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<p>The quantitative analysis of college students’ employment influencing factors is an effective path to tap into the employment problem of graduates and thus promote the high-quality development of the social economy. This paper constructs a high-quality employment logistic regression model using the logistic distribution function to study the relationship between high-quality employment of college students and their influencing factors, utilizes the correlation analysis method to measure the interdependence of variables, and tests the robustness of the model by replacing the variables and updating the model. The employment information of college students in city B is selected as a research sample to explore the regression effect of government and other factors on high-quality employment, and the socioeconomic level is tested as a mediating variable. And finally, the path to improve the employment quality of college students is designed. From the regression analysis of each factor and high-quality employment, the government level, college level, family level and individual level all show significant correlation at 1% level for high-quality employment, and the regression coefficients of the four levels are 0.126, 0.05, 0.034 and 0.077, which indicates that government policy is the key factor influencing college students’ high-quality employment. The proposed logistic regression model for high-quality employment can be used as a helpful reference for enhancing the high-quality employment of college students in universities.</p>
</abstract>

The quantitative analysis of college students’ employment influencing factors is an effective path to tap into the employment problem of graduates and thus promote the high-quality development of the social economy. This paper constructs a high-quality employment logistic regression model using the logistic distribution function to study the relationship between high-quality employment of college students and their influencing factors, utilizes the correlation analysis method to measure the interdependence of variables, and tests the robustness of the model by replacing the variables and updating the model. The employment information of college students in city B is selected as a research sample to explore the regression effect of government and other factors on high-quality employment, and the socioeconomic level is tested as a mediating variable. And finally, the path to improve the employment quality of college students is designed. From the regression analysis of each factor and high-quality employment, the government level, college level, family level and individual level all show significant correlation at 1% level for high-quality employment, and the regression coefficients of the four levels are 0.126, 0.05, 0.034 and 0.077, which indicates that government policy is the key factor influencing college students’ high-quality employment. The proposed logistic regression model for high-quality employment can be used as a helpful reference for enhancing the high-quality employment of college students in universities.

The Effective Guidance Path of High-Quality Employment for College Students in the Internet Perspective

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The quantitative analysis of college students’ employment influencing factors is an effective path to tap into the employment problem of graduates and thus...

The Effective Guidance Path of High-Quality Employment for College Students in the Internet Perspective

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 22. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0122

Schlüsselwörter
Logistic distribution function, Regression model, Mediating variables, Robustness, High quality employment

© 2024 Haiyan Gan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Effective Guidance Path of High-Quality Employment for College Students in the Internet Perspective

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 22. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0122

SchlüsselwörterLogistic distribution function, Regression model, Mediating variables, Robustness, High quality employment

© 2024 Haiyan Gan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Logistic distribution function, Regression model, Mediating variables, Robustness, High quality employment