ABSTRACT
A well-known problem in path analysis and structural equation modeling (SEM) is that even the largest and most comprehensive models cannot contain all of the causes of models’ endogenous variables. This violation of one of the underlying assumptions of path analysis and SEM gives rise to a commonly held belief that failure to include all relevant causes of endogenous variables may invalidate study results in path analysis and SEM. This problem has been referred to variously as the unmeasured variables problem (Duncan, 1975; James, 1980), the omitted variables problem (James, 1980; Kenny, 1979; Sackett, Laczo, & Lippe, 2003), left out variables error (LOVE; Mauro, 1990), a lack of perfect isolation (i.e., pseudo-isolation; Bollen, 1989), and lack of self-containment (James, Mulaik, & Brett, 1982). It has also been discussed as a particular type of model specification error (Hanushek & Jackson, 1977; Kenny, 1979). The omitted variables problem arises when the assumption that all relevant variables that influence the dependent (endogenous) variables are included in the model is violated. However, in the social sciences, this assumption is rarely, if ever, fulfilled.
