This paper evaluates the ability of two cross-sectional discretionary-accruals models, the Cross-Sectional Jones Model and the Cross-Sectional Modified Jones Model, to detect earnings management compared to their time-series counterparts. The study addresses the limitations of prior research, which often used non-random samples and did not control for potential research confounds. The authors argue that cross-sectional models offer several advantages, including a larger sample size and the ability to examine firms with shorter histories. They also emphasize the importance of controlling for variables such as book-to-market ratios, financial leverage, and earnings performance in earnings management studies. The study uses a matched-pair design to compare firms with qualified audit opinions to those with unqualified reports. Chi-square tests and logistic regression analyses are employed to assess the association between discretionary accruals and audit qualifications. The results show that all models, except the DeAngelo Model, are successful in detecting earnings management. However, when controlling for potential research confounds, only the two cross-sectional models continue to perform well. The findings suggest that cross-sectional models are more effective in detecting earnings management than their time-series counterparts. This is important for future earnings management research, as cross-sectional models can provide more reliable results due to their larger sample size and reduced survivorship bias. The study also highlights the importance of controlling for variables such as book-to-market ratios, financial leverage, and earnings performance in accruals management studies. The results indicate that these variables are significant in determining audit qualifications and that failure to control for them can lead to biased inferences. The study concludes that future research should prioritize the use of cross-sectional models to improve the accuracy and reliability of earnings management studies.This paper evaluates the ability of two cross-sectional discretionary-accruals models, the Cross-Sectional Jones Model and the Cross-Sectional Modified Jones Model, to detect earnings management compared to their time-series counterparts. The study addresses the limitations of prior research, which often used non-random samples and did not control for potential research confounds. The authors argue that cross-sectional models offer several advantages, including a larger sample size and the ability to examine firms with shorter histories. They also emphasize the importance of controlling for variables such as book-to-market ratios, financial leverage, and earnings performance in earnings management studies. The study uses a matched-pair design to compare firms with qualified audit opinions to those with unqualified reports. Chi-square tests and logistic regression analyses are employed to assess the association between discretionary accruals and audit qualifications. The results show that all models, except the DeAngelo Model, are successful in detecting earnings management. However, when controlling for potential research confounds, only the two cross-sectional models continue to perform well. The findings suggest that cross-sectional models are more effective in detecting earnings management than their time-series counterparts. This is important for future earnings management research, as cross-sectional models can provide more reliable results due to their larger sample size and reduced survivorship bias. The study also highlights the importance of controlling for variables such as book-to-market ratios, financial leverage, and earnings performance in accruals management studies. The results indicate that these variables are significant in determining audit qualifications and that failure to control for them can lead to biased inferences. The study concludes that future research should prioritize the use of cross-sectional models to improve the accuracy and reliability of earnings management studies.