We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean…

In social and behavioral sciences, data are typically not normally distributed, which can invalidate hypothesis testing and lead to unreliable results when being analyzed by methods developed for normal data. The existing methods of generating multivariate non-normal data typically create data according to specific univariate marginal measures…

High dimensional multivariate data, where the number of variables approaches or exceeds the sample size, is an increasingly common occurrence for social scientists. Several tools exist for dealing with such data in the context of univariate regression, including regularization methods such as Lasso, Elastic net, Ridge Regression, as well as the…

The objective of my dissertation is to create a general approach to evaluating IS/IT projects using Real Option Analysis (ROA). This is an important problem because an IT Project Portfolio (ITPP) can represent hundreds of projects, millions of dollars of investment and hundreds of thousands of employee hours. Therefore, any advance in the…

The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better…

In the common two-step structural equation modeling process, modifications are routinely made to the measurement portion of the model prior to assessing structural relations. The effect of such measurement model modifications on the structural parameter estimates, however, is not well known and is the subject of the current investigation. For a…

Monte Carlo results were used to evaluate procedures for discriminating between groups. A multiple testing version of Hotelling's T-squared and the Bonferroni procedure were most powerful for detecting at least one true difference, depending on conditions examined. A multiple Bonferroni procedure was superior in power for detecting all true…