Table 6.

Examples of data collection challenges concerning scientific quality and interpretation.

Questions of scientific quality, interpretation, and integration of data Missing self-report data due to inability or unwillingness to provide information Missing data in medical records or patients' personal records Conflicting data provided by different sources (patient vs. caregiver) or at different times Different information given by participants to nurses versus physicians versus researchers Different, sometimes conflicting data provided to different research personnel, depending on participant-researcher relationship, race or sex differences Difficulty determining the reason for contradictory information in real-time Difficulty capturing certain contextual data in real-time, such as gestures, tone, smell Potential for biased sampling when clinicians recommend or select patients for participations Participants have hard time remembering events in question or speaking about the research topic, but researchers are not aware of this Analyzing large volumes of qualitative and mixed data to assess data quality and validity in real- time is time consuming Difficulty interpreting whether participants' experience and perceptions align with the group to which they were assigned (e.g., diagnosis category, intervention vs. control condition)