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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 defines IVs, summarizes their origins, and describes their use in multiple regression, simultaneous equation models, factor analysis, latent variable structural equation models, and limited dependent variable models. It defines and contrasts three methods of selecting IVs: auxiliary instrumental variable, model implied instrumental variable, and randomized instrumental variable. It provides overidentification tests and weak IV diagnostics as methods to evaluate the quality of IVs. I review the use of IVs in models that assume heterogeneous causal effects. Another section summarizes the use of IVs in contemporary sociological publications. The conclusion suggests ways to improve the use of IVs and suggests that there are many areas in which IVs could be profitably used in sociological research.

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 concepts, and measurement error. This work provides an overview of latent variable SEMs. We present the equations for SEMs and the steps in modeling, and we provide three illustrations of SEMs. We suggest that the general nature of the model is capable of handling a variety of problems in the quantification of behavior, where the researcher has sufficient knowledge to formulate hypotheses.

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 Statistics in Medicine paper by Bollen, Noble, and Adair examined favorable fetal growth conditions (FFGC) as a latent variable. Their study of Filipino children from Cebu provided evidence consistent with treating FFGC as a latent variable that largely mediates the effects of mother's characteristics on birth weight, birth length, and gestational age. This innovative method may have widespread utility, but only if the model applies equally well across diverse settings. Our study assesses whether the FFGC model of Cebu replicates and generalizes to a very different population of children from North Carolina (N=705) and Pennsylvania (N=494). Using a series of structural equation models, we find that key features of the Cebu analysis replicate and generalize while we also highlight differences between these studies. Our results support treating fetal conditions as a latent variable when researchers test the fetal origins hypothesis. In addition to contributing to the substantive literature on measuring fetal conditions, we also discuss the meaning and challenges involved in replicating prior research.

▪ Abstract  The paper discusses the use of latent variables in psychology and social science research. Local independence, expected value true scores, and nondeterministic functions of observed variables are three types of definitions for latent variables. These definitions are reviewed and an alternative “sample realizations” definition is presented. Another section briefly describes identification, latent variable indeterminancy, and other properties common to models with latent variables. The paper then reviews the role of latent variables in multiple regression, probit and logistic regression, factor analysis, latent curve models, item response theory, latent class analysis, and structural equation models. Though these application areas are diverse, the paper highlights the similarities as well as the differences in the manner in which the latent variables are defined and used. It concludes with an evaluation of the different definitions of latent variables and their properties.

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 distinction between composite (formative) indicators and causal indicators and argues that validity is most relevant to the latter. Sound validity assessment of indicators is dependent on having an adequate overall model fit and on the relative stability of the parameter estimates for the latent variable and indicators as they appear in different models. If the overall fit and stability of estimates are adequate, then a researcher can assess validity using the unstandardized and standardized validity coefficients and the unique validity variance estimate. With multiple causal indicators or with effect indicators influenced by multiple latent variables, collinearity diagnostics are useful. These results are illustrated with a number of correctly and incorrectly specified hypothetical models.

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 variable SEMs that are more robust than ML to violations of both the model structure and distributional assumptions. Under less demanding assumptions, the MIIV-GMM estimators are consistent, asymptotically unbiased, asymptotically normal, and have an asymptotic covariance matrix. They are “distribution-free,” robust to heteroscedasticity, and have overidentification goodness-of-fit J -tests with asymptotic chi-square distributions. In addition, MIIV-GMM estimators are “scalable” in that they can estimate and test the full model or any subset of equations, and hence allow better pinpointing of those parts of the model that fit and do not fit the data. An empirical example illustrates MIIV-GMM estimators. Two simulation studies explore their finite sample properties and find that they perform well across a range of sample sizes.

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. A total of 695 female patients who received care from sexual assault nurse examiners at 13 United States emergency care centers and community-based programs completed standardized surveys 1 week after receiving sexual assault nurse examiners’ care for sexual assault. Most patients strongly agreed that the sexual assault nurse examiners provided high-quality care, including taking patients’ needs/concerns seriously, not acting as though the assault was the patient’s fault, showing care/compassion, explaining the sexual assault examination, and providing follow-up information. The perceptions did not vary by the patients’ demographic characteristics or preassault health status. Female patients who had been sexually assaulted and who were evaluated at 13 widely geographically distributed sexual assault nurse examiners’ programs consistently reported that the sexual assault nurse examiners provided high-quality, compassionate care.

IntroductionWorldwide, 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 and analysisWomen ≥18 years of age who present for emergency care within 72 hours of sexual assault to a network of treatment centres across the USA are approached for study participation. Blood DNA and RNA samples and brief questionnaire and medical record data are obtained from women providing initial consent. Full consent is obtained at initial 1 week follow-up to analyse blood sample data and to perform assessments at 1 week, 6 weeks, 6 months and 1 year. These assessments include evaluation of survivor life history, current health and recovery and experiences with treatment providers, law enforcement and the legal system.Ethics and disseminationThis study is approved by the University of North Carolina at Chapel Hill’s Institutional Review Board (IRB) and the IRB of each participating study site. We hope to present the results of this study to the scientific community at conferences and in peer-reviewed journals.

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 anticipated health; (2) a spontaneous assessment at the time of the survey; (3) a result of lagged effects from prior responses; (4) a function of life course processes; and (5) a combination of the preceding. Different perspectives suggest different longitudinal models, but evidence is lacking about which model best captures SRH trajectory. Using data from the National Longitudinal Study of Adolescent to Adult Health and the National Longitudinal Survey of Youth, we employ structural equation modeling to correct for measurement error and identify the best-fitting, theoretically guided models describing SRH trajectories. Results support a hybrid model that combines the lagged effect of SRH with the enduring perspectives, fitted with a type of autoregressive latent trajectory (ALT) model. This model structure consistently outperforms other commonly used models and underscores the importance of accounting for lagged effects combined with time-invariant effects in longitudinal studies of SRH. Interestingly, comparisons of this latent, time-invariant autoregressive model across gender and racial/ethnic groups suggest that there are differences in starting points but less variability in SRH trajectories from early life into adulthood.

Fixed- and random-effects models for longitudinal data are common in sociology. Their primary advantage is that they control for time-invariant omitted variables. However, analysts face several issues when they employ these models. One is the choice of which to apply; another is that FEM and REM models as usually implemented might be insufficiently flexible. For example, the effects of variables, including the latent time-invariant variable, might change over time. The latent time-invariant variable might correlate with some variables and not others. Lagged endogenous variables might be necessary. Alternatives that move beyond the classic FEM and REM models are known, but they involve estimators and software that make these extended models difficult to implement and to compare. This article presents a general panel model that includes the standard FEM and REM as special cases. In addition, it provides a sequence of nested models that provide a richer range of models that researchers can easily compare with likelihood ratio tests and fit statistics. Furthermore, researchers can implement our general panel model and its special cases in widely available structural equation models software.