Statistics and Probability Commons^™

Meta-Analysis Of Type I Error Rates For Detecting Differential Item Functioning With Logistic Regression And Mantel-Haenszel In Monte Carlo Studies, Eva Van De Water Ph. D.

Eva Van De Water

Differential item functioning (DIF) occurs when individuals from different groups who have equal levels of a latent trait fail to earn commensurate scores on a testing instrument. Type I error occurs when DIF-detection methods result in unbiased items being excluded from the test while a Type II error occurs when biased items remain on the test after DIF-detection methods have been employed. Both errors create potential issues of injustice amongst examinees and can result in costly and protracted legal action. The purpose of this research was to evaluate two methods for detecting DIF: logistic regression (LR) and Mantel-Haenszel (MH).

To …

A Comparison Of The Power Of The Discrete Kolmogorov-Smirnov And Chi- Square Goodness-Of-Fit Tests., Mike Steele, Neil Smart, Cameron Hurst, Janet Chaseling

Mike Steele

No abstract provided.

Reflecting On The Experience Sampling Method In The Qualitative Research Context: Focus On Knowledge Production And Power During The Data-Collection Process

Fredline MCormack-Hale

On Optimizing Multi-Level Designs: Power Under Budget Constraints, Todd C. Headrick, Bruno D. Zumbo

Todd Christopher Headrick

This paper derives a procedure for efficiently allocating the number of units in multi-level designs given prespecified power levels. The derivation of the procedure is based on a constrained optimization problem that maximizes a general form of a ratio of expected mean squares subject to a budget constraint. The procedure makes use of variance component estimates to optimize designs during the budget formulating stages. The method provides more general closed form solutions than other currently available formulae. As such, the proposed procedure allows for the determination of the optimal numbers of units for studies that involve more complex designs. A …

An Investigation Of The Rank Transformation In Multple Regression, Todd C. Headrick, Ourania Rotou

Todd Christopher Headrick

Real world data often fail to meet the underlying assumptions of normal statistical theory. The rank transformation (RT) procedure is recommended and used in the context of multiple regression analysis when the assumption of normality is violated. There is no general supporting theory of the RT. In view of this, the current study examined the Type I error and power properties of the RT in terms of multiple regression. The investigation included both additive and nonadditive models. Results indicated that there were severely inflated Type I error rates associated with the RT procedure under both normal and nonnormal distributions (e.g., …