Q01
Long Text
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AI Interview Data Quality Assessment
A meta-research coding instrument for researchers to systematically evaluate and compare data quality from AI-moderated interviews versus traditional qualitative methods. Designed for repeated use across multiple data sets. Estimated completion time: 12-15 minutes per data set evaluated.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
29 questions · ~4 min
Q02
Multiple Choice
Have you conducted, supervised, or formally analyzed qualitative research data within the past 12 months?
Q03
Long Text
How many years of experience do you have conducting or analyzing qualitative research?
Q04
Long Text
You are about to evaluate a set of qualitative interview data on several quality dimensions. Please have the transcript or data set you are coding readily available before proceeding. Rate each dimension based on the data set as a whole, not individual responses.
Q05
Long Text
How many distinct themes did you identify in this data set?
Q06
Long Text
Rate the extent to which respondents provided specific examples, anecdotes, or concrete details in their responses.
Q07
Long Text
Rate the degree to which respondents appeared genuinely engaged with the interview process.
Q08
Long Text
How confident are you that the data in this set would support reliable coding by multiple researchers?
Q09
Long Text
Compared to your typical experience with traditionally moderated qualitative data, how would you rate the overall quality of this data set?
Q10
Long Text
Which of the following best describes your primary professional role?
Q11
Multiple Choice
Have you personally reviewed or coded data collected through an AI-moderated interview tool (e.g., AI follow-up probes, automated qualitative interviewing)?
Q12
Multiple Choice
Which qualitative data collection methods have you used or analyzed in the past 2 years? (Select all that apply)
Q13
Multiple Choice
Which data collection method was used to produce the data set you are currently evaluating?
Q14
Long Text
Rate the degree to which the data set contained themes you did not anticipate before analysis.
Q15
Long Text
Rate the extent to which respondents elaborated beyond the minimum required to answer each question.
Q16
Long Text
Rate the degree to which responses appeared authentic and genuine rather than performative or superficial.
Q17
Long Text
How confident are you that this data set provides sufficient depth to generate actionable insights or theoretical contributions?
Q18
AI Interview
What specific strengths or weaknesses did you observe in this data set that influenced your quality ratings? Please describe any patterns, surprising findings, or methodological concerns.
Q19
Long Text
In which type of organization do you primarily conduct research?
Q20
Long Text
Prior to this evaluation, how would you describe your general attitude toward AI-moderated interviewing as a qualitative research method?
Q21
Long Text
Approximately how many individual responses or transcripts are in the data set you are evaluating?
Q22
Long Text
Rate the level of elaboration and detail present within the themes identified.
Q23
Long Text
Rate the extent to which responses included emotional, experiential, or personal content.
Q24
Long Text
Rate the prevalence of satisficing behaviors (e.g., minimal answers, repetitive phrasing, off-topic responses) in this data set.
Q25
Long Text
How confident are you that this data set adequately captures the range of experiences relevant to the research topic?
Q26
Long Text
Rate the diversity of perspectives or viewpoints represented across the data set.
Q27
Long Text
Rate the overall depth of responses in this data set.
Q28
Long Text
Rate how natural and conversational the flow of the interview felt based on the data.
Q29
Long Text
How willing would you be to base published research findings or strategic recommendations on this data set alone?
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.