The study by Huang, Curran, Keeney, Poposki, and DeShon aims to address the issue of insufficient effort responding (IER) in low-stakes surveys, which can compromise the quality of data in psychological and organizational research. The authors conducted two studies: an experimental study (Study 1) and a nonexperimental survey (Study 2) involving 725 undergraduates. **Findings:** - **Study 1** evaluated four indices—response time, long string, psychometric antonyms, and individual reliability coefficients—to detect IER. The results showed that these indices effectively measure the same underlying construct. - **Study 2** demonstrated that removing IER respondents improved the psychometric properties of the survey, including item interrelatedness, facet dimensionality, and factor structure. **Implications:** - The identification of effective IER indices can help researchers ensure the quality of their low-stake survey data. **Originality/Value:** - This study is the first to comprehensively evaluate IER detection methods using both experimental and nonexperimental designs, providing convergent validity evidence for various indices. **Keywords:** - Careless responding, Random responding, Inconsistent responding, Online surveys, Data screening The study also reviews existing approaches to IER detection, categorizing them into infrequency, inconsistency, pattern, and response time. The infrequency approach, while effective at detecting random responses, may not be suitable for IER due to potential confounding factors like impression management and faking.The study by Huang, Curran, Keeney, Poposki, and DeShon aims to address the issue of insufficient effort responding (IER) in low-stakes surveys, which can compromise the quality of data in psychological and organizational research. The authors conducted two studies: an experimental study (Study 1) and a nonexperimental survey (Study 2) involving 725 undergraduates. **Findings:** - **Study 1** evaluated four indices—response time, long string, psychometric antonyms, and individual reliability coefficients—to detect IER. The results showed that these indices effectively measure the same underlying construct. - **Study 2** demonstrated that removing IER respondents improved the psychometric properties of the survey, including item interrelatedness, facet dimensionality, and factor structure. **Implications:** - The identification of effective IER indices can help researchers ensure the quality of their low-stake survey data. **Originality/Value:** - This study is the first to comprehensively evaluate IER detection methods using both experimental and nonexperimental designs, providing convergent validity evidence for various indices. **Keywords:** - Careless responding, Random responding, Inconsistent responding, Online surveys, Data screening The study also reviews existing approaches to IER detection, categorizing them into infrequency, inconsistency, pattern, and response time. The infrequency approach, while effective at detecting random responses, may not be suitable for IER due to potential confounding factors like impression management and faking.