XeroGraph

XeroGraph evaluates data quality, categorizes the nature of missingness, and guides imputation decisions in datasets with missing values.

Key Features:

• Missingness Classification: Categorizes missing data into Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) types.
• Data Quality Evaluation: Assesses data quality to inform subsequent handling strategies.
• Imputation Method Comparison: Compares how various imputation methods influence underlying distributions to support selection of optimal strategies.
• Preliminary Assessments: Provides comprehensive preliminary assessments tailored to specific missing data mechanisms present in a dataset.

Scientific Applications:

• Missing Data Handling: Facilitates efficient handling of missing data in datasets across data-intensive research fields.
• Analysis Validity Improvement: Improves validity and reproducibility of research findings by enabling more accurate and transparent analyses.
• Imputation Strategy Selection: Guides selection of optimal imputation strategies based on the specific missing data mechanisms identified in a dataset.

Methodology:

XeroGraph uses statistical and predictive analysis to evaluate data quality, classify missingness types, and compare the influence of imputation methods on underlying distributions.