Analyses of multivariate data are frequently hampered by missing values. Until re-cently, the only missing-data methods available to most data analysts have been relatively ad hoc practices such as listwise deletion. Recent dramatic advances in theoretical and com-putational statistics, however, have produced a new generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simu-lation technique that replaces each missing datum with a set of m>1 plausible values. The m versions of the complete data are analyzed by standard complete-data methods, and the results are combined using simple rules to yield estimates, standard errors, and p-values that formally incorporate missing-data uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software programs currently available, and demonstrates their use on data from

We present an algorithm to impute missing values from given data alone, and analyse its performance. The proposed procedure is based on non-numeric rule based data analysis, and aims to maximise consistency of imputation from known values. In contrast to the prevailing statistical imputation algorithms, it does not make representational assumptions or presupposes other model constraints. Therefore, it is suitable for a wide variety of data-sets, and can be used as a pre-processing step before resorting to harder numerical methods.

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In recognition of the potentially critical role the family plays in substance use, the National Institute on Drug Abuse (NIDA) is expanding its school-based prevention efforts to include the family. For the developmentalist, this new focus raises challenging methodological issues. These issues largely reflect paradigmatic interests in change and multilevel systems, themes that are common to numerous developmental approaches: ecological (Bronfenbrenner 1979), contextual (Lerner and Kaufman 1985), interactive (Magnusson 1988), individual-socioecological (Valsiner 1987), and the lifecourse (Elder and O’Rand 1995). Since World War II, American family life has changed enormously. Demographers observe greater variability in the age at which marriages form, a decrease in fertility, and increases in marital dissolution; blended families; and alternatives to married living, including cohabitation and single-parent households (Cherlin 1988;