The utility and flexibility of recent advances in statistical methods for the
quantitative analysis of developmental data—in particular, the methods of individual
growth modeling and survival analysis—are unquestioned by methodologists, but have yet
to have a major impact on empirical research within the field of developmental psychopathology
and elsewhere. In this paper, we show how these new methods provide developmental
psychpathologists with powerful ways of answering their research questions about systematic
changes over time in individual behavior and about the occurrence and timing of life events. In
the first section, we present a descriptive overview of each method by illustrating the types of
research questions that each method can address, introducing the statistical models, and
commenting on methods of model fitting, estimation, and interpretation. In the following three
sections, we offer six concrete recommendations for developmental psychopathologists hoping to
use these methods. First, we recommend that when designing studies, investigators should
increase the number of waves of data they collect and consider the use of accelerated longitudinal
designs. Second, we recommend that when selecting measurement strategies, investigators
should strive to collect equatable data prospectively on all time-varying measures and should
never standardize their measures before analysis. Third, we recommend that when specifying
statistical models, researchers should consider a variety of alternative specifications for the time
predictor and should test for interactions among predictors, particularly interactions between
substantive predictors and time. Our goal throughout is to show that these methods are essential
tools for answering questions about life-span developmental processes in both normal and
atypical populations and that their proper use will help developmental psychopathologists and
others illuminate how important contextual variables contribute to various pathways of
development.