Latent trait analysis for teacher career development and capacity improvement in higher education institutions

Titolo Rivista: CADMO
Autori/Curatori: Wenjun Lin, Haiyu Song, Gonçalo Almeida, António Godinho
Anno di pubblicazione: 2021 Fascicolo: 2 Lingua: English
Numero pagine: 17 P. 46-62 Dimensione file: 407 KB
DOI: 10.3280/CAD2021-002005
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Qui sotto puoi vedere in anteprima la prima pagina di questo articolo.

Se questo articolo ti interessa, lo puoi acquistare (e scaricare in formato pdf) seguendo le facili indicazioni per acquistare il download credit.
Acquista Download Credits per scaricare questo Articolo in formato PDF

anteprima articolo

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

This study shows the influence of human resource management practices on the academic and non-academic staff and its impact on the higher education institution’s goals. A questionnaire based on the Cranet survey was used to gather data from 240 employees (academic and non-academic staff) from a public higher education institution. A phi-k correlation algorithm was used to verify the underlying correlation coefficients, statistical significance, and outliers within multiple data types. This algorithm allows a more personalized, understandable approach to reveal the human resource management practices that significantly impact the teacher career development and capacity improvement. In addition, the use of background variables to identify the groups of respondents allows the algorithm to discern the multidimensional data for a more personalized human resource management approach. Human resource management practices involving training development and staff were correlated to the institution’s goals. The phi-k correlation proved to be a suitable tool to shape structural models and latent trait analysis between multiple data types, which can overcome the drawbacks of Pearson and Cramer correlations when processing non-linear data. The presented research contributes to the literature by using the phi-k algorithm to process multiple data types. The proposed study with the phi-k algorithm is the first time applied to higher education institutions.

  1. Aboramadan, M., Albashiti, B., Alharazin, H., Dahleez, K.A. (2020), “Human resources management practices and organizational commitment in higher education: The mediating role of work engagement”, International Journal of Educational Management, 34 (1), pp. 154-174.
  2. Alayoubi, M.M., Al Shobaki, M.J., Abu-Naser, S.S. (2020), “Strategic leadership practices and their relationship to improving the quality of educational service in Palestinian Universities”, International Journal of Business Marketing and Management (IJBMM), 5 (3), pp. 11-26.
  3. Allui, A., Sahni, J. (2016), “Strategic Human Resource Management in Higher Education Institutions: Empirical Evidence from Saudi”, Procedia – Social and Behavioral Sciences, 235, pp. 361-371.
  4. Alqahtani, M., Ayentimi, D.T. (2021), “The devolvement of HR practices in Saudi Arabian public universities: Exploring tensions and challenges”, Asia Pacific Management Review, 26 (2), pp. 86-94.
  5. Baak, M., Koopman, R., Snoek, H., Klous, S. (2020), “A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics”, Computational Statistics and Data Analysis, 152, p. 107043.
  6. Beglar, D., Nemoto, T. (2014), Developing Likert-scale questionnaires. JALT2013 Conference Proceedings, pp. 1-8.
  7. Deze, W. (2018), Application of large data mining technology in colleges and universities, ACM International Conference Proceeding Series, pp. 86-89, DOI: 10.1145/3291801.3291834
  8. Gejingting, X., Ruiqiong, J., Wei, W., Libao, J., Zhenjun, Y. (2019), Correlation analysis and causal analysis in the era of big data, IOP Conference Series: Materials Science and Engineering, 563 (4), p. 042032.
  9. Goyal, M., Vohra, R. (2012), “Applications of Data Mining in Higher Education”, International Journal of Computer Science Issues, 9 (2), pp. 113-120.
  10. Halid, H., Kee, D.M.H., Rahim, N.F.A. (2020), “Perceived Human Resource Management Practices and Intention to Stay in Private Higher Education Institutions in Malaysia: The Role of Organizational Citizenship Behaviour”, Global Business Review, pp. 1-18, DOI: 10.1177/0972150920950906
  11. Khan, S., Khan, M.H., Mohmand, A.M., Misbah, S. (2020), “Impact of HR Practices on Employee Turnover and Job Satisfaction: Evidence from Pakistani Universities”, Review of Economics and Development Studies, 6 (3), pp. 607-624.
  12. Lecuona, O., Alvarado, J.M. (2017), Comparing Factor Analysis and Item Response Theory with multimodal latent distributions. In Department of Methodology, Faculty of Psychology, Universidad Complutense de Madrid, OSF Registries.
  13. Masri, N. El, Suliman, A. (2019), “Talent Management, Employee Recognition and Performance in the Research Institutions”, Studies in Business and Economics, 14 (1), pp. 127-140.
  14. Mattjik, M., Akbar, M., Yasin, M. (2020), “Managing human resources in a higher education institution: Managing the lecturers”, International Journal of Scientific and Technology Research, 9 (1), pp. 2360-2363.
  15. Nadarajah, S., Kadiresan, V., Kumar, R., Kamil, N.N.A., Yusoff, Y.M. (2012), “The Relationship of HR Practices and Job Performance of Academicians towards Career Development in Malaysian Private Higher Institutions”, Procedia – Social and Behavioral Sciences, 57, pp. 102-118.
  16. Pratolo, S., Mukti, A.H., Anwar, M. (2020), “Result-based Management Implementation in Higher Education Institution: Determinants and Impact on Performance”, Journal of Accounting and Investment, 21 (3), pp. 580-601.
  17. S. Abu-Oda, G., M. El-Halees, A. (2015), “Data Mining in Higher Education : University Student Dropout Case Study”, International Journal of Data Mining & Knowledge Management Process, 5 (1), pp. 15-27.
  18. Sang, X., Teo, S., Cooper, C., Bohle, P. (2013), “Modelling Occupational Stress and Employee Health and Wellbeing in a Chinese Higher Education Institution”, Higher Education Quarterly, 67, pp. 15-39.
  19. Schober, P., Schwarte, L.A. (2018), “Correlation coefficients: Appropriate use and interpretation”, Anesthesia and Analgesia, 126 (5), pp. 1763-1768.
  20. Shelby, L.B. (2011), “Beyond Cronbach’s Alpha: Considering Confirmatory Factor Analysis and Segmentation”, Human Dimensions of Wildlife, 16 (2), pp. 142-148.
  21. Stavrou, E.T., Brewster, C., Charalambous, C. (2010), “Human resource management and firm performance in Europe through the lens of business systems: Best fit, best practice or both?”, International Journal of Human Resource Management, 21 (7), pp. 933-962.
  22. Zhang, Y., Liu, T., Li, K., Zhang, J. (2017), “Improved visual correlation analysis for multidimensional data”, Journal of Visual Languages & Computing, 41, pp. 121-132.
  23. Zhao, Y. (2018), “Managing Chinese millennial employees and their impact on human resource management transformation: an empirical study”, Asia Pacific Business Review, 24 (4), pp. 472-489.

Wenjun Lin, Haiyu Song, Gonçalo Almeida, António Godinho, Latent trait analysis for teacher career development and capacity improvement in higher education institutions in "CADMO" 2/2021, pp 46-62, DOI: 10.3280/CAD2021-002005