Missing Data in Multiple Item Scales: A Monte Carlo Analysis of Missing Data Techniques
通过蒙特卡洛模拟比较了多种缺失数据处理技术，发现回归插补和个体均值替代在多项目量表中效果优于列表删除，对使用量表的研究者选择处理方法有参考价值。
Researchers in many fields use multiple item scales to measure important variables such as attitudes and personality traits, but find that some respondents failed to complete certain items. Past missing data research focuses on missing entire instruments, and is of limited help because there are few variables to help impute missing scores and the variables are often not highly related to each other. Multiple item scales offer the unique opportunity to impute missing values from other correlated items designed to measure the same construct. A Monte Carlo analysis was conducted to compare several missing data techniques. The techniques included listwise deletion, regression imputation, hot-deck imputation, and two forms of mean substitution. Results suggest that regression imputation and substituting the mean response of a person to other items on a scale are very promising approaches. Furthermore, the imputation techniques often outperformed listwise deletion.