Psychometric properties of the 18-item Indonesian Mental Toughness Questionnaire using the Rasch model and Machine Learning

The psychometric properties of the Indonesian version of the 18-item Mental Toughness Questionnaire (MTQ-18) remain vague. This study uses the Rasch model to elucidate these properties. In addition, boosting classification was adopted to assess the predictive validity of athletes’ achievement. The sample size comprised 400 athletes. According to the Martin-Loef likelihood-ratio test = 482, p = 1.0 and factor analysis of the Rasch residuals, the questionnaire tends to make unidimensional assumptions. The MADaQ3 = 0.074 shows the overall tendency of local independency across all items, with the majority clustered in moderate to low-level measures. Q11, Q15, and Q18 were clearly identified as showing gender bias, with significant effect sizes. According to the boosting classification, the performance between national vs no achievement (F1= 0.7, AUC= 0.56) and international vs no achievement (F1= 0.62, AUC= 0.58) was flagged as unsatisfactory predictive performance. In conclusion, the abridged questionnaire is not preferable for determining an individual’s future performance or achievement. Future studies are needed to develop a better version that is more unimpeded by gender bias, and to resolve the variability of the items.

Publication Details

Journal
Ubaya Repository (University of Surabaya)
Published
2025-05-31
Primary Topic
Mental Health via Writing
Type
article
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article

Psychometric properties of the 18-item Indonesian Mental Toughness Questionnaire using the Rasch model and Machine Learning

Ubaya Repository (University of Surabaya)
Mental Health via Writing
article

Psychometric properties of the 18-item Indonesian Mental Toughness Questionnaire using the Rasch model and Machine Learning

article en

Abstract

The psychometric properties of the Indonesian version of the 18-item Mental Toughness Questionnaire (MTQ-18) remain vague. This study uses the Rasch model to elucidate these properties. In addition, boosting classification was adopted to assess the predictive validity of athletes’ achievement. The sample size comprised 400 athletes. According to the Martin-Loef likelihood-ratio test = 482, p = 1.0 and factor analysis of the Rasch residuals, the questionnaire tends to make unidimensional assumptions. The MADaQ3 = 0.074 shows the overall tendency of local independency across all items, with the majority clustered in moderate to low-level measures. Q11, Q15, and Q18 were clearly identified as showing gender bias, with significant effect sizes. According to the boosting classification, the performance between national vs no achievement (F1= 0.7, AUC= 0.56) and international vs no achievement (F1= 0.62, AUC= 0.58) was flagged as unsatisfactory predictive performance. In conclusion, the abridged questionnaire is not preferable for determining an individual’s future performance or achievement. Future studies are needed to develop a better version that is more unimpeded by gender bias, and to resolve the variability of the items.

Ubaya Repository (University of Surabaya)
Gender equality
Openalex Percentile: Top 44%
Mental Health via Writing
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Psychometric properties of the 18-item Indonesian Mental Toughness Questionnaire using the Rasch model and Machine Learning · Ubaya Repository (University of Surabaya) (2025) | TGRS Research Map | TGRS