Missing Data Estimation in Developmental Research

Methods of cross-sectional and longitudinal missing data estimation are presented. Since the selection of a technique depends on the pattern of missing data, different patterns are discussed. Both imputation methods (substitution of individual data points) and direct parameter estimation methods (estimation of variance/covariance matrices and mean vectors) are presented. Examples for two of the more general approaches, (1) cross-sectional maximum likelihood estimation of parameters using a multiple-group LISREL model, and (2) direct parameter estimation of longitudinal missing data in the presence of a nested pattern of missingness, are included.

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