Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and…

This paper is part of a series of applied statistics monographs intended to provide supplementary reading for applied statistics students. In the present paper, derivations of the unbiased standard error of estimate for both the raw score and standard score linear models are presented. The derivations for raw score linear models are presented in…

The framework and procedures of generalizability theory are introduced and illustrated in this instructional module that uses a hypothetical scenario involving writing proficiency. Generalizability analyses are useful for understanding the relative importance of various sources of error and for designing efficient measurement procedures. (SLD)

This is the third module in a four-module training package for use in inservice seminars or workshops on performance appraisal and employee development. Module 3 focuses on the actual performance review process. The first part outlines requirements for constructing objective, workable employee development plans, in particular factors resulting in…