There is strong evidence that children who attend high-quality pre-kindergarten (pre-K) programs learn skills that benefit them in school and life. The price tag of most private programs puts them out of reach for some families, however. In response, many states and school districts fund pre-K programs to expand opportunities for their youngest…

States and localities throughout the United States are expanding their investments in pre-kindergarten (pre-K) programs. Although spending on publicly funded pre-K programs is well documented, relatively less is known about the true cost to deliver the programs, especially considering varying quality standards and accounting for the resources used…

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…

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…

This methodological brief introduces basic procedures and issues for conducting a high-quality meta-analysis in gifted education. Specifically, we discuss issues such as how to select a topic and formulate research problems, search for and identify qualified studies, code studies and extract data, choose and calculate effect sizes, analyze data,…

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those…

In the modern world information has become the new power. An increasing amount of efforts are being made to gather data, resources being allocated, time being invested and tools being developed. Data collection is no longer a myth; however, it remains a great challenge to create value out of the enormous data that is being collected. Data modeling…

Risks of life on the street caused by inclement weather, harassment, and assault threaten the unsheltered homeless population. We address some challenges of enumerating the street homeless population by testing a novel capture-recapture (CR) estimation approach that models individuals' intermittent daytime visibility. We tested walking and…

The relative precision of four methods of estimating missing data in principal components analysis was investigated. Artificial data with known characteristics, obtained from Cattell's "Plasmode: 30-10-4-2," was used with one third of the data on half of the variables being systematically eliminated. The four methods of missing data estimation…

Deliberations about social policy often center on estimates of harm or benefit generated by different interest groups. Problems in what is measured and how it is measured are illustrated by a discussion of research into sexual abuse and rape. Advocacy research is an unreliable foundation for social policy formation. (SLD)