The Survey Quality Predictor (SQP) helps researchers evaluate the expected measurement quality of survey questions before data collection even begins. By coding formal and linguistic features, such as wording, response scale, and mode of administration, SQP predicts key quality components like reliability, validity, and overall measurement quality. By doing so, SQP helps researchers design survey questions that are both clearer and more reliable to address measurement error prior to collecting data. The SQP tool demonstrates this with an applied example that compares two versions of the same question on satisfaction with the country’s economy using different response scales, showing how design choices lead to different predicted quality.

Further information: This tool was developed as part of the KODAQS project, a partnership between GESIS, the University of Mannheim, and LMU Munich.