Studies incorporating repeated observations of momentary phenomena are becoming more common in behavioral and medical science. Analysis of such data requires the use of statistical techniques that are unfamiliar to many investigators. Some common ways of analyzing momentary data are reviewed--aggregation strategies, repeated measures analysis of variance, pooled within-person regression, and two-stage estimation procedures for multilevel models--and are found to be usually suboptimal, possibly leading to incorrect inferences. A broad class of statistical models for multilevel data that can address many research questions typically asked of momentary data are then described. Analytic issues that merit careful consideration include the scaling of momentary variables, allowance for serial autocorrelation of residuals, and the treatment of coefficients that vary across individuals as fixed versus random effects.