Categorical data become increasingly ubiquitous in the modern big data era. In this dissertation, we propose novel statistical learning and inference methods for large-scale categorical data, focusing on latent variable models and their applications to psychometrics. In psychometric assessments, the subjects' underlying aptitude often cannot be…

We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such…

Presents a new approach for estimating missing observations together with the results of a Monte Carlo study of the relative strengths and weaknesses of this technique and three other available methods. These techniques are then examined with respect to their ability to use incomplete data to estimate the correlation matrix obtained using a full…

The basic notion of variability is generalized from a univariate context to a multivariate context using two matrix functions, a determinant, and a trace, yielding a number of alternative multivariate indices of shared variation. Some problems in the interpretation of tests of multivariate hypotheses are reviewed. (Author/BW)

Six major problems confronting attempts to use nonmetric multidimensional scaling to represent structures underlying similarity data are identified and the author's prospects for over-coming each of these problems are presented. (RC)

For a number of years, the California community colleges have used data from annual statewide surveys conducted by the Kern Community College District and the California Community College Trustees (CCCT) for comparative faculty salary information. Both the Kern and CCCT studies rely upon the device of selecting benchmark points (such as the…

An "undesigned" experiment is one in which the predictor variables are correlated, either due to a failure to complete a design or because the investigator was unable to select or control relevant experimental conditions. The traditional method of analyzing this class of experiment--multiple regression analysis based on a least squares…