This paper discusses statistical methods for studying relations between two sets of variables, when each set contains more than one variable. The number of methods discussed is about 20 or 25, depending on how one counts. About a third of the methods are old methods criticized here, another third are old methods mentioned favorably, and the rest are new methods published here for the first time.It has not been adequately recognized that problems involving two sets of variables arise frequently in almost every area of the behavioral sciences, as the following list of examples will attest:

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Guttmacher et al. | Peer Reviewed | Research and Practice | 235 RESEARCH AND PRACTICE  Objectives. This investigation examined the effectiveness of intensive efforts to include frequently absent students in order to reduce bias in classroom-based studies.Methods. Grade 10 students in 13 New York City high schools (n = 2049) completed self-administered confidential surveys in 4 different phases: a 1-day classroom capture, a 1-day follow-up, and 2 separate 1-week follow-ups. Financial incentives were offered, along with opportunities for out-of-classroom participation.Results. Findings showed that frequently absent students engaged in more risk behaviors than those who were rarely absent. Intensive efforts to locate and survey chronically absent students did not, however, significantly alter estimates of risk behavior. Weighting the data for individual absences marginally improved the estimates.Conclusions. This study showed that intensive efforts to capture absent students in classroom-based investigations are not warranted by the small improvements produced in regard to risk behavior esti-