When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

This Monte Carlo investigation provides some possible solutions to problems related to choosing multiple-comparison methods that maximize true rejections and minimize false ones. It has been argued that the traditional Bonferroni approach to multiple comparisons, which satisfies the statistician's family-wise Type I error concerns, could be…

A Monte Carlo investigation of Markov chain matrices was conducted to create empirical distributions for two statistics created from the transition matrices. Curve fitting techniques developed by Karl Pearson were used to deduce if theoretical equations could be fit to the two sets of distributions. The set of distributions which describe the…

Computer sampling from a multivariate normal spherical population was used to evaluate Type I error rates for a test of P = I based on Fisher's tanh(sup minus 1) variance stabilizing transformation of the correlation coefficient. (Author/TJH)

Outlines costs and benefits associated with different cross-validation strategies; in particular the way in which the study design affects the cost and benefits of different types of cross-validation. Suggests that the choice between empirical estimation methods and formula estimates involves a trade-off between accuracy and simplicity. (JAC)

Uses Monte Carlo methods to assess the relative power of the paired samples t test and Wilcoxon's signed-ranks test under 10 population shapes. Concludes that, insofar as these two statistics are concerned, the often-repeated claim that parametric tests are more powerful than nonparametric tests is not justified. (Author/CB)

Critical examination is made of the recent controversy over the value of Monte Carlo techniques in nonmetric multidimensional scaling procedures. The case is presented that the major relevance of Monte Carlo studies is not for the local minima problem but for the meaningfulness of the obtained solutions. (Author)

The purpose of this study was to examine the recovery of item parameters in simulated Automatic Item Generation (AIG) conditions, using Markov chain Monte Carlo (MCMC) estimation methods to attempt to recover the generating distributions. To do this, variability in item and ability parameters was manipulated. Realistic AIG conditions were…

In social science research there are a number of instruments that use a rating scale such as a Likert response scale. For a number of reasons, a respondent's response vector may not contain responses to each item. This study investigated the effect on a respondent's location estimate when a respondent is presented an item, has ample time to answer…

A Monte Carlo simulation approach was taken to investigate the effect of sample size on a variety of latent growth models. A fully balanced experimental design was implemented, with samples drawn from multivariate normal populations specified to represent 12 unique growth models. The models varied factorially by crossing number of time points,…

The purpose of this study was to determine: (1) the extent to which effect sizes vary by chance; (2) the proportion of standardized effect sizes that achieve or exceed commonly used criteria for small, medium, and large effect sizes; (3) whether standardized effect sizes are random or systematic across numbers of groups and sample sizes; and (4)…

Nonparametric procedures are often more powerful than classical tests for real world data, which are rarely normally distributed. However, there are difficulties in using these tests. Computational formulas are scattered throughout the literature, and there is a lack of availability of tables of critical values. This paper brings together the…

Real world data often fail to meet the underlying assumptions of normal statistical theory. Many statistical procedures in the psychological and educational sciences involve models that may include a system of statistical equations with non-normal correlated variables (e.g., factor analysis, structural equation modeling, or other complex…

Four of the most commonly used multiple comparison procedures were compared for pairwise comparisons and relative to control of per-experiment and experimentwise Type I errors when conducted as protected or unprotected tests. The methods are: (1) Dunn-Bonferroni; (2) Dunn-Sidak; (3) Holm's sequentially rejective; and (4) Tukey's honestly…