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In order to control for these effects, we regress the differences between the callback rates of the applicants on the same set of variables used in OLS regressions.
Using a 15-second piece of video showing the candidate knocking on the door, shaking hands, and being greeted by the interviewer, she asked a group of new participants to rate these applicants on the same criteria that the two trained interviewers had been using.
This unisex factor will allow you to evaluate female and male applicants on the same level.
References and contacts are another unisex factor that allows you to evaluate male and female applicants on the same level.
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With any luck, they might just outshine the other 2,999 applicants for the same place.
Thirdly, we send all the applicants to the same job offers, regardless of their religious closeness.
"Scorecards are different for different lenders, so it's not unusual for one lender to say 'yes' and another 'no' to the same applicant based on the same information," says James Jones, from Experian.
Unless an applicant's financial circumstances have improved significantly, an applicant may count on the same standard of support in subsequent years.
Type I errors lead to the over-estimation of the applicant's future performance, i.e. the selected applicant will perform on the same level or below the average of the rejected group.
Type II errors lead to the under-estimation of the applicant's future performance, i.e. the rejected applicant will perform on the same level or above the average of the selected group [38].
An entirely new group of interviewers, watching only these 15 seconds of each applicant, rated them on the same checklist that the earlier interviewers had used.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com