<p>
</p>
<p>Modelling CLASS admission is far easier and more accurate than modelling of individual admission probabilities. The papers and book written by Espenshade and his grad students at Princeton are an example. They constructed a very simple model of individual admissions, ignoring subjective data like transcript, essay, interview, and oboes, and merely assigning fixed numbers of points for SAT score interval, race, athlete or legacy status, US citizenship, and other objective factors available in their data set. They then found that when you run the individual-level model on the whole applicant pool, the predicted class composition closely approximates the true one. This method also reproduced qualitatively, and in some ways quantitatively, other known effects such as affirmative action and legacy preferences. </p>
<p>Those and other quantitative studies — all of which, so far, have used simple point systems and very basic statistical methods — lend credibility to several ideas:</p>
<ol>
<li><p>Simple models of individual admission probabilities are reasonable and feasible to construct.</p></li>
<li><p>Oversimplified models are closer to the truth than admissions offices would like to acknowledge. This is true whether or not admissions uses a point system or explicit algorithm of any kind. It is quite possible that the process itself is complicated, random and subjective but can be accurately caricatured by simple algorithms that tell most of the truth in most cases.</p></li>
<li><p>There is a meaningful relationship between data and prediction at the class level, and the individual level. For example, parameters in a model relating year-to-year changes in math SAT distribution to the class composition at engineering schools, may also be useful as parameters in a model of individual admission to those schools, and vice versa.</p></li>
</ol>