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Identifying change at the individual level is an important goal for researchers, educators, and clinicians. We present a set of statistical procedures for identifying individuals who depart from a normative change. Using Latent Change Scores models (LCS), we illustrate how the Individual Likelihood computed from a statistical model for change (lL) and from an alternative unrestricted model (ILsat) can be used to identify atypical trajectories in situations with several measurement occasions. Using LCS and linear regression, we also show how the observed and latent change residuals can be used to identify atypical individual change between 2 measurement occasions. We apply these methods to a measure of general verbal ability (from WISC-R), from a large sample of individuals assessed every 2 years from Grade I to 9. We demonstrate the efficiency of these techniques, illustrate their use to identify individual change in longitudinal data, and discuss potential applications in developmental research. [ABSTRACT FROM AUTHOR]