Confirmatory Factor Analysis (CFA) is conducted in the measurement model and there are two ways to conduct CFA through individual Confirmatory Factor Analysis or group Confirmatory Factor Analysis. It depends on how many items are in the construct and if the items in the construct have more than four, the measurement model analysis is conducted separately. Whereas, pooled CFA runs all measurement models at the same time. This Unidimensionality requirement can be met through the item deletion procedure that has a low factor loading value to reach the set level of fitness indexes. Items with a factor loading value of less than 0.6 are considered unimportant to the measurement of the construct and can be discarded (Chik & Abdullah, 2018). A total of 384 study samples were involved in this research, among East Cost Boarding School teachers in three (3) states on the East Coast of Peninsular Malaysia. Data were analyzed using the IBM-SPSS-AMOS (SEM) program version 21.0. Adjustment tests were conducted to ensure that the tested indicators truly represent the construct being measured and Confirmatory Factor Analysis was conducted in this study as a prerequisite that must be met. The findings of the study show that all the correlations between the constructs (Principal Instructional Leadership, the Acceptance of Technology Applications and Competency Teaching Teacher have a value less than 0.85 (<0.85) among East Coast Boarding school teachers in three (3) states (Kelantan, Pahang and Terengganu) on the East Coast of Peninsular Malaysia. The results of the Combined Confirmatory Factor Analysis of all measurement models (Pooled CFA), prove that all constructs have a strong relationship with each other to avoid the existence of multicollinearity problems.
Alias, H., & Husain, H. (2017). Structural Equation Modelling (SEM) & Manual Amos Graphic (edisi Bahasa Melayu) Modul 1.
Awang, Z. (2012). Research Methodology And Data Analysis. Penerbit Universiti Teknologi MARA Press (UiTM Press).
Awang, Z. (2015). SEM Made Simple: A Gentle Approach to Learning Structural Equation Modeling. Bandar Baru Bangi, MPWS Rich Resources.
Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Eribaum Associates.
Byrne, B. M. (2013). Structural Equation Modeling With Amos: Basic Concepts, Applications, And Programming (2nd Ed.). New York: Routledge.
Chik, Z., & Abdullah, A.H. (2018). Developing And Validating Instruments For Measurement Of Motivation, Learning Styles And Learning Disciplines For Academic Achievement. International Journal of Academic Research in Business and Social Sciences, 8(4), 594-605.
Hoque, A. S. M. M., Awang, Z., Jusoff, K., Salleh, F., and Muda, H (2017). Social Business Efficiency: Instrument Development and Validation Procedure using Structural Equation Modelling. International Business Management, 11(1), 222-231.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivatiate Data Analysis (6th ed.). New Jersey: Pearson Education International.
Kashif, M., Samsi, S. Z. M., Awang, Z., & Mohamad, M. (2016). EXQ: Measurement Of Healthcare Experience Quality In Malaysian Settings: A Contextualist Perspective. International Journal Of Pharmaceutical And Healthcare Marketing, 10(1), 27-47
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: The Guilford Press.
Schumucker, R. E., & Lomax, R. (2004). A Beginner’s Guide To Structural Equation Modeling (2nd Edition). Mahwah, New Jersey: Lawrence Erlbaun Associates Publishers.