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- All Subjects: Multiple Imputation
- Creators: Aiken, Leona
Description
Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers.
ContributorsMistler, Stephen (Author) / Enders, Craig K. (Thesis advisor) / Aiken, Leona (Committee member) / Levy, Roy (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Specific cultural variables have been found to protect against the onset of alcohol, tobacco and drug use among Latino adolescents. It has been suggested that targeting similar cultural components during the treatment of drug dependence and abuse for Latino adults may also enhance the effectiveness of the intervention, although few studies have explored this hypothesis. The current study attempted to remedy this disparity by exploring the potentially protective influence of two cultural variables, ethnic pride and family traditionalism, on self-efficacy to avoid drug use following residential substance abuse treatment among 99 Hispanic and 85 non-Hispanic White males. Results of the study indicate that higher levels of ethnic pride predict greater confidence to remain abstinent from drugs following substance abuse treatment, and that this relationship is stronger among Hispanic participants than non-Hispanic White participants. Family traditionalism was not a significant predictor of drug avoidance self-efficacy for either group, suggesting that some specific cultural variables may be better targets for substance abuse treatment than others. Study limitations and future directions for research and clinical practice are discussed.
ContributorsBoyd, Stephen James (Author) / Gonzalez Castro, Felipe (Thesis advisor) / Barrera, Jr., Manuel (Committee member) / Aiken, Leona (Committee member) / Arizona State University (Publisher)
Created2011