title: Multiplicative invalidity and its application to complex correlational models.
abstract: An ever-increasing proportion of social psychology researchers use various versions of complex correlational models such as path analyses or structural equation models and others to draw causal conclusions from correlational data. Critics of complex correlational models have pointed out that (a) misspecification errors are the rule rather than the exception, (b) one cannot draw causal conclusions from a set of correlations, (c) most researchers fail to adjust their correlations for attenuation due to unreliability, and (d) the measures researchers use may actually be measures of outside variables that are correlated with other variables in one's model. Rather than rehash the debates that go along with these criticisms, the author makes some assumptions that are extremely favorable to the complex correlational modeler in that all of these criticisms are disallowed. Nevertheless, even with these assumptions, the author shows how spurious direct and indirect effects are likely to be created by moderately valid measures when researchers compute complex correlations. The author concludes that until social psychologists are better able to deal with the issue of the validity of their measures, they should not use complex correlational models.
authors.affiliation: Department of Psychology, MSC 3452, PO Box 30001, New Mexico State University, Las Cruces, NM 88003-8001, USA. dtrafimo@nmsu.edu
authors.forename: David
authors.initials: D
authors.lastname: Trafimow
mesh_terms: D035841:Behavioral Research / Q000706:statistics & numerical data*; D015984:Causality; D006801:Humans; D015233:Models, Statistical; D011593:Psychology, Social / Q000706:statistics & numerical data*; D015203:Reproducibility of Results