Questioning “Boy Math” and “Girl Math”: A Measurement-Based Test of Gendered Financial Behaviour Profiles Among Indonesian Students
DOI:
https://doi.org/10.59247/ijase.v3i2.168Keywords:
Financial Literacy, Gender Differences, Measurement Invariance, K-Means Clustering, Explainable Machine LearningAbstract
The popular phrases “Boy Math” and “Girl Math” have transformed humorous spending explanations into a wider discussion about gender and money. This study uses the discourse as a cultural entry point rather than as a construct directly measured by PISA 2018, which predates the trend and does not assess meme-specific post-hoc spending rationalisations. Using complete-case data from 5,833 Indonesian students, the study examined financial autonomy, careful price behaviour, bank confidence, digital confidence, financial behaviour, and financial attitude through ordinal confirmatory factor analysis, gender measurement invariance, latent factor scores, k-means clustering, and a secondary Random Forest diagnostic. The measurement model showed acceptable fit (CFI = 0.992, TLI = 0.991, RMSEA = 0.063, SRMR = 0.059), and invariance was supported up to the strict level. The two-cluster solution separated students consistently across all six dimensions, indicating higher and lower financial engagement levels rather than qualitatively distinct reasoning styles. The unadjusted association between gender and cluster membership was not significant, χ²(1) = 2.135, p = 0.144, Cramer’s V = 0.019. In adjusted logistic regression using conventional standard errors, male students had slightly lower odds of belonging to the higher-engagement group after grade was controlled (OR = 0.864, p = 0.006). School-clustered standard errors were not reported in the supplied output, so the adjusted inferential result remains provisional. Given the gender-balanced profiles, negligible unadjusted effect size, and odds ratio close to one, the relationship is substantively small. Random Forest reconstructed the internally generated cluster labels with 97.27% out-of-bag accuracy; this represents classification fidelity rather than external predictive validity. The findings do not support treating “Boy Math” and “Girl Math” as clear empirical gender categories.
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