The Journal of College and University Student Housing - Volume 45, No. 2 - 56

Reverse-Engineering the RA Hiring Process

neer the previous year's component weights
to identify the combination of weights that
best predicted an RA's actual job performance,
which could then be employed in future hiring
practices. To systematically identify the best
combination of component weights, the total
weighted score was calculated for each candidate in 10-point increments for each component: Model 41 [40A | 20R | 40I], Model 42 [40A
| 30R | 30I], and Model 43 [40A | 40R | 20I] representing Model N [(Application Weight %) |
(Reference Weight %) | (Interview Weight %)].
This created 66 combinations, all of which
were run through a simple linear regression
with total weighted score serving as the predictor variable and performance evaluation score
serving as the dependent variable.

Of all 394 models run on the
collected Spring 2016 recruitment
data, 349 (or 88.6%) revealed
significant prediction capabilities of
Fall 2016 performance evaluations.

Of the 66 models run, 49 significantly
predicted future job performance. Significant
models ranged in variance from the least predictive model (Model 51 [50A | 50R | 0I]), explaining 6.5% of the variance in performance
evaluation scores, F(1, 46) = 4.285, p < .05, to
the most predictive model (Model 25 [20A | 30R
| 50I]), explaining 12.9% of the variance in performance evaluation scores, F(1, 46) = 7.956,

p < .01. As an interesting side note, the least
predictive (and insignificant) model was the
weighting combination that only considered
references (Model 11 [0A | 100R | 0I]), F(1, 46)
= 1.045, R2 = .001, p = .312, supporting the assumption that references alone are not a strong
metric from which to make hiring decisions.
The broad 10-point increment analysis
identified general weighting distribution
areas to conduct more analysis with increased
granularity (i.e., combinations roughly around
10-40 for application weights, 20-40 for
reference weights, and 40-70 for interview
weights). Therefore, an additional 124 total
weighted scores were created at 5-point increments for combinations falling in and around
those identified areas (e.g., Model 101 [15A | 25R
| 60I], Model 102 [15A | 30R | 55I], Model 103 [15A
| 35R | 50I]). As with the 10-point increment
models, the new models were all entered into
simple linear regressions with the total weighted score serving as the predictor variable and
performance evaluation scores serving as the
dependent variable.
Including both rounds of regression
models conducted thus far (190 total), 145 (or
76.3%) were significant predictors of performance evaluation scores. The new most predictive significant weighting combination was
Model 116 [20A | 25R | 55I], explaining 13.1%
of the variance in performance evaluation, F(1,
46) = 8.071, p < .01. The new least predictive
significant model was Model 163 [40A | 55R
| 5I], which explained 6.4% of the variance
in performance evaluation scores, F(1, 46) =
4.207, p < .05.
A review of models occurred similar to
the review of the 10-point weight increment

V O L U M E 4 5 , N O. 2 * 2 0 1 9

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The Journal of College and University Student Housing - Volume 45, No. 2

Table of Contents for the Digital Edition of The Journal of College and University Student Housing - Volume 45, No. 2

Contents
The Journal of College and University Student Housing - Volume 45, No. 2 - Intro
The Journal of College and University Student Housing - Volume 45, No. 2 - Cover1
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The Journal of College and University Student Housing - Volume 45, No. 2 - Cover3
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