The 2018 CE Data Quality Profile 

September 10, 2019 

Evan Hubener, Clayton Knappenberger, Yezzi Lee, and Lucilla Tan 

Consumer Expenditure Surveys Program Report Series 
Contents

Overview............................................................................................................................................................................................1

Highlights...........................................................................................................................................................................................2

1. Final disposition rates of eligible sample units (Diary and Interview Surveys)........................................................................3

2. Records use (Interview Survey)...................................................................................................................................................8

3. Information book use (Diary and Interview Surveys)..............................................................................................................10

4. Expenditure edit rates (Diary and Interview Surveys).............................................................................................................14

5. Income imputation rates (Diary and Interview Surveys).........................................................................................................18

6. Respondent burden (Interview Survey)....................................................................................................................................22

7. Survey mode (Interview Survey)................................................................................................................................................24

8. Survey time (Diary and Interview Surveys)...............................................................................................................................26

Conclusion.......................................................................................................................................................................................29
Consumer Expenditure Surveys - 2018 Data Quality Profile | 1

Overview 

In keeping with Statistical Policy Directory No. 1, covering the Fundamental Responsibilities of Federal Statistical Agencies, 

the Consumer Expenditure Surveys Program (CE) is committed to producing data that are of consistently high statistical 

quality, i.e., accurate, objective, relevant, timely, and accessible. CE has historically provided data users with a variety of 

metrics to evaluate overall data quality. Official tables provide standard errors, the public-use microdata user 

documentationprovides response rates, the program publishes data comparisons with other household survey estimates 

as well as the results of nonresponse bias studies, and the datasets contained in the public-use microdata provide 

variables and flags necessary for users to create their own quality measures. 

The Data Quality Profile (DQP) provides a comprehensive set of metrics that are timely, routinely updated, and accessible 

to users. For data users, the DQP metrics are an indication of quality and cover both the CE Quarterly Interview Survey 

(CEQ) and the CE Diary Survey (CED). For internal stakeholders, they also are actionable and provide a basis for survey 

improvements. Since the quality of survey estimates is affected by errors that can occurthroughoutthe survey lifecycle, it 

is expected that the set of DQP metrics will evolve over time as the CE continually researches methods to monitor and 

improve data quality. For each metric, a brief description is provided along with the results, which are tabulated and 

graphed. The DQP Reference Guide provides detailed descriptions of the metrics, computations, and methodology.

The metrics are reported in quarterly format, where the quarter is the quarter in which the survey data were collected. 

For example, “2018q1” refers to all surveys collected in the months of January, February, and March of 2018. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 2

Highlights

In this section, we highlight metric trends for their respectivereporting periods. Subsequent sections describe the 

individual metrics with detailed data tables.

Trends that are encouraging

 The rate of unedited total amount of family income before taxes continued to increase since 2017 for 

both CED and CEQ, due to declining rates of bracket imputation (Section 5).

 The increase in expenditure allocation rates in the CEQ beginning in 2017 is offset by an equal decline in 

expenditure imputation rates because of a process improvement for how missing data on cable, internet, 

and telephone bills are handledthat preserves more respondent provided data (Section 4). 

Trends for concern

 CED and CEQ response rates havecontinued to decline (Section 1). This is largely attributable to the 

continuing rise in refusal rates for both surveys. 

 Other nonresponse rates in the CED declined but were partially offset by an increase in the refusal rate. 

New metrics (2018 DQP): Additional metrics were introduced for the CEQ in the 2018DQP –an indicator of final 

wave respondents’ perception of survey burden (Section 6), a frequency of usage of the information booklet 

(Section 3), a measurement of the mode of collection (Section 7), and the median length of time necessary to 

complete the survey (Section 8). 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 3

1. Final disposition rates of eligible sample units (Diary and Interview Surveys)

Final disposition rates of eligible sample units reportthe final outcome of the field staff’s survey participation 

recruitment effort amongtheeligible sample. The CE classifies the final outcome of eligible sample units into four 

main final disposition categories: completed interview, nonresponse due to refusal, nonresponse due to 

noncontact, and nonresponsedue to otherreasons. Among the other nonresponse category is a subcategory called 

the non-response reclassification, where an edit check results in reclassifying a completed interview into a 

nonresponse. More information on the non-response reclassification edit, along with additional information on 

how we calculate response rates can be found in the DQP Reference Guide. 

Low response rates, examined with other indicators, may be indicative of potential non-response bias of a 

survey expenditure estimate if the nonresponse is correlated with that expenditure category. In addition, higher 

response rates are preferred for more precise estimates. We present unweighted response ratesin this report.

CED 

 Response rates have been declining since at least 2010, consistent with other federal household surveys 

and with voluntary surveys in general. 

 The dip in response rates in 2013 is attributed to the shutdown of the Federal Government.

 Refusal rates increasedby 6.5 percentage pointsin 2017q1. 

 Despite two quarters of higher CED response rates, the overall trend of declining response rates

continued, driven primarily by the continuing increase in refusal rates. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 4

Table 1.1 CED: distribution of final dispositions for eligible CUs (unweighted)

Quarter Number of

eligible CUs

Interview Refusal Noncontact Other 

Nonresponse

Row percent distribution

2016q1 5,050 58.4 16.8 5.8 18.9

2016q2 5,108 57.6 16.7 6.0 19.8

2016q3 5,076 57.9 17.9 4.9 19.3

2016q4 5,157 52.7 18.3 6.1 22.9

2017q1 4,972 57.1 24.8 6.0 12.0

2017q2 5,054 59.4 23.3 5.5 11.8

2017q3 4,916 59.1 23.3 5.1 12.5

2017q4 5,168 56.3 25.3 6.8 11.6

2018q1 5,032 55.5 25.0 6.9 12.7

2018q2 5,015 55.5 25.9 6.4 12.2

2018q3 5,014 57.8 24.8 6.2 11.2

2018q4 5,072 51.5 27.9 7.3 13.3
Consumer Expenditure Surveys - 2018 Data Quality Profile | 5

Table 1.2 CED: prevalence of nonresponse reclassifications (unweighted)

Nonresponse reclassifications

Quarter Number of

eligible CUs

Number of

other 

nonresponse

Number of

CUs

Other

nonresponse (%)

Eligible 

CUs (%)

2016q1 5,050 956 218 22.8 4.3

2016q2 5,108 1,009 257 25.5 5.0

2016q3 5,076 978 215 22.0 4.2

2016q4 5,157 1,181 311 26.3 6.0

2017q1 4,972 596 225 37.8 4.5

2017q2 5,054 595 250 42.0 4.9

2017q3 4,916 615 283 46.0 5.8

2017q4 5,168 601 227 37.8 4.4

2018q1 5,032 637 227 35.6 4.5

2018q2 5,015 613 241 39.3 4.8

2018q3 5,014 564 247 43.8 4.9

2018q4 5,072 677 205 30.3 4.0

CEQ 

 Response rates have been declining since at least 2010, consistent with with other federal household 

surveys and with voluntary surveys in general. 

 The dip in response rates in 2013 is attributed to the shutdown of the Federal Government.

 This decline is driven by refusal rates which rose to over 30 percent of eligible consumer units in 2017q4.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 6

Table 1.3 CEQ: distribution of final dispositions for eligible CUs (unweighted) 

Quarter Number of

eligible CUs

Interview Refusal Noncontact Other 

nonresponse

Row percent distribution

2016q1 10,123 63.5 25.7 6.6 4.2

2016q2 10,101 62.8 25.7 7.2 4.4

2016q3 10,037 63.5 25.7 6.0 5.0

2016q4 10,114 62.3 26.5 6.1 5.1

2017q1 10,113 61.4 28.7 5.3 4.6

2017q2 9,988 61.8 28.0 5.5 4.6

2017q3 9,954 61.2 28.9 5.1 4.9

2017q4 10,138 59.2 30.7 5.7 4.4

2018q1 10,077 58.7 31.1 5.7 4.5

2018q2 10,075 58.6 31.1 5.5 4.8

2018q3 10,053 57.4 32.6 5.5 4.5

2018q4 10,161 54.8 34.7 5.5 5.0
Consumer Expenditure Surveys - 2018 Data Quality Profile | 7

Table 1.4 CEQ: prevalence of nonresponse reclassifications (unweighted) 

Nonresponse reclassifications

Quarter Number of

eligible CUs

Number of 

other nonresponse

Number of 

CUs

Other 

nonresponse (%)

Eligible 

CUs (%)

2016q1 10,123 425 1 0.2 0.01

2016q2 10,101 441 3 0.7 0.03

2016q3 10,037 505 4 0.8 0.04

2016q4 10,114 513 4 0.8 0.04

2017q1 10,113 467 1 0.2 0.01

2017q2 9,988 462 21 4.6 0.21

2017q3 9,954 487 21 4.3 0.21

2017q4 10,138 445 15 3.4 0.15

2018q1 10,077 454 1 0.2 0.01

2018q2 10,075 486 1 0.2 0.01

2018q3 10,053 450 8 1.8 0.08

2018q4 10,161 504 5 1.0 0.05
Consumer Expenditure Surveys - 2018 Data Quality Profile | 8

2. Records use (Interview Survey)

This metric measures how many respondents used recordsin answering the CEQ survey questions. Examples of 

records include but are not limited to: receipts, bills, checkbooks, and bank statements. Records use isrecordedby 

the interviewer at the time of the interview.Respondents’ use of expenditure records result in lower 

measurement error, so a higher prevalence of records use is desirable.

CEQ 

 Records usage temporarily trended up throughout 2016, most noticeably for wave 1 respondents. This 

was likely due to a monetary incentive given to a subset of respondents for usingrecords as part of a field 

test during this period. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 9

Table 2.1 CEQ: prevalence of records use among respondents

Collection 

quarter

Wave Number of 

eligible CUs

Records None Missing

Row percent distribution

2016q1 wave 1 1,631 43.0 55.9 1.1

2016q1 wave 2&3 3,172 41.7 57.8 0.5

2016q1 wave 4 1,623 42.0 57.2 0.9

2016q2 wave 1 1,633 42.3 56.1 1.6

2016q2 wave 2&3 3,102 40.3 59.2 0.5

2016q2 wave 4 1,607 41.8 57.6 0.7

2016q3 wave 1 1,688 69.9 29.6 0.5

2016q3 wave 2&3 3,087 49.0 50.5 0.5

2016q3 wave 4 1,597 50.8 48.6 0.6

2016q4 wave 1 1,660 70.3 29.0 0.7

2016q4 wave 2&3 3,108 53.8 45.8 0.5

2016q4 wave 4 1,533 49.2 49.5 1.2

2017q1 wave 1 1,557 51.3 47.4 1.3

2017q1 wave 2&3 3,078 55.1 44.3 0.6

2017q1 wave 4 1,573 50.3 49.0 0.7

2017q2 wave 1 1,573 52.7 46.5 0.8

2017q2 wave 2&3 3,003 50.9 48.6 0.5

2017q2 wave 4 1,601 56.2 43.3 0.5

2017q3 wave 1 1,581 49.1 50.1 0.8

2017q3 wave 2&3 2,933 45.8 53.6 0.6

2017q3 wave 4 1,576 53.2 46.0 0.8

2017q4 wave 1 1,592 48.2 50.5 1.3

2017q4 wave 2&3 2,935 49.2 50.3 0.5

2017q4 wave 4 1,477 49.2 50.1 0.7

2018q1 wave 1 1,501 53.7 45.2 1.1

2018q1 wave 2&3 2,951 49.5 50.0 0.5

2018q1 wave 4 1,464 52.7 46.4 0.9

2018q2 wave 1 1,529 50.2 48.7 1.1

2018q2 wave 2&3 2,884 47.4 52.0 0.6

2018q2 wave 4 1,486 50.1 49.4 0.5

2018q3 wave 1 1,494 50.3 48.9 0.9

2018q3 wave 2&3 2,815 48.8 50.9 0.4

2018q3 wave 4 1,464 48.9 50.2 0.9

2018q4 wave 1 1,399 53.3 45.7 0.9

2018q4 wave 2&3 2,782 48.7 50.8 0.4

2018q4 wave 4 1,390 51.5 47.4 1.1
Consumer Expenditure Surveys - 2018 Data Quality Profile | 10

3. Information book use (Diary and Interview Surveys)

The information book is a recall aide the interviewer can provide the respondent. For the CEQ, it provides 

examples that can clarify the kinds of expenditures that each section/item code is intended to collect. For both 

CED and CEQ, it provides the response options for demographic questions and the income bracket response 

options. Thismetric measures the prevalence of information book useage among the respondents. Higher rates of 

usage are preferred since the use of such recall aides may alleviate under-reporting.

CED 

 The prevalence of information book use among CED respondents has declined 13.5 percentage points 

since 2016.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 11

Table 3.1 CED: prevalence of information book use among respondents

Quarter Number of

eligible CUs

No Yes Missing

Row percent distribution

2016q1 2,951 51.8 42.5 5.8

2016q2 2,942 51.0 41.8 7.2

2016q3 2,939 49.7 44.3 6.0

2016q4 2,720 48.3 44.6 7.1

2017q1 2,841 50.8 45.1 4.1

2017q2 3,003 52.7 43.9 3.4

2017q3 2,904 53.1 43.6 3.2

2017q4 2,910 57.3 39.7 3.0

2018q1 2,791 54.3 42.0 3.8

2018q2 2,781 59.2 37.7 3.1

2018q3 2,896 56.5 39.5 4.0

2018q4 2,611 58.6 38.3 3.1

CEQ 

 Information book useage, which for CEQ begins in 2016q1, is conditioned by wave. For wave 1, 

information book users represent almost half of respondents for the initial quarter; the is a jump in users 

in 2016q3 to 58 percent, but usage during wave 1 has declined since, and is now 49 percent for 2018q4. 

Usage during waves 2 and 3 and wave 4 is considerably lower, at around 30-40 percent; this could be due 

to higher rates of telephone interviews after the first interview or respondents becoming more familiar 

with the survey.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 12
Consumer Expenditure Surveys - 2018 Data Quality Profile | 13

Table 3.2 Prevalence of infobook useage among CEQ respondents

Quarter Wave No. CUs Used Did not use No Infobook Missing

Row percent distribution

2016q1 wave 1 1,631 48.9 18.9 31.1 1.1

2016q1 wave 2&3 3,172 35.0 17.3 47.2 0.5

2016q1 wave 4 1,623 33.5 16.8 48.9 0.9

2016q2 wave 1 1,633 47.6 19.7 31.2 1.6

2016q2 wave 2&3 3,102 34.9 18.2 46.4 0.5

2016q2 wave 4 1,607 34.8 16.9 47.6 0.7

2016q3 wave 1 1,688 58.2 15.2 26.1 0.5

2016q3 wave 2&3 3,087 39.3 16.1 44.1 0.5

2016q3 wave 4 1,597 35.3 14.6 49.5 0.6

2016q4 wave 1 1,660 56.1 13.1 30.1 0.7

2016q4 wave 2&3 3,108 40.1 14.8 44.7 0.5

2016q4 wave 4 1,533 35.6 16.8 46.3 1.2

2017q1 wave 1 1,557 49.6 15.4 33.7 1.3

2017q1 wave 2&3 3,078 39.6 12.7 47.1 0.6

2017q1 wave 4 1,573 33.8 14.8 50.7 0.7

2017q2 wave 1 1,573 53.8 17.0 28.4 0.8

2017q2 wave 2&3 3,003 38.9 15.4 45.2 0.5

2017q2 wave 4 1,601 39.0 13.4 47.1 0.5

2017q3 wave 1 1,581 52.6 16.6 30.0 0.8

2017q3 wave 2&3 2,933 38.3 16.2 44.9 0.6

2017q3 wave 4 1,576 38.5 13.9 46.8 0.8

2017q4 wave 1 1,592 50.1 15.9 32.8 1.3

2017q4 wave 2&3 2,935 37.1 15.4 47.0 0.5

2017q4 wave 4 1,477 35.2 14.8 49.3 0.7

2018q1 wave 1 1,501 50.2 16.5 32.2 1.1

2018q1 wave 2&3 2,951 37.2 14.5 47.7 0.5

2018q1 wave 4 1,464 34.4 13.9 50.9 0.9

2018q2 wave 1 1,529 47.5 17.7 33.6 1.1

2018q2 wave 2&3 2,884 36.4 16.3 46.7 0.6

2018q2 wave 4 1,486 34.5 16.8 48.1 0.5

2018q3 wave 1 1,494 48.1 20.6 30.5 0.9

2018q3 wave 2&3 2,815 36.8 15.9 47.0 0.4

2018q3 wave 4 1,464 33.9 14.9 50.3 0.9

2018q4 wave 1 1,399 49.0 17.3 32.8 0.9

2018q4 wave 2&3 2,782 35.6 15.9 48.1 0.4

2018q4 wave 4 1,390 32.4 16.7 49.9 1.1
Consumer Expenditure Surveys - 2018 Data Quality Profile | 14

4. Expenditure edit rates (Diary and Interview Surveys)

This metric measures the prevalence of reported expenditure data that are edited. Expenditure data edits are 

defined as changes made to the reported expenditure data by CE data processing, excluding changes due to 

calculations (e.g. conversion of weekly value to quarterly value) and top-coding/suppression of values for 

respondent confidentiality. Imputation, allocation, as well as manual edits are are performed for both the CEQ and 

CED:

 Imputation replaces missing or invalid responses with a valid value

 Allocation edits are applied when respondents provide insufficient detail to meet tabulation 

requirements. For example, if a respondent provides a non-itemized overall expenditure report for the 

category of fuels and utilities, that overall amount will be allocated to the target items mentioned by the 

respondent (such as natural gas and electricity). 

 Manual edits occur when certain cases are manually edited by CE economists based on research and 

expert judgment. 

Almost all edits in CED are allocations. The other edits category encompasses all other expenditure edits including 

manual edits. 

The need for expenditure data imputation results from missing data (item or price nonresponse). Thus, lower 

imputation rates are desirable. The need for expenditure data allocation is a consequence of responses that did 

not contain the required details of the item asked by the survey. Likewise, lower allocation rates are also 

preferred, and in general, lower data editing rates are preferred since that lowers the risk of processing error. 

However, edits based on sound methodology can improve the completeness of the data, and thereby reduce the 

risk of measurement error and non-response bias in survey estimates. Additionalinformation on expenditure edits 

are available in the DQP Reference Guide. 

CED

 The rate of edited reported expenditure records has been relatively constant around 10 percent since 

2016.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 15

Table 4.1 CED: reported expenditure records 

Type of edit

Allocated Other edit Unedited

Quarter Number of 

expn reports

Row percent distribution

2016q1 87,411 11.0 0.1 89.0

2016q2 84,232 11.4 0.1 88.5

2016q3 82,285 11.1 0.1 88.8

2016q4 80,515 10.9 0.1 89.0

2017q1 88,654 10.6 0.1 89.4

2017q2 92,985 10.9 0.1 89.0

2017q3 89,370 11.0 0.1 88.9

2017q4 92,031 10.3 0.1 89.7

2018q1 86,798 9.8 0.1 90.1

2018q2 87,649 9.9 0.1 89.9

2018q3 88,342 10.0 0.3 89.7

2018q4 80,129 10.3 0.2 89.5
Consumer Expenditure Surveys - 2018 Data Quality Profile | 16

CEQ 

 Overall expenditure edit rates remain constant with their 2015 levels

 Beginning in 2017q2, CE changed how cable, internet, and telephone utility expenditures are processed,

from imputing these expenditures, to allocating reported totals bills. This preserves more of the 

respondent provided data. 

 Thus, imputation rates declined 6.7 percentage points while allocation rates increased by 6.5 percentage 

points. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 17

Table 4.2 CEQ: reported expenditure records, edit type rate

Type of Edit

Allocated Imputed & 

allocated

Imputed Other 

edit

Unedited

Quarter Number of expn 

reports

Row percent distribution

2016q1 273,729 4.8 0.1 11.7 0.7 82.7

2016q2 268,405 4.7 0.1 12.1 0.7 82.4

2016q3 279,542 4.8 0.1 10.9 0.9 83.2

2016q4 276,290 4.9 0.1 10.5 0.8 83.6

2017q1 272,929 5.1 0.1 11.0 0.7 83.1

2017q2 276,568 11.6 0.2 4.3 0.5 83.4

2017q3 281,533 11.9 0.2 4.4 0.7 82.9

2017q4 277,032 11.8 0.2 4.3 0.6 83.0

2018q1 275,949 11.3 0.2 4.4 0.4 83.7

2018q2 270,726 11.5 0.2 3.9 0.5 83.9

2018q3 269,909 11.5 0.2 3.9 0.6 83.8

2018q4 259,508 11.5 0.2 3.8 0.5 84.0
Consumer Expenditure Surveys - 2018 Data Quality Profile | 18

5. Income imputation rates (Diary and Interview Surveys)

This metric describes the rate of editing performed on a consumer unit’s reported total income before taxes. This 

edit is based on three types of imputation methods, applicable to both the CEQ and CED:

1. Model-basedimputation: when the respondent indicates an income source but fails to report an amount 

of income received.

2. Bracket response imputation: when the respondent indicates the receipt of an income source, fails to 

report the exact amount of income but does provide a bracket range estimate of the amount of income 

received.

3. All valid blank conversion:when the respondent reports no receipt of income from any source, but the CE 

imputes receipt from at least one sourcewhen there is evidence that the CU has some income.

Since the need for imputation reflects item nonresponse or that insufficient item detail was provided, lower 

imputation rates are desirable for lowering measurement error. However, imputation based on sound 

methodology can improve the completeness of the data and reduce the risk of non-response bias. 

CED

 Between 2016 and 2018, the declining rate of mode-based imputation has driven a rising rate of 

unimputed total income before tax.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 19

Table 5.1 CED: income imputation rates for total amount of family income before taxes

Quarter Number 

of

eligible 

CUs

Valid 

blank 

converted

(AVB)

Bracket 

imputation 

only

Model 

imputation only

Model & bracket 

imputation

Not imputed

Row percent distribution

2016q1 2,951 3.2 18.0 21.5 6.0 51.3

2016q2 2,942 3.4 19.2 26.6 4.1 46.7

2016q3 2,939 3.2 19.4 22.4 5.5 49.4

2016q4 2,720 3.6 18.2 23.5 5.8 48.9

2017q1 2,841 1.8 19.4 19.7 5.9 53.2

2017q2 3,003 2.5 20.2 18.2 5.8 53.3

2017q3 2,904 1.8 19.2 18.8 4.8 55.4

2017q4 2,910 1.8 19.7 19.4 4.7 54.5

2018q1 2,791 1.9 18.9 18.7 4.1 56.5

2018q2 2,781 1.9 17.4 19.6 4.5 56.7

2018q3 2,896 1.5 18.4 21.3 5.1 53.8

2018q4 2,611 2.4 19.1 18.3 6.0 54.3
Consumer Expenditure Surveys - 2018 Data Quality Profile | 20

CEQ 

 Overall, the declining rate of model-based imputation has driven therising rate of unimputed total income 

before tax since 2015. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 21

Table 5.2 CEQ: income imputation rates for total amount of family income before taxes

Year Number 

of

eligible 

CUs

Valid 

blank 

converted

(AVB)

Bracket 

imputation only

Model 

imputation only

Model & bracket 

imputation

Not imputed

Row percent distribution

2016q1 6,426 1.6 19.4 18.6 4.6 55.8

2016q2 6,342 1.5 18.3 19.2 4.5 56.4

2016q3 6,372 1.6 19.2 17.6 4.6 57.1

2016q4 6,301 1.7 18.4 17.1 4.6 58.2

2017q1 6,208 1.9 19.6 17.2 4.3 57.0

2017q2 6,177 1.1 18.4 17.7 4.0 58.8

2017q3 6,090 1.3 17.4 18.6 4.3 58.4

2017q4 6,004 1.7 17.6 18.8 4.6 57.4

2018q1 5,916 1.5 17.5 18.0 4.6 58.4

2018q2 5,899 1.2 16.8 17.1 5.2 59.8

2018q3 5,773 1.4 17.9 16.6 4.7 59.4

2018q4 5,571 1.4 18.2 17.3 4.5 58.5
Consumer Expenditure Surveys - 2018 Data Quality Profile | 22

6. Respondent burden (Interview Survey)

This metric measures the prevalence of burden (“none”, “some”, “very”) respondents perceivefrom having 

participated in the final wave (wave 4) of the CEQ. The CEQ began continuously tracking self-reported respondent 

burden in 2017q2. A caveat to the interpretation of this metric is that since the burden question is asked of 

respondents only in theirfinal wave (wave 4) of the CEQ, this measure likely underestimates survey burden due to 

survivorship bias.

CEQ 

 Since 2017q2, the levels of respondent burden have fluctuated within a couple percentage points. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 23

Table 6.1 CEQ: respondents’ perceived burden in the final survey wave 

Collection 

quater

Number of

eligible CUs

Unanswered* Not burdensome Some burden Very burdensome

Row percent distribution

2017q2 1,601 3.2 34.2 52.5 10.0

2017q3 1,576 3.5 32.7 51.8 12.1

2017q4 1,477 2.6 33.6 52.7 11.0

2018q1 1,464 3.2 31.7 52.7 12.4

2018q2 1,486 2.6 32.4 52.8 12.2

2018q3 1,464 1.9 33.7 51.4 13.0

2018q4 1,390 2.9 34.2 50.8 12.1

*Unanswered due to valid blank / don’t know /refusal
Consumer Expenditure Surveys - 2018 Data Quality Profile | 24

7. Survey mode (Interview Survey)

This metric measures the prevalence of themodeof data collection. The interviewer can collect data for the CEQ in 

person, over the phone, or there can be a combination of the two modes. TheCEQ was designed to be an inperson interview. Higher prevalence of in-person data collection is preferred since the interviewer can actively 

prompt the respondent, as well as encourage the use of recall aids, thereby reducing the risk of measurement 

error. 

CEQ 

 Survey Mode also begins in 2016q1andis conditioned by wave. The preferred collection method for the 

CE Interview Survey is in-person, but there is some leeway to collect respondent data by phone when 

necessary. The percentage of surveys collected in person is high for first interviews, ranging between 

about 75 and 80 percent for the three years presented. The percentages drop considerably for second 

and third wave interviews, hovering around 60 percent in person, and they drop again for wave four 

interviews, ranging between about 55 and 60 percent, presumably as respondents become more familiar 

with the survey. 
Consumer Expenditure Surveys - 2018 Data Quality Profile | 25

Table 7.1 CEQ:survey mode

Quarter Wave Number of

CUs

All personal visit Equally split Telephone Missing

Row percent distribution

2016q1 wave 1 1,631 76.1 1.2 22.0 0.6

2016q1 wave 2&3 3,172 59.5 0.4 39.6 0.5

2016q1 wave 4 1,623 57.7 0.2 41.7 0.4

2016q2 wave 1 1,633 77.7 0.9 20.1 1.3

2016q2 wave 2&3 3,102 60.1 0.5 39.0 0.4

2016q2 wave 4 1,607 58.1 0.4 41.3 0.2

2016q3 wave 1 1,688 81.0 1.5 17.1 0.4

2016q3 wave 2&3 3,087 61.5 0.5 37.7 0.3

2016q3 wave 4 1,597 56.2 0.6 43.1 0.2

2016q4 wave 1 1,660 75.6 1.1 22.8 0.5

2016q4 wave 2&3 3,108 61.3 0.6 37.7 0.4

2016q4 wave 4 1,533 57.5 0.7 41.3 0.5

2017q1 wave 1 1,557 75.1 1.0 22.9 1.0

2017q1 wave 2&3 3,078 59.0 0.5 40.0 0.5

2017q1 wave 4 1,573 55.4 0.2 44.1 0.3

2017q2 wave 1 1,573 79.1 1.5 18.6 0.8

2017q2 wave 2&3 3,003 60.9 0.8 37.9 0.4

2017q2 wave 4 1,601 58.7 0.6 40.5 0.2

2017q3 wave 1 1,581 78.1 1.2 20.1 0.6

2017q3 wave 2&3 2,933 61.8 0.4 37.2 0.6

2017q3 wave 4 1,576 57.7 0.4 41.5 0.4

2017q4 wave 1 1,592 75.3 0.8 22.8 1.1

2017q4 wave 2&3 2,935 60.2 0.5 38.9 0.4

2017q4 wave 4 1,477 58.8 0.3 40.3 0.6

2018q1 wave 1 1,501 75.0 1.5 22.6 1.0

2018q1 wave 2&3 2,951 58.8 0.4 40.3 0.5

2018q1 wave 4 1,464 56.1 0.5 42.7 0.7

2018q2 wave 1 1,529 76.3 0.8 22.3 0.7

2018q2 wave 2&3 2,884 59.4 0.3 39.8 0.5

2018q2 wave 4 1,486 57.2 0.6 41.9 0.3

2018q3 wave 1 1,494 77.6 1.5 20.1 0.7

2018q3 wave 2&3 2,815 60.8 0.3 38.5 0.3

2018q3 wave 4 1,464 56.8 0.3 42.3 0.5

2018q4 wave 1 1,399 76.1 1.2 21.9 0.7

2018q4 wave 2&3 2,782 60.1 0.6 38.9 0.4

2018q4 wave 4 1,390 57.3 0.4 42.0 0.3
Consumer Expenditure Surveys - 2018 Data Quality Profile | 26

8. Survey time (Diary and Interview Surveys)

For the CEQ, this metric measures the median length oftime (survey time) to complete the interview. For the CED, 

this metric measures the survey time to complete the personal interview component that collects information

about income and demographics. Survey time is often used as an objectiveindicator for respondent burden; the 

longer the time needed to complete the survey, the more burdensome the survey. This is a concern because 

respondent burden negatively impacts both response rates and data quality. However, survey response time could 

also reflect the degree of engagement by the respondent, as an engaged and conscientious respondent could take 

longer to complete the survey because more extensive use of records or more complete reporting occur during the 

interview. In spite of this problem of ambiguity in interpreting survey response time as a standalone metric, it is 

nonetheless useful to track the median survey response time so that the impact of changes in survey features on 

this dimension of the survey design can be assessed. 

CED

 The time to complete the personal interview component of the CED has remained fairly constant around 

half-an-hour since 2016.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 27

Table 8.1 CED: median length of time (minutes) to complete the 

interview components (income and demographics)

Quarter Minutes

2016q1 34.0

2016q2 32.2

2016q3 33.1

2016q4 32.4

2017q1 32.5

2017q2 32.7

2017q3 32.0

2017q4 32.0

2018q1 31.2

2018q2 32.2

2018q3 34.2

2018q4 33.2

CEQ

 The increase in survey time from 2018q2 to 2018q3 could be explained by the s urvey changes (i.e. 

implementing point of purchase questions) in 2018q3.
Consumer Expenditure Surveys - 2018 Data Quality Profile | 28

Table 8.2 CEQ: time to complete survey

Quarter Number of

eligible CUs

Wave 1 Wave 2 

and 3

Wave 4

2016q1 6,421 69.9 47.8 51.9

2016q2 6,336 71.4 46.9 52.6

2016q3 6,367 76.9 49.0 53.9

2016q4 6,297 77.0 49.6 52.8

2017q1 6,202 71.3 50.2 52.9

2017q2 6,163 71.5 50.5 57.2

2017q3 6,081 71.5 50.4 57.1

2017q4 6,003 70.5 50.0 56.4

2018q1 5,910 73.4 51.5 56.2

2018q2 5,894 70.5 49.6 56.4

2018q3 5,771 77.0 53.8 60.3

2018q4 5,570 76.7 52.0 58.6
Consumer Expenditure Surveys - 2018 Data Quality Profile | 29

Conclusion

In 2018,some trends are encouraging, other trends warrant concern, or have an uncertain significance. For a brief 

summary of these findings, see the Highlightssection at the beginning of this report. The next issue of the CE Data 

Qualtiy Profilewill be be released in 2020, with metrics incorporating data through 2019.