The authors of the focus article in this issue have emphasized the continuing confusion among some researchers regarding various indicators used in structural equation models (SEMs). Their major claim is that causal indicators are not inherently unstable, and even if they are unstable they are at least not more unstable than other types of indicators. The authors describe effect indicators as conceptually dependent on a latent variable, while causal indicators influence the latent variable. Furthermore, they stress that causal indicators are different from composite indicators and covariates as described recently by Bollen and Bauldry (2011). Jue Wang, George Engelhard, Jr., and Zhenqiu Lu agree that "in measurement theory, causal indicators are controversial and little understood" (Bainter & Bollen, this issue, p. 125). For example, Howell, Breivik, and Wilcox (2007a) argued that the latent variables are open to interpretational confounding when causal indicators and other types of indicators are included in SEMs. In their commentary, Wang, Engelhard, and Lu briefly discuss types of indicators used in SEMs, and suggest additional steps to consider in clarifying both theoretical and empirical conceptualizations of indicators. The following two questions guide their comments: (1) How are different types of indicators defined in four models (reflective, formative, composite, and regression models)? and (2) What steps can be taken to aid in the clarification of indicators in these models?