This commentary highlights issues in a psychometric study, emphasizing statistical concerns and construct validity. The authors point out that the study incorrectly reported cumulative variances, which sum to over 100%, an impossibility. They also note the use of outdated methods for determining the number of factors, such as the Kaiser Criterion, instead of modern techniques like parallel analysis. The study failed to report eigenvalues or a scree plot, making it difficult to assess the validity of the factor structure. Additionally, the use of Varimax rotation, which assumes uncorrelated factors, may have led to misestimation of the factor structure. The authors recommend using oblique rotations when factors are expected to be correlated. They also emphasize the importance of reporting communalities accurately, as they cannot exceed 100%. The study's reported communalities were inconsistent with the factor loadings. The authors suggest calculating a range for eigenvalues based on assumed suppressed loadings. The study's results were found to be inconsistent with the reported factor loadings, and the authors suggest that the total variance explained by the three-factor construct should be between 25.54% and 44.34%. The authors also highlight the importance of replication and multiple samples in ensuring the generalizability of results. They recommend using confirmatory factor analysis for validation rather than exploratory factor analysis. The study used principal components analysis, which is not appropriate in the modern era. The authors conclude that exploratory analyses should not be used for validation and that researchers should report effect sizes with confidence intervals. The paper raises concerns about the validity of the study's results and the importance of using appropriate statistical methods in psychometric research.This commentary highlights issues in a psychometric study, emphasizing statistical concerns and construct validity. The authors point out that the study incorrectly reported cumulative variances, which sum to over 100%, an impossibility. They also note the use of outdated methods for determining the number of factors, such as the Kaiser Criterion, instead of modern techniques like parallel analysis. The study failed to report eigenvalues or a scree plot, making it difficult to assess the validity of the factor structure. Additionally, the use of Varimax rotation, which assumes uncorrelated factors, may have led to misestimation of the factor structure. The authors recommend using oblique rotations when factors are expected to be correlated. They also emphasize the importance of reporting communalities accurately, as they cannot exceed 100%. The study's reported communalities were inconsistent with the factor loadings. The authors suggest calculating a range for eigenvalues based on assumed suppressed loadings. The study's results were found to be inconsistent with the reported factor loadings, and the authors suggest that the total variance explained by the three-factor construct should be between 25.54% and 44.34%. The authors also highlight the importance of replication and multiple samples in ensuring the generalizability of results. They recommend using confirmatory factor analysis for validation rather than exploratory factor analysis. The study used principal components analysis, which is not appropriate in the modern era. The authors conclude that exploratory analyses should not be used for validation and that researchers should report effect sizes with confidence intervals. The paper raises concerns about the validity of the study's results and the importance of using appropriate statistical methods in psychometric research.