Latent Class Analysis of Consumer Expenditure Reports

Clyde Tucker1 Brian Meekins1 Paul Biemer2 October 2010

1 Bureau of Labor Statistics, 2 Massachusetts Ave, NE, Washington DC 20212 

2 RTI International, PO Box 12194, Research Triangle Park, NC 27709 

Abstract 

Previous research by Tucker et al. (2007), working with the Consumer Expenditure Interview Survey (CE), 

explores the efficacy of measurement error indicators such as: interview length, extent and type of records 

used, the monthly patterns of reporting, and income question missing in a latent construct. Later research by 

Tucker, Meekins, and Biemer (2008) extend this latent class model to include indicators of response 

behavior across multiple interviews in a panel. This research develops a number of plausible models which 

possess the qualities of reliability and validity, in that they appear to accurately capture measurement error, 

but prove unable to explain a large amount of the variance associated with expenditure reports. This work 

extends past research by including the relatively recently recorded indicators from the Contact History 

Instrument added to the CE since 2005. In addition, this work examines measurement error by mode of 

collection (also a recently collected item). 

Key Words: latent class model, measurement error, consumer expenditure

1. Introduction 

This work is part of a continuing effort to identify sources of measurement error in the Consumer 

Expenditure Interview Survey (CEIS), a household survey of expenditure reports of a variety of different 

commodity categories (e.g. furniture, clothing, utilities, etc.). Previous efforts have used Markov Latent 

Class Models to analyze patterns of item missing (where respondents do not report an expenditure in that 

category), and latent class models to identify characteristics of poor reporting consumer units (CUs). This 

work extends the work on the latter type of models by adding information pertaining to the experience of the 

consumer unit throughout all five interviews of the panel, developing a measure of the quality of expenditure 

reports with strong validity. 

2. Consumer Expenditure Survey 

The data used in this study consist of interviews collected in six years of the CEIS: 2005 through 2009. Each 

survey was designed to collect information on up to 95 percent of total CU expenditures. We define a CU as 

the members of a household who are related and/or pool their incomes to make joint expenditure decisions. 

In the CEIS, CUs are interviewed once every three months for five consecutive quarters to obtain the 

expenditures for 12 consecutive months. The initial interview for a CU is used as a bounding interview and 

these data are not used in the estimation. The survey is designed to collect data on major items of expense 

which respondents can be expected to recall for three months or longer. New panels are initiated every 

quarter of the year so that each quarter, 20 percent of the CUs are being interviewed for the first time. Only 

CUs completing and reporting an expense in wave 2 are used in this analysis, for a total of 29,347 

respondents. 

3. Previous Work 

For panel surveys such as the CEIS, a related statistical method referred to as Markov latent class analysis 

(MLCA) is available, which essentially relaxes the requirement that the replicate measurements pertain to 

the same point. Thus, this method of analysis is feasible for analyzing repeated measurements of the same 

units at different time points available in panel surveys. MLCA requires a minimum of three measurements 

of the same units, as would be the case for a panel survey where units are interviewed on three occasions. 

Section on Survey Research Methods – JSM 2010
The MLCA model then specifies parameters for both the period-to-period changes in the status of the item 

as well as the measurement error associated with measuring those changes. 

Previous work by the authors used MLCA to make aggregate estimates of underreporting in a category only 

by respondents reporting no expenditures in that category. Biemer (2000) applied the MLCA methodology 

to the CEIS in order to determine whether useful information on the magnitudes and correlates of screening 

question reporting error can be extracted directly from the CEIS panel data. Biemer and Tucker (2001) 

extended the earlier analysis using data from four consecutive quarters of the CEIS by considering CUs that 

were interviewed four consecutive times beginning in the first quarter of 1996 and ending in the last quarter 

of 1998. This allowed the authors to consider a wider-range of models including second-order Markov 

models. First order Markov models assume that a purchase or non-purchase at quarter q is affected only by 

quarter q-1 purchases or non-purchases. A second order Markov model assumes that both quarters q-1 and 

q-2 affect purchasing behavior at quarter q. Their analysis provided evidence of second-order Markov 

effects and recommended that second-order terms be included in the models. 

In Tucker, Biemer, and Vermunt (2002), model estimates with both unweighted and weighted data were 

compared. The results indicated that few differences were found between the two; therefore, given the ease 

of use, unweighted data were used in these analyses. A thorough examination of all explanatory variables 

considered in the previous studies was undertaken, and a reduced set of the most powerful ones was 

identified. A new diagnostic technique was developed and used to evaluate the validity of the models. In 

2003, Tucker, Biemer, and Meekins developed methodology for estimating the amount of the missing 

expenditures. 

Unlike the previous work, a micro-level approach incorporating measures specific to a given interview was 

used by Tucker, Biemer, Meekins, and Shields (2004) to examine underreporting for total expenditures. A 

latent variable that adequately accounted for the shared variance among a set of observed response error 

indicators was created. The observed variables were based on information collected from each CU during 

the interview. The latent variable was believed to be a better measure of underreporting than any of the 

observed variables taken individually. Each CU then was assigned to a particular class of the latent variable 

representing its hypothesized level of expenditure underreporting based on the CUs values on the observed 

variables. See Tucker (1992) for an earlier empirical example. 

For this analysis the authors used only second interview data and examined reporters of expenditures while 

ignoring nonreporters. They wished to develop a model separate from covariates with only indicators of the 

quality of response. The authors began with the simplest identifiable model composed of three indicators 

(each with three classes) and a latent variable with three classes. From this point they ran all possible 

combinations of three indicators for a three class latent variable. The analysis was further extended by 

examining restricted models based on the hypothetical relationship of some of the indicators with the latent 

variable, thus ordering the latent classes in what we believed to be an interpretable manner. These 

“restricted” models were compared to the unrestricted models to aid in interpretability and choices of model 

fit. Some of the indicators are dichotomous. These were entered into the best three variable models along 

with other combinations to create four-indicator models. The goal was to develop a latent variable 

(preferably ordered) that indicated the quality of responses, such that poor reporters could be easily 

identified. 

 

Models were estimated using lEM, LCA software developed by Vermunt (1997). Model selection was based 

on a number of objective and subjective measures. The authors primarily used the Bayesian Information 

Criteria (BIC), the L2 test statistic, and the dissimilarity index. However, for each model the authors 

examined the conditional probabilities of the latent variable given each value of each indicator. In this way 

we assessed the relative influence of each indicator and the degree to which an indicator effectively 

differentiated the respondents with respect to the classes of the latent variable 

Section on Survey Research Methods – JSM 2010
Using these methods a “best” model was selected. Latent classes aligned with expenditure means as 

expected. Those with lower expenditure means had higher levels of underreporting. For example, those in 

the low underreporting class had a total expenditure mean of $10,625, while those in the high underreporting 

class had a mean of $6,948 

In Tucker, Biemer, and Meekins (2005), the authors continued with a more in-depth exploration of microlevel measures of underreporting. In this analysis, only second wave data are used from those respondents 

actually reporting expenditures in the commodity classes under study (57,184 families interviewed in 1996 

through 2001). Thus, we first were interested in the response errors for those respondents reporting 

expenditures and not those who said they had no expenditures in these categories. Again, the authors 

assumed response errors come largely in the form of underreports. 

In this case, a refined set of manifest indicators of response error were created. These indicators are listed 

below, with the coding scheme used for each: 

1. Number of contacts the interviewer made to complete the interview (1=0-2; 2=3-5; 3=6+) 

2. The ratio of respondents to total number of household members (1=<.5; 2>.5) 

3. Whether the household was missing a response on the income question (1=present; 2=missing) 

4. The type and frequency of records used. This variable indicates whether a respondent used bills or 

their checkbook to answer questions, and how often they did so. (1=never; 2=single type or 

sometimes; 3=multiple types or always) 

5. The length of the interview (1<45min; 2=45-90; 3>90) 

6. A ratio of expenditures reported for the last month of the 3 month reporting period to the total 

expenditures for the 3 months (1<.25; 2=.25-.5; 3=>.5) 

7. A combination of type of record used and the length of the interview. (1=poor; 2=fair; 3=good) as 

shown below for the combined variable. 

8. Number of expenditure questions within commodity category for which a response was imputed or 

allocated. 

For each of seven expenditure categories: children’s clothing, women’s clothing, men’s clothing, furniture, 

electricity, minor vehicle expenses, and kitchen accessories, we began with the simplest identifiable model 

composed of three indicators and a latent variable with three classes. Models were again estimated using 

lEM. Only three manifest variables were used to maximize cell sizes in the manifest tables. We ran all 

possible combinations of three indicators for each expenditure class. The analysis involved both “restricted” 

and “unrestricted” models. Restricted models forced a hypothesized ordering of the manifest indicators to 

the latent response error (ordering the latent classes in what we believed to be an interpretable manner), 

while unrestricted models did not. Based on comparisons of the results from restricted and unrestricted 

models, it was decided to proceed with only restricted models from that point. Combinations of four and five 

manifest indicators were examined, but all models with more than four variables were of little value. Again, 

we ran models with several different sets of starting values to avoid reaching only a local solution. 

. 

The selection of the best model for each expenditure category was based primarily on the BIC and the 

Dissimilarity Index. The same set of manifest indicators were not used for the best model in each case, but 

the statistical diagnostics confirm a good fit for all final models chosen. 

The authors also extended the use of substantive diagnostics used in earlier work. For each model they 

examined both conditional probabilities of the latent variable given each value of each indicator and the 

conditional probabilities of each indicator given each value of the latent variable. In addition, they also 

examined the actual probabilities of a case being in a particular latent class given its manifest cell location, 

as well as the proportion of cases assigned to each manifest cell by the latent class model. 

Section on Survey Research Methods – JSM 2010
To gain a further understanding of the models, the authors again turned to the expenditure means for the 

three latent classes. The results, while not completely disconfirming, were not that promising. Across all 

seven categories of expenditures we analyzed, we found that the three classes of the latent variable failed to 

distinguish CUs based on their expenditures. However, for kid’s clothing, women’s clothing, and kitchen 

accessories, two separate groups could be identified that met our expectations. 

By including CUs that reported no expenditures in our analysis, the authors found that, for most 

commodities, mean expenditure amounts increased monotonically across the latent scale, and the three 

means were significantly different from one another. 

Research by Tucker, Biemer, Meekins, Kesselman (2006) advanced the effort by examining a much larger 

number of commodity categories (29) and more rigorously examining and validating the results of the latent 

class models. The “final” model for each of the 29 commodities and overall were selected in a similar 

manner to past research, using both objective statistics and subjective diagnostic tools. Based on the results 

of the models CUs were then assigned to certain classes of reporting in the same way as previous research. 

The classification variable, corresponding to proo, fair, and good reporting quality was then regressed on a 

number of demographics in order to assess the content validity of the latent variable. After finding similar 

patterns across all commodity categories and verifying the results of the latent class modeling, the authors 

regressed the expenditure mean for each commodity category and overall on the latent classification 

controlling for key demographics, examining the contribution of the latent variable in predicting 

expenditure, controlling for demographic variables (such as one would use in weighting or nonresponse 

adjustment). Consistent with previous research the results of this research provided validation for the latent 

class approach to modeling measurement error, but a model that could differentiate levels of underreporting 

(given a report) remained elusive, while models classifying CU’s by whether they erroneously omit a report 

altogether were more successful. 

Other research by Meekins, Tucker, and Biemer (2008) used the latent construct developed in Tucker et al. 

(2006) to examine the relationship of measurement error to subsequent wave nonresponse and bias. It was 

found that those in the poorest category of reporting were somewhat less likely to respond in subsequent 

interviews, volunteered expenditure reports in fewer categories, and had more sharply declining overall 

expenditure amounts in subsequent interviews than their counterparts in the fair and good reporting 

categories. In other research (Tucker, Biemer, Meekins 2009), included indicators that characterized the 

experience of the CU throughout the course of the entire panel. The new indicators included: 

 1. Number of completed interviews. 

2. Pattern of attrition combined with the number of completed interviews (those with a pattern of 

attrition as opposed to a sporadic nonresponse pattern were further penalized). 

 3. Average number of commodity categories for which CU had at least one expenditure report. 

4. The number of interviews in which the third month’s expenditure to the quarter was between 0.25 

and 0.5. 

 5. Panel averages of some of the interview level indicators. 

The three best fitting models selected from this research incorporated indicators found to be important in 

previous work and new panel indicators. Two models utilized three indicators, while one model used four 

indicators to examine the quality of expenditure reports. The three indicator models used the indicators: 

missing on income, length of interview, and average number of commodity categories to differentiate three 

and four latent classes of reporting quality. The four indicator model used all of those used in the three 

indicator models combined with the number of good interviews in the panel. Following the same process as 

prior research, these models were validated with demographic and process variables. The models showed 

good differentiation of expenditure estimates, even when controlling for demographics and process 

Section on Survey Research Methods – JSM 2010
variables. Of particular note is the contribution of the interaction between income level and the latent class 

variable to this model. At very high or very low incomes the relationship between level of reporting and 

reported expenditure is significantly stronger. Indeed, the contribution of the interaction term is much higher 

than the direct effect of the latent construct. When examining bias (second quarter reported expenditure – 

fourth quarter reported expenditure), we find similar results. The variable derived from the latent class 

analysis showed good differentiation in the expected direction. The authors concluded that the latent 

construct was indeed measuring the quality of reporting, but either lacked the sensitivity needed to 

adequately predict underreporting, or measurement error is not a strong predictor of the average expenditure 

reported by the CU or the amount of bias, as measured by the coarse measure 

4. Current Research 

The current research advances prior work in a two significant ways. Firstly, the data are more current, 

extending from the second quarter of 2005 to the second quarter of 2009. The more current data also 

incorporate information collected in the Contact History Instrument (CHI). These data were not collected 

prior to the second quarter of 2005. These data capture a number of attributes of contact attempts made by 

field interviewers, including the number of contacts, mode of contact (phone or in-person), and reasons for 

refusal or noncontact that were recorded by the interviewer. This information is quite detailed. The authors 

hypothesized that these could add strength to the models constructed in previous work. 

The contact history data were refined into indicators for each wave and for the overall panel for each second 

quarter respondent. In order to combine the detailed reasons for refusal and noncontact (by simple tally), the 

authors sought guidance from research conducted by Maitland, Casas-Cordero, and Kreuter (2009) and 

Dixon (2009). Utilizing factor analysis these authors identified the same factors from the CHI in two 

different surveys. The strongest factors related for reluctance could be grouped by privacy concerns, 

reluctance, and hostility. Only two factors were identified for reasons for noncontact (gatekeepers or barriers 

and “other”). 

The new indicators are as follows: 

1. Indicators of reluctance based on privacy concerns 

2. Indicators of reluctance based on time concerns 

3. Indicators of especially hostile refusal 

4. Any reluctance 

5. Indicators of noncontact based on gatekeepers or other barriers 

6. Indicators of noncontact based on other problems 

7. Proportion of attempts made in-person 

8. Proportion of completed interviews that were completed by phone 

Model selection was conducted under two separate strategies. The first strategy ran indicators that were 

examined in previous research in all possible combinations of three and four and ran the “new” CHI 

indicators in separate latent class analysis also with all possible combinations of three and four indicators. 

The best performing indicators in these models were then combined. Strategy two grouped all indicators by 

theoretical concept. Three groups were formed: 1. reluctance indicators; 2. noncontact indicators; 3. all other 

indictors. After finding the best model using the reluctance and noncontact indicators, other variables were 

added to the model and tested for fit. Multiple iterations with random start values were used in order to 

avoid local maxima. Models were initially selected based on fit, reducing the many possible combinations of 

classes and indicators to a relative few candidates. The remaining candidates were then evaluated based on 

the relationship of the latent construct to the indicators and other subjective criteria. Five “best” models were 

selected. Although three and four category latent class constructs were examined, all of the best fitting 

models had three latent classes. 

Section on Survey Research Methods – JSM 2010
1. Old model: This model was based on the best fitting indicators of those examined in previous 

research. This was done to confirm these results with the more recent data. Indicators: Income 

missing; Record use combined with interview length; Average number of commodity categories for 

which CU reported an expenditure; Number of completed interviews 

2. CHI model: This model consisted of only the “new” CHI indicators. Indicators: Reluctance due to 

privacy concerns; Tally of all noncontact problems; Average number attempts. 

3. Combination model: This model combined the best fitting indicators from the Old and CHI models. 

Indicators: All the “Old” model indicators; Average number attempts. 

4. Reluctance model: This model combined the best fitting indicators that indicate a degree of 

reluctance on the part of the CU. Indicators: Income missing; Record use combined with interview 

length; Number of completed interviews; Reluctance due to time constraints. 

5. Noncontact model: This model combined the best fitting indicators that indicate difficulty in 

making contact with the CU. Indicators: Record use combined with interview length; Tally of 

noncontact problems; Average number of attempts. 

After these five models were selected the CU was assigned a latent class value based on the probability of 

being in that class given the indicators in the model. Expenditure means were found for each latent class 

assignment. The models were further validated by regressing demographic variables on the latent class 

assignment using proportional odds models. Expenditure means were regressed on the latent class 

assignment together with demographic variables. The mean expenditure (across CUs) is significantly lower 

in the fourth wave interviews compared to that of the second. It is commonly thought that CUs, on average, 

underreport their expenditures in the fourth wave. Utilizing this as an indicator of poor reporting, the authors 

regress the latent class assignment on the proportional difference between the total expenditure reported on 

the second and fourth panel waves of the interview and the standard deviation of the total expenditure across 

all completed interviews within the panel. The formula for the proportional difference follows:

( ) 





− >

≤ =

42242

42

;/

;0

XXXXX

XX

Error

5. Results 

Although acceptable, the models emerging in the current latent class analysis generally have poorer fit than 

those selected as the best models in prior research. Table 1, shows the class probabilities associated with 

each of the latent variables. Note the “previous best” is the best model from previous research, whereas the 

Old model uses the same indicators but is estimated with the current (2005-2009) data. The difference in the 

size of the first two classes is somewhat striking considering the only difference in these two are the 

timeliness of the data. 

Section on Survey Research Methods – JSM 2010
Table 1: Class Probabilities 

 Poor Fair Good 

Previous Best .203 .232 .565 

Old .137 .281 .582 

CHAI .245 .434 .321 

Combo .140 .285 .575 

Reluctance .138 .309 .554 

Noncontact .279 .574 .148 

Table 2 shows the average overall expenditure per quarter by latent class and model. Corresponding with the 

change in the relative size of the class mentioned above, unlike the Previous Best model the mean overall 

expenditure does not differ across the first two classes of the Old model. Other models, including the 

Combo, Noncontact, and CHI models, suffer from the same lack of differentiation including. 

Table 2: Average Overall Expenditure by Latent Class Variable 

 Poor Fair Good 

Previous Best 6,946.84 8,920.20 11,985.71 

Old 10,359.92 10,032.08 13,231.43 

CHI 12,365.20 12,022.62 10,895.20 

Combo 10,725.39 10,492.81 13,014.24 

Reluctance* 10,385.60 11,124.69 12,708.89 

Noncontact 12,774.90 12,432.04 10,678.05 

Looking at the expenditure by some of the commodity types for the Combo and Reluctance models, we see 

similar inconsistent findings. For some commodities, such as electricity and trash collection, furniture, and 

dental care, the latent constructs perform as expected. For other categories, such as sports equipment, 

apparel, and television and other electronics, we see that the latent classes do not perform as expected, with 

mean expenditure often lowest in the second class. Other models performed as poorly. 

Section on Survey Research Methods – JSM 2010
Table 3: Mean Commodity Expenditure by Latent Class

Combo Poor Fair Good 

Electricity 267.02 301.61 331.37 

Trash 7.18 13.08 19.73 

Sports 24.86 20.57 33.49 

Furniture 30.70 42.87 76.90 

Kitchen 

Accessories 5.14 11.48 23.55 

Major Vehicle 

Repairs 17.20 24.67 43.72 

Gas 98.03 113.94 135.79 

TV 104.71 75.94 113.59 

Women’s Apparel 117.36 97.86 127.00 

Men’s Apparel 74.04 59.72 74.99 

Kid’s Apparel 47.89 41.94 54.59 

Dental 15.86 31.40 61.72 

Reluctance Poor Fair Good 

Electricity 262.42 316.52 325.77 

Trash 7.18 14.82 19.04 

Sports 24.91 22.69 32.41 

Furniture 25.76 54.20 73.00 

Kitchen 

Accessories 4.81 14.77 22.41 

Major Vehicle 

Repairs 15.38 29.25 42.47 

Gas 98.56 122.61 132.01 

TV 96.15 82.80 110.52 

Women’s Apparel 111.43 108.34 121.25 

Men’s Apparel 74.51 66.22 71.95 

Kid’s Apparel 46.01 47.94 51.74 

Dental 15.12 37.94 59.53 

The relationship of the demographic variables with any of the latent class variables were consistently in the 

expected direction. For example, CUs that rent showed a higher propensity of being in the lowest latent 

class, followed by the second lowest, and finally the best class. Table 4 shows the proportional odds 

coefficients for the Combo, Reluctance, CHI, and Noncontact models. 

Section on Survey Research Methods – JSM 2010
Table 4: Proportional Odds Model Results 

Latent 

Variable Combo Reluctance CHI Noncontact

Exp(b) PR(X

2

) Exp(b) PR(X

2

) Exp(b) PR(X

2

) Exp(b) PR(X

2

)

Famsize 1 .887 .0145 1.129 .0102 .907 .0030 1.099 .0838

Famsize 2 .948 .8415 1.060 .9456 .999 .0869 1.024 .1812

Age 1.007 <.0001 1.007 <.0001 1.025 <.0001 1.021 <.0001

Educ .962 .2868 .990 .7562 .970 .3119 0.988 .6784

Inc rank1 .782 .0009 .920 .9954 1.211 <.0001 1.407 <.0001

Inc rank2 .809 .0039 .846 <.0001 1.071 .2961 1.138 .0877

Race 1.420 <.0001 1.439 <.0001 1.263 <.0001 1.135 <.0001

Tenure .805 <.0001 .880 .0003 .931 .0310 .962 .2140

Urban .878 .0477 .947 .3516 .833 .0006 .855 .0019

Cell 

complete .929 .2783 1.016 .8026 .918 .1496 .865 .0099

Maxrescaled R

2 .018 .016 .055 .057

Table 5 shows the results of the average expenditure per CU per wave regressed on the latent construct 

controlling for demographics. The marginal gain from introducing the latent construct is small but 

statistically significant. The effect sizes of the demographics are not significantly diminished by the 

introduction of the latent construct. The means of the expenditure by latent class controlling for the 

demographics variables in the model are significantly different but are not in the expected direction, where 

the Fair category of the latent construct has the lowest means expenditure. These results are similar for a 

number of commodity categories (not shown), although for some commodities the latent construct does 

perform as expected controlling for demographics.

Section on Survey Research Methods – JSM 2010
Table 5: MANCOVA Results: Total Expenditures Combo Model

 Baseline model With LV 

 Estimate p-value Estimate p-value 

 F 1569.60 <.0001 971.82 <.0001 

 R2

 .41 .41

F[Contribution of Latent variable]: 46.93 <.0001 

F[Contribution of Interaction Term]: 8.32 <.0001

Total Expenditure: Least Squared Means Controlling for All Other Variables in 

the Model* 

Class Mean 

p-values for differences in LSMean 

Poor Fair Good 

Poor 10,263.94 <.01 .89 

Fair 9,165.52 <.01 <.01 

Good 10,383.46 .89 <.01 

*Scheffe adjustment for multiple comparisons 

Table 6 shows summarizes the results of regressing the proportional difference of the Wave 2 expenditure 

and Wave 4 expenditure measure on the latent construct controlling for demographic variables. Again the 

contribution of the latent construct to the model is statistically significant, however the adjusted R2

 is 

extremely small overall. In this model the latent construct performs better than in the model for total 

expenditure. where the proportional difference (a sign of bad reporting) is smaller for the Good reporting 

group and highest for the Poor reporting group. The differences are statistically significant. 

Table 6: MANCOVA Results: Proportional Difference Combo Model 

 Baseline model With LV 

 Estimate p-value Estimate p-value 

 F 1.48 .1296 8.41 <.0001 

 R2

 .00 .00

F[Contribution of Latent variable]: 27.52 <.0001 

F[Contribution of Interaction Term]: 7.15 <.0001 

Total Expenditure: Least Squared Means Controlling for All Other Variables in 

the Model* 

Class Mean 

p-values for differences in LSMean 

Poor Fair Good 

Poor .207 .04 <.01 

Fair .148 .04 <.01 

Good .117 <.01 <.01 

*Tukey-Kramer adjustment for multiple comparisons 

6. Discussion 

Overall, the latent constructs did not perform as well as they did in previous research. While the model fit 

was still good, the CHI variables grouped more closely with themselves than with any other indicators and 

seemed to contribute little to the efficacy of the latent construct in predicting reporting error. Overall the 

Section on Survey Research Methods – JSM 2010
strongest indicators across models were income missing, record use combined with interview length, number 

of completed interviews, reluctance due to time constraints, and average number of attempts. These 

indicators were also strong in previous research. As in previous research the latent constructs lack the 

sensitivity needed to adequately predict underreporting, or measurement error is not a strong predictor of the 

average expenditure reported by the CU and the amount of bias, as measured by total expenditure, or the 

proportional difference in Wave 2 and Wave 4 expenditure reports. 

Unlike previous research we do not consistently show differences in expenditure amounts in the expected 

direction across the three classes. For many commodity categories and for overall expenditure we can only 

differentiate between two classes of reporting quality. The current latent constructs appear to be relatively 

blunt instruments (although the only instruments we have), and are probably not useful for adjustment as 

they do not explain much of the variation in expenditure or change in expenditure. 

Future research will attempt to develop indicators that may be able to further differentiate reporting quality 

among expenditure reporters. One direction is indicated from the results of Tables 1 and 2. The models 

emphasizing noncontact (CHI and Noncontact) have very different class probabilities than models 

emphasizing reluctance (Combo and Reluctance). In addition, the expenditure means across class are quite 

different for these models. It is possible that utilizing two different, two-class latent constructs, 

corresponding to the CU’s reason for bad reporting, will result in a more effective model. 

 

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