In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Causal indicators have conceptual unity and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variable(s). Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects and composites are a matter of convenience. The failure to distinguish the “three Cs” has led to confusion and questions such as: are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points.

Numerous studies document that higher education is associated with a reduced likelihood of depression. The protective effects of higher education, however, are known to vary across population subgroups. This study tests competing theories for who is likely to obtain a greater protective benefit from a college degree against depression through an analysis of data from the National Longitudinal Study of Adolescent Health and recently developed methods for analyzing heterogeneous treatment effects involving the use of propensity scores. The analysis examines how the effects of two “treatments” (at least some college education and attaining at least a four-year college degree) on latent depressive symptomology vary by background disadvantage, as indicated by having a low propensity for completing some college or attaining a four-year college degree. Results indicate that people from disadvantaged backgrounds realize a greater protective effect of higher education, either completing some college or attaining a four-year degree, against depressive symptomology than people from advantaged backgrounds. This pattern is more pronounced for people who attain at least a four-year degree than for people who complete at least some college education.

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This paper proposes and tests a life course model of self-rated health (SRH) extending from late childhood to young adulthood, drawing on three waves of panel data from the U.S. National Longitudinal Study of Adolescent Health (Add Health). Very little research has examined SRH during the early decades, or whether and how these self-assessments reflect experiences in the family of origin. Background characteristics (parental education, income, and family structure), parental health conditions (asthma, diabetes, obesity, migraines), and early health challenges (physical abuse, presence of a disability, and parental alcoholism and smoking) predict SRH from adolescence to young adulthood. These experiences in the family-of-origin are substantially mediated by the young person’s health and health behaviors (as indicated by obesity, depression, smoking, drinking, and inactivity), although direct effects remain (especially for early health challenges). Associations between SRH and these mediators (especially obesity) strengthen with age. In turn, efforts to promote healthy behaviors in young adulthood, after the completion of secondary school, may be especially strategic in the promotion of health in later adulthood.

A college degree is associated with a range of health-related benefits, but the effects of higher education are known to vary across different population subgroups. Competing theories have been proposed for whether people from more or less advantaged backgrounds or circumstances will gain greater health-related benefits from a college degree. This study draws on data from the National Longitudinal Study of Adolescent Health (Add Health) and recently developed models for analyzing heterogeneous treatment effects to examine how the effect of obtaining a college degree on the self-rated health of young adults varies across the likelihood of obtaining a college degree, a summary measure of advantage/disadvantage. Results indicate that a college degree has a greater effect on self-rated health for people from advantaged backgrounds. This finding differs from two recent studies, and possible reasons for the contrasting findings are discussed.

Purpose Discrimination has been identified as a major stressor and influence on immigrant health. This study examined the role of perceived discrimination in relation to other factors, in particular, acculturation, in physical and mental health of immigrants and refugees. Methodology/approach Data for US adults (18+ years) were derived from the National Epidemiologic Survey on Alcohol and Related Conditions. Mental and physical health was assessed with SF-12. Acculturation and perceived discrimination were assessed with multidimensional measures. Structural equation models were used to estimate the effects of acculturation, stressful life effects, perceived discrimination, and social support on health among immigrants and refugees. Findings Among first-generation immigrants, discrimination in health care had a negative association with physical health while discrimination in general had a negative association with mental health. Social support had positive associations with physical and mental health and mediated the association of discrimination to health. There were no significant associations between discrimination and health among refugees, but the direction and magnitude of associations were similar to those for first-generation immigrants. Implications Efforts aiming at reducing discrimination and enhancing integration/social support for immigrants are likely to help with maintaining and protecting immigrants’ health and well-being. Further research using larger samples of refugees and testing moderating effects of key social/psychosocial variables on immigrant health outcomes is warranted. Originality/value This study used multidimensional measures of health, perceived discrimination, and acculturation to examine the pathways between key social/psychosocial factors in health of immigrants and refugees at the national level. This study included possibly the largest national sample of refugees.

Background Poor self-rated health (SRH) and elevated inflammation and morbidity and mortality are robustly associated in middle- and older-aged adults. Less is known about SRH-elevated inflammation associations during young adulthood and whether these linkages differ by sex. Methods Data came from the National Longitudinal Study of Adolescent Health. At Wave IV, young adults aged 24–34 reported their SRH, acute and chronic illnesses, and sociodemographic and psychological characteristics relevant to health. Trained fieldworkers assessed medication use, BMI, waist circumference, and also collected bloodspots from which high-sensitivity CRP (hs-CRP) was assayed. The sample size for the present analyses was N=13,236. Results Descriptive and bivariate analyses revealed a graded association between SRH and hs-CRP: Lower ratings of SRH were associated with a higher proportion of participants with hs-CRP > 3 mg/L and higher mean levels of hs-CRP. Associations between SRH and hs-CRP remained significant when acute and chronic illnesses, medication use, and health behaviors were taken into account. When BMI was taken into account, the association between SRH and hs-CRP association fully attenuated in females; a small, but significant association between SRH and hs-CRP remained in males. Conclusion Poor SRH and elevated hs-CRP are associated in young adults, adjusting for other health status measures, medication use, and health behavior. In males, SRH provided information about elevated hs-CRP that was independent of BMI. In females, BMI may be a better surrogate indicator of global health and pro-inflammatory influences compared to SRH.

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.