A Four-Factor Model of Executive Functions in Indonesian Children: Development and Validation of a Parent-Reported Scale
| Authors | : | |
| DOI | : | https://doi.org/10.64600/vo2-no2-43-51 |
| Issue | : |
Vol. 2 No. 2 (2025)
Section: Articles |
| Published | : | Dec 18, 2025 |
| Pages | : | 43-51 |
| Keywords | : | Construct validity , Executive functions, Indonesia, Instrument, Parent rating |
Abstract
Background: Executive functions (EF) are higher-order cognitive processes essential for learning, self-regulation, and social adaptation in childhood. Despite extensive research in Western contexts, validated EF instruments remain limited in Indonesia. This study aimed to develop and validate a parent-rated EF scale for Indonesian children, based on Diamond’s (2013) four-domain model comprising interference control, response inhibition, working memory, and cognitive flexibility.
Method: Skala Fungsi Eksekutif Anak (SK-FEA) was developed by Rexsy Taruna and administered to parents of 549 typically developing children aged 4–12 years. The instrument included 24 items across four subscales, each rated on a 5-point Likert scale. Descriptive statistics were calculated, concurrent validity was examined through intercorrelations among subscales, and construct validity was tested using confirmatory factor analysis (CFA) with a WLSMV estimator.
Result: Descriptive analyses indicated adequate score variability across subscales. All subscales were positively and significantly correlated (r = 0.51–0.71, p < 0.001), supporting concurrent validity. CFA confirmed the hypothesized four-factor structure with excellent fit indices, χ²(246) = 276.12, p = .091, CFI = .999, TLI = .998, RMSEA = .015 (90% CI [.000, .024]), SRMR = .048. All items loaded significantly on their intended factors (λ = 0.31–0.80, p < 0.001).
Conclusion: Findings provide strong evidence for the construct validity of the EFRS as a parent-rated measure of EF in Indonesian children. The instrument captures both the distinctiveness and interrelatedness of EF domains, offering a culturally relevant tool for research and practice. Further studies should examine external validity, predictive validity, and measurement invariance across diverse populations.
References
Baggetta, P., & Alexander, P. A. (2016). Conceptualization and operationalization of executive function. Mind, Brain, and Education, 10(1), 10–33. https://doi.org/10.1111/mbe.12100
Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child Development, 81(6), 1641–1660. https://doi.org/10.1111/j.1467-8624.2010.01499.x
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Press.
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168. https://doi.org/10.1146/annurev-psych-113011-143750
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11(1), 19–23. https://doi.org/10.1111/1467-8721.00160
Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. https://doi.org/10.1037/1082-989X.9.4.466
Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186–204. https://doi.org/10.1016/j.cortex.2016.04.023
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis. Cengage.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford Press.
Li, C. H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48, 936–949. https://doi.org/10.3758/s13428-015-0619-7
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions. Current Directions in Psychological Science, 21(1), 8–14. https://doi.org/10.1177/0963721411429458
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315
Ribner, A. D., Willoughby, M. T., & Blair, C. B. (2017). Executive function buffers the association between early math and later academic skills. Early Childhood Research Quarterly, 39, 43–54. https://doi.org/10.1016/j.ecresq.2016.11.001
Toplak, M. E., West, R. F., & Stanovich, K. E. (2013). Practitioner review: Do performance-based measures and ratings of executive function assess the same construct? Journal of Child Psychology and Psychiatry, 54(2), 131–143. https://doi.org/10.1111/jcpp.12001
Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6(4), 354–360. https://doi.org/10.1111/j.1750-8606.2012.00246.x


