<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The professional identity of grid workers is a significant expression of social service consciousness, and this paper utilizes statistical analysis methods to study its related factors. Under the framework of SPSS 22.0, the non-robust paradigm square of traditional PCA is replaced by a robust measure, and the AWL-RPCA model is constructed by combining the introduced data adaptive learning. Meanwhile, to address the data smoothness problem of quantile regression, the PDQR model was proposed by using the method of constructing panel data to optimize the control of individual heterogeneity. Taking the gridmen in L street of a city as the research object, the related factors of occupational identity were mined based on the AWL-RPCA model, and then the regression equation was obtained by the PDQR model. The degree of influence of the independent variables on the occupational identity of the gridmen from the highest to the lowest is the working status, the level of education, the pressure of helping and teaching, the appraisal satisfaction, the support and coordination satisfaction, and the propaganda satisfaction, and the regression coefficients are 8.36, 3.14, 5.08, 3.60, -4.35, and 3.32, respectively. Under the guidance of the statistical analysis of the SPSS, the construction of the gridmen team can be optimized in a better way.</p>
</abstract>

The professional identity of grid workers is a significant expression of social service consciousness, and this paper utilizes statistical analysis methods to study its related factors. Under the framework of SPSS 22.0, the non-robust paradigm square of traditional PCA is replaced by a robust measure, and the AWL-RPCA model is constructed by combining the introduced data adaptive learning. Meanwhile, to address the data smoothness problem of quantile regression, the PDQR model was proposed by using the method of constructing panel data to optimize the control of individual heterogeneity. Taking the gridmen in L street of a city as the research object, the related factors of occupational identity were mined based on the AWL-RPCA model, and then the regression equation was obtained by the PDQR model. The degree of influence of the independent variables on the occupational identity of the gridmen from the highest to the lowest is the working status, the level of education, the pressure of helping and teaching, the appraisal satisfaction, the support and coordination satisfaction, and the propaganda satisfaction, and the regression coefficients are 8.36, 3.14, 5.08, 3.60, -4.35, and 3.32, respectively. Under the guidance of the statistical analysis of the SPSS, the construction of the gridmen team can be optimized in a better way.

Professional identity of grid workers based on SPSS 22.0

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Professional identity of grid workers based on SPSS 22.0"}

The professional identity of grid workers is a significant expression of social service consciousness, and this paper utilizes statistical analysis methods to...

Professional identity of grid workers based on SPSS 22.0

Publicado en línea: 22 nov 2023

Páginas: -

Recibido: 06 ene 2023

Aceptado: 02 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01210

Palabras clave
SPSS 22.0, AWL-RPCA model, PDQR model, Grid workers, Professional identity

© 2023 Zhenjiang Hou et al., published by Sciendo

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

Professional identity of grid workers based on SPSS 22.0

Publicado en línea: 22 nov 2023

Páginas: -

Recibido: 06 ene 2023

Aceptado: 02 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01210

Palabras claveSPSS 22.0, AWL-RPCA model, PDQR model, Grid workers, Professional identity

© 2023 Zhenjiang Hou et al., published by Sciendo

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

Palabras clave
SPSS 22.0, AWL-RPCA model, PDQR model, Grid workers, Professional identity