Consumer Expenditure Survey Measurement Error Study Phase 1 

Proposal on Tracking Measurement Error in the CE 

Roger Tourangeau 

Westat 

Brandon Kopp and Lucilla Tan

Bureau of Labor Statistics 

Final: 2013.03.12 
Proposal on Tracking Measurement Error in the CE | 1

Introduction 

The primary objective of this proposal is to provide recommendations for the development of 

metrics that can be used on an ongoing basis to track measurement error in the Consumer Expenditure 

Survey (CE) over time. These recommendations draw heavily from the Study Team’s preceding work on 

the Report on the State of Knowledge of Measurement Error for the CE (Tourangeau et al., 2013), 

specifically, from the review of past research approaches adopted to investigate measurement error in the 

CE. If the recommendations made here are adopted, the error measures may also help shed light on the 

sources of reporting error and the expenditures categories that are more or less susceptible to reporting 

error. 

Although the intent of this effort is to assess and track the level of measurement error in CE data, 

some of the proposed indicators (e.g., comparing CE estimates with external benchmarks) reflect all 

sources of error in the CE (e.g., nonresponse errors). Still, we think these indicators will be useful in 

tracking the overall level of error over time and across any changes in CE protocols. 

This proposal is organized into five sections: 

I. a summary of the strengths and weaknesses of past approaches taken to investigate 

measurement error in the CE; 

II. a description of a multi-method-indicators (MMI) approach to track measurement error; 

III. an outline of issues for further research that should be conducted prior to implementation of 

the MMI methodology; 

IV. an outline of research projects that should be conducted following implementation of the MMI 

to further refine the methodology; and 

V. an assessment of the components of the MMI methodology against a set of criteria to assist 

with resource planning for implementing this proposal. 

I. Strengths and weaknesses of past methods used to investigate measurement error 

A variety of methods have been used in the past to investigate measurement error in the CE. (For a 

more detailed review of each method, see Section III of Tourangeau et al., 2013). Each method has its 

characteristic flaws: 

 Indicators that are based solely on CE data or information about the data collection process are 

at best indirect measures of error. Consider, for example, the proportion of CE respondents 

consulting bills or other records during the Interview Survey; although we expect reports 

based on record-aided recall to be more accurate than other reports, we cannot say how 

accurate either type of report is; 

 External indicators (comparing estimates from the CE to an external data source) are also 

flawed. While item comparability is generally accounted for in comparing CE estimates to 

other data sources, the sources and magnitude of error in the external data sources are often 

either unknown or not taken into account. 

 Record check studies require considerable staff resources and are expensive to carry out. In 

addition, the respondent provided records are likely to give incomplete coverage of the 

universe of expenses incurred during the reference period. 
Proposal on Tracking Measurement Error in the CE | 2

Table 1 highlights the major strengths and weaknesses of methods that have been used in the past to 

investigate measurement error in the CE. 

Table 1. Major Strengths and Weaknesses of Past Methods for Studying CE Measurement Error

Method Strength Weakness

1. Internal comparisons

1a. Interview Survey vs. Diary 

Survey

1b. Within a Survey: comparing 

across waves, or groups within a 

wave

No added data collection costs

incurred to obtain these data.

Easier to establish concordance 

of expenditure categories 

between diary and interview 

since CE controls both 

instruments.

Differences in estimates between 

two comparison groups within an 

instrument is indicates error in 

one or both groups.

This method lacks an objective 

―true value‖. The measures 

chosen to represent the 

underlying ―true,‖ or at least 

better, values (e.g., the Diary 

Survey, the most recent month of 

the reference period, the first 

wave of a panel survey, etc.) are 

likely to suffer from their own 

measurement errors, which are 

generally also unknown. 

Between the Interview and Diary 

Surveys, the Diary Survey is 

typically assumed to be more 

accurate, on the assumption that 

diary keepers promptly record 

expenses so there is little recall 

error. However, there has yet to 

be a definitive validation of this 

assumption. 

The use of internal data 

comparisons to track 

measurement error before and 

after a redesign may be 

problematic because the changes 

made to the survey could affect 

both the ―true‖ value and the 

comparison, making any changes 

in the apparent level of error 

difficult to interpret.

1c. Latent class analysis No added data collection costs

incurred to obtain these data.

Can be used to identify potential 

predictors of reporting error.

Response error latent variable for 

classifying respondents was a 

better measure of underreporting 

than any of the observed 

indicators taken individually.

Less effective in predicting the 

level of reporting error across 

latent classes:

 ―true‖ values are modelbased.

 these models often rely on 

very strong assumptions that 

may not be met, in practice. 
Proposal on Tracking Measurement Error in the CE | 3

Table 1. Major Strengths and Weaknesses of Past Methods for Studying CE Measurement Error

Method Strength Weakness

1d. Multilevel models When validation data are 

available, this approach can be 

used to show which types of 

items are most susceptible to 

error and the item characteristics 

that can account for the variation 

in measurement error.

Lack of availability of validation 

data for sufficiently large, 

representative sample of 

respondents and item categories.

2. Comparison to external data 

sources 

2a. Personal Consumption 

Expenditure (PCE, National 

Accounts)

The PCE is the only single

source of nationally 

representative data that covers 

the range of expenditure 

categories that the CE does.

Sources and magnitudes of error 

in the PCE are not well 

understood.

2b. Comparison to other 

household surveys

These data are based on 

household reports, like the CE. 

Some of these household surveys 

have built-in validation features –

e.g., the Medical Expenditure 

Panel Survey (Household 

Component) uses medical 

provider information (regarded as 

less prone to error than 

household reports) to supplement 

or replace respondent reports; the 

Residential Energy Consumption 

Survey (RECS) obtains the 

household’s energy billing data 

from the energy provider.

Other surveys have high response 

rates which minimize nonresponse errors. 

The sources and magnitude of 

error in these other surveys are 

not well understood.

Adjustments may be needed to 

render estimates from the CE and 

the other household survey 

sources more comparable in 

scope and definition.

Iterations of some surveys (e.g., 

RECS) are conducted too far 

apart to make them practical as 

an ongoing source of 

comparisons.

3. Validation (records check) of 

reported expenditures

A validation approach comes 

closest to obtaining true values 

for verifying a respondent’s 

reported expenditure. 

A representative sample is costly 

and resource intensive to 

conduct. Past CE validation 

studies have used small, 

convenience samples.

It is difficult to ensure that every 

reported expense generates a 

record, or to verify reports that 

are omitted or included in error.

4. Check of balance between 

expenditures and income (after 

accounting for savings and debt)

The size of the gap between 

income and total expenditures is 

an easily understood indicator of 

the accuracy in overall reporting, 

Reporting accuracy is not 

measured at the item or category 

level.
Proposal on Tracking Measurement Error in the CE | 4

Table 1. Major Strengths and Weaknesses of Past Methods for Studying CE Measurement Error

Method Strength Weakness

since the money coming into or 

going out of the household is 

accounted for.

Small scale lab study indicated 

that conducting real-time 

calculations based on respondent 

reports and providing 

interviewers and respondents 

with feedback useful for 

improving survey reports was not 

viable. 

Comprehensive measurements 

are required for all components 

of the balance check.

Source: Section III of Tourangeau et al., 2013

As we noted in our State of Knowledge report, the Geisen et al. (2011) study, which compared 

survey reports with respondent records, leads to different conclusions from the studies that compare CE 

estimates to external benchmarks. Their study suggests that overreporting and underreporting of 

expenditure amounts occur about equally often in the CE. In contrast, the CE-PCE comparisons almost 

uniformly suggest that the CE estimates are underestimates. The varied strengths and weaknesses among 

the methods imply no single method or indicator can adequately assess measurement error, and thus we 

advocate a multi-method-indicators (MMI) approach to track measurement error in the CE. 

II. Proposed multi-method indicators (MMI) approach

The approach we propose to assess and monitor measurement error consists of three components: 

1) Internal indicators, which are based solely on the CE data themselves and paradata (other 

information regarding the CE data collection process); 

2) External indicators, which involve comparisons of key CE survey estimates to similar 

estimates from other data sources; and 

3) Periodic but regular record check studies, including those incorporated into the production 

sample (e.g., via a methods panel). 

The internal indicators and external indicators together make up a ―vector of flawed indicators.‖

The indicators are flawed because each of them is susceptible to measurement and other errors, but each 

provides useful, easily understood measures of overall error. In addition to identifying these indicators, 

ongoing research will also be needed to investigate the inter-relationship between the indicators. 

The third component of the MMI approach, record check/validation studies, could be used to 

show which item categories are reported well or poorly in the CE. This information also provides a basis 

for confidence in findings from the comparison of these categories to external benchmarks, given no 

changes in the methodology of the benchmark source. For example, if the validation study suggests a 

given expenditure is underreported, we can be more confident that an increase in the CE/external 

benchmark ratio represents improved reporting in the CE. We believe it is important to move away from 

assuming that higher levels of reporting indicate higher levels of accuracy. 
Proposal on Tracking Measurement Error in the CE | 5

If all three components suggest that CE estimates are improving or getting worse in terms of 

overall level of error, it will provide greater confidence than trends based on any single approach can. 

Differences across indicators may suggest that the CE is getting more or less accurate for some 

expenditure categories but not for others, or may suggest that some indicators are too flawed to be useful 

and should be dropped. We describe the three components in more detail below and offer preliminary 

recommendations about indicators to include in each component. 

II.1. Internal Indicators 

The first component of the MMI approach encompasses internal indicators derived from CE data 

and paradata. A number of investigators have attempted to estimate the level of measurement error in the 

CE using data from the CE itself. For example, Yan and Copeland (2010) compared Wave 2 reports in 

the CE Quarterly Interview survey with those from later rounds (see also Shields and To, 2005, and 

Silberstein, 1990, for similar efforts). In a series of papers from 2004 to 2011, Tucker, Meekins, and 

Biemer (2004, 2008, 2010, 2011a, 2011b) have examined a variety of internal error indicators using latent 

class models; they have identified a set of variables that seemed to cluster together, allowing them to 

classify CE respondents into three groups with different overall levels of reporting error. Based on our 

review of this past work, we have identified several indicators that seem particular promising to us and 

that are relatively inexpensive to collect and monitor. 

Selection criteria. The criteria for selecting internal indicators for inclusion in the MMI approach 

are the following: 

1) Relevance to different sources of reporting error, such as recall error, conditioning through the 

survey experience itself, and satisficing; 

2) Past research showing the indicators are highly predictive of reporting errors; 

3) Availability of the data to produce the indicator repeatedly over time; 

4) Usefulness for improving survey operations (we intentionally exclude respondent 

demographic characteristics even though some of these characteristics have been associated 

with poor reporting behavior) 

An illustrative set of internal indicators might be: 

 In the Diary Study, interviewer assessments of the diary keeper’s level of diligence in 

recording of entries in the diary before pickup versus data collected by recall ; 

 The ratio of the number of entries in diary week one and diary week two; 

 The percentage of respondents who use records during the Quarterly Interviews; 

 The average length of the interviews; 

 The average number of contact attempts needed to complete Quarterly Interviews. 

These five indicators reflect different potential sources of measurement error. The first and the 

third are measures of the likely level of recall error in the CE data. As we noted in the State of 

Knowledge report, respondents may forget a purchase entirely or may remember the purchase but forget 

the amount. Filling out the Diary ahead of time or consulting records during the Interview are likely to 

reduce the frequency of such errors. The decline in reporting over time (our second indicator above) is 

thought to reflect ―conditioning,‖ particularly for regularly purchased items; conditioning refers to the 

increased tendency for respondents to take shortcuts the longer they participate in a diary or panel survey. 

The final internal indicator — the average of contact attempts — presumably reflects the Consumer 
Proposal on Tracking Measurement Error in the CE | 6

Unit’s (CU) overall reluctance to take part in the survey and the level of effort the members of the CU are 

likely to put into providing accurate data during the Quarterly Interview. 

II.2. External Indicators 

A common method for estimating the accuracy of a survey is to compare the survey estimates to 

some external benchmark and this forms the second component of the MMI approach. In the best case, 

the external benchmark is error-free or at least much less error-prone than the survey of interest. In 

reality, external benchmarks are likely to have error from various sources. One of the most commonly 

used benchmarks for the CE is the Personal Consumption Expenditure (PCE) component of the National 

Income and Product Accounts (NIPA). Unfortunately, the accuracy of the PCE estimates is not entirely 

clear. Secondly, disparities between the CE estimate and the external estimate reflect not only 

measurement error but also differences in coverage, nonresponse, sampling, and other sources of error. 

Despite these problems, we believe it will be helpful to compare CE estimates to several external 

benchmarks. These comparisons have conventionally been reported as ratios (CE estimate/External 

estimate). The CE estimates have generally been lower than the NIPA PCE estimates for a given 

expenditure category, but are sometimes lower and sometimes higher than comparable estimates from 

other surveys, such as the American Community Survey (ACS) and the Medical Expenditure Panel 

Survey (MEPS) (see Tables 5a through 5f in Tourangeau et al., 2013). We recommend that BLS use 

several benchmark comparisons for any given category rather than relying on just one benchmark, giving 

more weight to the trend in the ratios than to their absolute values. 

Selection criteria for categories for comparison to external data. The categories selected should be 

representative as a set (that is, they should cover a range of different expenditure types) and should have 

the following additional features: 

1) Cover categories that differ in the likely availability of records; 

2) Include both regular (e.g., rent/mortgage, utilities) and irregular (e.g., clothing) expenditure 

categories ; 

3) Cover both large and small expenditures; 

4) Focus on categories in which the external source uses a definition that is reasonably consistent 

with the CE definition. Where possible, the sources of error in the external source and the 

magnitudes of these errors should be made explicit (to the extent that they have been identified 

and quantified). 

The following is an illustrative list of potential external benchmarks; the percentage given in parenthesis 

indicates the average expenditure for each category as a share of average total expenditure from the 

official CE data table (Table 52: Shares of average annual expenditures and sources of income, 

Consumer Expenditure Survey, 2011): 

1) Comparisons with other surveys 

a) ACS estimates for rent (6.1%) and mortgage (6.4%); 

b) ACS estimates for utilities and fuel (7.5%); 

c) Residential Energy Consumption Survey (RECS) estimates for utilities and fuel; 

d) MEPS estimates for hospitalization and health insurance (Healthcare 6.7%); 

e) MEPS estimates for medical and health; 

f) PSID estimates for medical and health. 
Proposal on Tracking Measurement Error in the CE | 7

2) Comparisons with PCE 

The proposed categories for comparisons with the PCE are among those that have the greatest 

comparability between the two sources in terms of definitional concordance (see SOK Report 

Table 6, Passero (2012)). 

a) Household appliances (major and small appliances 0.6%); 

b) Rent (6.1%) and utilities (7.5%); 

c) Food purchased offsite (Food away 5.3%); 

d) Women’s and girl’s clothing (1.5%); 

e) Men’s and boy’s clothing (0.8%). 

II.3. Periodic Record Check Study 

The final component of the MMI approach is to compare the survey reports to an external record at 

the respondent level. This information establishes a basis for confidence in findings from the comparison 

of these categories to external benchmarks. Geisen et al. (2011) used this approach to evaluate CE reports 

with a small convenience sample. They administered an abbreviated version of the CE Interview Survey, 

asked respondents to collect financial records for any expenditures covered by the survey, and then 

compared the reported answers with the values indicated on the records. 

We propose that BLS adopt a record validation approach similar to that used by Geisen et al. 

(2011). Validation studies should be carried out regularly, though not necessarily with as high a 

frequency as the other methods proposed in this report. To help reduce costs, the respondent burden 

involved in collecting records, and the burden on BLS to process the data from those records, a subset of 

items or CE categories could be chosen for this periodic evaluation. 

Selection criteria for categories to be included in validation study. By design, these categories 

should be similar to those used in the external benchmarking indicators because it would be useful to 

compare the conclusions derived from different methods for the same expenditure categories. Thus, 

following the illustrative categories proposed in Section II.2, the categories for periodic record check 

might include: 

1) Women’s and girl’s clothing; 

2) Men’s and boy’s clothing; 

3) Rent and utilities; 

4) Food purchased offsite; and 

5) Hospitalization and health insurance. 
Proposal on Tracking Measurement Error in the CE | 8

III. Issues for Preliminary Research before Implementation 

We recommend several lines of research before attempting to implement the collection and 

monitoring of these error indicators. 

1) Investigate the feasibility and cost of collecting the internal and external indicators 

recommended here on a routine basis. 

2) Conduct feasibility tests to develop better protocols for obtaining records for more expenditure 

categories and for a higher percentage of survey reports. While the Geisen et al. (2011) 

Records Study and the Sjoblom and Lee (2012) Records Information and Feasibility of Use 

Study provided useful information about the viability of this approach, several questions 

remain about how it can be used in a production or program evaluation environment. These 

questions can/should be addressed in smaller scale studies prior to full implementation. Some 

issues for further investigation for these preliminary studies include: 

a) Determine whether it is less burdensome to ask the respondent to collect records of all 

their expenses or to collect records only for select categories. 

b) Explore what expenditure categories and what types of records raise privacy concerns, 

making record-based data collection more difficult and make those records that are 

provided less representative of the population. Future research should address the issue 

of nonresponse in record collection and assess the potential for bias. 

c) Determine the appropriate sample size for a records validation study that would be used 

to make inferences about the nature of measurement error in the CE and make 

recommendations for future changes. There is variability in respondents’ ability to 

accurately report their expenditures, the likelihood the expenditures generate records, 

and the ability of the respondents to retrieve the records. To produce reliable results, a 

large sample will be needed. If finer detail is desired for demographic characteristics 

(e.g., income level, household size) then an even larger sample would be needed. The 

Geisen et al. (2011) and the Sjoblom and Lee (2012) studies had 115 and 152 

participants, respectively. From these studies, it is clear that the sample should be much 

larger (≈1000 completed cases) and, if possible, it should be nationally representative. 

One possibility is to use a subsample of an outgoing cohort of the CE Interview Survey 

sample or to incorporate validation within an ongoing methods panel. 

d) Find out how we can obtain records for a higher percentage of reported expenditures. 

Geisen and her colleagues were able to compare only 36 percent of the reported 

expenditures to household records. Participants in that study were asked following the 

initial interview to collect records from the previous three months. They were not 

warned to do this ahead of time so could only collect the records they had held on to. 

The authors of that report suggest asking respondents in advance to gather relevant 

records prior to the interview. Future research should be directed toward developing 

procedures that maximize respondent participation in the record collection task 

(including the use of incentives) while minimizing the potential for bias. 

e) Attempt to access respondents’ electronic records more effectively. It is also clear from 

both the Geisen et al. (2011) and the Sjoblom and Lee (2012) studies that many 

respondents were either unable or unwilling to provide printed copies of electronic 

financial records. These types of records are likely to become more common as time 
Proposal on Tracking Measurement Error in the CE | 9

goes on, so their collection would be of particular importance. Any future records 

validation study would need to motivate respondents to provide these records, either 

through monetary incentives or by making the process of printing or uploading the 

records easier and less costly for the respondent. 

f) Find methods to improve on the Geisen et al. (2011) methodology to measure under- and 

overreporting of expenditures (as opposed to amounts). In the methodology used in that 

study, if a respondent failed to report an expenditure for which there was no record, 

there was no way to detect the underreporting of that expenditure. Poor memory for an 

expenditure and lack of a receipt are both likely when low-dollar value expenditures are 

made. Collecting secondary records, such as bank or credit card statements could 

identify some of these missing records/reports, but not those made using cash. To detect 

overreporting, it may require asking respondents to collect records from periods before

the reference period used in the interview and using those records to check whether and 

how often expenses incurred prior to the reference period were erroneously reported. 

IV. Issues for ongoing research after implementation 

1) Once the external indicators begin to be collected, their interrelationships should be examined 

on an ongoing basis. Are the indicators unidimensional or multidimensional? One way to 

determine this would be to create a time series of indicators and to carry out a factor analysis 

of the indicator values. It may be that the relationships among the indicators are captured by a 

few factors. If so, the factor loadings could serve as weights for one or more composites that 

combine several or all of the indicators. 

2) A similar type of factor analysis should also be conducted for the set of internal indicators. 

3) Ongoing research to identify more effective internal indicators will be needed, especially if the 

CE survey design changes. 

V. Additional considerations regarding implementation 

The resources needed to carry out an approach like the one proposed here is a critical consideration 

for its implementation. As a first step towards acquiring this understanding, we offer a preliminary set of 

criteria and describe them for each component of the MMI in Table 2. 

 

 Cost: What inputs are needed to develop each MMI component? 

 Duration for development: How long will the development efforts take? 

 Applicability: Is the component applicable only to the current CE design or will it remain 

applicable to other designs? 

 Periodicity: How often can the indicators be tracked? 

 

We emphasize that our estimates regarding the costs and durations in Table 2 are intended only to 

highlight the relative differences across the components of the MMI methodology and are not intended to 

represent estimates of actual cost and duration. 
Proposal on Tracking Measurement Error in the CE | 10 

Table 2. Considerations for MMI Implementation Planning 

Internal indicators External comparisons Periodic Record 

Check Studies

Cost Very low cost

Primary resource need 

is staff time to conduct

analysis of the data. 

Additional cost may be 

incurred if additional 

data are needed and 

require cognitive testing 

of new questions.

Very low cost

Primary resource need is 

staff time to conduct 

analysis of the data. 

High Cost (> $500k)

Primary tasks involve:

1) Development of 

effective records 

collection 

protocols,

2)Interviewer 

training, 

3)Instrument 

development, 

4) Systems 

development to 

process collected 

records. 

Duration for 

development

< 1 year < 1 year for sources 

currently used.

1-2 years to find and 

analyze other sources for 

additional expenditure 

categories.

> 2 years

Applicable only to 

current CE design

No No Remains relevant if 

respondent recall 

remains a key feature 

of any future design

Periodicity Quarterly Annual (depends on the 

periodicity of data 

release for external

benchmarks).

Every 3 to 5 years

Note: Cost and duration estimates in this table are intended only to highlight the relative differences 

between the components of the MMI methodology. 
Proposal on Tracking Measurement Error in the CE | 11 

Summary 

Prior research to assess measurement error in the CE has used a variety of methods, ranging from 

small scale cognitive and records validation studies, to comparison with other data sources, to 

multivariate models of varying statistical complexity. Each of these methods focused on different aspects 

of measurement error (e.g., estimating the magnitude of measurement of error, distributional features of 

measurement errors, characteristics of respondents or items that are correlated with misreporting 

expenditures). The varied strengths and weaknesses of the methods imply that, although no single method 

or indicator can adequately assess measurement error, each method offers potentially useful information 

about the nature of measurement error in the CE surveys. Thus, we recommend a multi-methodindicators (MMI) approach to track measurement error in the CE. We recommend that this approach 

include three components: the use of CE data and paradata, comparisons with external benchmarks, and 

periodic but regular record validation studies. This approach offers the potential for convergent validity, 

or the triangulation of measurement error in specific item categories, that no single indicator can provide. 

We do not provide a definitive list of internal and external indicators for the MMI approach, but 

instead provide criteria for their selection and offer recommendations for pre- and post-implementation 

research to provide additional evidence about the usefulness of the indicators that are ultimately selected. 

Our proposed MMI methodology builds on previous research, as well as experience with small 

scale records collection studies. This increases the likelihood of the proposal to meet one of the overall 

project goals of ―having in place, practical and replicable methods and /or metrics for monitoring and 

evaluating changes in measurement error by the end of 2014” (see Table 1. Overall goals of the CE 

Measurement Error, No. 4 in the project Statement of Work, dated July 23, 2012). 

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