This article addresses statistical concerns and invalid construct validity in psychometric studies, particularly focusing on a problematic study that claimed to explain over 100% of the variance with three factors. The authors highlight common methodological issues, such as reporting cumulative rather than unique communality, using outdated methods for determining the number of factors, and failing to report eigenvalues or a scree plot. They emphasize the importance of accurate reporting of communalities, clear descriptions of extraction and rotation techniques, and addressing cross-loadings. The article also recommends using confirmatory factor analysis (CFA) for validation, rather than exploratory factor analysis (EFA), to ensure results are more replicable and generalizable. Additionally, it suggests reporting effect sizes and confidence intervals to enhance the practical significance of findings. The authors conclude by emphasizing the need for rigorous peer review and best practices to improve the quality and reliability of psychometric studies.This article addresses statistical concerns and invalid construct validity in psychometric studies, particularly focusing on a problematic study that claimed to explain over 100% of the variance with three factors. The authors highlight common methodological issues, such as reporting cumulative rather than unique communality, using outdated methods for determining the number of factors, and failing to report eigenvalues or a scree plot. They emphasize the importance of accurate reporting of communalities, clear descriptions of extraction and rotation techniques, and addressing cross-loadings. The article also recommends using confirmatory factor analysis (CFA) for validation, rather than exploratory factor analysis (EFA), to ensure results are more replicable and generalizable. Additionally, it suggests reporting effect sizes and confidence intervals to enhance the practical significance of findings. The authors conclude by emphasizing the need for rigorous peer review and best practices to improve the quality and reliability of psychometric studies.