Like many developmental psychopathology researchers, I began my career wishing I had longitudinal data. With longitudinal data, I felt I would be one step closer to identifying the predictors, and perhaps the causes, of childhood psychopathology. At the very least, I would no longer be obliged, at the end of every paper, to issue the standard apology for having derived all my conclusions from cross-sectional studies. Now, fortunate enough to have accumulated longitudinal data, I spend much of my time coping with their incumbent frustrations. In this article, I have two primary goals. One is to outline some of the problems often associated with the analysis of longitudinal data. The second is to suggest some methodological and statistical approaches that might be helpful in coping with these problems. In this effort, I use examples drawn from my work on depression in children; however, the general points should be relevant to other areas of developmental psychopathology as well.Underlying many longitudinal data analytic problems is the stability of the construct of interest. Throughout this article, I use the term stability to mean the constancy of individual differences over time. According to this definition, high stability suggests that individuals who score high on a construct (relative to their peers) at one point in time continue to score high (relative to their peers) at subsequent points in time. Conversely, individuals who score low (relative to their peers) continue to score low. I do not use the term to refer to the constancy of an individual's scores relative to his or her self over time. Thus complete and perfect stability can exist despite the fact that every individual's score changes over time, as long as all of these changes occur in the same direction and to the same extent (Nesselroade, 1988(Nesselroade, , 1991.In psychopathology research, measures of disorders often show rather high levels of stability over time, a phenomenon that becomes problematic when one attempts to predict or explain change in such measures. Predicting change in a highly stable phenomenon can be frustrating (if not pointless). The problem becomes evident in the classic regression approach to non-experimental, longitudinal data. Let us imagine a study in which the investigator hypothesizes that stressful life events at time 1 (represented by S 1 ) generate or predict depression at time 2 (D 2 ). The investigator wants to control (statistically) for prior levels of depression (D 1 ). A regression approach to this problem would be symbolized as D 2 = β 0 + β 1 D 1 + β 2 S 1 , in which the size and significance of β 2 would be taken as support for the hypothesis. A problem emerges when D is highly stable. As the correlation between D 1 and D 2 increases, D 1 explains more and more of the variance in D 2 (i.e., β 1 gets quite large), leaving less variance for S 1 to explain (i.e., β 2 becomes quite small). For example, if depression has a stability of .5, the range of possible values for β 2 extends from 0 to as high as .75. I...