Summary
Contents
Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a non-technical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.
Assumptions
Assumptions
Researchers often try to make the case that people who have missing values on a particular variable are no different from those with observed measurements. It is common, for example, to present evidence that people who do and do not report their income are not significantly different on a ...