Business School/Lingnan Normal University, Zhanjiang 524048, China
Principal components analysis (PCA) is a multivariate data analysis technique whose main purpose is to reduce the dimension of the observations and thus simplify the analysis and interpretation of data, as well as facilitate the construction of predictive models. In this paper, principal component analysis is used to analyze the indicators of teachers’ teaching ability, calculate the scores of each teacher in each principal component and comprehensive scores by using SPSS. It realizes the comprehensive ranking of teachers’ teaching evaluation, thus providing quantitative basis for each link of teachers’ teaching evaluation.
Principal Component; Eigen Values; Variance; Factor Matrix; Teaching Evaluation
Meng Yi, Application of Principal Component Analysis in Teaching Evaluation. Frontiers in Sport Research (2019) Vol. 1: 34-42. https://doi.org/10.25236/FSST.080106.
 Jamilah, M.; Zakaria, A., Md. Shakaff, A.Y., Idayu, N.; Hamid, H., Subari, N., Mohamad, J(2012). Principal Component Analysis-A Realization of Classification Success in Multi Sensor Data Fusion. In Principal Component Analysis-Engineering Applications; InTech: Philadelphia, PA, USA, 2012; pp. 1–25.
 Paul, L.C., Suman, A.A., Sultan, N(2013). Methodological Analysis of Principal Component Analysis (PCA) Method. Int. J. Comput. Eng. Manag, no.16, pp.32–38.
 Simar, L(2004). Applied Multivariate Statistical Anaylsis, Springer.
 Dan Armeanu, Leonard Lache(2006). Application of the Model of Principal Components analysis on Romanian insurance market. theoretical and applied economics, pp. 11-20.
 Ramsay, J. O. (2011), ‘Functional data analysis’, McGill University, Canada. Accessed 16th March.
 Shang, H. L. & Hyndman, R. J. (2011), ‘Nonparametric time series forecasting with dynamic updating’, Mathematics and Computers in Simulation, vol. 81, no.7, 1310–1324.
 Maindonald and Braun(2007). Data Analysis and Graphics Using R: An Example-based Approach. Cambridge University Press.