title: SPSS macros to compare any two fitted values from a regression model.
abstract: In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.
authors.affiliation: Human Sciences Division, Northern Ontario School of Medicine, Thunder Bay, ON, Canada, P7B 5E1. bweaver@lakeheadu.ca
authors.forename: Bruce
authors.initials: B
authors.lastname: Weaver
authors.forename: Sacha
authors.initials: S
authors.lastname: Dubois
mesh_terms: D000465:Algorithms; D016001:Confidence Intervals; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D012984:Software