Missing Data Pattern Analyzer

Pro v1.0.0 1 view

Analyze missing data patterns in research datasets and recommend appropriate handling strategies with statistical tests and implementation code.

What You Get

Get complete missing data analysis including mechanism testing, strategy recommendations, R/Python code templates, and publication-ready methods text - replacing hours of manual analysis with statistically rigorous guidance.

The Problem

Researchers face missing data in surveys, longitudinal studies, and clinical trials but struggle to determine if data is MCAR, MAR, or MNAR, and which handling method (deletion, imputation, FIML) is appropriate. Incorrect choices lead to biased results and publication rejections.

The Solution

Systematic missing data analysis that calculates missingness statistics, runs Little's MCAR test and logistic regression to identify predictors, determines the missingness mechanism, recommends evidence-based handling strategies, and provides ready-to-use R/Python code with publication-ready methods section text.

How It Works

  1. 1 Calculate missing data summary statistics including percentage missing per variable and complete case counts
  2. 2 Classify missingness pattern as univariate, monotone, or arbitrary and generate visualization code
  3. 3 Test missingness mechanism using Little's MCAR test and logistic regression for predictors
  4. 4 Determine mechanism (MCAR/MAR/MNAR) by synthesizing test results with theoretical reasoning
  5. 5 Recommend handling strategy (deletion, multiple imputation, or FIML) with justification
  6. 6 Provide implementation code templates in R (mice, lavaan) and Python (sklearn)
  7. 7 Generate publication-ready methods section text and sensitivity analysis recommendations

What You'll Need

  • Dataset with missing values (CSV or described tabular data)
  • Variable names, types, and sample size
  • Missing counts per variable
  • Research context (survey, longitudinal, clinical trial)

Get This Skill

Requires Pro subscription ($9/month)

Have Feedback?

Help us improve this skill by sharing your ideas and suggestions.

Request Improvements