Treatment Of Missing Data At The Second Level Of Hierarchical Linear Models

Abstract

The problem of missing data at the second level of a two-level hierarchical data structure was investigated. Using data generated to simulate the 1982 High School and Beyond data set, five missing data treatments—listwise deletion, overall mean substitution, group mean substitution, the expectation maximization (EM) algorithm, and multiple imputation—were examined under four manipulated conditions: number of Level 2 variables, Level 2 sample size, Level 1 intercept-slope correlation, and percentage of missing data. Listwise deletion, group mean substitution, and the EM algorithm performed equally well for the variable having missing values. For the variables having no missing data, listwise deletion and the EM algorithm performed satisfactorily. Only listwise deletion performed well in estimating random effects except when the Level 2 sample size was 30 and 40% of the data were missing. The practical implications of the findings are discussed.