Q-Sat AI

A Machine Learning-Based Decision Support System for Data Saturation Determination in Qualitative Studies

The determination of sample size in qualitative research often relies on subjective criteria such as data saturation. Q-Sat AI(Qualitative Saturation AI) introduces a data-driven, machine learning-based decision support system to estimate sample sizes objectively — without compromising the interpretive nature of qualitative inquiry.

AI Powered 85% Accuracy Research-Based

Prediction Result

Prediction Result

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Research Parameters

Broader scope requires more participants; narrow scope requires fewer.
Experienced researchers need fewer participants.
High participant expertise reduces sample size.
More interviews per participant reduce sample size.
Longer interviews yield more data per participant.
Longer observation periods reduce sample size.
Homogeneous groups require fewer participants.
Unique participants provide richer data, reducing sample size.
Multiple data sources reduce the need for a large sample.
High-quality data reduces sample size.
About the System

This system estimates sample size using advanced machine learning models trained on over 3,000 qualitative research cases.

  • 85% accuracy rate
  • 6 different research designs
  • 11 different parameters
  • Machine Learning Models
Important Notes
  • Prediction results are for guidance purposes
  • You may adjust based on your research topic
  • Consider data saturation criteria
  • Don't forget to consult expert opinion