Applied Statistics 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 …

Asymptotic Behavior Of A T Test Robust To Cluster Heterogeneity, Douglas G. Steigerwald

Douglas G. Steigerwald

We study the behavior of a cluster-robust t statistic and make two principle contributions. First, we relax the restriction of previous asymptotic theory that clusters have identical size, and establish that the cluster-robust t statistic continues to have a Gaussian asymptotic null distribution. Second, we determine how variation in cluster sizes, together with other sources of cluster heterogeneity, affect the behavior of the test statistic. To do so, we determine the sample specific measure of cluster heterogeneity that governs this behavior and show that the measure depends on how three quantities vary over clusters: cluster size, the cluster specific error …

Imputation Procedures For American Community Survey Group Quarters Small Area Estimation, Chandra Erdman, Chaitra Nagaraja

Chaitra H Nagaraja

No abstract provided.

Optimal Experimental Design With The Sigma Point Method, René Schenkendorf, Andreas Kremling, Michael Mangold

René Schenkendorf

Using mathematical models for a quantitative description of dynamical systems requires the identification of uncertain parameters by minimising the difference between simulation and measurement. Owing to the measurement noise also, the estimated parameters possess an uncertainty expressed by their variances. To obtain highly predictive models, very precise parameters are needed. The optimal experimental design (OED) as a numerical optimisation method is used to reduce the parameter uncertainty by minimising the parameter variances iteratively. A frequently applied method to define a cost function for OED is based on the inverse of the Fisher information matrix. The application of this traditional method …