Recommendations to expand Cohen's (1988) rules of thumb for interpreting effect sizes are given to include very small, very large, and huge effect sizes. The reasons for the expansion, and implications for designing Monte Carlo studies, are discussed.

The Type I and II error properties of the t test were evaluated by means of a Monte Carlo study that sampled 8 real distribution shapes identified by Micceri (1986Micceri ( , 1989 as being representative of types encountered in psychology and education research. Results showed the independent-samples t tests to be reasonably robust to Type I error when (a) sample sizes are equal, (b) sample sizes are fairly large, and (c) tests are two-tailed rather than one-tailed. Nonrobust results were obtained primarily under distributions with extreme skew. The / test was robust to Type II error under these nonnormal distributions, but researchers should not overlook robust nonparametric competitors that are often more powerful than the t test when its underlying assumptions are violated.Along with Pearson's chi-squared test, the independent-samples t test must be counted among the best-known statistical procedures in current use. Given its familiarity and utility, it is not surprising that over the years, this test has received an inordinate amount of attention from statistical researchers. Much of this attention has focused on the question of robustness (or lack thereof) of the t statistic to departures from the underlying assumption of population normality.Although there is some disagreement on the subject (see Bradley, 1978), the prevailing view seems to be that the independent-samples t test is reasonably robust, insofar as Type I errors are concerned, to non-Gaussian population shape so long as (a) sample sizes are equal or nearly so, (b) sample sizes are fairly large (Boneau, 1960, mentions sample sizes of 25 to 30), and (c) tests are two-tailed rather than one-tailed. Note also that when these conditions are met and differences between nominal alpha and actual alpha do occur, discrepancies are usually of a conservative rather than of a liberal nature.

The Council for Exceptional Children conducted an online Web survey to obtain information on the instructional practices and attitudes of educators as they relate to self-determination and student involvement in the individualized education program (IEP) process. We obtained 523 usable responses from teachers, administrators, and related services professionals. Although respondents highly valued both student involvement in IEPs and self-determination skills, only 8% were satisfied with the approach they were using to teach self-determination. Only 34% were satisfied with the level of student involvement in IEP meetings. Implications include the need for longitudinal research and technical assistance, targeting administrators, general educators, and special educators beginning in the elementary grades, to improve the capacity of schools to deliver self-determination instruction.

The purpose of this article is to provide an empirical comparison of rank-based normalization methods for standardized test scores. A series of Monte Carlo simulations were performed to compare the Blom, Tukey, Van der Waerden and Rankit approximations in terms of achieving the T score's specified mean and standard deviation and unit normal skewness and kurtosis. All four normalization methods were accurate on the mean but were variably inaccurate on the standard deviation. Overall, deviation from the target moments was pronounced for the even moments but slight for the odd moments. Rankit emerged as the most accurate method among all sample sizes and distributions, thus it should be the default selection for score normalization in the social and behavioral sciences. However, small samples and skewed distributions degrade the performance of all methods, and practitioners should take these conditions into account when making decisions based on standardized test scores.

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