- May 30, 2017
- May 25, 2017
Congratulations to Jing Liu for receiving 2017 National Academy of Education/Spencer Dissertation Fellowship.
Jing Liu is a Ph.D. candidate in Economics of Education at the Stanford Graduate School of Education. He earned his B.A. in Economics in 2011 and M.A. in Economics of Education in 2013, both from Peking University, China. He also earned a M.A. in Economics in 2016 from Stanford University. His research mainly focuses on using computational social science methods, especially “text as data”, to measure beneficial teacher and peer practices and evaluate their effects in K-16 classrooms. His work has appeared in Journal of Policy Analysis and Management and AERA Open.
- May 22, 2017
- Jason Fletcher on Environmental Shaping of Effects of Individual Endowments in Processes of Social MobilityMay 09, 2017
- May 08, 2017
Researchers Camille Whitney of Mindful Schools and Jing Liu of Stanford University tracked class-by-class attendance for more than 50,000 middle and high school students in an urban district from 2007-08 to 2012-13. They found that missing individual classes accounted for as many total missed days as full-day absences—added all up, the chronic absenteeism rate rose from 9 percent to 24 percent of the district's secondary students.
- May 08, 2017
- May 02, 2017
In a separate study, Heather Hough, Demetra Kalogrides, and Susanna Loeb of Stanford found 5 percent of the differences in schools' math growth in elementary school and 6 percent of the differences in math growth in middle schools, as well as 11 percent of the differences in high schools' graduation rates, could be explained by differences in their school climate and student-reported social skills. That was the case even after controlling for school demographics and quality indicators, like teacher quality. Combining the results of the student social-skills surveys and school climate surveys accounted for 21 percent of the difference in math scores for the lowest-performing 5 percent of low-performing schools.
- April 27, 2017
"We really emphasize that the news articles on shortages tend not to be all that nuanced and the issue of shortage is a lot more nuanced than the public policy discussion," Dan Goldhaber told Education Week. He is director of the Center for Education Data and Research at the University of Washington and a coauthor of the report along with Thomas S. Dee, the associate dean for faculty affairs at Stanford University.
- April 26, 2017
- April 25, 2017
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.
- April 20, 2017
- April 19, 2017
- Congratulations to sean reardon, a newly elected member of the American Academy of Arts and SciencesApril 12, 2017
- April 03, 2017
- March 27, 2017
Through the Stanford-SFUSD partnership I had the opportunity to use the district's data to evaluate the TK program. I leveraged the age eligibility requirements to compare the outcomes of students who attended TK to students who attended San Francisco's universal prekindergarten program. TK differs from the universal prekindergarten program in that it is folded into the larger K-12 system, employs teachers that are more highly educated and compensated, and offers a more academically focused curriculum established by SFUSD.
- March 27, 2017
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.