The price of admission: rethinking how Americans pay for college
In: Economics of education review, Band 21, Heft 4, S. 395
ISSN: 0272-7757
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In: Economics of education review, Band 21, Heft 4, S. 395
ISSN: 0272-7757
In: Economics of education review, Band 38, S. 124-138
ISSN: 0272-7757
In: Economics of education review, Band 25, Heft 6, S. 575-590
ISSN: 0272-7757
In: The journal of human resources, Band 37, Heft 3, S. 653
ISSN: 1548-8004
In: Economics of education review, Band 18, Heft 1, S. 117-132
ISSN: 0272-7757
In: The annals of the American Academy of Political and Social Science, Band 671, Heft 1, S. 69-91
ISSN: 1552-3349
A growing number and proportion of students rely on student loans to assist with the costs of postsecondary education. Yet little is known about how first-generation students use federal loans to finance their education. In this article, we examine each of the decisions that culminate in student indebtedness: the decision to apply for aid, whether to borrow, and how much to borrow. We find significant differences by generational status at each step of the student borrowing process. First-generation students are more likely to apply for financial aid, borrow, and take out larger loans than their peers, after controlling for a rich set of covariates for costs and financial resources. We find that student characteristics cannot fully explain these observed differences in borrowing outcomes across generations.
In: Journal of policy modeling: JPMOD ; a social science forum of world issues, Band 20, Heft 4, S. 223-226
ISSN: 0161-8938
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types.
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