An exploration of the application of PLS path modeling 

approach to creating a summary index of respondent 

burden 

Scott Fricker, Craig Kreisler, and Lucilla Tan 

U.S. Bureau of Labor Statistics, 2 Massachusetts Avenue NE, Washington, DC 20212 

ABSTRACT 

The potential effect on respondent burden is a major consideration in the evaluation of 

survey design options, so the ability to quantify the burden associated with alternative 

designs would be a useful evaluation tool. Furthermore, the development of such a tool 

could facilitate more systematic examination of the association between burden and data 

quality. In this study, we explore the application of Partial Least Squares path modeling 

to construct a burden score. Our data come from a phone-based, modified version of the 

Consumer Expenditure Interview Survey in which respondents were asked post-survey 

assessment questions on dimensions thought to be related to burden – e.g., effort, survey 

length, and the frequency of survey requests (Bradburn, 1978). These dimensions served 

as the latent constructs in our model. We discuss model development and interpretation, 

assess how the measured items relate to our latent constructs, and examine the extent to 

which the resulting burden scores covary with other survey measures of interest. 

KEY WORDS: path modeling, respondent burden 

1. Introduction 

The Consumer Expenditure Survey (CE), sponsored by the U.S. Bureau of Labor 

Statistics, is currently undertaking a multiyear research effort to redesign the CE in order 

to improve data quality. The current Interview Survey instrument asks respondents to 

recall detailed out-of-pocket household expenditures over a 3-month reference period, a 

process acknowledged to be burdensome to the respondent. Since it is commonly 

assumed that respondent burden is associated with the quality of respondent reporting, 

the evaluation of survey design options should also take account of their potential effect 

on respondent burden. 

In prior experiments of alternative survey design options, data on multiple indirect 

indicators of respondent burden were collected, but assessment of these indicators have 

typically taken the form of bivariate analyses, such as the comparison of the frequency 

distribution of these indicators among treatment groups, or cross-tabulations of these 

indicators with sample characteristics or other indicators of reporting characteristics. A 

summary measure of burden that is based on all these indicators would provide a way to 

rank the respondents on a “burden continuum,” and thus allowing the investigation of 

how survey characteristics of interest change on this continuum.
2. The Study 

The objective of this exploratory study was to learn to apply the methodology of partial 

least squares (PLS) path models to develop a summary index of respondent burden from 

multiple indirect indicators of burden. This exploratory research used data from a smallscale field test (Creech et al. 2011). The field test survey instrument was based on a 

shortened version of the ongoing Consumer Expenditure Quarterly Interview Survey, 

which collects detailed household expenditure information. One of the treatment 

conditions in this field test was designed to investigate the effect of a shortened reference 

period on the quality of expenditure reporting. The research reported in this paper is 

based on the final wave interview data from that treatment group (Recall) and the Control 

group in the field test. The Recall group was asked about their expenditures for a onemonth reference period, and their survey panel consisted of 4 waves, with each interview 

taking place over 4 consecutive months. The Control group was asked to recall 

expenditures for a 3-month reference period, and their survey panel consisted of 3 waves, 

with each interview taking place three months apart. Data collection for both treatment 

groups began in June 2010, ending in November 2010 for the Recall group and February 

2011 for the Control group. 

The interviews were conducted by computer-assisted telephone interview (CATI). In 

each wave, respondents were asked about their purchases during the reference period on 

major appliances, clothing, vehicle operating expenses, non-health insurance, health 

insurance, education, subscriptions, trips, and average weekly expenses on food and 

beverage. At their end of their final interview, respondents were asked a series of postsurvey assessment questions (PSAQs) about their survey experience: how burdensome 

they found the survey to be (PSAQ_3), their interest in survey content (PSAQ_1), their 

perception of difficulty in responding to the survey questions (PSAQ_2a), their 

perception about the length of the interview (PSAQ_4), the use of recall aids, the 

appropriateness of the frequency of survey requests (PSAQ_3a), and the appropriate 

number of contact attempts (PSAQ_3b). From the contact attempt history for the sample 

units, we identified sample units for whom refusal conversion assistance was requested at 

least once over their survey panel. 

3. Methodology 

3.1 Data 

As noted above, the present analyses were conducted on the final wave interview data 

from the Recall and Control groups since the post-survey assessment questions are asked 

only in the final interview. The final wave response rate was 64.9 percent for the Control 

group (N eligible = 735) and 59.4 percent for the Recall group (N eligible = 1006).1 A 

requirement of the software we used to perform our analyses required complete case data 

(i.e. there are no missing values on the items used in the model), so only respondents who 

provided valid answers to all the items used in the analysis were included in our study 

sample. Selected demographic characteristics of the study sample appear in Table 1. 

Differences in characteristics between the two groups were generally less than 3 

percentage points, with the exception of respondents in the Recall group being more 

 

1

The response rate definition used was AAPOR response rate definition RR4 [see page 44, AAPOR(2011)], 

with the proportion of eligibility among cases with “unknown” final disposition assumed to be 0.33.
likely to be in the 35-64 age range, and more likely to have attained at least a High 

School degree. 

3.2 Partial Least Squares Path Models 

Partial Least Squares path modeling (PLS) is a multivariate data analysis technique that 

provides researchers the opportunity to simultaneously assess the measurement of the 

constructs (or latent variables – we use these terms interchangeably), and test hypotheses 

on all the relationships among the constructs within the same analysis. This technique is 

designed to explain variance, and is suited for predictive applications and theorybuilding. PLS performs an iterative set of factor analyses and ordinary least squares 

regressions until the difference in the average R2 of the constructs become non-significant 

(p.27 in Gefen et al. 2000). It should be noted that some researchers consider PLS to be 

one type of structural equation modeling (SEM), while other researchers contend that 

PLS is a form of regression and not “mainstream” SEM (e.g. Rouse and Corbitt 2008), 

which is based on analysis of covariance structures that explicitly models measurement 

errors. 

Assumptions. The PLS technique imposes less stringent assumptions on normality of the 

data and measurement scales than covariance-based structural equation model (SEM) 

methods. It can be applied to small samples, accommodate both reflective and formative 

measurement models, and contain many constructs and indicators without leading to 

estimation problems (p.279-281, Henseler et al. 2009). However, the technique assumes 

the relationship between the observed variables and their constructs is linear. And 

although PLS has low sample size requirements, it may not have an advantage in 

detecting statistical significance in small sample sizes. In addition, PLS parameter 

estimates are asymptotically correct (large sample size and large number of indicators per 

latent variable; p.296, Henseler et al. 2009). 

The PLS framework consists of two inter-related models: (1) the measurement model, 

which describes the assignment of the observed items (or indicators) to each unobserved 

construct , and (2) the structural model, which describes the relationship among the set of 

constructs. Both models are explicitly defined by the analyst, and depicted in a path 

diagram. The direction of relationships between a construct and its item pool (the 

indicators associated with a construct) can be described as a reflective or formative. With 

a reflective construct, the items are assumed to reflect variation in the construct; thus a 

change in the construct is manifested as a change in all its items. With a formative 

construct, it is assumed that the observed items represent different dimensions of the 

construct, and so the items need not be correlated with each other. For our objective of 

constructing an index of respondent burden, we used a formative measurement model. 

Algorithm. We performed the PLS analysis using the functions plspm() and 

plspm.groups() in the plspm package in R software (Sanchez and Trinchera 2012). The 

algorithm optimizes the explained variance (estimates R2) of the endogenous latent 

variables, and uses bootstrapping to determine significance levels of estimated 

parameters. The key parameters estimated by the plspm algorithm are the item weights 

(i.e., the scalar coefficients in the linear equation relating the items to their associated 

latent variable), loading coefficients (i.e., the correlations between items and their 

associated latent variable), and path coefficients (i.e., the estimated coefficients of the 

structural model). For detailed discussions on how the algorithm produces the estimated 

parameters, see Sanchez (2009) (also Hensler et al. 2009). 
Model assessment. PLS path modeling lacks a well-identified global optimization 

criterion, so there is no global fitting function to assess the goodness-of-fit criterion of the 

model (Vinzi et al. 2010). Instead, different criteria are used to assess partial model 

structures. A systematic application of these criteria involves first assessing the 

measurement model, then the structural model (Henseler et al. 2009, p.298). In the 

assessment of the measurement model, different criteria are used for reflective and 

formative constructs due to the different nature of the relationship between the items and 

the construct intended for measurement. For formative measurement model (the focus of 

our analyses), the criteria for validity at the item level are that item weights should attain 

statistical significance, and there should not be multicollinearity of items within an item 

pool (Henseler et al. 2009, p. 298-304). At the construct level, the formative index should 

have external validity (i.e. explain a large proportion of the variance of an alternative 

reflective measure of the focal construct), and nomological validity (i.e. the relationships 

between the formative index and other constructs in the path model that are known from 

prior research should be significant). For the structural model, the constructs should be 

distinct (cross-correlations between latent variables should be less than 0.5), the 

proportion of variance (R2) for the endogenous construct should be at least 0.1, and the 

path coefficients should be significant. 

3.3 The structural model 

We adopted Bradburn‟s (1978) seminal research on factors that contribute to a 

respondent‟s perception of survey burden as the basis of our structural model. Bradburn 

identified four dimensions of burden: (1) length of the interview, (2) amount of effort 

required by the respondent, (3) amount of stress experienced by the respondent, and (4) 

the frequency with which the respondent is interviewed. In addition to underscoring the 

multidimensional nature of respondent burden, Bradburn emphasized that 

“burdensomeness” is a subjective characteristic of the task, “the product of an interaction 

between the nature of the task and the way it is perceived by the respondent” (pg. 36). As 

the nature of the question answering task becomes more difficult, all things being equal, 

the task may be perceived as more burdensome. Conversely, as positive elements of the 

task (e.g., interest in the survey topic or perceived importance of the data) become more 

salient, the task may be perceived as less burdensome. Fricker et al. (2011) used recursive 

partitioning analysis on data from the same field study to classify sample units in the 

Recall group into homogeneous groups according to their responses to post-survey 

questions about the burden dimensions. They found that respondents‟ perception of 

survey length was the dimension most strongly associated with reported burden, followed 

by respondent‟s interest in survey content, and the frequency of interview requests (“too 

many” pre-interview calls) and respondents‟ perceived difficulty in responding to survey 

questions were the next dimensions associated with reported burden. 

In our study data, we have indicators for three of the four dimensions of respondent 

burden (PB): perception of survey length (PL), perception of effort (PE), and perception 

of frequency of the interview requests (PF). Taking account of the findings by Fricker et 

al. (2011), we posited the following two endogenous relationships in the structural model 

for PLS analysis: 
1. Perception of survey burden: we hypothesize that perceived length of the survey (PL), 

perceived effort (PE), and perceived frequency of survey request (PF) influence a 

respondent‟s perception of survey burden (PB). 

2. Perception of survey length: in addition, if a respondents needs to exert more effort to 

respond to survey questions, this may lead him/her to perceive it takes a longer time to 

complete the interview. Similarly, the frequency of survey requests and/or the attempts to 

contact a sample unit to conduct the survey could increase the saliency of the survey, 

which may in turn exaggerate any negative perceptions the respondent already has about 

taking the survey – such as the length of time it takes to complete the survey. Thus, the 

amount of effort a respondent exerts, and the frequency of survey requests made of the 

respondent could both influence the respondent‟s perception of survey length. 

3.4 Measurement model 

A description of the items for the constructs in the structural model appears in Table 2. 

The constructs are the burden dimensions, PL, PE, PF, and PB. All the items were treated 

as formative indicators in the measurement model since they represent different 

dimensions of the respondent burden construct, and we use them to construct the 

respondent burden index. The frequency distributions of the items are shown in Table 3. 

The scale mean of categorical variables (mean for continuous variables) and standard 

deviations are presented in Table 4. For the analysis, the item data were standardized to 

mean of 0, variance of 1. 

3.5 Burden index 

We define the respondent burden index as a value assigned to a respondent that 

represents the degree to which he or she perceives burdened from the survey experience, 

where perceived burden is “measured” by the relationships specified in the structural 

model in section 3.2. Having obtained the scores of the exogenous latent variables from 

the measurement model, and the estimated path coefficients of the structural model for 

the Control-Recall data, the burden index for each respondent i, pb_mani, was computed 

as a linear combination of the estimated path coefficients and the respondent‟s latent 

variable scores based on the two endogenous relationships for PB and PL: 

 

PB PE PF PL       eb fb lb PL PE PF     el fl

, , , 

, , , , 

, , 

_ * * *

 * * *( * * )

 ( * )* ( * )*

i EB PE i FB PF i LB PL i

EB PE i FB PF i LB EL PE i FL PF i

EB LB EL PE i FB LB FL PF i

pb man Score Score Score

Score Score Score Score

Score Score

  

    

     

  

   

   
4. Findings 

4.1 Model assessment 

The loading of each item with its associated latent variable (shown in bold) and its cross 

loading on other latent variables are shown in Table 5. An item‟s loading on its 

associated latent variable is greater than its cross-loading on other latent variables in the 

model, which provides some evidence for construct validity. The weights of the items, 

with the exception of f1numatm, were all significant (Table 6). The correlation between 

items for item pools with only 2 indicators (e1int, e2ease) did not exceed 0.15, and the 

estimated magnitudes of multicollinearity for item pools with more than 2 indicators, 

(f1numatm, f2numwave, f3numatmp) and (l2timest, l3size, l4timep), computed as the 

mean of variance inflation factors, were less than 1.2, indicating that multicollinearity 

among items was not a problem. Taken together, these results indicate that the 

measurement model is valid. The cross-correlations between the latent variables were all 

less than 0.5 (Table 7), the estimated path coefficients were all significant (p<0.05), and 

the R2 for both endogenous latent variables, perceived length and perceived burden, were 

greater than 0.1. The path model diagram, with the estimated item weights and path 

coefficients are shown in Figure 1. 

4.2 Impact of Treatments on Burden 

Having obtained the scores of the exogenous latent variables from the measurement 

model and the estimated path coefficients of the structural model for the Control-Recall 

data, the burden index pb_mani was computed. The histograms of the burden index by 

treatment group are shown in Figure 2. The histograms suggest that there may be 

relatively more respondents with higher index values in the Recall group. Results from a 

Wilcoxon-Mann-Whitney test confirm what was visually apparent: the distribution for 

the Recall group is shifted right; burden index values were generally higher for the Recall 

group than the Control group (W = 125355.5, p=0.0281, 1-tailed test). 

In addition, the PLS path model analyses also provides insights into how the groups 

differ on the paths in the structural model of perceived burden. The estimated path 

coefficients for the structural model depicted in Figure 1 are shown on the left panel of 

Table 8, and the path coefficients by treatment group appear in the right panel of Table 8. 

These results indicate that the perception of frequency (PF) had a smaller effect on 

perception of length (PL) for the Control group than it did for the Recall group 

(difference -0.17, p=0.0190), but there were no other differences between the groups on 

the other path relationships in the structural model. 

4.3 Burden and Survey Outcomes 

We also examined the distribution of the respondent burden index by selected panel 

survey characteristics of interest. The distribution of the burden index for intermittent 

respondents is shifted right relative to the distribution for respondents who completed all 

waves of the survey request (see left panel of Figure 3, W=108543.5, p<0.0001, 1-tailed 

test). The distribution of the burden index of respondents who had at least one refusal 

conversion noted in their contact attempt history is shifted right relative to the 

distribution of respondents who had none (see right panel of Figure 3, W = 47242.5, p = 

0.0009, 1-tailed test). However, there was no evidence of an association between the 

burden index with total expenditures and prevalence of refusal or don‟t know reports – 
two indirect indicators of data quality typically used by the Consumer Expenditure 

Survey Program. 

5. Discussion 

The results of this exploratory study suggest that the PLS path modeling approach to 

constructing a burden index is promising: it not only permits construction of the index, 

but it also can provide insights into how different treatment conditions may differentially 

affect burden dimensions. This is useful information when considering which features to 

target for intervention in an ongoing survey or in a survey redesign, and for evaluating 

the impact of those changes. 

As with any exploratory research, our study had a number of limitations, and in the 

process of conducting our analyses and examining the results, we learned valuable 

lessons that will improve future work in this area. First, when we examined the 

distribution of our burden index, we noticed that respondents tended to be chunked at 

regular intervals rather than dispersed evenly along a burden continuum. One potential 

reason for this is that the range and diversity of the burden index values is a function of 

the number of different covariate patterns possible among the indicators used in the 

measurement model. The relatively limited range of the index values and their lack of 

„smoothness‟ may be problematic in that the index values may not sufficiently 

differentiate between respondents across a “burden continuum” if we are trying to study 

the association of burden with a relatively large variety of survey measures of interest. 

We plan to extend our PLS path modeling approach in the future by incorporating an 

expanded set of items related to the different dimensions of burden. 

It also is important to systematically develop and test the burden items that you intend to 

serve as the basis of a summary index of the latent „burden‟ construct. As we noted 

earlier, the burden items used in this study were taken from an existing dataset, and many 

of the items were based on questions used in the empirical burden literature. 

Additionally, we examined but found no evidence for collinearity among our indicators. 

However, a full item development process would ideally involve some activities which 

we did not perform – e.g., pre-testing items to assess content and indicator specification, 

expanded testing of external validity, etc. (see, e.g., Arnett et al., 2003, or Helm, 2005). 

As such, it is unclear the extent to which our items covered the burden construct, so 

inferences from the model described in Section 4 should be viewed primarily as 

illustrative of working through the PLS methodology. 

Finally, our model specification and our substantive interpretations of the results were 

somewhat influenced by our choice of analytic methods and tools. For example, we 

decided to use the plspm package in R because it offered many advantages over other 

programs: it is free; the barriers to entry are low (coding is straightforward and flexible, 

documentation is available, etc.); and it can be used with all of the other R data analysis 

options. However, other software for implementing PLS path modeling (e.g. SmartPLS) 

may permit investigations of other features of this approach that were not available in the 

plspm package (e.g. exploring a model in which there is only one latent variable - burden 

- as in Helm, 2005). Additionally, the application of other data reduction methods to the 

analysis of burden-related items certainly merits further investigation. However, the 

results of this study illustrate the potential advantages of these approaches generally to 

better understanding conceptualizations and measures of respondent burden, which will 

ultimately aid its management and reduction. 
Table 1. Selected sample characteristics of study sample

Characteristic Control Recall

N 431 543

Respondent age group

 missing 0.5 0.4

 <25 3.3 3.5

 25-34 59.2 53.8

 35-64 36.0 41.4

 65+ 1.2 0.9

Respondent Education attainment

 missing 0.7 0.7

 Less than High School 6.5 7.2

 High school graduate 21.6 24.9

 Some college 29.2 26.9

 Undergraduate 24.6 20.8

 Postgraduate 17.2 19.5

Respondent‟s race (respondent can identify 

with more than one race)

 White 88.6 89.3

 Black 7.4 5.5

Hispanic origin

 missing 0.7 0.6

 1 6.5 6.5

 2 92.8 93.0

CU size

 1 26.9 26.7

 2 37.1 39.4

 3+ 36.0 33.9

Housing tenure

 Owner 89.6 92.1

 Rent 8.6 6.5

 Other 1.9 1.5
Table 2. Measurement model: indicators used for respondent burden dimensions

Item Variable name 

in analysis

Question & response options

Dimension: perceived length (PL)

Perceived survey length (final 

wave)

l4timep Do you feel that the length of today's 

interview was too long, too short, or about 

right?

1= too short; 2= about right; 3= too long 

Household size l3size Number of members in the household

Estimated time (final wave) l2timest How long do you think today's interview 

took? Response in minutes

Dimension: perceived effort (PE)

Interest in survey topic e1int How interesting was this survey to you?

1=very interested; 2=somewhat; 3=not very; 

4=not at all

Perceived ease in responding to 

survey

questions 

e2ease How difficult or easy was it for you to answer 

the questions in this survey?

1=easy; 2=some easy; 3=some difficult;

4=very difficult

Dimension: perceived frequency (PF)

Number of contact attempts f1numatm Number of contact attempts recorded in the 

contact history for the sample unit

Perceived appropriateness of 

number of contact attempts 

f3numatmp Thinking about the number of phone calls 

you received before each interview, would

you say that it was too many, or did it seem 

like a reasonable number?

1=Reasonable; 2=Too many

Perceived appropriateness of 

number of survey requests in 

survey panel 

f2numwave Over the course of the survey, you were 

asked to participate in (3 interviews for CON 

group, and 4 for REC group) interviews. 

Would you say that this was too many 

interviews, or did it seem like a reasonable 

number?

1=Reasonable; 2=Too many

Dimension: perceived burden (PB)

Perceived burden (final wave)* b1burdenp How burdensome was this survey to you?

1= not at all burdensome; 2= not very; 3= 

somewhat; 4= very 

* Typically, there should be more than 1 indicator for a latent construct, and all facets of the construct should be 

covered by indicators (although it is not necessary for every facet to be measured by multiple indicators – Helm 

(2005), p. 100). For the Perceived Burden dimension, we were constrained by the number of indicators available 

for each dimension in the analysis.
Table 3. Items used in PLS analysis: frequency 

distribution of scaled items

ITEM CONTROL

(n=431)

RECALL

(n=543)

e1int

 1=very interested 21.1 16.6

 2-somewhat 52.4 49.9

 3=not very 16.2 19.2

 4=not at all 10.2 14.4

e2ease

 1=easy 44.6 53.0

 2=some easy 38.3 35.2

 3=some difficult 15.8 10.1

 4=very difficult 1.4 1.7

f2numwave

 1=reasonable 67.5 58.0

 2=too many 32.5 42.0

f3numatmp

 1=reasonable 71.0 69.2

 2=too many 29.0 30.8

l4timep

 1=too short 0.2 1.3

 2=about right 82.8 78.5

 3=too long 16.9 20.3

b1burdenp

 1=not at all burdensome 34.3 26.3

 2=not very 29.5 28.6

 3=somewhat 31.3 37.8

 4=very 4.9 7.4

Table 4. Items used in PLS analysis: scale mean, means, and standard deviations

Control (n=431) Recall(n=543)

ITEM Mean SD Mean SD

e1int 2.2 0.9 2.3 0.9

e2ease 1.7 0.8 1.6 0.7

f1numatm* 5.7 5.6 5.5 5.4

f2numwave 1.3 0.5 1.4 0.5

f3numatmp 1.3 0.5 1.3 0.5

l2timest* 20.7 11.7 18.2 8.6

l3size* 2.5 1.4 2.4 1.4

l4timep 2.2 0.4 2.2 0.4

b1burdenp 2.1 0.9 2.3 0.9

* indicates mean of integer variables; otherwise, mean of scale for categorical variables are shown.
Table 5. Item loadings and cross loadings

Item PE PF PL PB

e1int 0.85 0.36 0.25 0.43

e2ease 0.65 0.15 0.23 0.30

f1numatm 0.08 0.30 0.07 0.13

f2numwave 0.32 0.82 0.27 0.32

f3numatmp 0.27 0.84 0.24 0.35

l2timest 0.14 0.07 0.40 0.16

l3size 0.05 0.06 0.23 0.10

l4timep 0.30 0.31 0.95 0.30

b1burdenp 0.49 0.40 0.33 1.00

Bolded numbers are loadings of each item with its associated construct 

Table 6. Estimated item weights

Latent variable Item Weight 95LCI 95UCI

Perceived effort (pe) e1int 0.77 0.69 0.84

e2ease 0.53 0.44 0.63

Perceived frequency (pf) f1numatm 0.02 -0.10 0.13

f2numwave 0.57 0.48 0.69

f3numatmp 0.62 0.50 0.72

Perceived length (pl) l2timest 0.21 0.07 0.35

l3size 0.20 0.10 0.32

l4timep 0.90 0.82 0.97

Perceived burden (pb) b1burdenp 1.00 1.00 1.00

Bootstrapping was used to compute significance of estimated weights.

Table 7. Correlations between latent variables

pe pf pl pb

pe 1.00

Pf 0.36 1.00

pl 0.31 0.31 1.00

pb 0.49 0.40 0.33 1.00

Table 8. Estimated path coefficients for the structural model 

Overall (combined data) By group

Path

Control 

and Recall 

(C + R)

SE 95LCI 95UCI Control

(C)

Recall

(R)

Difference

(C – R) P - value

pe->pl 0.22 0.04 0.17 0.28 0.26 0.22 0.04 0.5704

pe->pb 0.37 0.03 0.32 0.42 0.40 0.34 0.06 0.2587

pf->pl 0.23 0.04 0.17 0.29 0.13 0.30 -0.17 0.0190

pf->pb 0.22 0.03 0.17 0.28 0.25 0.19 0.06 0.3167

pl->pb 0.15 0.03 0.09 0.19 0.16 0.17 -0.01 0.9401

Standard errors and confidence intervals computed through bootstrapping. 
Figure 1. Estimated item weights and path coefficients 
 

Figure 2. Histogram of the respondent burden index by treatment group 
Figure 3. Distribution of respondent burden index by selected survey panel characteristics 
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