Procedures for treating missing data in the statistical analysis of survey data are reviewed. The main topics covered are: (1) how to assess the nature of missing data especially with regard to randomness, (2) a comparison of listwise and pairwise deletion, and (3) methods for using maximum information to estimate (a) parameters or (b) missing values. any large data set it is unlikely that complete information or any large data set it is unlikely that complete information will be present for all the cases. In surveys which rely on respondents' reports of behavior and attitudes, it is almost certain that some information is either missing or in an unusable form. Although statisticians have long appreciated that the existence of such missing information can change an ordinarily simple statistical analysis into a complex one (e.g., Orchard and Woodbury, 1972) and responded to this challenge by producing enormous amounts of literature (see, for example, Afifi and Elashoff, 1966;Hartley and Hocking, 1971;Orchard and Woodbury, 1972;Press and Scott, 1974), there is little indication that survey researchers have paid much attention to the literature. When faced with such missing-data problems, most survey researchers are likely to choose either a listwise deletion or pairwise deletion, and then proceed to interpret the resulting statistics as usual. 1 The primary objective of this paper is to review and organize the procedures for handling missing data, having in mind the practical needs of survey researchers with a relatively complex analysis problem but with little statistical sophistication. To make the task manageable, we will confine our discussion mostly to the situation in which variables are measured at least on an interval scale. Other situations will be dealt with only when such excursion is simple and does not interrupt the flow of the presentation. For researchers with specific problems not discussed in this paper, a brief bibliographical note is included.

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In comparative study, it is argued that (1) the standardization of variables and scales should be separated from the habitual use of standardized coefficients; (2) the use of standardized coefficients implies standardizing every variable using group specific standards, and, therefore, it is not appropriate even if some variables have group specific metrics or some variables do not possess commonly accepted metrics; and (3) the explicit standardization of some or all variables can be fruitfully combined with the use of unstandardized coefficients.

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