Knafl presents examples of the use of one of these macros for nonlinear growth curve modeling.
SAS® (SAS Institute, Inc., Cary, NC) macros have been developed to support adaptive regression. Adaptive methods were originally developed for nonlinear modeling in the Poisson regression context with Poisson distributed count outcomes, but the methods extend to linear regression with continuous outcomes treated as normally distributed, as often used in mediation analyses, as well as logistic regression with discrete outcomes. To provide example analyses using these methods. To present an approach for assessing linearity of mediation relationships and for conducting nonlinear mediation analyses, based on the adaptive regression methods of Knafl and Ding, and 2. Methods are needed to assess linearity of mediation relationships and to conduct mediation analyses when the underlying relationships are nonlinear. To fully address nonlinearity requires more complex methods like nonparametric regression. Standard polynomials sometimes can be effectively used to address nonlinearity in predictors, but not in general. Pearl has developed a general approach for quantifying direct, indirect, and total effects allowing for nonlinearity as well as for categorical variables. A few authors have addressed nonlinearity in the mediation context. Mediating relationships, however, can be nonlinear.
Relationships underlying mediation are commonly treated as linear in Y, X, and M. Since then, they have undergone a variety of refinements. Methods for addressing mediation have existed for some time. Mediation is an important issue considered in the behavioral, medical, and social sciences, addressing situations where the effect of a predictor variable X on an outcome (or dependent or response) variable Y is explained to some extent by an intervening, mediator variable M. ConclusionsĪdaptive methods provide an effective way to incorporate possibly nonlinear monotonicity into mediation relationships. Example moderated monotonic mediation and simulation analyses are also presented. ResultsĮxample monotonic mediation analyses are provided assessing linear and monotonic mediation of the effect of family functioning ( X) on a child’s adaptation ( Y) to a chronic condition by the difficulty ( M) for the family in managing the child's condition. MethodsĮxisting adaptive regression methods based on fractional polynomials are extended here to address nonlinearity in mediation relationships, but assuming those relationships are monotonic as would be consistent with theories about directionality of such relationships. Methods are needed for assessing when mediation relationships can be treated as linear and for estimating them when they are nonlinear. These relationships, however, can be nonlinear. While these methods continue to undergo refinement, the relationships underlying mediation are commonly treated as linear in the outcome Y, the predictor X, and the mediator M.
Methods for addressing mediation have been available for some time. It addresses situations where the effect of a predictor variable X on an outcome variable Y is explained to some extent by an intervening, mediator variable M. Mediation is an important issue considered in the behavioral, medical, and social sciences.
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