scholarly journals Adolescent recovery capital and recovery high school attendance: An exploratory data mining approach.

2019 ◽  
Vol 33 (8) ◽  
pp. 669-676 ◽  
Author(s):  
Emily A. Hennessy ◽  
Andrew J. Finch
2018 ◽  
Vol 17 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Emily E. Tanner-Smith ◽  
Andrew J. Finch ◽  
Emily A. Hennessy ◽  
D. Paul Moberg

2020 ◽  
Vol 21 (8) ◽  
pp. 1104-1113
Author(s):  
Emily E. Tanner-Smith ◽  
Lindsey M. Nichols ◽  
Christopher M. Loan ◽  
Andrew J. Finch ◽  
D. Paul Moberg

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1624-P
Author(s):  
ERIN M. TALLON ◽  
MARK A. CLEMENTS ◽  
DANLU LIU ◽  
KATRINA BOLES ◽  
RACHEL A. STUCK ◽  
...  

2021 ◽  
pp. 003804072110573
Author(s):  
Lei Lei

Many developing countries have experienced increasing spatial inequality, but little is known about the effect of community disadvantages on educational attainment in these societies. Using data from the China Family Panel Studies (2010–2016), I examine the effect of community socioeconomic status (SES) on the transition into high school in urban and rural China, and I explore several mechanisms explaining the community effects. I adopt the generalized propensity score method to estimate the potential probability of high school entrance at different levels of community SES. Results show that community SES is positively associated with high school attendance in both urban and rural China, and the relationship is stronger in more disadvantaged communities in both contexts. In urban areas, the effect of community SES is partly attributable to collective socialization and children’s academic performance. In rural areas, spatial accessibility to high schools and children’s academic performance are the salient mechanisms.


2008 ◽  
pp. 2566-2582
Author(s):  
Jeff Zeanah

This chapter discusses impediments to exploratory data mining success. These impediments were identified based on anecdotal observations from multiple projects either reviewed or undertaken by the author and are classified into four main areas: data quality; lack of secondary or supporting data; insufficient analysis manpower; lack of openness to new results. Each is explained, and recommendations are made to prevent the impediment from interfering with the organization’s data mining efforts. The intent of the chapter is to provide an organization with a structure to anticipate these problems and to prevent the occurrence of these problems.


1902 ◽  
Vol 10 (7) ◽  
pp. 558-565
Author(s):  
F. D. Boynton

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