Highlighting New Faculty Member Craig Enders
Date published: 9/16/2015
Missing data are a pervasive research problem that cuts across a variety of substantive disciplines. For example, participants in a psychology study may refuse to answer specific questionnaire items, or patients in a longitudinal clinical trial may drop out prematurely. Applying unsophisticated analysis approaches (e.g., excluding cases with missing data) can bias estimates of associations among variables and reduce the sensitivity to detect treatment effects.
My methodological research program has focused on developing and applying modern statistical methods for addressing missing data, principally maximum likelihood estimation, multiple imputation, and Bayesian estimation. These techniques were initially developed in mathematical statistics, sometimes making strong assumptions about the nature of the data. Because these assumptions are often not met in psychology data, problems arise when behavioral scientists attempt to apply modern analytic approaches. My goal has been to identify the domains in which these procedures produce adequate results and to develop new methods in domains in which they are not adequate. My research develops new models for the treatment of missing data for common statistical analyses methods, among them structural equation modeling, growth curve modeling, and multilevel modeling.
Most recently, I have been developing imputation methods (procedures that fill in the missing scores with replacement values) for multilevel data structures where observations are nested within higher-order organizational units (e.g., repeated measures within individuals; children within families; students within schools). Multilevel data are ubiquitous throughout psychology and the social sciences, yet methods for dealing with missing data in this context have received relatively little attention in the methodological literature. I was recently award a three-year grant from the Institute of Educational Sciences to develop statistical software for dealing with missing data in multilevel analyses.
I am originally from the Midwest and received a B.A. in Psychology from University of Nebraska - Lincoln. During my undergraduate years, I developed an interest in psychometrics, and I subsequently did my Ph.D. work in psychometrics and quantitative methods at the University of Nebraska in Educational Psychology. Prior to joining the faculty at UCLA in 2015, I was a professor in the Department of Psychology at Arizona State University.