Timeline for Dataset of grad admission acceptance rate based on undergrad school
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| S Jul 13, 2019 at 1:55 | history | suggested | Glorfindel | CC BY-SA 4.0 |
broken image fixed (click 'rendered output' or 'side-by-side' to see the difference); for more info, see https://gist.github.com/Glorfindel83/9d954d34385d2ac2597bbe864466259f
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| Jul 12, 2019 at 20:22 | review | Suggested edits | |||
| S Jul 13, 2019 at 1:55 | |||||
| Jan 6, 2016 at 23:08 | comment | added | ff524 | This is interesting data. It's different from what the OP requested in two ways: besides for being about a different stage (as acknowledged), it only includes successful applicants, not rejected applicants. i.e. assuming these 51 school are the only schools in existence, you can use it to say "N% of hires at Z come from Y" or "M% of Y graduates who get positions end up at Z." But you can't say "N% of applicants to Z from Y are hired," which would be analogous to what OP wanted for grad admissions ("X % of students from undergraduate school Y applying to graduate school Z are admitted"). | |
| Jan 6, 2016 at 22:14 | comment | added | BrianH | This is exactly the link that came to mind when I read the question, but I was having trouble recalling where I read it. Thanks for posting it! I also recall something like a network analysis of "longest connection length", with the general idea that "for top institutions, it really is a small world" - that at top institutions they had 1-link long connections to everywhere, while at smaller institutions it might take 4+ links to get some other department. The one's I'm thinking of don't answer the OPs questions either though, I'm afraid. | |
| Jan 6, 2016 at 20:35 | history | edited | WBT | CC BY-SA 3.0 |
Added image
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| Jan 6, 2016 at 20:22 | comment | added | WBT | The key issue is lack of motivation/incentives to gather such a data set: for what purpose would it be used? There are so few large colleges/CS programs that limiting to those wouldn't tell you much, and analyzing random noise is not likely to be helpful in answering a motivating question. Data could be aggregated over years (skipping doesn't reduce the PII specificity issue) at the cost of current/predictive value (programs change over time). Data collection should be motivated by a specific question, so as to guide decision-making between the trade-offs involved. | |
| Jan 6, 2016 at 20:16 | comment | added | Franck Dernoncourt | Thanks, how about large colleges/CS programs, and perturbing the data (e.g., random noise or skip years)? | |
| Jan 6, 2016 at 20:13 | history | answered | WBT | CC BY-SA 3.0 |