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Predicting With Logistic or Linear Regression: Will It Make a Difference in Who Is Selected for Pilot Training?

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TLDR
In this paper, a pilot selection database is used to demonstrate that the ranking of pilot training applicants is nearly identical for both linear and logistic regression methods and that both methods would select the same applicants.
Abstract
Selecting individuals for pilot training is typically accomplished by determining what human characteristics are predictive of training success using ordinary linear regression. Frequently, however. the measure of success is passing or failing training. Some critics suggest that ordinary linear regression is inappropriate for such a dichotomous criterion and recommend using logistic regression. For the practitioner the question becomes: "Will it make a difference in who is selected for pilot training?" A pilot selection database is used to demonstrate that the ranking of pilot training applicants is nearly identical for both methods. The rank correlations between the methods was .9967. Therefore, both linear and logistic regression would select the same applicants. Other considerations favoring the use of linear regression are discussed.

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The predictive validity of the MCAT exam in relation to academic performance through medical school: a national cohort study of 2001-2004 matriculants.

TL;DR: Both UGPAs and MCAT total scores are strong predictors of academic performance in medical school through graduation, not just the first two years, and these relationships generalize across medical schools.
Journal ArticleDOI

Group Differences on US Air Force Pilot Selection Tests

TL;DR: In this article, the authors examined the impact of race and ethnic group differences on the operational composites and tests used to select applicants for U. S. Air Force officer commissioning programs and for pilot training.
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Multiple test batteries as predictors for pilot performance: A meta‐analytic investigation

TL;DR: In this article, the authors investigated the predictive validity of TBs' composite scores for several criteria of pilot performance and proposed a classification scheme of six categories representing the most common composite scores in selection assessment: Acquired Knowledge, Perceptual Processing, Motor Abilities, Controlled Attention, General Ability, and Work Sample.
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Improving the Pilot Selection System: Statistical Approaches and Selection Processes

TL;DR: In this article, the authors compare three statistical approaches to model candidate performance: multiple linear regression, linear discriminant analysis, and logistic regression, and discuss how system operators can adjust each of the processes to deal with a pilot shortage.
References
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Journal ArticleDOI

IV.—A Note on Karl Pearson's Selection Formulæ

TL;DR: In this article, the authors adopt the shortened vector and matrix notation introduced by Aitken (I) to evaluate the effect of selection on the means, variances and covariances of a multivariate population.
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Predicting pilot and navigator criteria : not much more than g

TL;DR: A comparison of the validity of psychometric g and specific ability of job knowledge, s, for predicting pilot and navigator criteria was conducted as mentioned in this paper, which demonstrated that g was the best predictor of all criteria and s contributed little beyond g.
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A logistic regression model for personnel selection

TL;DR: In this paper, a two-parameter logistic regression model for personnel selection is proposed and a uni-fied approach is provided for studying selection, validity generalization, employee classification, selection bias, and utility-based fair selection.
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Short-Term Retest Reliability of an Experimental U.S. Air Force Pilot Candidate Selection Test Battery

TL;DR: In this paper, the authors examined the short-term retest reliability of the Basic Attributes Test (BAT) battery and found that there was a moderate correlation between subjects' first and second administration test composites (Pearson r =.56, Spearman rho =.55).
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