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Len Cook: Hero or Zero of the 2001 Census? OR A look at the impact of disclosure control on aggregate census outputs
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1. The need for SDC v
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2. The pre-2001 ‘consensus’
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Imputation (District level)
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Imputation (ward level)
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Item Imputation rate Professional qualifications17.2 Hours worked 8.0 Workplace postcode 7.8 Religion 7.6 Employment status 6.6 Qualifications 6.2 Long-term illness 3.9 Tenure 3.4 Ethnic group 2.9 Age 0.5 Sex 0.4 Imputation (item level)
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Other protective measures Thresholding (of spatial output) Aggregation and top-coding Barnardisation Data access barriers Time Imperfect local knowledge
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0 1 2 3 … 3. The 2001 Census controversy
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4. SDC approaches Exact details not published, but: UK 2001: ‘Small Cell Adjustment rounding to 0 or 3 Australia/New Zealand: Round to Base 3 Canada: Round to Base 5 UK 1991: ‘Barnadisation’ prob = 0.1 (or 0.02?)
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5. SDC Confidence Intervals Bnd_0.02 0.2(n-1) 0.5 Bnd_0.1 0.5(n-1) 0.5 SCA_3 1.0(n-1) 0.5 Base_3 1.2(n-1) 0.5 Base_5 2.0(n-1) 0.5
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5. SDC Confidence Intervals
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6. Impacts of SDC? Unknown! Solution? Summarise impact across a range of tables/counts/types of analysis
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6. Impacts of SDC? Solution: Summarise impact of SDC by table/count type population rarity level of geographical aggregation method of SDC type of analysis
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All counts
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Internal counts
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Marginal counts
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Percentages
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Ranking
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Correlation across 30 areas [wrong signs]
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Bivariate regression across 30/100 areas [coefficient error > 1 SE]
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Multivariate regression across 30 areas [Difference in r 2 ]
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7. Responses to SDC Univariate counts marginals Largest possible building blocks Minimise spatial units used Can estimate context-specific CI, given number of 1s and 2s in data being analysed
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8. Len Cook, Hero or Zero? 0 1 2 3 …
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Ward level variability in tabular counts
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8. Len Cook, Hero or Zero? 0 1 2 3 …
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