College baseball has grown in popularity over the course of the last few years, as is evidenced by the increased national and regional broadcasts of games on television. For some schools, baseball is the third revenue-generating sport; however, the sport has not received much attention in the academic sports literature. This study seeks to fill the void in the extant literature and examines the rise in expenditures and revenues, and their impact on winning, using a sample of Power Five Conference baseball teams from 2007 to 2014. ANOVA, linear regression and logistic regression were used to find that there is a moderate, positive relationship between expenditures and success. Specifically, expenditures account for 18% of the variance in winning, and a 5% increase in win percentages is evidenced with every additional million-dollar investment in the sport. Additionally, the same investment increases the odds of qualifying for the National Collegiate Athletic Association tournament by 248%, for a Super Regional by 112%, and for the College World Series by 127%. The implications of these findings, as well as recommendations to frame decision-making at the administrative level are included here.

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The research community focuses on conducting research with the purported goal of improving educational practice, yet the two communities largely remain disjointed. This chapter explores the major disconnects between research and practice from the perspectives of both the practice and the research communities, and we present strategies for establishing stronger connections based on the results of our literature analysis. We argue that examining the research–practice gap through the lens of absorptive capacity provides elucidations about the disconnects, and it facilitates the organization of research-based strategies. As a result, both communities are able to jointly determine what constitutes quality research evidence and attenuate the gap between research and practice.

In this chapter, the authors describe the specific research skills to be developed for prospective principals in preparation for effective data use for school improvement. Relevant background information is provided regarding effective data use leadership, definition of data literacy, standards for principal preparation in data use, research on teaching research methods, and a comparison of the research process and the data-informed decision-making (DIDM) process. These skills are organized and reported in the chapter by steps in the DIDM research process. These steps include goal setting/problem formation, using previous research, planning for data collection, obtaining or collecting data, analyzing data (transforming data into information), and interpreting/taking action/making decisions.

We conducted a seroprevalence survey to estimate the true number of infections with SARS-CoV-2, the virus that causes COVID-19, in King County as of August 2020 by measuring the proportion of residents from who had antibodies against the virus. Participants from 727 households took part in a cross-sectional address-based household survey with random and non-random samples and provided dried blood spots that were tested for total antibody against the viral nucleocapsid protein, with confirmatory testing for immunoglobulin G against the spike protein. The data were weighted to match King County’s population based on sex, age group, income, race, and Hispanic status. After weighting and accounting for the accuracy of the tests, our best overall estimate of anti-SARS-CoV-2 seroprevalence in King County as of August 2020 is 3.9% (95% confidence interval (CI) 2.4%-6.0%) with an effective sample size of 589. Comparing seroprevalence with positive test reports, our survey suggests that viral testing underestimated incidence by a factor of about five and suggests that the proportion of cases that were serious (based on hospitalization) or fatal was 2.4% and 0.8%, respectively. Prevalence varied by subgroup; households reporting incomes at or below $100,000 in 2019 had nearly five times higher estimated antibody prevalence than those with incomes above $100,000. Those reporting non-White/non-Asian race had roughly seven times higher estimated antibody prevalence than those reporting White race. This survey was noteworthy for including people of all ages; among all age groups, the weighted estimate of prevalence was highest in older teens and young adults and lowest in young children, although these differences were not statistically significant.