A method of introducing a controlled degree of skew and kurtosis for Monte Carlo studies was derived. The form of such a transformation on normal deviates is given. Analytic and empirical validation of the method is demonstrated. (Author/JKS)

Stochastic approximation is suggested as a useful technique in areas where individuals have a goal firmly in mind, but lack sufficient knowledge to design an efficient, more traditional experiment. One potential area of application for stochastic approximation is that of formative evaluation. (CTM)

Used Monte Carlo simulation to compare the properties of a goodness-of-fit (GOF) procedure and a test statistic developed by E. Bartfay and A. Donner (2001) to the likelihood ratio test in assessing the existence of extra variation. Results show the GOF procedure possess satisfactory Type I error rate and power. (SLD)

A Monte Carlo simulation was conducted to study the Type I error rate and power of the 1994 approximation developed by R. A. Alexander and D. M. Govern as an alternative to the analysis of variance "F" test. Conditions under which this test is the best approach are discussed. (SLD)

A Monte Carlo study compared the accuracy of different estimates of the standard error of correlations corrected for restriction in range. The procedure suggested by P. Bobko and A. Rieck (1980) generated the most accurate estimates of the standard error. Aspects of accuracy are discussed. (SLD)

Monte Carlo study results show that the runs test yields markedly asymmetrical error rates in the two tails and that neither directional nor nondirectional tests are satisfactory with respect to Type I errors. The test is not recommended for evaluating the independence of errors in time-series regression models. (SLD)

Compared two parametric procedures for detecting differential item functioning (DIF) using the graded response model (GRM), the GRM-likelihood ratio test and the GRM-differential functioning of items and tests, with a nonparametric DIF detection procedure, Poly-SIBTEST. Monte Carlo simulation results show that Poly-SIBTEST showed the least amount…

Estimated statistical power for three randomization tests used with multiple-baseline designs using Monte Carlo methods. For an effect size of 0.5, none of the tests provided an adequate level of power, and for an effect size of 1.0, power was adequate for the Koehler-Levin test and the Marascuilo-Busk test only when the series length was long and…

Demonstrates how substantive researchers can use a Monte Carlo study to decide on sample size and determine power. Presents confirmatory factor analysis and growth models as examples, conducting these analyses with the Mplus program (B. Muthen and L. Muthen 1998). (SLD)

A Monte Carlo simulation examined the Type I error rates and power of four tests of the null hypothesis that a correlation matrix equals the identity matrix. The procedure of C. J. Brien and others (1984) was found to be the most powerful test maintaining stable empirical alpha values. (SLD)

The Type I error rates and statistical power of nine selected multiple comparison procedures were compared in a Monte Carlo study. The Peretz, Ryan, and Fisher-Hayter tests were the most powerful, and differences among these procedures were consistently small. Choosing among these procedures might be based on their calculational complexity. (SLD)

The normality of number distributions generated by various random-number generators were studied, focusing on when the random-number generator reached a normal distribution and at what sample size. Findings suggest the steps that should be followed when using a random-number generator in a Monte Carlo simulation. (SLD)

The problem of chance model modifications under varying levels of sample size, model size, and severity of misspecification in confirmatory factor analysis models was examined through Monte Carlo simulations. Findings suggest that practitioners should exercise caution when interpreting modified models unless sample size is quite large. (SLD)

Illustrates that sampling variance can be very large when a three-wave quasi simplex model is used to obtain reliability estimates. Also shows that, for the reliability parameter to be identified, the model assumes a Markov process. These problems are evaluated with both real and Monte Carlo data. (SLD)

Explains a situation in which the standard nonparametric one-sample runs test gives anomalous results and describes a procedure that allows the maximum run length to be determined empirically through a Monte Carlo permutation test. Illustrates the new procedure with examples from suicide research and psycholinguistics. (SLD)