Using the information that users have shared publicly on trackitt.com, I made some graphs and did some basic hypothesis testing. In order to get the data, I simply scraped all the webpages using R, and cleaned the data by removing incomplete cases.
Percentage of applicants in each category, and the correspondent percentages of accepted, pending, and denied cases
## types percentage accepted pending denied
## 5 EB1A 3.358 71.84 17.352 10.350
## 6 EB1B 3.900 92.14 5.505 2.359
## 1 EB1C 7.498 82.41 15.951 1.431
## 2 EB2 58.137 91.59 7.104 1.090
## 3 EB2-NIW 6.328 83.84 13.651 1.939
## 4 EB3 20.778 89.37 7.577 2.706
Countries leading in the number of applicants
## EB2-NIW
##
## India China South Korea Iran Taiwan Pakistan
## 22.536 16.721 9.855 7.431 4.281 2.342
## All categories
##
## India China South Korea Pakistan Philippines Canada
## 63.356 6.226 2.730 2.152 1.983 1.769
How about checking if there is any significant difference between countries, in NIW category?
## types percentage accepted pending denied
## 1 South Korea 15.601 87.70 12.295 0.000
## 2 India 35.678 82.08 15.054 2.509
## 3 China 26.471 89.37 8.696 1.932
## 4 Iran 11.765 79.35 18.478 2.174
## 5 Taiwan 6.777 86.79 11.321 1.887
## 6 Pakistan 3.708 79.31 13.793 6.897
Seems like there is a difference between Pakistan and South Korea (pvalue = 0.0383)! But if we correct for multiple-hypothesis testing, the difference is not significant.
What about for all categories?
## types percentage accepted pending denied
## 1 India 81.002 89.05 8.770 1.9524
## 2 South Korea 3.490 90.64 8.989 0.1873
## 3 Pakistan 2.751 85.99 8.789 3.8005
## 4 Philippines 2.536 82.99 14.948 2.0619
## 5 China 7.960 90.48 8.128 1.3957
## 6 Canada 2.261 91.33 7.225 1.1561
And there is no difference between any other countries.
Focusing on the approved case, I was curious to see what’s the difference between the total time it takes to be approved between different categories. Categories are further divided by their processing speed.
Obviously, premium processing is way faster than regular processing, but alas, it’s not available for EB2-NIW!
Focusing on EB2-NIW, we see that the processing speed has a clear periodic pattern!
Does total time it takes to be accepted depend on nationality? It seems like Chinese, Indians and Pakistanis have a longer wait time than Iranians, South Koreans and Taiwanese. Not sure if it’s because of the priority date, or what.
Again in EB2-NIW, you can either apply for I-140 concurrently with I-485, or not. But would that change the processing time, or the likelihood to be approved?
## Concurrent application
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10 124 190 235 327 1040 55
## Non-concurrent application
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5 145 218 257 374 877 117
## types percentage accepted pending denied
## 1 concurrent 40.63 87.48 10.54 1.392
## 2 non-concurrent 59.37 81.36 15.78 2.313
Applying concurrently decreases the process time for NIW I-140 (pvalue = 0.022), for almost 4 weeks. But how does it affect the chance of being rejected? It seems like the rate of rejection goes up to 2.3%, from 1.4%, if you apply non-concurrently (but that difference is not significant).
So how do pending cases accumulate over time? It seems like the number of pending cases in 2014 is significantly higher than the last 6 years. Isn’t this the underlying reason we are here now?
How about RFE? How does receiving one changes the chance of getting approved?
## types percentage accepted pending denied
## 1 no 79.48 84.65 14.33 0.5081
## 2 yes 20.52 80.71 11.02 7.4803
If you don’t get RFE, chances to be rejected is 0.5%. If you get a RFE, rejection rate goes up to 7%! And the difference is significant (pvalue = 0.0228). About the application center, there is no significant difference between Nebraska and Texas centers, in terms of the outcome.