This page contains information on the methodology used to calculate and collect CE information and the quality of the CE data. Also included are links to the CE public use microdata documentation and files, and general articles and research papers using CE data including documents in the CE Library.
BLS Handbook of Methods
OPLC Program Comparisons
Frequently Asked Questions (FAQs)
Data Quality in the CE
The Consumer Expenditure Survey (CE) has historically provided some limited metrics for data users to evaluate the overall quality of output provided in its products. Published tables provide standard errors, the public-use microdata user guide provides response rates, and the public-use microdata datasets provide all the variables and flags necessary for users to create his or her own quality measures. There has long been a recognition for the need for more comprehensive data quality metrics that are timely and routinely updated, and accessible to data users from a single source. However, there is also recognition of the high cost in terms of resources and commitment to identifying appropriate metrics and establishing the information base necessary to routinely produce reports on survey data quality. In order for this effort to be sustainable, the benefits from it must be relevant and useful to survey operations and data users.
The CE Data Quality Profile (Prototype version 2) is the second in a series of iterations towards developing a single reference on a comprehensive set of CE data quality metrics that are timely and routinely updated for the Consumer Expenditure Interview Survey (CEQ) and the Consumer Expenditure Diary Survey (CED). Recognizing the benefits of “learning-by-doing” – using cumulative experience to provide CE with a practical understanding of what resources are needed and how best to deploy them to routinely produce a Data Quality Profile (DQP) - the first iteration DQP1 produced a small set of metrics with very limited resources. In Prototype version 2, the set of metrics was expanded, and its presentation format modified. It is our goal to publish an annual Data Quality Profile.
Articles and Research Papers
How to Understand Variances
Last Modified Date: January 29, 2018