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Edit and Imputation o f the 2011 Abu Dhabi Census Glenn Hui and Hanan AlDarmaki

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Edit and Imputation o f the 2011 Abu Dhabi Census Glenn Hui and Hanan AlDarmaki Statistics Centre - Abu Dhabi. UNECE CES Work Session on Statistical Data Editing (Oslo, Norway, 26 September 2012). Outline. Census Overview Edit and Imputation Methodologies

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slide1

Edit and Imputation of the 2011 Abu Dhabi Census

Glenn Hui and Hanan AlDarmaki

Statistics Centre - Abu Dhabi

  • UNECE CES
  • Work Session on Statistical Data Editing
  • (Oslo, Norway, 26 September 2012)
slide2

Outline

  • Census Overview
  • Edit and Imputation Methodologies
  • Societal Differences and Challenges
  • Performance Analysis
  • Data Editing in the 2005 Census
  • Conclusions
slide3

2011 Abu Dhabi Census Overview

  • First census conducted by SCAD
  • Main collection via CAPI, October 2011
  • 20 questions
  • Three methodologies used for edit and imputation, each with its own purpose:
    • Donor
    • Deterministic
    • Manual
slide4

Edit and Imputation Methodologies

  • Donor Imputation
  • Canadian Census Edit and Imputation Systemv4.5 (CANCEIS) hot deck module
  • Substitutes invalid value with value from “donor” record
  • Deterministic Imputation
  • Correct data via hard-coded rules (SAS)
  • Applied mostly for out-of-scope responses
  • Manual Imputation
  • Manually check and modify data.
  • Difficult cases like very large households.
slide5

Societal and Cultural Differences

  • Very large household sizes: ~5 persons average
    • Contrast to typical ~2.5 averages in Western countries
    • Error rates increase with family size; used less exacting DLTs to account for this
      • Households of 17+ treated as individual records, with some manual imputation as well
slide6

Societal Differences continued

  • Complex relationships in large households
    • Extended families
    • High proportion of household servants
    • Multiple wives – special consistency rules required
  • Large Expatriate Population
    • Many live in shared living arrangements
    • Significant portion live in employer-provided camps
    • Shares and collectives treated as 1-person households
slide7

Imputation Performance

  • Test Data: Starting with clean data, introduced two types of errors: missing data and “interchange” errors.
  • Most performance measures from Euredit project (Charlton, 2003)
  • Example Statistics
  • Predictive Accuracy: R2 generated by regressing true on imputed values, used to assess predictive ability.
  • Estimation Accuracy: Difference in means of true and imputed values, m1, used to assess aggregate imputation accuracy.
    • Imputation performance for Age

Charlton, J. C. (2003).“Evaluating New Methods for Data Editing and Imputation - Results from the Euredit Project”, UNECE Statistical Data Editing Work session. Madrid, Spain.

slide8

Data Editing in the 2005 Census

  • 2005: Manual and Deterministic imputation
  • Phase 1: validation edits, outlier detection via SQL
    • Small subset imputed via deterministic imputation
  • Phase 2: Most failed records corrected manually
  • Comparison to 2011
  • 2005 performance unknown
  • 2005: Three methodologists, several months’ preparation
  • 15 data clerks, 4+ months
  • 2011: Two methodologists, 5 months total
slide9

Conclusions

  • Modern edit and imputation methodology successfully applied in distinct cultural context
    • Reliable results
    • Measurable changes
    • More efficient approach
  • Special thanks to CANCEIS E&I unit, Statistics Canada
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