Psychometric Evaluation of the IST: Reliability, Validity, and Network Approaches with Demographic Comparisons in Indonesian Defense Selection Contexts: Evaluasi Psikometri IST: Pendekatan Reliabilitas, Validitas, dan Jaringan dengan Perbandingan Demografis dalam Konteks Seleksi Pertahanan Indonesia

Sukmo Gunardi

doi:10.21009/JPPP.151.10

Authors

Sukmo Gunardi Prodi Psikologi Industri, Fakultas Teknik, Universitas Nurtanio Bandung, 40192, Indonesia

DOI:

https://doi.org/10.21009/JPPP.151.10

Keywords:

ist, psychometrics, defense selection, gender differences, reliability

Abstract

This study examined the psychometric properties of the Intelligenz-Struktur-Test (IST) among candidates of the Indonesian Air Force and Pertahanan University. Data were scored dichotomously (A = 1; others = 0). Analyses included Classical Test Theory (reliability indices), Confirmatory Factor Analysis (CFA), Exploratory Factor Analysis (EFA), and network psychometrics (Graphical Gaussian Models). Measurement invariance across gender and age was evaluated descriptively. Reliability was acceptable (α = .76-.85; ω = .78-.86). CFA indicated the bifactor model fit best (CFI = .93, RMSEA = .05). Network analysis identified numerical reasoning (ZR) parcels as most central (strength up to 2.81). Gender networks were configural and congruent (>.85), with no significant mean differences (p >.05). Age correlated negatively with ZR (r = -.18) and positively with WA (r = .12). IST is a reliable and valid instrument for defense selection in Indonesia. Results highlight both a general factor and domain-specific abilities, with ZR central to the network structure. This study contributes methodologically by integrating latent and network models in a high-stakes context.

Keywords: IST, reliability, validity, CFA, network analysis

References

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1

Jokste, I., Trups-Kalne, I., Lubenko, J., Millere, I., & Kolesnikova, J. (2024). Adaptation of the Intelligence Structure Test, Latvian version: Psychometric properties. Frontiers in Psychology, 15, Article 1319983. https://doi.org/10.3389/fpsyg.2024.1319983

Liu, Y., Yin, H., Xin, T., Shao, L., & Yuan, L. (2019). A comparison of differential item functioning detection methods in cognitive diagnostic models. Frontiers in Psychology, 10, 1137. https://doi.org/10.3389/fpsyg.2019.01137

Malkewitz, C. P., Schwall, P., Meesters, C., & Hardt, J. (2023). Estimating reliability: A comparison of Cronbach’s alpha, McDonald’s omega, and the greatest lower bound. MethodsX, 10, 102032. https://doi.org/10.1016/j.mex.2023.102032

Martinková, P., Drabinová, A., Liaw, Y.-L., Sanders, E. A., McFarland, J. L., & Price, R. M. (2017). Checking equity: Why differential item functioning analysis should be a routine part of developing conceptual assessments. CBE-Life Sciences Education, 16(2), rm2. https://doi.org/10.1187/cbe.16-10-0307

Murman, D. L. (2015). The impact of age on cognition. Seminars in Hearing, 36(3), 111–121. https://doi.org/10.1055/s-0035-1555115

Rodríguez-Cancino, M., & Concha-Salgado, A. (2023). WISC-V measurement invariance according to sex and age: Advancing the understanding of intergroup differences in cognitive performance. Journal of Intelligence, 11(9), 180. https://doi.org/10.3390/jintelligence11090180

Tarigan, M., & Fadillah, F. (2022). Quality analysis of Intelligence Structure Test 2000 Revision (IST 2000R) items in Indonesian. Psyche: Jurnal Psikologi, 9(1), 103–116. https://doi.org/10.15575/psy.v9i1.14977