Abstract
In many psychological experiments, interaction effects in factorial analysis of variance (ANOVA) designs are often estimated using total scores derived from classical test theory. However, interaction effects can be reduced or eliminated by nonlinear monotonic transformations of a dependent variable. Although cross-over interactions cannot be eliminated by trans formations, the meaningfulness of other interactions hinges on achieving a measurement scale level for which nonlinear transformations are inappropriate (i.e., at least interval scale level). Classical total test scores do not provide interval level measurement according to contemporary item response theory (IRT). Nevertheless, rarely are IRT models applied to achieve more optimal measurement properties and hence more meaningful interaction effects. This paper provides several condi tions under which interaction effects that are estimated from classical total scores, rather than IRT trait scores, can be misleading. Using derived asymptotic expecta tions from an IRT model, interaction effects of zero on the IRT trait scale were often not estimated as zero from the total score scale. Further, when nonzero inter actions were specified on the IRT trait scale, the esti mated interaction effects were biased inward when estimated from the total score scale. Test difficulty level determined both the direction and the magnitude of the biased interaction effects. Index terms: facto rial designs, interaction effects, interval measurement, item response theory, level of measurement, measure ment scales, statistical inference.