Supporting the use of separate analyses of measurement and structural portions of latent or mixed manifest and latent variable models, limited information (single equation) procedures are presented for estimating structural parameters. These procedures are recommended for testing specific causal hypotheses and locating specific structural model…

Monte Carlo methods are used to explore the accuracy of a method for establishing confidence intervals for variance component estimates in generalizability studies. Previous research has shown that variance component estimation errors due to sampling are often larger than suspected. (Author/CM)

The accuracy of three analytical formulas for shrinkage estimation and four empirical techniques were investigated in a Monte Carlo study of the coefficient of cross-validity in multiple regression. Substantial statistical bias was evident for all techniques except the formula of M. W. Brown (1975) and multicross-validation. (SLD)

A method for multidimensional scaling that is highly resistant to the effects of outliers is described. Some Monte Carlo simulations illustrate the efficacy of the procedure, which performs well with or without outliers. (SLD)

The parallel analysis method for determining the number of components to retain in a principal components analysis has received a recent resurgence of support and interest. However, researchers and practitioners desiring to use this criterion have been hampered by the required Monte Carlo analyses needed to develop the criteria. Two recent…

Using Monte Carlo methods, with item response data generated for a variety of test characteristics, procedures are developed for estimating item and ability parameters in homogeneous, unidimensional tests with person characteristic functions for different levels of the total raw score distribution. (SLD)

Procedures for implementing parallel analysis (PA) criteria in practice were compared, examining regression equation methods that can be used to estimate random data eigenvalues from known values of the sample size and number of variables. More internally accurate methods for determining PA criteria are presented. (SLD)

A new parametric maximum likelihood procedure is proposed for estimating ultrametric trees for the analysis of conditional rank order proximity data. Technical aspects of the model and the estimation algorithm are discussed, and Monte Carlo results illustrate its application. A consumer psychology application is also examined. (SLD)

A Monte Carlo study was conducted to compare the efficiency of Listwise deletion, Pairwise deletion, Allvalue, and Samemean methods in estimating the correlation matrix from data that had randomly occurring missing values. The four methods were compared in a 3x3x4 factorial design representing sample size, proportion of incomplete records in the…

The estimation of mean and standard errors of the eigenvalues and category quantifications in generalized non-linear canonical correlation analysis (OVERALS) is discussed. Starting points are the delta method equations. The jackknife and bootstrap methods are compared for providing finite difference approximations to the derivatives. Examining the…

A normally distributed data set of 1,000 values--ranging from 50 to 150, with a mean of 50 and a standard deviation of 20--was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from…