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Gomes, A. & Dias, J. G. (2015). Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling. International Journal of Aviation Psychology. 25 (2), 108-121
A. P. Gomes and J. M. Dias, "Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling", in Int. Journal of Aviation Psychology, vol. 25, no. 2, pp. 108-121, 2015
@article{gomes2015_1731964924180, author = "Gomes, A. and Dias, J. G.", title = "Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling", journal = "International Journal of Aviation Psychology", year = "2015", volume = "25", number = "2", doi = "10.1080/10508414.2015.1130489", pages = "108-121", url = "http://www.tandfonline.com/doi/abs/10.1080/10508414.2015.1130489" }
TY - JOUR TI - Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling T2 - International Journal of Aviation Psychology VL - 25 IS - 2 AU - Gomes, A. AU - Dias, J. G. PY - 2015 SP - 108-121 SN - 1050-8414 DO - 10.1080/10508414.2015.1130489 UR - http://www.tandfonline.com/doi/abs/10.1080/10508414.2015.1130489 AB - Latent growth mixture modeling is a statistical approach that models longitudinal data, grouping individuals who share similar longitudinal data patterns into latent classes. We evaluated the application of this method in a sample of ab initio pilot applicants (N = 297), using longitudinal data collected from a military flight-screening program (where the applicants flew seven required flights), resulting in a final pass–fail outcome. Results showed the existence of a two-class solution (Cluster 1 presented an initially higher performance and contained 75% of the Pass candidates) and the psychomotor coordination and general adaptability showed a significant effect. ER -