SOLAS

SOLAS for Missing Data Analysis, developed in conjunction with Prof. Donald B. Rubin, Harvard University, 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. Multiple Imputation techniques include:

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

Single Imputation techniques include:

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

Other Important Features in SOLAS:

• Unique Missing Data Pattern …more
• Script Language Facility …more
• Post Imputation Analyses …more

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. Read more about SOLAS for Missing Data Analysis:

• SOLAS in Use – case study (PDF / 136 KB)
• Essay: “Software for Multiple Imputation”, Donald B. Rubin (PDF / 88 KB)