An Analysis on the Difference of Research Competency of Engineering Doctoral Students with Various Characteristics Based on SPSS
- DOI
- 10.2991/978-94-6463-034-3_87How to use a DOI?
- Keywords
- Engineering and Technical Talents; Research Competency; Engineering Doctoral Students; SPSS
- Abstract
Engineering and technical talents have become the “dancing partners” of AI and the “bodyguards” of big data security. As a reserve of high-level engineering and technical talents, the research competency of engineering doctoral students will affect the speed of technological innovation. The paper takes 163 engineering doctoral students from a research university in Liaoning as the research object, using SPSS to analyse the difference of research performance under various characteristics. ANOVA shows that significant differences exist in gender, undergraduate and master’s degrees universities, interdisciplinary and admission type. Regression analysis shows that admission type affects the research performance of engineering doctoral students. The results provide a scientific basis for the cultivation of high-level innovative scientific and technological talents in engineering technology.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xiaoqing Xu PY - 2022 DA - 2022/12/23 TI - An Analysis on the Difference of Research Competency of Engineering Doctoral Students with Various Characteristics Based on SPSS BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 843 EP - 854 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_87 DO - 10.2991/978-94-6463-034-3_87 ID - Xu2022 ER -