Practitioners use various indicators to screen for meaningless, careless, or fraudulent responses in Internet surveys. This study employs an experimental design to test whether multiple post hoc indicators are applicable to identify cases with low data quality. Findings suggest that careless responses are most reliably identified by questionnaire completion time, while a measure for within-subscale correlation structure is most indicative of fake responses. This paper discusses the different indicators' benefits and drawbacks, explains their computation, and proposes an index and a cut off value for completion speed. Given the tested estimates for data quality, removal of suspicious cases is only suggested if a significant amount of meaningless data is expected.