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Missing-data imputation

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CHAPTER 25Missing- data imputationMissing data arise in almost all serious statistical analyses. In this chapter wediscuss a variety of methods to handle missing data , including some relatively simpleapproaches that can often yield reasonable results. We use as a running example theSocial Indicators Survey, a telephone survey of New York City families conductedevery two years by the Columbia University School of Social Work. Nonresponsein this survey is a distraction to our main goal of studying trends in attitudes andeconomic conditions, and we would like to simply clean the dataset so it could beanalyzed as if there were no missingness.

In R, missing values are indicated by NA’s. For example, to see some of the data ... To decide how to handle missing data, it is helpful to know why they are missing. ... on sex, race, and education—but this is a lot more plausible than assuming that the probability of nonresponse is constant, or that it depends only on one of these ...

  Data, Value, Missing, Imputation, Plausible, Missing data imputation

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