The purpose of this study was to examine doctoral students' preparation in statistics in the field of education. A national survey was conducted focusing on 27 quantitative methods (QM) programs. One QM professor from each program was identified and asked to describe and evaluate the training of QM and non-QM doctoral students at his or her institution. The vast majority of professors indicated that most or all of the students in their QM programs received training in the "old standard" procedures, analysis of variance, multiple regression , and traditional multivariate procedures, while fewer than half of the professors indicated that most or all of their QM students received training in more recent procedures such as bootstrapping and multilevel models. Professors were also asked to rate the skills of the QM students' training in areas such as mathematical statistics and computer skills on a scale from "Weak" to "Strong." Most professors gave high marks to their QM students' skills with statistical packages, but gave much more mixed ratings of their QM students' training in mathematical statistics. Nearly half of the professors thought that most of their QM students could have benefited from one to two additional statistics courses. Results are discussed in terms of training of future doctoral students. (Contains 10 tables.) (Author/SLD)