Important new work by Reardon and his collaborators shows that not only test scores but also racial test score gaps vary dramatically across American school districts. In this latter paper, Reardon and coauthors report that while racial/ethnic test score gaps average around 0.6 standard deviations across all school districts, in some districts the gaps are almost nonexistent while in others they exceed 1.2 standard deviations.
"The combination of rising income inequality and rising tuitions has meant that middle-class families increasingly can’t afford private schooling," said Sean Reardon, a Stanford University professor of poverty and inequality in education, who co-authored the study with Harvard University economist Richard Murnane.
Stanford's Educational Opportunity Monitoring Project has dug deeply into available data, searching for what other factors beyond poverty might be influencing the black-white achievement gap.
Researcher Sean Reardon studied the multiple factors that contribute to the gap, using more than 200 million test scores from schools and districts across the country.
Reardon and his fellow researchers wanted to see which factors are most closely correlated with the achievement gap. They looked at two sets of factors that account for about three-fourths of the gap.
The main way well-off families choose schools is by choosing where to live. Increasingly, they’re settling in districts where most children look like theirs. “Rich districts are being created, and leaving middle-to-poor districts behind,” said Ann Owens, a sociologist at the University of Southern California, citing research she conducted with Sean Reardon of Stanford University and Christopher Jencks of Harvard.
Whether the goal is to identify and describe trends and variation in populations, create new measures of key phenomena, or describe samples in studies aimed at identifying causal effects, description plays a critical role in the scientific process in general and education research in particular. Descriptive analysis identifies patterns in data to answer questions about who, what, where, when, and to what extent. This guide describes how to more effectively approach, conduct, and communicate quantitative descriptive analysis. The primary audience for this guide includes members of the research community who conduct and publish both descriptive and causal studies, although it could also be useful for policymakers and practitioners who are consumers of research findings. The guide contains chapters that discuss the important role descriptive analysis plays; how to approach descriptive analysis; how to conduct descriptive analysis; and how to communicate descriptive analysis findings.
Research on school climate; shifts in race, income and gender-based achievement gaps; learning tools and approaches; and more appeared in the 20 most popular journal articles published by the American Educational Research Association in 2016. Based on the number of times they were accessed online, the following were the most popular AERA research articles published in 2016.