Discusses the different strategies employed by three practical nonmetric multidimensional scaling algorithms using Monte Carlo techniques. (Author/RK)

The standard Pearson correlation coefficient is a biased estimator of the true population correlation, ?, when the predictor and the criterion are range restricted. To correct the bias, the correlation corrected for range restriction, r-sub(c), has been recommended, and a standard formula based on asymptotic results for estimating its standard…

Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a population measured by multiple categorical indicators. Latent transition analysis (LTA) is a type of LCA that facilitates addressing research questions concerning stage-sequential change over time in longitudinal data. Both approaches have been used with…

Two Monte Carlo studies were conducted to explore the Type I error rates in moderated multiple regression (MMR) of observed scores and estimated latent trait scores from a two-parameter logistic item response theory (IRT) model. The results of both studies showed that MMR Type I error rates were substantially higher than the nominal alpha levels…

The standardized group mean difference, Cohen's "d", is among the most commonly used and intuitively appealing effect sizes for group comparisons. However, reporting this point estimate alone does not reflect the extent to which sampling error may have led to an obtained value. A confidence interval expresses the uncertainty that exists between…

Intraclass correlation and Cronbach's alpha are widely used to describe reliability of tests and measurements. Even with Gaussian data, exact distributions are known only for compound symmetric covariance (equal variances and equal correlations). Recently, large sample Gaussian approximations were derived for the distribution functions. New exact…

A new test assembly algorithm based on a Monte Carlo random search is presented in this article. A major advantage of the Monte Carlo test assembly over other approaches (integer programming or enumerative heuristics) is that it performs a uniform sampling from the item pool, which provides every feasible item combination (test) with an equal…

The performance of methods for detecting publication bias in meta-analysis was evaluated using Monte Carlo methods. Four methods of bias detection were investigated: Begg's rank correlation, Egger's regression, funnel plot regression, and trim and fill. Five factors were included in the simulation design: number of primary studies in each…

The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…

Conducted a Monte Carlo computer simulation of the ANOVA design to detect job differences. The design proved reasonably powerful in detecting differences. A second study used Monte Carlo methods to analyze the viability of techniques for determining job differences. Offers guidelines for selecting statistical techniques. (Author/RC)

This study examines the performance of a new method for assessing and characterizing dimensionality in test data using the NOHARM model, and comparing it with DETECT. Dimensionality assessment is carried out using two goodness-of-fit statistics that are compared to reference X[2] distributions. A Monte Carlo study is used with item parameters…

Within-class regression is a method, developed in this paper, of comparing a large number of nonequivalent groups. This study indicated that within-class regression was a less biased method of data analysis and will yield more accurate estimates of treatment effects than analysis of covariance. (PN)

This paper explores the application of a technique for hierarchical item response theory (IRT) calibration of complex constructed response tasks that has promise both as a calibration tool and as a means of evaluating the isomorphic equivalence of complex constructed response tasks. Isomorphic tasks are explicitly and rigorously designed to be…

Introduces the Monte Carlo method which is shown to represent an attractive alternative to the hypergeometric model for identifying the levels at which random retrieval performance is exceeded in retrieval test collections and for overcoming some of the limitations of the hypergeometric model. Practical matters to consider when employing the Monte…

In order to evaluate standard setting procedures, apart from the more commonly applied approach of simply comparing the derived standards or failure rates across various techniques, this study investigated the errors of classification associated with the contrasting groups procedures. Monte Carlo simulations were employed to produce…