The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift,…

This article addresses the problem of estimating the proportion Pi[subscript S] of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is…

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. Classification of imbalanced data is an…

In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but then within each arm, are grouped together (e.g., within classrooms/schools, through shared case managers, in group therapy sessions, through shared doctors, etc.) to receive…

In this article, we consider estimation of parameters of random effects models from samples collected via complex multistage designs. Incorporation of sampling weights is one way to reduce estimation bias due to unequal probabilities of selection. Several weighting methods have been proposed in the literature for estimating the parameters of…

Notes that family researchers can examine extent of self-selection bias in samples by using range of "plausibly correlated characteristics" such as marriage and divorce public records. Provides extensive case example of analyses and discusses compensation techniques of weighting and hazard rate models. (Author/NB)

Model-based methods for estimating the population mean in stratified clustered sampling are described. The importance of adjusting the weights is assessed by an approach considering the sampling variation of the adjusted weights and its (variance) components. The resulting estimators are more efficient than the jackknife estimators for a variety…

The mathematical derivation of the statistics used for inference in some linear models assumes that the values of the independent variables are measured without error. This assumption is often disregarded when these models are utilized in research. This study is an investigation of the consequences of the violation of this assumption for one…

The 11 papers in this volume were presented at the 1996 American Statistical Association (ASA) meeting in Chicago (Illinois), August 4 through 8. This is the fourth collection of ASA papers of particular interest to users of National Center for Education Statistics (NCES) survey data published in the "Working Papers" series. The…

This study explored the assumption that the percentile rank of a group of students should be expected to remain the same on a post-test as on a pretest when the treatment of the group in the intervening period was the same as the treatment of the norms population. This assumption is basic to the use of the norm-referenced model for the assessment…