Presentation is loading. Please wait.

Presentation is loading. Please wait.

The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey Adela Luque (U.S. Census Bureau) Brittany.

Published byGenevieve Angel Modified over 10 years ago

Similar presentations

Presentation on theme: "The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey Adela Luque (U.S. Census Bureau) Brittany."— Presentation transcript:

1 The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey Adela Luque (U.S. Census Bureau) Brittany Bond (U.S. Department of Commerce) J. David Brown & Amy OHara (U.S. Census Bureau) FedCASIC March 2014 1

2 Disclaimer Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau All results have been reviewed to ensure that no confidential information on individual persons is disclosed 2

3 Overview Motivation Objectives Data & Methodology Background on Anonymous Identifier Assignment Process Expected Effects Results Conclusions 3

4 Motivation Record linkage can enrich data, improve its quality & lead to research not otherwise possible - while reducing respondent burden & operational costs Linking data requires common identifiers unique to each record that protect confidentiality Census Bureau assigns Protected Identification Keys (PIKs) via a probabilistic matching algorithm: PVS (Personal Identification Validation System) Not possible to reliably assign a PIK to every record, which may introduce bias in data analysis 4

5 Objectives What characteristics are associated with the probability of receiving a PIK? That is, what is the nature of the bias introduced by incomplete PIK assignment? Help researchers understand nature of bias, interpret results more accurately, adjust/reweight linked analytical dataset Examine bias using regression analysis - before & after changes in PVS. Do alterations to PVS improve PIK assignment rates as well as reduce bias? NORC (2011) described some demographic and socio- economic characteristics of those records not getting a PIK 5

6 Data & Methodology 2009 & 2010 American Community Survey (ACS) – processed through PVS Ongoing representative survey of the U.S. population Socioeconomic, demographic & housing characteristics 50 states & DC - Annual sample approximately 4.5 million person records Probit model for 2009 and 2010 separately Dependent variable = 1 if person record received a PIK (0 otherwise) Covariates: Demographic characteristics: age, sex, race and Hispanic origin Socio-economic characteristics: employment status, income, poverty status, marital status, level of education, public program participation, health insurance status, citizenship status, English proficiency, military status, mobility status, and household type Housing and address-related characteristics: urban vs. rural, type of living quarter, age of living quarter ACS replicate weights Report marginal effects 2009 & 2010 results compared – before & after changes to PVS 6

7 Background on PVS Probabilistic match of data from an incoming file (e.g., survey) to reference file containing data from the Social Security Administration enhanced with address data obtained from federal administrative records If a match is found, person record receives a PIK or is validated 7

8 Background on PVS Initial edit to clean & standardize linking fields (name, dob, sex & address) Incoming data processed through cascading modules (or matching algorithms) Only records failing a given module move on to the next Impossible to compare all records in incoming file to all records in reference file blocking Data split into blocks/groups based on exact matches of certain fields or part of fields – probabilistic matching within block 8

9 Background on PVS 2009 PVS Modules Verification – Only for incoming files w/ SSNs Geosearch looks for name/dob/gender matches after blocking on an address or address part (within 3-digit ZIP area) Namesearch looks for name & dob matches within a block based on parts of name/dob Each module has several passes – different blocking & matching strategies 9

10 Background on PVS 2010 PVS Enhancements ZIP3 Adjacency Module looks for name/dob/gender/address matches after blocking on address field parts in areas adjacent to 3-digit ZIP area DOB Search Module looks for name/gender/dob matches after blocking on month & day of birth Household Composition Search Module looks for name/dob matches for unmatched records that are seen in past at same address with PIKed record Inclusion of ITINs in reference file 10

11 Expected Effects Less likely to obtain a PIK: Insufficient or inaccurate person identifying info in incoming record Issues w/ data collection or withholding due to language barriers, trust in govt., privacy preferences Identifying info in incoming file & reference file more likely to differ Address info differs/not updated Movers, rent vs. own, certain types of housing Record not in government reference files Newborns, recent immigrant, very poor/unemployed/no govt. program recipient 11

12 Results – Overall Validation Rates 12 Sources: 2009 & 2010 ACS

13 Probit Results 13 Sources: 2009 & 2010 ACS

14 Probit Results 14 Note: Dotted bars indicate that change in marginal effect from 2009 to 2010 is not statistically significant.

15 Probit Results 15

16 Probit Results 16 Note: Dotted bars indicate that change in marginal effect from 2009 to 2010 is not statistically significant.

17 Probit Results 17 Note: Dotted bars indicate that change in marginal effect from 2009 to 2010 is not statistically significant.

18 Probit Results 18

19 Probit Results 19

20 Probit Results 20 Note: Dotted bars indicate that change in marginal effect from 2009 to 2010 is not statistically significant.

21 Probit Results 21

22 Probit Results 22

23 Probit Results 23 Note: Dotted bars indicate that change in marginal effect from 2009 to 2010 is not statistically significant.

24 Probit Results 24

25 Probit Results 25 Note: Dotted bars indicate that change in marginal effect from 2009 to 2010 is not statistically significant.

26 Probit Results 26

27 Conclusions Mobile persons, those with lower income, unemployed, in process of integrating in economy/society, non-participants in government programs are less likely to be validated Renters, movers, mobile homes Low income, non-employed, most minorities, non-U.S. citizens, poor English Non-participants of govt. program, uninsured, non-military Researchers may wish to reweight observations based on validation propensity 27

28 Conclusions Changes to PVS system Increased overall validation rate by 4.5 percentage points Reduced validation differences across most groups from 2009 to 2010 Record linkage research can lead to higher PIK assignment rates and less bias 28

29 Thank you! adela.luque@census.gov adela.luque@census.gov 29

Download ppt "The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey Adela Luque (U.S. Census Bureau) Brittany."

Similar presentations

1 The Foreign-Born Population in the United States: 2003.

1 The Foreign-Born Population in the United States: 2003.
© GfK 2012 1 Using ancillary information to stratify and target young adults and Hispanics in national ABS samples J. Michael Dennis, Charles DiSogra,

© GfK Using ancillary information to stratify and target young adults and Hispanics in national ABS samples J. Michael Dennis, Charles DiSogra,
National Prevention Strategy

National Prevention Strategy
1 Inducements–Call Blocking. Aware of the Service?

1 Inducements–Call Blocking. Aware of the Service?
Advanced Piloting Cruise Plot.

Advanced Piloting Cruise Plot.
Chapter 1 The Study of Body Function Image PowerPoint

Chapter 1 The Study of Body Function Image PowerPoint
THE 2004 LIVING CONDITIONS MONITORING SURVEY : ZAMBIA EXTENT TO WHICH GENDER WAS INCORPORATED presented at the Global Forum on Gender Statistics, Accra.

THE 2004 LIVING CONDITIONS MONITORING SURVEY : ZAMBIA EXTENT TO WHICH GENDER WAS INCORPORATED presented at the Global Forum on Gender Statistics, Accra.
Lisa Dubay, Ph.D., Sc.M. Johns Hopkins Bloomberg School of Public Health and Center for Children and Families Getting to the Finish Line:

Lisa Dubay, Ph.D., Sc.M. Johns Hopkins Bloomberg School of Public Health and Center for Children and Families Getting to the Finish Line:
1 Probabilistic Linkage: Issues and Strategies Craig A. Mason, Ph.D. University of Maine

1 Probabilistic Linkage: Issues and Strategies Craig A. Mason, Ph.D. University of Maine
1 The SEP Gradient, Race, or the SEP Gradient and Race: Understanding Disparities in Child Health and Functioning Lisa Dubay, PhD, ScM The Urban Institute.

1 The SEP Gradient, Race, or the SEP Gradient and Race: Understanding Disparities in Child Health and Functioning Lisa Dubay, PhD, ScM The Urban Institute.
TABLE OF CONTENTS CHAPTER 1.0: Trends in the Overall Health Care Market Chart 1.1: Total National Health Expenditures, 1980 – 2010 Chart 1.2: Percent.

TABLE OF CONTENTS CHAPTER 1.0: Trends in the Overall Health Care Market Chart 1.1: Total National Health Expenditures, 1980 – 2010 Chart 1.2: Percent.
Figure 1. There Are 13.3 Million Uninsured Young Adults Ages 19–29, 30 Percent of the Nonelderly Uninsured, 2005 Source: Analysis of the March 2006 Current.

Figure 1. There Are 13.3 Million Uninsured Young Adults Ages 19–29, 30 Percent of the Nonelderly Uninsured, 2005 Source: Analysis of the March 2006 Current.
1 The Academic Profession and the Managerial University: An International Comparative Study from Japan Akira Arimoto Research Institute for Higher Education.

1 The Academic Profession and the Managerial University: An International Comparative Study from Japan Akira Arimoto Research Institute for Higher Education.
NTTS conference, February 18 – 20 2009 New Developments in Nonresponse Adjustment Methods Fannie Cobben Statistics Netherlands Department of Methodology.

NTTS conference, February 18 – New Developments in Nonresponse Adjustment Methods Fannie Cobben Statistics Netherlands Department of Methodology.
1 ACS Statistical Issues and Challenges: One-, Three-, and Five-year Period Estimates Michael Beaghen U.S. Census Bureau New Jersey State Data Center Annual.

1 ACS Statistical Issues and Challenges: One-, Three-, and Five-year Period Estimates Michael Beaghen U.S. Census Bureau New Jersey State Data Center Annual.
Statistical Significance and Population Controls Presented to the New Jersey SDC Annual Network Meeting June 6, 2007 Tony Tersine, U.S. Census Bureau.

Statistical Significance and Population Controls Presented to the New Jersey SDC Annual Network Meeting June 6, 2007 Tony Tersine, U.S. Census Bureau.
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13

Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13

Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Exit a Customer Chapter 8. Exit a Customer 8-2 Objectives Perform exit summary process consisting of the following steps: Review service records Close.

Exit a Customer Chapter 8. Exit a Customer 8-2 Objectives Perform exit summary process consisting of the following steps: Review service records Close.
Monitoring School District Human Resource Cost Pressures Presented by Tom Gallagher October 30, 2012 Research & Planning Wyoming Department of Workforce.

Monitoring School District Human Resource Cost Pressures Presented by Tom Gallagher October 30, 2012 Research & Planning Wyoming Department of Workforce.

Similar presentations

Ads by Google