Small sample sizes contribute to several problems in research and knowledge advancement. This conceptual replication study confirmed and extended the inflation of type II errors and confidence intervals in correlation analyses of small sample sizes common in kinesiology/exercise science. Current population data (N = 18, 230, & 464) on four…

Context: A perennial problem in applied statistics is the inability to justify strong claims about cause-and-effect relationships without full knowledge of the mechanism determining selection into treatment. Few research designs other than the well-implemented random assignment study meet this requirement. Researchers have proposed partial…

In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…

Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, researchers assess only part of the sample on a high-quality expensive measure, while the entire sample is given a more inexpensive, but biased measure. The…

This report from the Career and Technical Education (CTE) Research Network Lead team provides final results from an evaluability assessment of CTE programs that feasibly could be evaluated using a rigorous experimental design. Evaluability assessments (also called feasibility studies) are used in education and other fields, such as international…

Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts…

It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…

This paper provides a framework for developing sampling designs in mixed methods research. First, we present sampling schemes that have been associated with quantitative and qualitative research. Second, we discuss sample size considerations and provide sample size recommendations for each of the major research designs for quantitative and…

Institutional researchers frequently use national datasets such as those provided by the National Center for Education Statistics (NCES). The authors of this chapter explore the adjustments required when analyzing NCES data collected using complex sample designs. (Contains 8 tables.)

This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…

Statistical significance is misused in educational and psychological research when it is applied as a method to establish the reliability of research results. Other techniques have been developed which can be correctly utilized to establish the generalizability of findings. Methods that do provide such estimates are known as invariance or…