The utility and flexibility of recent advances in statistical methods for thequantitative analysis of developmental data—in particular, the methods of individualgrowth modeling and survival analysis—are unquestioned by methodologists, but have yetto have a major impact on empirical research within the field of developmental psychopathologyand elsewhere. In this paper, we show how these new methods provide developmentalpsychpathologists with powerful ways of answering their research questions about systematicchanges over time in individual behavior and about the occurrence and timing of life events. Inthe first section, we present a descriptive overview of each method by illustrating the types ofresearch questions that each method can address, introducing the statistical models, andcommenting on methods of model fitting, estimation, and interpretation. In the following threesections, we offer six concrete recommendations for developmental psychopathologists hoping touse these methods. First, we recommend that when designing studies, investigators shouldincrease the number of waves of data they collect and consider the use of accelerated longitudinaldesigns. Second, we recommend that when selecting measurement strategies, investigatorsshould strive to collect equatable data prospectively on all time-varying measures and shouldnever standardize their measures before analysis. Third, we recommend that when specifyingstatistical models, researchers should consider a variety of alternative specifications for the timepredictor and should test for interactions among predictors, particularly interactions betweensubstantive predictors and time. Our goal throughout is to show that these methods are essentialtools for answering questions about life-span developmental processes in both normal andatypical populations and that their proper use will help developmental psychopathologists andothers illuminate how important contextual variables contribute to various pathways ofdevelopment.