Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social,…

We explain how to simulate both univariate and bivariate raw data sets having specified values for common summary statistics. The first example illustrates how to "construct" a data set having prescribed values for the mean and the standard deviation--for a one-sample t test with a specified outcome. The second shows how to create a bivariate data…

The work of R. MacCallum et al. (1992) was extended by examining chance modifications through a Monte Carlo simulation. The stability of post hoc model modifications was examined under varying sample size, model complexity, and severity of misspecification using 2- and 4-factor oblique confirmatory factor analysis (CFA) models with four and eight…

Multiple-hypothesis testing in the context of a correlation matrix is used to compare the statistical power of the original Bonferroni with six modified Bonferroni procedures that control the overall Type I error rate. Three definitions of statistical power are considered: (1) the ability to detect at least one true relationship; (2) the ability…

In the present study Monte Carlo methods were employed to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. Sampling error influences were investigated across variations in variable and sample (n) sizes, and across variations in average within-set correlation sizes and in…

This study developed and investigated an empirical sampling distribution of the congruence coefficient. The effects of sample size, number of variables, and population value of the congruence coefficient on the sampling distribution of the congruence coefficient were examined. Sample data were generated on the basis of the common factor model and…

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…

A population data set was randomly generated from which a random sample was drawn. This sample was randomly divided into two data sets, one of which was used to generate parameter estimates, which were then used in the second data set for cross-validation purposes. The best variable subset models were compared between the two data sets on the…

This study examined how four commonly used test equating procedures (linear, equipercentile, Rasch Model, and three-parameter) would respond to situations in which the properties or the two tests being equated were different. Data for two tests plus an external anchor test were generated from a three parameter model in which mean test differences…

Sampling distributions for ten tests for comparing population variances in a two group design were generated for several combinations of equal and unequal sample sizes, population means, and group variances when distributional forms differed. The ten procedures included: (1) O'Brien's (OB); (2) O'Brien's with adjusted degrees of freedom; (3)…

Four discrimination indices were compared, using score distributions which were normal, bimodal, and negatively skewed. The score distributions were systematically varied to represent the common circumstances of a military training situation using criterion-referenced mastery tests. Three 20-item tests were administered to 110 simulated subjects.…