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Decision timelines for particular universities and programs derived from the gradcafe data + GRE/GPA distributions

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     Hi all.

 

Tired of waiting for graduate committees’ decisions I estimated decision timelines myself based on gradcafe data. For each university and program in albums below you will find three graphs:

  1. Decision timeline as a cumulative sum of decisions (accept, reject, interview, waitlist) as a function of time between Jan 1 and May 1 for the last five years combined.
  2. Boxplots of GRE Q and GRE V for people who reported both scores.
  3. Histogram of GPAs (from 2.5 to 4.0 with 0.1 step).  

 

Here is the list of programs I analyzed (some important notes below):

 

Computer science PhD https://imgur.com/a/cXaEs 

Computer Science MS https://imgur.com/a/u3joC 

Electrical Engineering PhD https://imgur.com/a/ra3Eh 

Electrical Engineering MS https://imgur.com/a/KUGrD 

Economics PhD https://imgur.com/a/NzlYm 

Economics MS https://imgur.com/a/JfgSk 

Statistics PhD https://imgur.com/a/mB5UC 

Statistics MS https://imgur.com/a/tXowL 

Mathematics (applied and pure) PhDhttps://imgur.com/a/d0821 

Chemistry PhD https://imgur.com/a/U5x91 

Physics (applied and pure) PhD https://imgur.com/a/35tTy 

Chemical Engineering PhD https://imgur.com/a/Tng2r 

Literature PhD https://imgur.com/a/LDKpT 

Anthropology PhD https://imgur.com/a/d5ub4 

Bioengineering PhD https://imgur.com/a/RpTSD 

Philosophy PhD https://imgur.com/a/ihoGS 

Biology PhD https://imgur.com/a/FWhoD 

 

How to use the graphs?

I used this data to decrease my own misery. Now that I know decision timelines of universities and programs I applied to, I can refresh gradcafe less and concentrate on more useful stuff more. Also, it is interesting to explore differences between different universities/programs. For example, some universities do gradual accepts rejects/accepts and others do it in waves. Some programs start early (chemistry) and some — later (CS). Keep in mind, that there may be errors in my analysis so use this data at your own risk.  

How reliable are timelines?

I personally trust them (but I am biased). In general, it depends on curve shapes and available data. If there are more than 100 observations overall — I would consider that data to be pretty reliable. If there are characteristic ‘steps’ — it is a good sign because may indicate internal deadlines for waves of accepts/rejects. But the number of admissions/rejections records in the data is definitely inflated by question records (i.e. ‘to poster below: what program?”). I filtered some, but definitely not all of them. Also, bear in mind that department policies can change.

How reliable are GRE/GPA?

Somewhat reliable. There is noise, mistakes (i.e. switched Q/V) and self-report bias. For example, salty people with good scores may more likely report rejections and lucky people with low GPAs may less likely report accepts. But for some universities which publish admission statistics (for example, Duke), calculated GRE/GPA medians are pretty close to reported averages (I didn’t calculate means, sorry). Also, we can’t affect GPA/GRE right now, so it is mostly for entertainment.

How did you do it?

  1. Scraped and parsed all gradcafe results.
  2. Selected all records from Jan 1 2013 to May 1 2017 and combined data for all years together, so all data is based on five year period.
  3. For each university and program in question I built a cumulative sum of decisions as a function of days since beginning of the year.
  4. For analysis of GRE I only chose records which included both Q and V scores.
  5. For analysis of GPA I used only 4-point scale grades and didn’t convert other scales to it (i.e. 10-point).
  6. Selection of universities/programs was done by regular expressions so there can be some noise added by incorrect parsing. For example, “University of Washington” may both mean Seattle and St. Louis. I tried to avoid it the best I could but there can be mistakes nonetheless.

How did you choose universities/programs?

Voluntarily, so there are a lot of omissions. Sorry, if your university/program is not there. Also, bear in mind that programs may overlap (for example ‘Computer Science’ and ‘Electrical Engineering’). Finally, I excluded uni/program from analysis if there were less than 30 observations.

Will you share your code/data?

I am thinking about it, but undecided yet.


Hope it helps and good luck with the admissions!



 

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This is great! I encourage you to share your code/data on something like github, so it would be easy for someone to update it in future years (so that you don't have to repeat it each year)

It would be really cool to see the acceptance decision timeline distributions for all disciplines on one plot, just out of curiosity. 

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> It would be really cool to see the acceptance decision timeline distributions for all disciplines on one plot, just out of curiosity. 

Great question. Here you go: https://imgur.com/a/jR53K

 I encourage you to share your code/data on something like GitHub

I will definitely release my code on GitHub I am just undecided to do it before or after my admission results. 

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32 minutes ago, there_is_a_wait_so_long said:

> It would be really cool to see the acceptance decision timeline distributions for all disciplines on one plot, just out of curiosity. 

Great question. Here you go: https://imgur.com/a/jR53K

 I encourage you to share your code/data on something like GitHub

I will definitely release my code on GitHub I am just undecided to do it before or after my admission results. 

Awesome :)

Intriguing that for PhDs, the timeline for acceptances isn't earlier than rejections as I would have thought! 

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33 minutes ago, TakeruK said:

Intriguing that for PhDs, the timeline for acceptances isn't earlier than rejections as I would have thought! 

1

Yes, surprising to me as well. But it heavily varies by university/program. Some reject first and the accept second,  others do exactly the opposite. Also, it is really to cool to see steps in accepts/rejects corresponding to first/second/third weeks of Feb/Mar. 

Edited by there_is_a_wait_so_long

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17 hours ago, there_is_a_wait_so_long said:

     Hi all.

 

Tired of waiting for graduate committees’ decisions I estimated decision timelines myself based on gradcafe data. For each university and program in albums below you will find three graphs:

  1. Decision timeline as a cumulative sum of decisions (accept, reject, interview, waitlist) as a function of time between Jan 1 and May 1 for the last five years combined.
  2. Boxplots of GRE Q and GRE V for people who reported both scores.
  3. Histogram of GPAs (from 2.5 to 4.0 with 0.1 step).  

 

Here is the list of programs I analyzed (some important notes below):

 

Computer science PhD https://imgur.com/a/cXaEs 

Computer Science MS https://imgur.com/a/u3joC 

Electrical Engineering PhD https://imgur.com/a/ra3Eh 

Electrical Engineering MS https://imgur.com/a/KUGrD 

Economics PhD https://imgur.com/a/NzlYm 

Economics MS https://imgur.com/a/JfgSk 

Statistics PhD https://imgur.com/a/mB5UC 

Statistics MS https://imgur.com/a/tXowL 

Mathematics (applied and pure) PhDhttps://imgur.com/a/d0821 

Chemistry PhD https://imgur.com/a/U5x91 

Physics (applied and pure) PhD https://imgur.com/a/35tTy 

Chemical Engineering PhD https://imgur.com/a/Tng2r 

Literature PhD https://imgur.com/a/LDKpT 

Anthropology PhD https://imgur.com/a/d5ub4 

Bioengineering PhD https://imgur.com/a/RpTSD 

Philosophy PhD https://imgur.com/a/ihoGS 

Biology PhD https://imgur.com/a/FWhoD 

 

How to use the graphs?

I used this data to decrease my own misery. Now that I know decision timelines of universities and programs I applied to, I can refresh gradcafe less and concentrate on more useful stuff more. Also, it is interesting to explore differences between different universities/programs. For example, some universities do gradual accepts rejects/accepts and others do it in waves. Some programs start early (chemistry) and some — later (CS). Keep in mind, that there may be errors in my analysis so use this data at your own risk.  

How reliable are timelines?

I personally trust them (but I am biased). In general, it depends on curve shapes and available data. If there are more than 100 observations overall — I would consider that data to be pretty reliable. If there are characteristic ‘steps’ — it is a good sign because may indicate internal deadlines for waves of accepts/rejects. But the number of admissions/rejections records in the data is definitely inflated by question records (i.e. ‘to poster below: what program?”). I filtered some, but definitely not all of them. Also, bear in mind that department policies can change.

How reliable are GRE/GPA?

Somewhat reliable. There is noise, mistakes (i.e. switched Q/V) and self-report bias. For example, salty people with good scores may more likely report rejections and lucky people with low GPAs may less likely report accepts. But for some universities which publish admission statistics (for example, Duke), calculated GRE/GPA medians are pretty close to reported averages (I didn’t calculate means, sorry). Also, we can’t affect GPA/GRE right now, so it is mostly for entertainment.

How did you do it?

  1. Scraped and parsed all gradcafe results.
  2. Selected all records from Jan 1 2013 to May 1 2017 and combined data for all years together, so all data is based on five year period.
  3. For each university and program in question I built a cumulative sum of decisions as a function of days since beginning of the year.
  4. For analysis of GRE I only chose records which included both Q and V scores.
  5. For analysis of GPA I used only 4-point scale grades and didn’t convert other scales to it (i.e. 10-point).
  6. Selection of universities/programs was done by regular expressions so there can be some noise added by incorrect parsing. For example, “University of Washington” may both mean Seattle and St. Louis. I tried to avoid it the best I could but there can be mistakes nonetheless.

How did you choose universities/programs?

Voluntarily, so there are a lot of omissions. Sorry, if your university/program is not there. Also, bear in mind that programs may overlap (for example ‘Computer Science’ and ‘Electrical Engineering’). Finally, I excluded uni/program from analysis if there were less than 30 observations.

Will you share your code/data?

I am thinking about it, but undecided yet.


Hope it helps and good luck with the admissions!

Our lord and savior has revealed themselves. I love this. Thank you!

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Thank you for channeling nervous energy in such a useful way!

Are you taking requests?  ;)

If so ... 

  • Linguistics
  • Arabic / "Middle East" / "Near East" / "Middle Eastern" / "Near Eastern" (just about every program has a different name, but that should catch most of them!)

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By request, added two more. Please bear in mind that some programs can be too specific (i.e. Arabian studies), thus not having enough data for particular university (I use 30 observations threshold). 

Biostatistics PhD https://imgur.com/a/UQ2e9

Linguistics PhD https://imgur.com/a/MauHg

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1 hour ago, there_is_a_wait_so_long said:

By request, added two more. Please bear in mind that some programs can be too specific (i.e. Arabian studies), thus not having enough data for particular university (I use 30 observations threshold). 

Biostatistics PhD https://imgur.com/a/UQ2e9

Linguistics PhD https://imgur.com/a/MauHg

Marry me.

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Great work! Could you do some of the Social Sciences as well? 

Sociology, Geography?

Edited by 94AVL

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Wow, that Literature one is ... sobering. Y'all have my sympathies.

image.png.cad1c0ece7fee8a9f1ceb5f7385b7f55.png

If this doesn't work out with the Arabic thing, I think I'm going to switch to Computer Science ....

image.png.890d23bdc390500eabcdccd317518174.png

Edited by semling

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1 hour ago, 94AVL said:

Great work! Could you do some of the Social Sciences as well? 

Sociology, Geography?

Sociology is in one of the answers above. Geography I will check if there is enough data.  

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