At the U.S. Bureau of the Census, data in economic surveys may occasionally be missing as a result of a company's failure to respond to a certain question, for example. In addition, values of other variables may require editing because they are clearly implausible. Implausible (outlying) values may arise as a result of the failure of the respondent to understand the survey question. This paper develops a strategy for cleaning survey data with missing and outlying values in three stages: 1) detection of outlying cases; 2) detection of outlying values within outlying cases; and 3) imputation of likely values for missing and/or outlying and edited values. Methodological tools include distance measures, graphical procedures, and maximum likelihood and robust estimation for incomplete multivariate normal data. Data from the Annual Survey of Manufactures (ASM) are used to illustrate the method.

Others in Series

Working Paper

On Using Different Time Series Forecasting Model for Each Forecast ...

August 12, 1983

On Using Different Time Series Forecasting Model for Each Forecast Lead

Working Paper

Coverage Error Models for Census and Survey Data

September 1983

Coverage Error Models for Census and Survey Data

Working Paper

Seasonal Decomposition of Deterministic Effects

January 10, 1984

Seasonal Decomposition of Deterministic Effects

View All

Related Information

WORKING PAPER

Statistical Research Reports and Studies

Page Last Revised - October 28, 2021

Some content on this site is available in several different electronic formats. Some of the files may require a plug-in or additional software to view.

Is this page helpful?
Thumbs Up Image

Yes

NO THANKS

255 characters maximum

255 characters maximum reached

Thank you for your feedback.
Comments or suggestions?

Top