SOLAS software description

SOLAS for Missing Data Analysis provides researchers with a range of imputation techniques in an easy to use, validated software application. SOLAS offers principled approaches to analyzing data with missing values, featuring 6 different imputation techniques and its own script language so that you can easily record your imputation choices.

SOLAS multiple Imputation techniques include:

-  Predictive Model Based Multiple Imputation
-  Propensity Score Based Multiple Imputation
-  (Non Parametric Approach Based on Propensity Scores and the Approximate Bayesian Bootstrap)

SOLAS single Imputation techniques include:

- Hot Deck Imputation
- Predicted Mean Imputation (using Regression)
- (Ordinary Least Squares Method & Discriminant Method)
- Last Value Carried Forward (LVCF / LOCF)
- Group Mean

Other Important Features in SOLAS:

- Unique Missing Data Pattern
- Script Language Facility
- Post Imputation Analyses

The incorrect analysis of datasets with incomplete data can lead to biased analysis and incorrect inference. SOLAS, with its 6 different imputation techniques, unique missing data pattern feature and script language facility, is the missing data software most research statisticians and data analysts choose when working with incomplete data or missing values.

Ordering

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