than substantive theory guides the specific model selected. This opens the possibility that alternative models might better correspond to the...

We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

The current article is a rejoinder to "A Call for Theory to Support the Use of Causal-Formative Indicators: A Commentary on Bollen and Diamantopoulos." Our article comments on the 6 research questions raised by Hardin (2017) in his constructive commentary on our original article (i.e., "In Defense of Causal-Formative Indicators: A Minority Report")...

"We propose to change the default P-value threshold forstatistical significance for claims of new discoveries from 0.05 to 0.005."

This article provides a brief overview of confirmatory tetrad analysis (CTA) and presents a new set of Stata commands for conducting CTA. The tetrad command allows researchers to use model-implied vanishing tetrads to test the overall fit of structural equation models (SEMs) and the relative fit of two SEMs that are tetrad-nested. An extension of t...

Researchers apply sampling weights to take account of unequal sample selection probabilities and to frame coverage errors and nonresponses. If researchers do not weight when appropriate, they risk having biased estimates. Alternatively, when they unnecessarily apply weights, they can create an inefficient estimator without reducing bias. Yet in pra...

The "fetal origins" hypothesis suggests that fetal conditions not only affect birth characteristics such as birth weight and gestational age, but also have lifelong health implications. Despite widespread interest in this hypothesis, few methodological advances have been proposed to improve the measurement and modeling of fetal conditions. A Statis...

Experiencing a relationship with God is widely acknowledged as an important aspect of personal religiosity for both affiliated
and unaffiliated young adults, but surprisingly few attempts have been made to develop measures appropriate to its latent,
multidimensional quality. This paper presents a new model for measuring relationships with God based...

The term ‘indicator’ refers to a variable that is an imperfect representation of a social science concept. First, this article distinguishes different types of indicators (effect, causal, and composite indicators). Second, it describes the construction of measurement models, which are models that specify the links between latent variables (represen...

Alcohol-related problems have traditionally been conceptualized and measured by an effect indicator model. That is, it is generally assumed that observed indicators of alcohol problems are caused by a latent variable. However, there are reasons to think that this construct is more accurately conceptualized as including at least some causal indicato...

Causal-formative indicators directly affect their corresponding latent variable. They run counter to the predominant view that indicators depend on latent variables and are thus often controversial. If present, such indicators have serious implications for factor analysis, reliability theory, item response theory, structural equation models, and mo...

Readings of blood pressure are known to be subject to measurement error, but the optimal method for combining multiple readings is unknown. This study assesses different sources of measurement error in blood pressure readings and assesses methods for combining multiple readings using data from a sample of adolescents/young adults who were part of a...

In measurement theory causal indicators are controversial and little-understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning intended...

Structural equation models refer to general statistical procedures for multiequation systems that include continuous latent variables, multiple indicators of concepts, errors of measurement, errors in equations, and observed variables. An analysis that uses structural equation models has several components. These include (a) model specification, (b...

Several existing methods have been shown to consistently estimate causal direction assuming linear or some form of nonlinear relationship and no latent confounders. However, the estimation results could be distorted if either assumption is violated. We develop an approach to determining the possible causal direction between two observed variables w...

This chapter examines the consequences of measurement error. It begins with the consequences for the mean and variance of one variable. Then the chapter explains the covariance, correlation, and the simple regression between two imperfectly measured variables. The author follows this with the topics of measurement error in multiple regression and i...

This chapter introduces extensions of general structural equation model. It treats alternative model representations that enable some of the implicit constraints of the usual model to be removed. The chapter talks about equality and inequality constraints, and interaction and quadratic terms. It discusses instrumental variable estimators and finall...

This chapter explores the nature of causality, the conditions of causation, and the limits of causal modeling. It examines threats to isolation or pseudo-isolation. The chapter describes some of the limits of structural equation modeling. The three subsections are (1) model-data versus model-reality consistency, (2) experimental and nonexperimental...

This chapter examines structural equation models with observed variables. First, they are the most common structural equation models. Second, these models are a special case of the more general structural equation procedures with latent variables that are discussed in the chapter. The major topics of the chapter-model specification, the implied cov...

This chapter discusses three basic tools essential to understand structural equation models. They are model notation, covariances, and path analysis. Latent random variables represent unidimensional concepts in their purest form. The latent variable model encompasses the structural equations that summarize the relationships between latent variables...

This chapter talks about confirmatory factor analysis technique. It begins with the relation between exploratory and confirmatory factor analysis. The chapter moves to model specification for confirmatory factor analysis, followed by sections on the implied covariance matrix, identification, estimation, the evaluation of model fit, comparisons of m...

The general structural equation model, the topic of this chapter, represents a synthesis of these two model types. It consists of a measurement model that specifies the relation of observed to latent variables and a latent variable model that shows the influence of latent variables on each other. The chapter topics are as follows: model specificati...

This chapter explores measurement models from a structural equation perspective. The focus is specification of measurement models. It begins with an introduction to the nature of measurement. This is followed by two sections, one on validity and the other on reliability. The author contrast traditional definitions and measures of validity and relia...

The common maximum likelihood (ML) estimator for structural equation models (SEMs) has optimal asymptotic properties under ideal conditions (e.g., correct structure, no excess kurtosis, etc.) that are rarely met in practice. This paper proposes model-implied instrumental variable – generalized method of moments (MIIV-GMM) estimators for latent vari...

Selecting between competing structural equation models is a common problem. Often selection is based on the chi-square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with structural equation models compared to other fit indices. This arti...

The fetal origins hypothesis emphasizes the life-long health impacts of prenatal conditions. Birth weight, birth length, and gestational age are indicators of the fetal environment. However, these variables often have missing data and are subject to random and systematic errors caused by delays in measurement, differences in measurement instruments...

This study examines the adverse consequences of use of hierarchical linear modeling (HLM) to analyze ratings collected by
multiple raters in longitudinal research. The most severe consequence of using HLM that ignores rater effects is the biased
estimation of both level 1 and level 2 fixed effects and the potential for incorrect significance tests....

Causality was at the center of the early history of structural equation models (SEMs) which continue to serve as the most popular approach to causal analysis in the social sciences. Through decades of development, critics and defenses of the capability of SEMs to support causal inference have accumulated. A variety of misunderstandings and myths ab...

Instrumental variable (IV) methods provide a powerful but underutilized tool to address many common problems with observational sociological data. Key to their successful use is having IVs that are uncorrelated with an equation's disturbance and that are sufficiently strongly related to the problematic endogenous covariates. This review briefly def...

Introduction The analysis of long-term monitoring data is increasingly important; not only for the discovery and documentation of changes in environmental systems, but also as an enterprise whose fruits validate the allocation of effort and scarce funds to monitoring. In simple terms, we may distinguish between the detection of change in some ecosy...

Bayes factors (BFs) play an important role in comparing the fit of statistical models. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using exact BFs. Instead, it is approximated, often using the Bayesian Information Criterion (BIC) or a variant of BIC. The authors provide a comparison of sever...

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of varia...

Quantifying behavior often involves using variables that contain measurement errors and formulating multiequations to capture the relationship among a set of variables. Structural equation models (SEMs) refer to modeling techniques popular in the social and behavioral sciences that are equipped to handle multiequation models, multiple measures of c...

Although the literature on alternatives to effect indicators is growing, there has been little attention given to evaluating causal and composite (formative) indicators. This paper provides an overview of this topic by contrasting ways of assessing the validity of effect and causal indicators in structural equation models (SEMs). It also draws a di...

Multiequation models that contain observed or latent variables are common in the social sciences. To determine whether unique parameter values exist for such models, one needs to assess model identification. In practice analysts rely on empirical checks that evaluate the singularity of the information matrix evaluated at sample estimates of paramet...