survey-design

To calculate Cronbach's alpha and ICC for Likert scale survey data, the tool computes internal consistency and inter-rater reliability using pingouin. It requires numeric survey item data to perform these reliability metrics and evaluate measurement quality.

Detecting straight-lining and speedy responders in Qualtrics CSV data involves applying response-quality filtering and instructed-response-item logic. The tool identifies low-quality responses and supports clean export workflows for Qualtrics and LimeSurvey datasets.

Designing attention checks for a research questionnaire involves embedding instructed-response-item logic into the survey. This approach catches low-quality responses during pilot testing, ensuring interpretable and usable results before main analysis.

Yes, you can use this tool to plan Likert scale construction and pilot testing. It guides response format selection, mitigates acquiescence via reverse-scoring, and plans scale structure to achieve good measurement quality for your research survey.

Reverse scoring and item-total correlation filtering are needed for reliability analysis to mitigate acquiescence bias and assess internal consistency. Computing corrected item-total correlations helps identify and remove poorly designed items before analysis.

Yes, the survey validation process works with LimeSurvey CSV exports. It supports questionnaire data cleaning and preparation specifically oriented for Qualtrics and LimeSurvey CSV formats to produce a cleaned dataset ready for your main study.