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Quality Metrics for Assessing the Impact of Editing and Imputation

on Economic Data

Broderick E. Oliver

and

Katherine Jenny Thompson

Office of Statistical Methods and Research for Economic Programs

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Outline

• Motivation for the study

• Quality Metrics (Formulas)

• Quality Metrics (Actual Results)

• Future Research

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Motivation

Economic Directorate conducted a series of

studies to evaluate the editing efficiency of

selected surveys and censuses.

1. What value is added from subjecting the same

record to multiple editing phases?

2. What is the impact of editing and imputation on

the final data?

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Development of Quality Metrics

• Assess overall changes to “reported” data at the:– Micro level– Macro level

• Examine– the size of change to reported data.– the source of change to reported data.

• Determine which changes had greatest impact on final tabulations

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Key Terms

• Critical Item• Reported Data• Final Data• Data Flag

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Metric 1• Item Level (Critical Items)• Percentage of records with reported values

whose value was changed by editing/imputation

•Where: yi = 1 if reported value final value

• 0 otherwise. and n = number of records

1001METRIC 1

n

n

iyi

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Metric 2• Item Level (Critical Items).• The percentage of changes to the records with reported values

that is attributable to analyst correction versus machine correction.

Where ai = 1 if reported value final value and source is analyst correction.

0 otherwise.

mi = 1 if reported value final value and source is machine correction.

0 otherwise

n = number of records.

100A2METRIC 1

na

n

i i

100M2METRIC 1

nm

n

i i

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Metric 3

• Item Level (Critical Items).• The source of change of the reported data.• The size of change of the reported data.• The impact of the changes on the final tabulations.

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Metric 3: Tabular Format: (Item Level)

Source of

Change

(1)

Change Category

(2)

No. of Records

(3)

Tabulated

(Weighted)

Reported

(4)

Tabulated

(Weighted)

Edited

(5)

Percent

Difference

(6)

Sum of the

Absolute

Difference

(7)

Average

Absolute

Difference

(8)

Analyst

Correction

1.0 < R/E < 1.1 n x y (y-x)*100/x z z/n 1.1 R/E < 9 9 R/E < 90

90 R/E < 900

R/E 900

No Change

R/E=1

Totals Total 3 Total 4 Total 5

Percent Difference

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Metrics Applied to:

• Annual Wholesale Trade Survey (AWTS)• Annual Survey of Manufactures (ASM)

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Annual Wholesale Trade Survey(AWTS)

• Sample Survey• Approximately 8,000 wholesale businesses• Critical Items:

– Sales– Total Purchases– Total Inventories

• Processed in Standard Economic Processing System (StEPS)

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AWTS Editing/Imputation• StEPS Automatic Processing Flow

– Simple Imputation Module: Data “clean up”– Edit Module: Identifies “suspicious” values– General Imputation module: Replaces “suspicious” values

• Item Flagging– Can identify four distinct sources of change:

• Analyst Correction• Analyst Impute• Machine Correction• No Change

• “Cycling” between analyst and machine corrections

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Annual Survey of Manufactures (ASM)

• Sample Survey• 55,000 establishments• Critical Items:

– Cost of Materials– Employment– Annual Payroll– Receipts

• Processed in the Economic Census System– Plain Vanilla Editing Module

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ASM Editing/Imputation• ASM Automatic Processing Flow

– Pre-editing Module: Data filling and clean up– Plain Vanilla Edit Modules

• Ratio (editing/imputation)• Balancing (editing/imputation)

• Item Flagging– Can identify three sources of change:

• Analyst correction/impute (cannot distinguish)• Machine impute• No change

• “Cycling” between analyst and machine

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Illustration of Metric 1: AWTS

– Relatively few of the reported values for each critical item changed.

– Changes to these records had a great impact on final tabulations.

Critical Item

No. of records

with reported values

No. of records changed

Percent Reported

Amount

(Weighted)

In Millions

Edited

Amount

(Weighted)

In Millions

Percent

Difference

Sales 4,819 238 4.9% $41,156,147 $2,156,439 - 93.9 %

Purchases 4,628 403 8.7% $4,486,157 $1,953,140 - 56.5%

Inventories 4,334 326 7.5% $21,392,659 $256,920 - 98.8%

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Illustration of Metric 1: ASM

– Relatively few of the reported values for each critical item changed.

– Except for employment, changes to these records had a “small” impact on final tabulations

Critical Item No. of records

with reported values

No. of records changed

Percent Reported

Amount

(Weighted)

In Millions

Edited

Amount

(Weighted)

In Millions

Percent

Difference

Cost of Materials

35,908 3,520 9.8% $2,157 $1,936 -10.2%

Employment 31,603 4,032 12.8% 12 6 - 44.6%

Annual Payroll

30,756 454 1.5% $293 $291 - 0.4%

Receipts 38,074 4,157 10.9% $4,320 $3,647 - 15.6%

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Illustration of Metric 2: AWTSCritical Item Source of

ChangeNo.

Records Changed

Percent of Total

Average Absolute Difference Between Reported and Edited

Amount

(In Millions)

Ratio of

AC to AI and

AC to MI

Sales AC 221 92.9% $175,545 -----

AI 15 6.3% $653 269/1

MI 2 0.8% $55 3150/1

Purchases AC 363 90.1% $7,404 -----

AI 37 9.2% $289 26/1

MI 3 0.7% $79 94/1

Inventories AC 285 87.4% $74,196 -----

AI 15 4.6% $73 1011/1

MI 26 8.0% $39 1914/1

AC = Analyst Correction; AI = Analyst Impute; MI = Machine Impute

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Illustration of Metric 2: ASMCritical Item Source of

ChangeNo.

Records Changed

Percent of Total

Average Absolute Difference

Ratio of

AC to MI

Cost of Materials

AC 940 26.7% $188,435 --

MI 2,580 73.3% $63,468 3/1

Receipts AC 2,723 65.5% $270,852 --

MI 1,434 34.5% $74,262 4/1

AC = Analyst Correction MI = Machine Impute

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Key Findings With Metric 3: AWTS

• Analyst corrections accounted for the majority of the changes to all three critical items

• Correction of “rounding” errors– Corrected by analysts– Most substantive impact on tabulations– Relatively few records

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Key Findings Metric 3: ASM

• A high percentage of changes to reported data fell into the “small change” categories. – For Cost of Materials, machine imputes made the majority

of these small changes (74.7 percent).– For Receipts, analysts made the majority of these changes

(68.4 percent).

• Correction of “rounding” errors:– Corrected equally by analyst and machine– Most substantive impact on tabulations– Relatively few records

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Study Highlights/Key Findings• Importance of rounding errors:

– Small number of cases– Resolved generally by analysts in AWTS– Resolved by analysts and machine in ASM

• Large proportion of small changes in ASM:– Identified potential edit parameter problems

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Advantages of Standardized Metrics

• Allowed for direct comparisons between different programs.

• Uncovered different areas of investigation in different programs.

• Facilitated “buy-in” from all parties via development process.

• Provides baseline measures for future investigation.

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Future Research

• Apply metrics at various processing stages (AWTS).

• Apply metrics at industry level.

• Examine the number of times the records are subjected to changes.

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Contact Information

broderick.e.oliver@census.gov

katherine.j.thompson@census.gov