Is it okay to use ordinal variables in procedures like regression or path analysis that assume interval data?
In a recent review of the literature on this topic, Jaccard and Wan (1996) conclude that, for many statistical tests, rather severe departures (from intervalness ) do not seem to affect Type I and Type II errors dramatically. Standard citations to literature showing the robustness of correlation and other parametric coefficients with respect to ordinal distortion are Labovitz (1967, 1970) and Kim (1975). Others are Binder (1984) and Zumbo and Zimmerman (1993). Use of ordinal variables such as 5-point Likert scales with interval techniques is the norm in contemporary social science. Use of scales with fewer values not only violates normality assumptions but also runs a heightened risk of confounding difficulty factors as discussed in the section below on use of dichotomies. Researchers should be aware that there is an opposing viewpoint. Thomas Wilson (1971), for instance, concludes, “the ordinal level of measurement prohibits all but the weakest inferences concerning the fit between da
