Abstract This dataset presents anonymized participant responses collected through the Trait Profiler implementation of the Mach IV Scale (Machiavellianism Test). The assessment was administered online between 2026-05-21 and 2026-06-01, resulting in a final sample of 46 completed responses. Data collection was conducted through the Trait Profiler assessment platform developed by Deen Mohd. Participants voluntarily completed the assessment for educational, self-exploratory, and research purposes. All records were anonymized prior to publication and no personally identifiable information is included in this dataset. The released dataset contains participant-level response data, assessment scores, and associated measurement information generated according to the scoring procedures of the Mach IV Scale (Machiavellianism Test). Researchers may use these data for psychometric evaluation, reliability analysis, exploratory and confirmatory factor analysis, normative investigations, score distribution analysis, and methodological demonstrations. Dataset Characteristics Within the present sample, scores ranged from 28 to 55, producing an observed range of 27 points. The average score was 51.15, while the median score was 54. Variability within the sample was moderate, with a standard deviation of 5.25. The estimated 95% confidence interval for the sample mean ranged from 49.63 to 52.67. These findings provide an empirical overview of score distributions observed among individuals who voluntarily completed the assessment through the Trait Profiler platform. The data should not be considered representative of any national or clinical population; however, they may provide valuable reference information for comparative studies and future psychometric research. Contents of the Dataset The dataset includes anonymized item responses, calculated assessment scores, completion dates, and statistical information derived from participant submissions. The data are distributed in CSV format to facilitate reuse across statistical software environments including R, Python, SPSS, JASP, Jamovi, and Stata.