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What is between treatment variance made up of?
In short, it is common to call “between variance” the variance among treatments, that is, how much of the variation in the response variable(s) is explained by your explanatory variable(s) ; and the “within variance” is the variance left unexplained (how much variation there is within treatments after removing the …
Why do we need to use the variance to compare the means?
So, when we divide them up in a way that matters, we get huge variation between and little variation within. When we divide them in a silly way, we get huge variation within and little variation between. That’s why we look at variances to compare means.
When to use single imputation or multiple imputation?
Imputation is one of the key strategies that researchers use to fill in missing data in a dataset. By using various calculations to find the most probable answer, imputed data is used in place of actual data in order to allow for more accurate analyses. There are two different types of imputation:
Why is a covariance matrix used in multiple imputation?
Meaning that a covariance (or correlation) matrix is computed where each element is based on the full set of cases with non-missing values for each pair of variables. This method became popular because the loss of power due to missing information is not as substantial as with complete case analysis.
What’s the difference between ” within ” and ” between ” variance?
The between variance is how much your estimates change one imputation to another — it’s what gets added to your analysis by imputing more than one dataset. To illustrate, consider a simple dataset, where we want to run a linear regression where X1 and X2 predict Y. First, we generate the data:
How to get an estimate of the between variance?
To get an estimate of the between variance, you’d want to look at how much the coefficients for X1 and X2 vary across the three imputations.