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Publication
A retenção de militares dos quadros permanentes na força aérea: os dados como fonte de informação e previsão
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| 3.09 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Este estudo investiga a problemática das saídas voluntárias dos militares dos Quadros
Permanentes (QP) da Força Aérea Portuguesa, desenvolvendo um modelo preditivo para
identificar militares com maior probabilidade de pedir abate. Utilizando uma abordagem
quantitativa, recorreu-se a métodos de machine learning, nomeadamente Regressão Logística,
Naive Bayes, Análise Discriminante Linear, Análise Discriminante Quadrática e Árvores de
Decisão. A base de dados foi composta por 2176 militares, divididos entre aqueles que solicitaram
abate e os que permaneceram em serviço. A análise identificou variáveis sociodemográficas e
estruturais significativas, como a idade, a especialidade, o número de louvores e a nota da ficha
de avaliação. O modelo de Regressão Logística (RL) apresentou a melhor performance,
permitindo calcular um índice de probabilidade de saída, com utilidade prática para a gestão de
recursos humanos. Apesar de limitações, como a baixa sensibilidade e o uso de um conjunto
limitado de variáveis, o estudo oferece contributos importantes para a retenção de talento na
Força Aérea e sugere a inclusão de mais variáveis e abordagens avançadas em investigações
futuras.
This study investigates the issue of voluntary departures of permanent staff (QP) from the Portuguese Air Force, developing a predictive model to identify military personnel with a higher likelihood of requesting departure. Using a quantitative approach, were used machine methods learning, namely Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis and Decision Trees. The database consisted of 2179 military personnel, divided between those who requested discharge and those who remained in service. The analysis identified significant sociodemographic and structural variables, such as age, specialty, commendations, and evaluation grade. The Logistic Regression (LR) model showed the best performance, allowing the calculation of a probability of departure index, with practical utility for human resources management. Despite limitations, such as low sensitivity and the use of a limited set of variables, the study offers important contributions to talent retention in the Air Force and suggests the inclusion of more variables and advanced approaches in future research.
This study investigates the issue of voluntary departures of permanent staff (QP) from the Portuguese Air Force, developing a predictive model to identify military personnel with a higher likelihood of requesting departure. Using a quantitative approach, were used machine methods learning, namely Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis and Decision Trees. The database consisted of 2179 military personnel, divided between those who requested discharge and those who remained in service. The analysis identified significant sociodemographic and structural variables, such as age, specialty, commendations, and evaluation grade. The Logistic Regression (LR) model showed the best performance, allowing the calculation of a probability of departure index, with practical utility for human resources management. Despite limitations, such as low sensitivity and the use of a limited set of variables, the study offers important contributions to talent retention in the Air Force and suggests the inclusion of more variables and advanced approaches in future research.
Description
Keywords
Retenção Rotação Métodos de Machine Learning Retention Rotation Machine Learning Methods