Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique

The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using several variations of DupER Augmentation (based on different imputation methodologies, deletion rates, and duplication rates), analyzed in BILOG-MG 3, and results are compared to those obtained from analyzing the raw data. Additional manipulated variables include test length and sample size. Estimates are compared using seven different evaluative criteria. Results are mixed and inconclusive. DupER augmented data tend to result in larger root mean squared errors (RMSEs) and lower correlations between estimates and parameters for both item and ability parameters. However, some DupER variations produce estimates that are much less biased than those obtained from the raw data alone. For one DupER variation, it was found that DupER produced better results for low-ability simulees and worse results for those with high abilities. Findings, limitations, and recommendations for future studies are discussed. Specific recommendations for future studies include the application of Duper Augmentation (1) to empirical data, (2) with additional IRT models, and (3) the analysis of the efficacy of the procedure for different item and ability parameter distributions.

Infrastructure Support for Controlled Experimentation with Software Testing and Regression Testing Techniques

Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the design of our infrastructure. The paper then describes the infrastructure that we are creating in response to these challenges, and that we are now making available to other researchers, and discusses the impact that this infrastructure has and can be expected to have.

Overabundant deer: Better management through research

Overabundance of white-tailed deer (Odocoileus virginianus) continues to challenge wildlife professionals nationwide, especially in urban settings. Moreover, wildlife managers often lack general site-specific information on deer movements, survival, and reproduction that are critical for management planning. We conducted radio-telemetry research concurrent with deer culling in forest preserves in northeastern Illinois and used empirical data to construct predictive population models. We culled 2,826 deer from 16 forest preserves in DuPage County (1992-1999) including 1,736 from the 10 km2 Waterfall Glen Forest Preserve. We also radio-marked 129 deer from 8 preserves in DuPage and adjacent Cook County (1994-1998). Recruitment was inversely associated with deer density suggesting a classic density-dependent response. Female deer were philopatric and 20% of adult males dispersed. Survival was high for all sex and age classes, and deer-vehicle collisions accounted for >55% of known mortalities. Based upon data from other areas, early attempts to apply population models to deer at Waterfall Glen Forest Preserve were not useful. The subsequent quantification of the density-dependent recruitment response and use of other empirical data strengthened the predictive capability of models. Our experience illustrates the importance of understanding demographics of overabundant deer in order to set realistic objectives and make sound management decisions.

"100 CHALLENGES FACING STUDENTS OF COLOR IN THE 21st CENTURY"

"It is particularly critical to assess the impact, given the empirical data available, on institutions in California, Texas, Florida and Washington which anti-affirmative action laws and court orders have been passed/handed down."