Objectives The current paper examined the variability of predictors of changes in adolescent smoking across linear and nonlinear analytic models.
Design Three analytic models typically used to model adolescent smoking behaviour were tested: one linear model of change (standard linear), one static linear model (pre-post linear) and one nonlinear model of change (cusp catastrophe). Variability in model composition was assessed by examining the pattern of variables achieving statistical significance and proportion of variance explained.
Methods Model testing was conducted on data from Australian adolescents successfully tracked through a 12-month longitudinal study of smoking (N = 779). The survey measured demographics, self-reported smoking, smoking among friends and family, self-esteem, neuroticism, coping, stress and risk taking.
Results The results indicated that while predictors of change were invariant across analytic models explanatory power varied markedly. Models of change in smoking that included simple, interacted or polynomial forms of initial conditions (past behaviour) explained more than four times the variance of models without.
Conclusions These results justified confidence in the predictors of change in adolescent smoking across analytic models. A secondary implication was that more research into past behaviour's role in the context of dynamical models of adolescent smoking and other health behaviour is needed.