Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…

Adopting a two-phase explanatory sequential mixed methods research design, the current study examined the impact of student teaching experiences on pre-service teachers' readiness for technology integration. In phase-1 of quantitative investigation, 2-level growth curve models were fitted using online repeated measures survey data collected from…

Despite our best efforts as evaluators, program implementation failures abound. A wide variety of valuable methodologies have been adopted to explain and evaluate the "why" of these failures. Yet, typically these methodologies have been employed concurrently (e.g., project monitoring) or to the post-hoc assessment of program activities.…

A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the…

Every day, students collect, organize, and analyze data to make decisions. In this data-driven world, people need to assess how much trust they can place in summary statistics. The results of every survey and the safety of every drug that undergoes a clinical trial depend on the correct application of appropriate statistics. Recognizing the…

The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

Extension professionals often assess community needs to determine programs and target audiences. Data can be collected through surveys, focus group and individual interviews, meta-analysis, systematic observation, and other methods. Knowledge gaps are identified, and programs are designed to resolve the deficiencies. However, do Extension…

Background: Individuals with intellectual and developmental disabilities (IDD) experience high rates of physical and mental health problems; yet their health care is often inadequate. Information about their characteristics and health services needs is critical for planning efficient and equitable services. A logical source of such information is…

This study details the collection, analysis, and interpretation of data from a national multi-institutional longitudinal mixed methods study of college impact and student development of liberal arts outcomes. The authors found three sets of practices in the quantitative data that corroborated with the themes that emerged from the qualitative data:…

Provides a brief overview of single-subject research designs. This method exercises its power by examining changes in single subjects' responses over time across experimental conditions. Describes a classroom project in which students collect repeated measures of their own behavior and graph the data. (MJP)