This study investigates the extent to which analytic power can be increased through the inclusion of siblings in a data set and the concomitant use of random coefficient multilevel models. Analyses of real-world data regarding the predictors of young adult alcohol use illustrate how parallel single-level analyses of a 1-child-per-family data set and multilevel analyses of a data set including all siblings in each family would be conducted. A simulation study, closely based on the illustrative analyses, compares the empirical power to detect main, moderation, and mediation effects under three conditions: (a) single-level analyses of 1-child-per-family data, (b) multilevel analyses of all-siblings data, and (c) single-level analyses of independent data with sample size equivalent to the all-siblings condition. Supplementary analyses are conducted to determine the conditions under which greater analytic power could be achieved with the addition of siblings to a data set than with the addition of a lesser number of independent individuals at equivalent cost.