Dealing with Missing Data

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*'''Option 3''' is to replace the missing values, called imputation. There is little agreement about whether or not to conduct imputation. There is some agreement, however, in which type of imputation to conduct. [[Image:Fe40.png]] - You typically do NOT conduct Mean substitution or Regression substitution. Mean substitution is replacing the missing value with the mean of the variable. Regression substitution uses regression analysis to replace the missing value. Regression analysis is designed to predict one variable based upon another variable, so it can be used to predict the missing value based upon the subject’s answer to another variable. The favored type of imputation is replacing the missing values using different estimation methods. The “Missing Values Analysis” add-on module in SPSS contains the estimation methods.
*'''Option 3''' is to replace the missing values, called imputation. There is little agreement about whether or not to conduct imputation. There is some agreement, however, in which type of imputation to conduct. [[Image:Fe40.png]] - You typically do NOT conduct Mean substitution or Regression substitution. Mean substitution is replacing the missing value with the mean of the variable. Regression substitution uses regression analysis to replace the missing value. Regression analysis is designed to predict one variable based upon another variable, so it can be used to predict the missing value based upon the subject’s answer to another variable. The favored type of imputation is replacing the missing values using different estimation methods. The “Missing Values Analysis” add-on module in SPSS contains the estimation methods.
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Revision as of 08:21, 3 February 2008

How do I deal with missing values? Irrespective of whether the missing values are random or non-random, you have three options when dealing with missing values.






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