Robert Lussier, COPAFS Representative

The Council of Professional Associations on Federal Statistics (COPAFS) acts as the advocate for the development and dissemination of high-quality federal statistics. Member organizations include professional associations, businesses, research institutes, and others interested in federal statistics. Through COPAFS, members have an opportunity to review and have an effect on issues including timeliness, quality, confidentiality, and relevance of data. COPAFS holds quarterly meetings, the last one being on June 1, 2012. Detailed minutes and copies of the overheads used by the presenters can be found on COPAFS’ website.

As part of his executive director’s report, Ed Spar described an Office of Management and Budget memo clamping down on the conference activities of federal agencies. With agencies now encouraged to organize conferences in-house, COPAFS conference activity is being affected.

Spar also talked about the budget situation, especially related to the economic census and the American Community Survey (ACS). Spar described the $20 million cut from the economic census as a misunderstanding in Congress that is likely to be reversed. There are two bills on the ACS. One would eliminate it; the other would make response to it voluntary.

Spar also described the recently released 2010 census coverage measurement numbers. He credited the U.S. Census Bureau for reporting the gross errors (undercount and overcount) for geographic detail, including large counties.

Finally, Spar noted that the next Federal Committee on Statistical Methodology Policy seminar is scheduled for December 4–5.

A Review of Plans for the Bureau of Transportation Statistics

Patricia Hu, the new director of the Bureau of Transportation Statistics (BTS), described her organization as collecting, analyzing, and reporting transportation data and ensuring the cost-efficient use of resources in monitoring transportation’s contributions to the economy. BTS, established in 1992 under the Intermodal Surface Transportation Efficiency Act, is part of the Department of Transportation’s Research and Innovative Technology Administration. However, BTS is not the only statistical agency within the department. The Federal Highway Administration spearheads the National Household Travel Survey.

Hu described specific BTS programs. Reflecting the growing importance of freight, the Commodity Flow Survey (CFS) collects—from 100,000 shippers—data on domestic freight shipments by commodity types, origins, and destinations. A Trans Border Freight Data program supplements the CFS. Hu also described programs on passenger travel, such as the collection of monthly data on airline enplanements, on-time performance, and ground delays. There is also a 2010 National Census of Ferry Operators and a GIS-Based Intermodal Passenger Connectivity Database. It is this program that has identified that Americans in rural counties are losing access to inter-city transportation options.

BTS is engaged in modernizing data programs, such as a web response option for CFS, and the streamlined tracking of airline information. BTS also promotes data access and is getting into the development of mobile apps, web engineering, and data visualization. Other initiatives include the reintroduction of the Journal of Transportation Statistics, the re-energizing of the ASA’s Transportation Statistics Interest Group, and the coordination of transportation statistics and definitions across North America.

Measuring Sexual Identity in NCHS Surveys

Jennifer Madans of the National Center for Health Statistics (NCHS) presented work in progress on measuring sexual identity in NCHS surveys. She started by describing the need to better understand the health of sexual minority groups, as there is evidence of health disparities, and a need for data to help address them.

Collecting data on sexual identity is not straightforward. One challenge is the complexity of concepts such as sexual orientation, sexual attraction, sexual behavior, and sexual identity. Further complicating matters is that it can change over time and with context (who is asking). There also are issues with the varied use and comprehension of terms in the media and across subgroups. Different groups relate to terms differently. For example, sexual nonminorities tend to talk more about what they are not, rather than what they are. They might report that “I’m not gay.” They also might not know what terms such as “heterosexual” and “bisexual” mean. In contrast, sexual identity tends to be highly salient to sexual minorities such as lesbian, gay, bisexual, and transgender persons.

Madans then reviewed some of the ways sexual identity has been asked about in the National Health and Nutrition Examination Survey (NHANES) and the National Survey of Family Growth (NSFG). In the NSFG, about 6% of respondents do not answer sexual identity, which gives “missing” a higher frequency than some of the target groups. Missing responses are less of a problem in the NHANES, but they are not random. For example, missing responses have been more common among Hispanics. The 2006–2008 NSFG includes improvements to wording and allows people to write in what they mean by “something else.” The number of “missing” is sharply reduced overall, but remains high in some groups.

The plan is to add questions about sexual identity to the National Health Interview Survey (NHIS)—a larger and interviewer-conducted survey. Goals for the new questions are to reduce misclassification (especially for nonminorities), reduce “something else” and “don’t know” responses, and sort nonminority from minority cases. Testing of the new NHIS questions will continue through 2012, and full implementation is targeted for January 2013.

How Good Are the Annual Social and Economic Supplement Earnings Data? A Comparison to SSA Detailed Earnings Records

Fritz Scheuren of the National Opinion Research Center described research that matches records from the Current Population Survey’s 2006 Annual Social and Economic Supplement (ASEC) with Social Security’s 2005 Detailed Earnings Records (DER). The joint project of Census Bureau and Department of Health and Human Services compares the two to gauge their consistency on income earned in calendar year 2005.

Scheuren described the handling of income imputation in cases of ASEC nonresponse, noting the difference between item imputes—where a specific question is unanswered—and whole imputes—where the entire supplement is imputed. Imputation is a potential factor in the comparison, as poverty rates differ by imputation status.

In comparing ASEC and DER data, Scheuren noted DER is not a gold standard, as it misses some persons and sources of earnings. DER and ASEC can differ in a number of other ways, so that an ASEC-DER comparison is not pure apples to apples.

Results were tabulated for persons age 15 and above with earnings. Overall, 52% had ASEC and DER incomes within $10,000 of each other, and 79% were within $20,000. For records without imputation, 61% were within $10,000 and 88% were within $20,000. Records with whole imputes were least consistent, with only 24% within $10,000 and 72% within $20,000. The correspondence also is strong when looking at the poverty population. When DER is substituted for ASEC, the majority of persons do not change poverty status, and that result holds across demographic groups.

Still to be studied are persons with no ASEC-DER match and those with highly dissimilar incomes.

Managing and Analyzing Longitudinal Data

Patricia Ruggles of Orlin Research, Inc. described the company she runs. It provides products to enhance the use of complex data in social science research. For example, analysis of longitudinal data is complex. Sample attrition, weighting problems, and inconsistencies in response across waves of a survey complicate matters. Creating the necessary links can be difficult in packages such as SAS or SPSS—or highly inefficient, as one would have to track, for example, each person’s income for each month of the survey. Analysts often shy away from using large longitudinal data sets such as the Survey of Income and Program Participation (SIPP), or they use such databases only for cross-sectional analyses.

Ruggles described three basic steps in the use of longitudinal analysis:

1. Understanding the data
2. Preparing data for analysis
3. Performing analyses

Using 2008 SIPP as an example, Ruggles expanded on these steps and described how her company’s products can help with each.

The next COPAFS meeting will take place September 21, 2012.