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ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.