Application of Structural Equation Modeling (SEM) in Estimating the Contributing Factors to Satisfaction of TASKA Services in East Coast Malaysia


  • Azrul Fazwan Kharuddin Graduate School of Business, Universiti Tun Abdul Razak, MALAYSIA
  • Norazura Azid SMK Jeram, Selangor, MALAYSIA
  • Zaida Mustafa School of Education & Humanities, Universiti Tun Abdul Razak, MALAYSIA
  • Ku Faridah Ku Ibrahim School of Education & Humanities, Universiti Tun Abdul Razak, MALAYSIA
  • Darvinatasya Kharuddin School of Mathematics, Universiti Sains Malaysia, Pulau Pinang, MALAYSIA



Structural Equation Modeling, Measurement Model, Goodness of fit index, Validity and Reliability


This research shows the application of the Structured Equation Modeling (SEM) to obtain the best model for studying the relationship between the more efficient and accurate against the findings and the interpretation of the variables. The objectives of the study are to assess the reliability of the developed instrument and to test construct validity of the research instruments in estimating the contributing factors to TASKA service satisfaction. The proportionate stratified random sampling method was used to select a total of 61 TASKAs from three states on the east coast of Malaysia consisting of 273 parents and guardians which are currently using the TASKA services. Validity and reliability of the measurement model in the analysis using Structural Equation Modeling (SEM) method. Measurement models were data-based and fit based on the fit index (CMF) χ2 = 3230.541, with degrees of freedom (df) = 902, CMIN / df = 3.582 (≤5.0), CFI & TLI (≤0.9), and RMSEA = 0.070 (≤0.1). Based on the results obtained, all indices meet standardized metric and assessment tools have proven to be a good instrument. The results show that there are 5 factors that influence parents' satisfaction with the quality of services offered by TASKA. Analysis found that the combination of manage, grow, fees, activity and cost constitutes a strong association to estimate a complete structured equation model while supported by demographic factors such as education level, occupation, location, distance, agency, status and age of children to strengthen the TASKA selection factor. Research shows that this TASKA service model can assist as a guide in improving the existing quality for future improvement. Furthermore, it can be used as a module in providing the best quality of services to the satisfaction of parents and guardians.



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Ahmetoğlu, E., & Acar, İ. H. (2017). Parents'satisfaction With Their Children's Educational Experiences In Early Childhood Period. Electronic Turkish Studies, 12(6).

Austin, P. C., Lee, D. S., & Leckie, G. (2020). Comparing a multivariate response Bayesian random effects logistic regression model with a latent variable item response theory model for provider profiling on multiple binary indicators simultaneously. Statistics in Medicine.

Berti, S., Cigala, A., & Sharmahd, N. (2019). Early Childhood Education and Care Physical Environment and Child Development: State of the art and Reflections on Future Orientations and Methodologies. Educational Psychology Review, 1-31.

Berti, S., Cigala, A., & Sharmahd, N. (2019). Early Childhood Education and Care Physical Environment and Child Development: State of the art and Reflections on Future Orientations and Methodologies. Educational Psychology Review, 1-31.

Bertrand, J., Butler, E., Fesseha, E., Maich, K., McCuaig, K., McLean, C., ... & Gabrielle, Y. (2019, January). Quality Early Childhood Education the Need for Special Education Services: A Symposium. In 2019 Conference of the Canadian Society for the Study of Education.

Briere, J., Runtz, M., Eadie, E., Bigras, N., & Godbout, N. (2017). Disengaged parenting: Structural equation modeling with child abuse, insecure attachment, and adult symptomatology. Child Abuse & Neglect, 67, 260-270.

Byrne B.M. (2013) Structural equation modeling with AMOS: basic concepts, applications, and programming. Routledge, New York

Capmourteres V, Anand M (2016) Assessing ecological integrity: a multi-scale structural and functional approach using structural equation modeling. Ecol Indic

Cava-Tadik, Y., Brown, G. L., & Mangelsdorf, S. C. (2020). Fathers’ Satisfaction With Physical Affection Before and After the Birth of a New Baby: Cross-Parent Effects and Associations With Family Dynamics. Journal of Family Issues, 41(4), 415-436.

Collier, J. E. (2020). Applied Structural Equation Modeling using AMOS: Basic to Advanced Techniques.

Garn, A. C., & Webster, E. K. (2018). Reexamining the factor structure of the test of gross motor development–Second edition: Application of exploratory structural equation modeling. Measurement in Physical Education and Exercise Science, 22(3), 200-212.

Godbout, N., Daspe, M. È., Runtz, M., Cyr, G., & Briere, J. (2019). Childhood maltreatment, attachment, and borderline personality–related symptoms: Gender-specific structural equation models. Psychological trauma: theory, research, practice, and policy, 11(1), 90.

Hajizadeh, A., & Zali, M. (2016). Prior knowledge, cognitive characteristics and opportunity recognition. International Journal of Entrepreneurial Behavior & Research.

Hoyle RH (2011) Structural equation modeling for social and personality psychology. Sage, London

Hu, B. Y., Yang, Y., Wu, H., Song, Z., & Neitzel, J. (2018). Structural and process predictors of Chinese parental satisfaction toward early childhood education services. Children and Youth Services Review, 89, 179-187.

Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2017). A comparison of methods for uncovering sample heterogeneity: Structural equation model trees and finite mixture models. Structural equation modeling: a multidisciplinary journal, 24(2), 270-282.

Jamaludin, H., & Mohamad, B. (2018). The Relationship between Service Quality and Parent Satisfaction in Early Childhood Education: A Study among Malaysian Government Servants at Putra Jaya. Global Business and Management Research, 10(3), 486.

Kline RB (2006) Reverse arrow dynamics. Formative measurement and feedback loops. In: Hancock GR, Mueller RO (eds) Structural equation modeling: A second course. Information Age Publishing, Greenwich

Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018). A novel measure of effect size for mediation analysis. Psychological Methods, 23(2), 244.

Maydeu-Olivares, A., Shi, D., & Fairchild, A. J. (2019). Estimating causal effects in linear regression models with observational data: The instrumental variables regression model. Psychological methods.

McNaughton, D. (1994). Measuring parent satisfaction with early childhood intervention programs. Topics in Early Childhood Special Education, 14(1), 26-48.

Mohajan, H. K. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University. Economic Series, 17(4), 59-82.

Ning, B., Ghosal, S., & Thomas, J. (2019). Bayesian method for causal inference in spatially-correlated multivariate time series. Bayesian Analysis, 14(1), 1-28.

Papadaki, D., Bakas, D. N., Karamitsos, D., & Kirkham, D. (2019). Big data from social media and scientific literature databases reveals relationships among risk management, project management and project success. Project Management and Project Success (September 26, 2019).

Parkes, A., Sweeting, H., & Wight, D. (2016). What shapes 7-year-olds’ subjective well-being? Prospective analysis of early childhood and parenting using the Growing Up in Scotland study. Social psychiatry and psychiatric epidemiology, 51(10), 1417-1428.

Rentzou, K. (2017). Using rating scales to evaluate quality early childhood education and care: reliability issues. European Early Childhood Education Research Journal, 25(5)

Rusbult, C. E., Martz, J. M., & Agnew, C. R. (1998). The investment model scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size. Personal relationships, 5(4), 357-387.

Scheiner, C. W., Baccarella, C. V., Feller, N., Voigt, K. I., & Bessant, J. (2016). Organisational and individual unlearning in identification and evaluation of technologies. International Journal of Innovation Management, 20(02), 1650017.

Shao Y, Bao W, Chen D, Eisenhauer N, Zhang W, Pang X, Xu G, Fu S (2015) Using structural equation modeling to test established theory and develop novel hypotheses for the structuring forces in soil food webs. Pedobiologia 58(4):137–145

Shipley, B. (2016). Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference with R. Cambridge University Press.

Silver, R. B., Newland, R. P., Hartz, K., Jandasek, B., Godoy, L., Lingras, K. A., ... & Seifer, R. (2017). Integrating early childhood screening in pediatrics: A longitudinal qualitative study of barriers and facilitators. Clinical Practice in Pediatric Psychology, 5(4), 426.

Wang, M., Turnbull, A. P., Summers, J. A., Little, T. D., Poston, D. J., Mannan, H., & Turnbull, R. (2004). Severity of disability and income as predictors of parents' satisfaction with their family quality of life during early childhood years. Research and Practice for Persons with Severe Disabilities, 29(2), 82-94.

Zainudin, A. (2012). Research Methodology and Data Analysis 5th Edition. Shah Alam: Universiti Teknologi MARA Publication Center (UiTM Press).




How to Cite

Kharuddin, A. F., Azid, N., Mustafa, Z., Ku Ibrahim, K. F., & Kharuddin, D. (2020). Application of Structural Equation Modeling (SEM) in Estimating the Contributing Factors to Satisfaction of TASKA Services in East Coast Malaysia. Asian Journal of Assessment in Teaching and Learning, 10(1), 69–77.