Using Biodata to Select Air Traffic Controllers
Abstract: Biodata factors were examined as predictors of training performance for candidate air traffic control specialists (ATCSs). These factors, which have been shown to predict controller training performance in previous research, were highest educational degree achieved, grade point average both in high school overall and in high school math courses, aviation operations experience, pilot licenses held, and age. Results from logistic regression analyses were only partially supportive of previous research. Age was the most consistent (inverse) predictor of training success. Most of the other factors did not predict training success. Differences between these results and previous research might be attributed to differences in the criterion measures, samples, and generational differences. Overall, the evidence for using the assessed biodata factors for selection was weak. We suggested that a new biodata instrument be developed to assess and identify experiences to predict performance of the next generation of cont...
Summary (3 min read)
- The selection of air traffic control specialists (ATCSs; referred to as controllers) has been the subject of on-going research by the Federal Aviation Administration (FAA) from its beginnings in 1958.
- Two recent events within the FAA have led to renewed interest in biodata as a predictor of controller training performance.
- The IRP recommended that the FAA incorporate these factors into its air traffic controller recruitment and selection processes.
- AT-SAT has been found to be a hiring “barrier” for African-American, Hispanic/Latino, and female applicants (Outz & Hanges, 2012).
- The sample for this study was a group of candidate air traffic controllers hired by the FAA and entering the FAA Academy from February 2007 through December 2011 (N =2,662).
- Controllers within terminal facilities organize the flow of air traffic into and out of airports.
- As indicated in the FAA’s (2014) most recent controller workforce plan, the training target time by size of terminal facility is 17 months for small (levels 4-6), 24 months for medium (levels 7-9), and 29 months for large (levels 10-12) facilities.
- There are many reasons why trainees may request a transfer.
- The unsuccessful category is for trainees that did not complete training; however, lack of success was not due to poor performance but rather some issue, such as failing a medical exam or security screening, retirement, or even death.
- There were seven predictors and one criterion measure used in this study.
- For their criterion measure, the authors used training outcome data from both the FAA Academy and the NTD to determine the training status (successful or unsuccessful) of the trainees in their sample as of June 2014.
- If the authors excluded age from the analyses, one or more of the biodata items may predict training status merely due to its association with age.
- BQ items selected for analysis were those shown to either predict the training performance of candidate controllers in the past and/or to assess a subset of the proposed quality ranking factors.
- Trainees assigned to an en route center were successful less often than expected (652/690) and unsuccessful more often than expected (284/247).
- Logistic regression was used to model the relationship of ATSAT score, controller age upon entering the FAA Academy, and the five-biodata items to training status of trainees assigned to an en route center or terminal facility.
- For both analyses, AT-SAT score and age were continuous variables.
- The authors excluded trainees with missing data on any of the variables entered into the model.
- The final number of trainees included in the analyses was 1,900 (841 en route center trainees; 1,059 terminal facility trainees).
- Shown in Table 3 are the values and frequency counts for the biodata items entered into the logistic regression.
- For trainees assigned to an en route center, the initial logistic regression model of AT-SAT score, age at entry on duty, and biodata items on training status resulted in correct classifications of 64.6% of the trainees.
- Thus, the authors interpreted the original model with the outliers.
- A significant relationship was found between two of the assessed biodata items and training status after accounting for AT-SAT score and age, as indicated by the chi-square for the third block (χ2 (2) = 21.48, p < .001).
- To test the linearity of the logit, the authors ran the logistic regression analysis again, including predictors that were the interaction of each continuous variables and the log of itself (Field, 2009).
- In their test for multicollinearity, all tolerance values were greater than .1, and VIF values were less than 10.
- For trainees assigned to a terminal facility, the initial logistic regression model of AT-SAT score, age at entry on duty, and biodata items on training status resulted in correct classifications of 61.3% of the terminal trainees.
- An analysis of the data, with 13 outliers (studentized residuals greater than 2.58 or less than -2.58) removed, resulted in a model in which 64.1% of the trainees were correctly classified.
- In reviewing both regression models (with or without the outliers), the authors found that the same variables were included and excluded in the models.
- Nevertheless, as shown in Table 5, the logistic regression coefficients for AT-SAT score and age were statistically significant.
- There was no relationship between any of the biodata items and training status for the trainees assigned to a terminal facility.
- In the current research, the authors investigated the validity of five biodata items as predictors of controller training status after accounting for aptitude (e.g., AT-SAT score) and age at entry on duty for trainees assigned to en route centers and terminal facilities.
- As expected, the authors found AT-SAT score and age were related to training status for trainees assigned to both en route centers and terminal facilities.
- It was somewhat surprising that the authors did not find high school GPA or having a degree from a CTI school to predict training status.
- It is possible that by including HS math GPA in this study, which the authors found to predict training status of en route trainees, they obscured the relationship between training status and high school GPA seen in previous research.
- It is also possible that the criterion measure used in this study, which differed from previous research studies, had an impact.
- One potential explanation for these findings is that CTI graduates were more likely than non-CTI graduates to be assigned to more complex facilities (e.g., en route centers and higher-level terminal facilities) and thus, less likely to succeed in training than non-CTI graduates.
- The proportion of CTI graduates assigned to an en route center, rather than a terminal facility, was 47.9%.
- With large sample sizes, such as the authors have here, even small differences may be statistically significant.
- There are 36 schools in the program, and except for a common core in aviation-education supplied and required by the FAA, the schools vary in what they teach and even how the information is taught.
- Thus, it is possible that training performance of CTI graduates was influenced by differences in the CTI programs and the processes used to select from the CTI applicants.
- Results for the other biodata items, specifically HS math GPA and holding any pilot’s certificate, differed between options.
- A brief description extracted from the FAA’s controller workforce plan (FAA, 2014) is provided in Table 6 (also see Broach, 2013).
- Controllers in a combination tower and TRACON facility use both visual means and radar to control traffic within their terminal airspace.
- Results from job analyses have indicated that the aptitudes (work requirements) required to control air traffic are similar across option (en route and terminal) and facility types within the terminal option (Nickels, Bobko, Blair, Sands, & Tartak, 1995).
- A limitation of this research was the use of only one criterion measure related to training status.
- There is a need to consider multiple outcome measures, especially measures related to onthe-job performance of air traffic controllers.
- Ultimately, the goal is to select, place, and train successful air traffic controllers, not merely identify candidates likely to succeed in training.
- The authors findings are somewhat consistent with previous research, especially their findings regarding predictors of success in training as an en route controller.
- The authors findings also provide additional support for the utility and validity of AT-SAT in predicting training status of trainees (Broach et al., 2013).
- The authors results demonstrated yet again an inverse relationship between age at entry on duty and training performance.
- Younger controllers performed better in training than did older controllers.
- The FAA makes exceptions to the age policy for retired military controllers and previous FAA controllers.
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Cites background from "Using Biodata to Select Air Traffic..."
...didates’ advancement in the selection process include age, intelligence, attention and multitasking ability, as well as general motivation , ....
Cites background from "Using Biodata to Select Air Traffic..."
...As mentioned previously, a significant amount of research has suggested that age has a substantial impact on success rates (see Pierce et al., 2014)....
...Researchers have found age at entry to be a consistent and powerful (inverse) predictor of training performance (see Pierce et al., 2014, for a review)....
"Using Biodata to Select Air Traffic..." refers background in this paper
...However, it is probably the type of job assessed, as well as the items used to predict performance, that will influence the likelihood and degree of subgroup differences in biodata (Hough et al., 2001; Imus et al., 2011)....
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Q1. What are the contributions mentioned in the paper "Using biodata to select air traffic controllers" ?
In a recent study, Broach et al. this paper used the CBAS as a predictor of controller training performance.