Longitudinal Analysis

Instructor(s): Michael Berbaum, Public Health

Longitudinal analysis is the study of short series of observations obtained from many respondents over time and is also referred to as panel analysis (of a cross-section of time series), or repeated measures, or growth curve analysis (polynomials in time), or multilevel analysis (where one level is a sequence of observations from respondents). Longitudinal analysis is used for panel surveys, experiments, and quasi-experiments in health and biomedicine, education and psychology, and the evaluation of prevention and treatment programs. This course treats the statistical basis and practical application of linear models for longitudinal normal data and generalized linear models for longitudinal binary, count, and ordinal data. The approach involves inclusion of random effects in linear models to reflect within-person cross-time correlation. Techniques for irregularly observed (unequally spaced) data will be covered. The technical level will be at Track II, with interludes at Track III (matrix algebra, probability distributions). Examples and exercises will use both standard and special-purpose software. Participants should have a good understanding of linear regression and analysis of variance.