Missing Data & Outliers
Turn data cleaning into a STROBE paragraph
Missing Data & Outliers
Turn data cleaning into a STROBE paragraph
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Description
Enter your sample size, missing data rate and pattern (MCAR/MAR/MNAR), the missing data method you used (such as listwise deletion or multiple imputation), and your outlier detection method (Z-score, IQR, or Mahalanobis) to generate an academic paragraph that can be added directly to the “Data Preprocessing” section of your paper. If there is a methodological inconsistency between your chosen method and the missing data pattern—for example, using listwise deletion with an MNAR pattern—it will be clearly identified. Suitable for graduate students and researchers conducting survey or clinical data analysis who want to write their data preprocessing section quickly and accurately.
Recommended by
Shuting@YouMind
Why we love this skill
Turns data-cleaning steps into a STROBE-compliant, reviewer-defensible Data Preprocessing section, with special attention to consistency among missing-data patterns, imputation choices, outlier thresholds, and the final sample.
Best for
Suitable for students and researchers analyzing survey or clinical data.
How to use this Skill
Provide your input
Provide the sample size, missing data rate and pattern, method used, outlier approach, and final sample information.
Run the Skill
The assistant evaluates your data preprocessing process for methodological consistency and turns it into academic prose.
Review your result
Receive a data preprocessing paragraph for your paper, a rationale for the method, and a power analysis warning.
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Information
- Version
- v2
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
- Use cases
- Writing & contentResearch & analysis
- What you get
- Article
