in the disciplines relevant to its mission; the implementation of ongoing quality assurance programs to improve data validity and reliability and to improve the processes of compiling, editing, and analyzing data; and the development of a strong and continuing relationship with appropriate professional organizations in the fields of statistics and relevant subject-matter areas.


To carry out its mission, a Federal statistical organization assumes responsibility for determining sources of data, measurement methods, methods of data collection and processing while minimizing respondent burden; employing appropriate methods of analysis; and ensuring the public availability of the data and documentation of the methods used to obtain the data. Within the constraints of resource availability, a statistical organization continually works to improve its data systems to provide information necessary for the formulation of public policy.

Beyond this, the OMB has directed each federal agency to issue its own information quality guidelines, and further guidelines have been issued by USDA, NASS, and ERS (U.S. Department of Agriculture, 2006; National Agricultural Statistics Service, 2007a; Economic Research Service, 2003).

More recently, OMB has issued detailed Standards and Guidelines for Statistical Surveys (U.S. Office of Management and Budget, 2006b), a comprehensive guide to developing and managing surveys in such a way as to obtain OMB approval for their conduct. In these guidelines, quality standards for the various stages of survey operations have been spelled out in some detail. The topics covered range from satisfactory survey response rates to the development of sampling frames to drawing of inferences from the data. The panel refers to these standards and guidelines in the report when discussing issues of compliance and noncompliance.

GUIDE TO THE REPORT

Following this introduction, we lay out the contemporary issues facing American agriculture and the relevant uses of ARMS data to address them in Chapter 2. These uses include those driven by congressional mandates and by agency and research community needs. Chapter 3 outlines the organizational structure behind ARMS and the collaborative management of NASS and ERS. Issues of sample and survey design, data collection, nonresponse, imputation, and estimation are addressed in the next three chapters. Chapter 7 provides a framework for analysis of complex surveys and issues related to inclusion of survey design in estimation. Data user concerns, including dissemination of data and opportunities for user feedback and training are addressed in Chapter 8. Chapter 9 summarizes the panel’s conclusions and recommendations.



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