Methods for the analysis of within-subjects effects in multivariate groups by trials repeated measures designs are considered in the presence of heteroscedasticity of the group variance-covariance matrices and multivariate nonnormality. Under a doubly multivariate model approach to hypothesis testing, within-subjects main and interaction effect…

The Johnson-Neyman (J-N) technique (P. Johnson and N. Neyman, 1936) is used to determine areas of significant difference in a criterion variable between two or more groups in situations of linear regression. In using this technique, researchers have encountered difficulties with results, possibly related to the J-N technique's sensitivity to…

Recent research using single-stage procedures to test the equality of the means of J independent normal distributions when variances are unequal have proven unsatisfactory in controlling Type I errors and power. A method for dealing with the problem of unequal sample sizes while implementing two-stage procedures is discussed. (TJH)

The effectiveness of smoothing in reducing random errors in equipercentile equating of a short writing assessment with two raters, two prompts, with scores ranging from zero to five was examined. Thirteen methods were examined: no equating, three presmoothing, three postsmoothing, three combination presmoothing and postsmoothing, mean equating,…

The efficacy of two improvement-over-chance or "I" effect sizes, derived from predictive discriminant analysis (PDA) and logistic regression analysis (LRA), were investigated for two-group univariate mean comparisons. Data were generated under selected levels of population separation, variance pattern, sample size, and distribution…

Unfortunately, researchers do not usually have measurement instruments that provide perfectly reliable scores. Therefore, the researcher may want to account for the level of unreliability by appropriately increasing the sample size. For example, the results of a pilot study may indicate that a particular instrument is not as reliable with a given…

The primary objective of this study was to investigate how incorporating prior information improves estimation of item parameters in two small samples. The factors that were investigated were sample size and the type of prior information. To investigate the accuracy with which item parameters in the Law School Admission Test (LSAT) are estimated,…

As the 1994 publication manual of the American Psychological Association emphasized, "p" values are affected by sample size. As a result, it can be helpful to interpret the results of statistical significant tests in a sample size context by conducting so-called "what if" analyses. However, these methods can be inaccurate…

This study presents and evaluates a method of standardization that may be used by test practitioners to standardize classical item statistics when sample sizes are small. The effectiveness of this standardization approach was compared through simulation with the one-parameter logistic (1PL) and three parameter logistic (3PL) models based on the…

Demonstrates low probability that nonequivalence will produce erroneous inferences in small samples. Sees probability of erroneous inference in absence of true treatment effect as generally no greater than nominal Type I error rate. Seems unlikely that small samples have biased inferences drawn from past psychotherapy outcome research. Cites other…

Reviews research literature to determine psychometric validity of Strong Interest Inventory with Black samples. Found only eight relevant studies. Results found little evidence of Strong's psychometric validity with Black samples. (Author/CM)

Investigated empirical Type I error rates and the power of omega (index of answer copying developed by J. Wollack, 1997) when item and trait (theta) parameters were unknown and estimated from datasets of 100 and 500 examinees. Type I error was unaffected by estimating item parameters, with power slightly lower for the smaller sample. (SLD)

Continues a series on topics in research methodology, statistics, and data analysis techniques for the library and information sciences. Focuses on the basics of sampling for surveys or experimental work, including rationale, terminology, technique, alternative methods, and sample size. (Author/LRW)

Outlines a method for comparing completely standardized solutions in multiple groups. The method is based on a correlation structure analysis of equal-size samples and uses the correlation distribution theory implemented in the structural equation modeling program RAMONA. (SLD)

Reviews interval estimates of the Pearson, Kendall tau-alpha, and Spearman correlates and proposes an improved standard error for the Spearman correlation. Examines the sample size required to yield a confidence interval having the desired width. Findings show accurate results from a two-stage approximation to the sample size. (SLD)