Job Intrinsic Security Stability Algorithm Model Based On Factor Analysis Method

• Published in: 2024 Second International Conference on Data Science and Information System (ICDSIS) pp. 1 - 5
• Main Authors: Lu, Libin, Nie, Leigang, Ye, Wende, Jia, Heng, Qin, Ji
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.05.2024
• Subjects: algorithm model; Analytical models; Computational modeling; Data models; Job intrinsic security stability; key factors; KMO test; Loading; Reliability; Security; Stability criteria
• DOI: 10.1109/ICDSIS61070.2024.10594064

Job intrinsic security stability is an important indicator for assessing the consistency and reliability of jobs under different conditions. This study proposes a job stability algorithm model based on the KMO(Kaiser-Meyer-Olkin) test to assess the appropriateness of using factor analysis on data and factor analysis method, aiming to evaluate the stability level of jobs by analyzing various factors within the job. The model collects data related to the stability status of jobs, assesses the suitability of the data through the KMO test to determine if it is suitable for factor analysis, and uses an appropriate factor extraction method to extract key factors. Factor validation is then conducted, and reliable indicators of the factors, such as Cronbach's alpha coefficient, and validity indicators, such as the proportion of variance explained, are calculated. Based on the extracted factors and their loading, scores or indices for job stability are calculated, and an appropriate method is selected to compute the comprehensive assessment of job stability. This algorithm model provides an effective method for evaluating job stability. By extracting and analyzing key factors within jobs, it is possible to objectively assess the consistency and reliability of jobs. The application of this model can help improve job stability and provide guidance for optimization and improvement in the job execution process.