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Why Choose SOLAS?

SOLAS for Missing Data Analysis was developed with guidance from Donald B. Rubin, the inventor of Multiple Imputation.
SOLAS 4.0 offers 5 different methods for multiple imputation in addition to 4 single imputation techniques. You choose the most appropriate method for your particular data set.
Amazing new graphics in SOLAS 4.0 allow you to visualize your missing data issues like never before.

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SOLAS In Action – Practical Business Uses

Pharma



The incorrect analysis of datasets with incomplete data can lead to biased analysis and incorrect inference. With 5 multiple imputation techniques SOLAS for Missing Data Analysis is a must to aid you with imputation, analysis and sensitivity reports. National Research Council Recommendations of How To Handle Missing Data.

Survey



The Predictive Mean Matching method in SOLAS, as described by Roderick J. Little (1988), works well with very large survey data. Also, new plots and graphics allow you to find irregularities in your data immediately and identify issues from faulty mechanics to flawed survey questions.

Academic



Academic researchers will love the new collapse missing data pattern feature which allows you to quickly and simply interpret missing values in your data. Academic organizations can also benefit from special offer pricing, get your price quote here.

Government



SOLAS for Missing Data Analysis is now available in 64-bit. This new capability will allow government agencies to perform its analysis on much larger datasets due to SOLAS’s great processing power.