When direct measures of response error are not available in multiwave panel data, latent Markov chain models can provide a useful framework for analysis of response errors. These models permit the estimation of real change and response error even when the observations are widely spaced. This methodology is illustrated with Social Security Administration panel data from the years 1971, 1972, and 1974 (see Frohlich 1975 and Social Security Administration 1979a on self-reported disability status. The model shows much less real change than that indicated from inspection of raw turnover tables. Direct estimates of measurement error from a later survey (1978) give a proximate confirmation of the model results.

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