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Measurement Errors in Self-Reports of 

Consumer Expenditures: Are Errors Attributable 

to Respondents or Expenditures?

Charles Q. Strohm

Emily Geisen

Ashley Richards

Brandon Kopp
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Correlates of Recall Error

 We know a host of factors affect recall ...

– Event (e.g., frequency, elapsed time, salience, distinctiveness)

– Respondent (e.g., age, education, gender)

– Survey design (e.g., mode, question wording)

 ... but what is their relative importance?
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Two Extremes of Measurement Error

All Variance 

On 

Respondent 

Level

All Variance 

On 

Expenditure

Level

Good 

respondent

Bad 

respondent

Average 

respondent

Average 

respondent
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Research Questions

1. How much variance in measurement error is on the 

expenditure versus respondent level?

2. What respondent and expenditure characteristics are 

associated with measurement error?

3. How much of the variance in measurement error is 

accounted for by these respondent and expenditure 

characteristics?
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Consumer Expenditure Records Study

 Designed to investigate self-reports of U.S. Consumer 

Expenditure Quarterly Interview Survey

 115 respondents completed two CAPI interviews

– Convenience sample from two sites (DC, NC)

– Interview 1: Self-reports of consumer expenditures

– Interview 2: Comparison of records with self-reports

 Analytic sample:

– 104 respondents

– 939 expenditures

– Mean of 9.0 expenditures per respondent (SD = 8.2), with 

maximum of 34 expenditures
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Measures

 Dependent variable: Measurement Error

– Records are gold standard

– Percent difference between record and self-report

 Independent variables 

Respondent Expenditure

Gender Type (housing, phone, utilities, appliance

Age furniture, clothing, misc.)

Employment Amount

Income Time since expenditure

Education

Location (DC, NC)
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Analytic Approach

 Multi-level models

– Level 1: Expenditure

– Level 2: Respondent

 Sequential models

(1) Random intercept model

(2) Include respondent characteristics

(3) Include expenditure characteristics

 Test for cross-level interactions
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Variance in Random Intercept Model

187 

(7%)

2,379

(93%)

Intercept variance 

(i.e., variance on 

respondent level)

Residual variance 

(i.e., variance on 

expenditure level)
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Respondent Characteristics

Characteristic ß Z

Male 4.3 1.0

Age (years) -0.2 -1.3

Employed (not employed) 9.5 2.2

Low income (high) 8.5 1.4

Medium income (high) 0.8 0.2

HS or less (college) -1.5 -0.3

Some college (college) -1.4 -0.3

DC (NC) -16.0 -2.5

Parameters are from a multi-level model predicting measurement error 

(percent difference between record and self-report).

Reference category is in parentheses.

Model includes random intercept and expenditure characteristics.
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Expenditure Characteristics

Characteristic ß Z

Phone (housing) 0.1 0.0

Utilities (housing) 20.4 2.4

Appliance (housing) -4.5 -0.5

Furniture (housing) 17.6 1.8

Clothing (housing) 14.0 1.7

Misc. (housing) 3.4 0.4

Amount (dollars) -.0038 -0.7

2 months ago (current/last) -5.0 -1.2

3 months ago (current/last) -2.3 -0.5

Parameters are from a multi-level model predicting measurement error 

(percent difference between record and self-report).

Reference category is in parentheses.

Model includes random intercept and respondent characteristics.
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Limitations

 Small-scale study based on convenience sample

 Unknown whether results apply to topics other than 

expenditures

 We explained little variance on expenditure level (4%)
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Summary

 Key Findings

– 93% of variance is on expenditure level: we are close to the 

extreme of all variance due to expenditures

– Greater measurement error among employed & NC respondents

– Utilities, furniture, clothing had greater measurement error

 Implications

– Academics: research on expenditure characteristics

– Survey practitioners: administrative records, question design
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More Information

Charles Q. Strohm

Survey Methodologist

RTI International

919.541.8798

cstrohm@rti.org

The findings and conclusions in 

this presentation are those of the 

authors and do not necessarily 

reflect official views of the U.S. 

Bureau of Labor Statistics.