Reducing Response Burden in the American Community Survey Proceedings of a Workshop (2016) / Chapter Skim
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5 Using Improved Sampling and Other Methods to Reduce Response Burden
5 Using Improved Sampling and Other Methods to Reduce Response Burden
Pages 55-78
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From page 55... ...
CENSUS BUREAU RESEARCH ON MATRIX SAMPLING Mark Asiala (Census Bureau) focused on a report written by a group of Census Bureau staff who looked at the feasibility of using matrix sampling or other techniques to reduce respondent burden.
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Matrix Sampling: dividing the ACS questionnaire into possibly • overlapping subsets of questions, and then administering these subsets to different subsamples of the initial sample Option 4. Administrative Records Hybrid: using alternative data sources as • a direct substitution for survey data collection, potentially in a hybrid approach by including the question on the survey in certain geographic areas to address coverage gaps in the alternative data, or to assist in periodically refining sta tistical models that use the administrative records to meet data needs SOURCE: U.S.
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From page 57... ...
SOURCE: Mark Asiala presentation at the Workshop on Respondent Burden in the American Community Survey, March 9, 2016. Available: http://sites. nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_173161.
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SOURCE: Mark Asiala presentation at the Workshop on Respondent Burden in the American Community Survey, March 9, 2016. Available: http://sites. nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_173161.
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1 1 1 1 1 1 2 2 2 3 3 3 2 2 2 OR 1 1 1 2 2 2 3 3 3 3 3 3 1 1 1 2 2 2 3 3 3 10 FIGURE 5-3 Illustration of option 3: Comprehensive matrix sampling. SOURCE: Mark Asiala presentation at the Workshop on Respondent Burden in the American Community Survey, March 9, 2016.
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1 1 1 1 1 2 2 2 2 11 FIGURE 5-4 Illustration of option 4: Administrative records hybrid. SOURCE: Mark Asiala presentation at the Workshop on Respondent Burden in the American Community Survey, March 9, 2016.
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If the Census Bureau does not create a complete microdata file, this would introduce concerns about the user friendliness of the public-use file. On the other hand, constructing a complete file introduces issues with the techniques for imputing the missing data.
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Gonzalez said he would take a broad perspective on utilizing matrix sampling to reduce respondent burden, provide a definition of matrix sampling, discuss the design of matrix samples, identify implications of simple
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This has led the ACS to consider the possibility of dividing the lengthy ACS questionnaire into subsets of questions and then administering each subset to subsamples of the full sample. This is referred to as matrix sampling, he explained, but it is also referred to as a split questionnaire design -- these designs ensure that every question is administered to at least some portion of the sample.
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Finally, survey operations are more complicated and case management systems must be modified in order to keep track of the various forms. Balanced against these disadvantages, Gonzalez referred to work on matrix sampling or split questionnaire designs by Chipperfield and Steel (2012)
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Since the ACS collects information from heterogeneous target populations, the survey methods literature concludes that collecting information on heterogeneous target populations from standardized instruments may be suboptimal. Incorporating auxiliary information about sample units in the ACS can have positive effects on data quality and burden reduction.
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The second design option under consideration involves the integration of data sources, which would comingle ACS or other survey data with information provided in other data sources such as administrative records, other surveys, or organic data sources to either replace, edit, impute, or use in estimation in some way. The motivation for considering these methods is similar to that of matrix sampling, that is, to reduce respondent burden, improve some dimension of data quality, and yield some cost savings.
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From page 67... ...
He stated he would cover burden and information needs in the ACS; research-based results, empirical findings, and common-sense observations on planned missingness data designs; four options for incorporating planned missingness or matrix sampling in the ACS; and methodological and empirical issues that are involved. He explained that the burden of the ACS can be classified as individual respondent burden, aggregate sample burden, system (data producer)
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is a pure matrix-sampling design and is usually exemplified by a set of core questions and then modular components. In contrast to the multiphase design, which creates a monotonic missing data problem, the SQD creates a generalized missing data problem.
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His recommendation was to modularize by year and treat the 5-year interval as a split questionnaire design. Heeringa also addressed a series of methodological and empirical issues in applying planned missingness in the ACS.
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S and analytic datasets would need attention, and the Census Bureau may wish to consider implementing a user-managed approach to the planned missing data. GENERAL DISCUSSION Workshop steering committee cochair Joseph Salvo observed that the 1970 census started from a strong base -- the long form had a substantial sample with items asked of 5, 15, and 20 percent samples.
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• Only data collected on the ACS form should be labeled ACS data. Administrative data should clearly be labeled as such, and he stressed that linked data are not ACS data.
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However, administrative data have a huge role in imputation of ACS data, and the Census Bureau needs to allow users to link the administrative data in an easy way. Matrix Sampling, Maximum Likelihood Approaches, and Multiple Imputation Paul Biemer (RTI International)
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Biemer uses loglinear path models to specify relationships among C, A, B, R, and S The response mechanisms that are assumed depend on the model assumed for R and S -- either an assumption that the response indicators R and S are related to the core (a missing at random assumption)
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From page 74... ...
If complete case analysis were used, a huge number of cases would be discarded in the analysis. Based on this experience, he urged the Census Bureau to explore FIML, stating it is a viable approach for interval and model estimation and matrix sampling and is an alternative to multiple imputation methods.
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The undertaking will be complicated, he said. If the goal is to produce estimates of one variable at a time, there is no need to intercorrelate variables, and various split questionnaire designs will work adequately in many cases.
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In order to appropriately design an approach to reducing burden, he urged the Census Bureau to consider a series of questions: What does the agency want to collect about the population over this period of time? Who are the clients?
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The method provides an indication of the ability to recover information or conversely the fraction of missing information from these sorts of planned missing data designs. He also noted mass imputation, which was created by Donald Rubin to permit imputation of rectangularized datasets because software could not handle anything but complete data, would strike out cases with any missing data (Rubin, 1987)
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From page 78... ...
Heeringa and O'Muircheartaigh concurred that the idea is promising. The difference from the matrix sampling discussed earlier is that immediately a burden propensity is inserted into the process to produce an estimate.
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