Two Best Ways to Fix Missing Data in SPSS

In this video, I demonstrated how to use "Regression Base Imputation and Expectation-Maximization Algorithm" to fix missing data in a dataset as the two different methods I considered as the "Two Best Ways to Fix Missing Data in SPSS".

In this video, the illustrations are simplified and made easy to follow and replicate. So, I encourage you to see or watch this video to the end.

If the size of your dataset is not large, or if the length of your dataset is short, and trying to fix the missing data in it by deleting some rows or observation as necessary will reduce the size or length of your dataset, and might make you lose some useful information. So, using "REGRESSION BASE IMPUTATION (RBI) and EXPECTATION-MAXIMIZATION ALGORITHM (EMA) can help to preserve the size and length of your dataset and the inherent information without introducing outliers that can make the data bias.

So, regardless of the size of your data and the type of your data, always use RBI or EMA as the the best method to fix the missing data in your dataset.

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