Overview of Today’s Workshop

Joe Schafer

U.S. Census Bureau

Federal Committee on Statistical Methodology

Joseph.L.Schafer@census.gov

Opinions expressed are those of the author and are not necessarily the views or policies of 

the United States Census Bureau.
Three Workshops

Reporting on Quality of Integrated Data

Workshop 1: Quality of Input Data

December 1, 2017

Workshop 2: Quality of Data Processing

January 25, 2018

Workshop 3: Quality of Output Data / Synthesis

February 26, 2018
What “Data Processing” Entails

in the Context of Integrated Data

1. Record Linkage

Exact match, probabilistic match, privacy-preserving record linkage

2. Using Multiple Frames

Drawing samples from two or more frames to improve coverage or reduce costs

3. Statistical Matching / Data Fusion

Joining two or more non-overlapping samples by variables shared in common, then applying modeling

or imputation techniques to handle the missing values

4. Models for Combining Aggregate Statistics or Estimates

Combining estimates from different sources at national, subnational or subpopulation levels,

as in Small-Area Estimation
What “Data Processing” Entails

in the Context of Integrated Data

5. Dimension Reduction / Feature Extraction

Techniques for summarizing unstructured data (e.g. images, freeform text)

6. Harmonization

Combining information across datasets in the presence of mode effects, differing definitions or

granularities (e.g. variables measured at differing time periods or levels of geography)

7. Edit and Imputation

Other types of cleaning after data sources are combined

8. Adjusting for Representativeness

Making combined data more representative of the intended population (reweighting, benchmarking,

poststratification, calibration, ...)
What “Data Processing” Entails

in the Context of Integrated Data

9. Estimation

Computing estimates of population quantities and associated measures of uncertainty

10. Disclosure Avoidance

Techniques for preventing re-identification or de-anonymization of individual records

11. Provenance and Curation of Metadata

Preserving information about data sources, dictionaries, audit trails
Prioritizing the Topics

Which of these topics are

• substantially more complicated or qualitatively different when 

combining multiple data sources? 

• less familiar to statisticians and methodologists?

• not well covered by existing standards for quality and transparency?

• not as well covered by existing literature (e.g. on Small Area Estimation 

or Total Survey Error)?

• not already covered in Workshop 1?
Topic Priority (L/H)

1. Record linkage H

2. Multiple frames L

3. Statistical matching / data fusion H

4. Combining aggregate statistics or estimates (as in SAE) L

5. Dimension reduction / feature extraction L

6. Harmonization across data sources H

7. Edit and imputation L

8. Adjusting for representativeness L

9. Estimation L

10. Disclosure avoidance H

11. Provenance / curation of metadata L
Features of Workshop

• Speakers from academia, federal agencies, research firms

• Many are participating from remote locations (WebEx)

• Presentations may be less formal than typical seminar

• Focus less on methods, more on quality issues

• Extra time in each session for questions, comments, open discussion

• Rapporteurs from JPSM will synthesize what we learn
Session 1: Record Linkage

• Main presentation by Rebecca Steorts (Duke University) (40 min)

• Comments and discussion by Bill Winkler (Census Bureau) (10 min)

• Questions, comments, discussion (25 min)