Nonresponse Bias Analysis of BMI Data

in the Eating & Health Module

Karen S. Hamrick1

USDA Economic Research Service

khamrick@ers.usda.gov

Abstract: This paper presents analysis on the respondents who did not report height and/or weight in the American 

Time Use Survey Eating & Health Module. Analyses focusing on correlations between BMI and time use could be 

biased if those who do not report height and/or weight differ significantly in other observable characteristics from 

the rest of the survey respondents. Results show that any item nonresponse bias using the Eating & Health Module 

height and weight data appears to be small. As a consequence, the Eating & Health Module data can be used in

analyses of body mass index (BMI) and time use patterns. 

Keywords: time use, American Time Use Survey, Eating & Health Module, nonresponse bias, item nonresponse, 

Body Mass Index, BMI, dissimilarity index, paradata

Introduction

The USDA Economic Research Service (ERS) Eating & Health Module, a supplement to the American Time Use 

Survey (ATUS), collected information on respondents’ height and weight in order to have data on time use and 

Body Mass Index (BMI) for obesity research. Some respondents refused to report their height and/or their weight, 

and so a BMI could not be calculated. Bias would occur if those with missing BMI had different time use patterns 

than other respondents, resulting in under- or over-estimates of average time spent in various activities. 

Eating & Health Module

The Bureau of Labor Statistics (BLS) ATUS is a continuous survey program that began in 2003, collecting time use 

data for almost every day of the year, with U.S. Census Bureau conducting the interviews.2

 One individual age 15 

or older from each sampled household is interviewed about his or her activities for the 24-hour period from 4 a.m. 

the day before the interview to 4 a.m. of the interview day. The ATUS data also include demographic, labor force 

participation, and household information. The Eating & Health Module supplement (EH Module) developed by 

ERS and funded by ERS and National Cancer Institute, was fielded from January 2006 to December 2008, 

producing 3 full years of data. Over 2006-08 the ATUS and EH Module resulted in 37,832 completed interviews of 

individuals age 15 or over.3

 Weighting factors were calculated by the U.S. Census Bureau so that when applied to 

the individual respondent data, the ATUS and EH Module produce nationally representative estimates. 

The EH Module contains questions on eating patterns; height, weight, and health status; Supplemental Nutrition 

Assistance Program (formerly Food Stamp Program)4

participation and income; meals obtained at school by 

household children; and grocery shopping and meal preparation. Since the ATUS is a time use survey, it does not 

include food intake information. Nonetheless, the data provide important information on eating/drinking duration, 

frequency, and context that allow for the characterization of eating patterns of different groups. 

Collecting height and weight information in order to calculate BMI allows for research on time use patterns by BMI. 

Because obesity is the most common food and nutrition-related health problem in the United States, understanding 

patterns of activities and time spent in various activities helps to understand how behavior of those who are obese 

differ from that of others. 

 

1 The views expressed here are those of the author and are not necessarily those of the Economic Research Service or the U.S. 

Department of Agriculture.

2 The ATUS conducts interviews every day of the year except on Thanksgiving and Christmas.

3 A small number of respondents completed the ATUS survey but did not complete the EH Module questions. Over 2006-08, 

there were a total of 82 of these cases, 0.2 percent.

4 As of October 1, 2008, the Food Stamp Program was renamed as the Supplemental Nutrition Assistance Program (SNAP).
Relevant Literature

 

Considerable research had been conducted on unit nonresponse in household surveys, the situation in which the 

sampled individual or household does not participate in the survey. The concern is that estimates using the survey 

data will have an increased variance, and in addition, there may be bias in the estimates, that is, a difference between 

an estimate using survey data and the actual population value, if those who do not participate have different 

characteristics related to the survey topic than those who do respond to the survey. The literature in this area is 

extensive, and good overviews are provided by Groves (2006), Groves, et al. (2002), and Bethlehem, Cobben, and 

Schouten (2011). 

In the area of nonresponse in time use data, Abraham, Maitland, and Bianchi (2006) investigated unit nonresponse 

in the ATUS, and concluded, “We find little support for the hypothesis that busy people are less likely to respond to 

the ATUS but find considerable support for the hypothesis that people who are weakly integrated into their 

communities are less likely to be contacted.” (p. 676) However, reweighting the estimates to account for this 

nonresponse produced only modest differences in time use. 

Abraham, Helms, and Presser (2008) also looked at nonresponse in the ATUS in relation to estimates of time spent 

in volunteer work and unit nonresponse. They investigated whether the social processes that lead to survey 

participation also lead to volunteering, which would result in an overrepresentation of volunteers in the ATUS. 

They concluded that indeed there is a connection between the causes of volunteering and the causes of survey 

participation, meaning that time use estimates would contain bias in time spent in volunteering and other prosocial 

activities. However, they do not recommend an adjustment for the bias. 

O’Neill and Sincavage (2004) conducted a response analysis survey on ATUS nonrespondents in order to 

understand respondent refusals. They found that one-third of the ATUS nonrespondents experienced CPS-related 

participant fatigue, and stated “...that their decision not to participate in ATUS was because of their previous CPS 

participation. Several nonrespondents stated that they were tired of the Census Bureau calling them and they felt that 

participating in CPS was doing more than their share of the work.” (p. 6)

Item nonresponse occurs when an individual or household participates in a survey, but does not provide information 

for one or more questions. The respondent may think that the information requested by the question is too sensitive 

to reveal, such as income.

5

 The individual may think that the question is not directly related to the survey topic, and 

so not important to answer. Or, the individual may not know the answer and does not want to spend time 

researching the answer. It is also possible that the individual does not know the answer and cannot obtain the 

answer. The research on item nonresponse is not as extensive as that on unit nonresponse, however Bethlehem, 

Cobben, and Schouten (2011, chapter 14), Dixon (2005), and Mason, Lesser, and Traugott (2002) provide good 

overviews. 

Fricker and Tourangeau (2010) investigated nonresponse, reluctant respondents, and data quality in the Current 

Population Survey and in the ATUS. Although their focus was unit nonresponse and data quality, they studied item 

nonresponse as well. Their data quality indicators for ATUS were “(1) total number of diary activities reported; (2) 

missing diary reports of basic daily activities; (3) round values for activity durations; and (4) item nonresponse on 

ATUS labor-force questions.” (p. 941) Among their findings was that respondents with high nonresponse 

propensity scores had fewer activities reported in their time diaries. In addition, they examined respondents who 

were refusal conversions, that is, those who originally refused to participate in the ATUS but later agreed to be

interviewed. They found that data quality was worse for refusal conversion respondents than for others.

Another area of research is late respondents and data quality. Triplett et al. (1996) studied late respondents 

(converted-refusal cases) using the 1993-94 time diary study conduced by the Environmental Protection Agency. 

They concluded that the converted-refusal respondents consistently provided less information than other 

respondents. Friedman, Clusen, and Hartzell (2003) investigated the characteristics of late respondents to the Health 

Care Survey of Department of Defense Beneficiaries—are the characteristics of late respondents different from 

other respondents? They used the “continuum of resistance” model that posits “...individuals who require the most 

contacts before participating in a survey are also the most resistant to being interviewed, and the most resistant a 

 

5 Tourangeau, Rips, and Rasinski (2000, chapter 9) provide a good discussion of sensitive questions and nonresponse.
respondent, the more similar he is to the most resistant individuals in the population—the nonrespondents.” (p. 992) 

They studied these late respondents, those who needed the most callbacks before participating, as a way of gaining 

insight into nonrespondents. They found that indeed the late respondents had different characteristics than non-late 

respondents, and also late respondents were more likely to have “don’t know,” “not applicable,” or just blank 

responses. As a consequence, their responses may be less reliable than non-late respondents.

 

Missing BMI—Characteristics of Respondents

The Eating & Health Module asked ATUS respondents their height and weight, which allows the calculation of 

body mass index (BMI):

6

I'm going to switch topics and ask you a few final questions about your physical health that might 

affect how you use your time. In general, would you say that your health is Excellent, Very Good, 

Good, Fair, or Poor? How tall are you without shoes? How much do you weigh without shoes?

Although EH Module BMI is calculated from self-reported height and weight in ATUS, researchers have found that 

differences between self-reported BMI and measured BMI are small, and also that self-reported values are 

acceptable to use for analysis of nonelderly adults.

7

 In addition, the expected underreporting of BMI (through 

underreporting of weight and overreporting of height) in the EH Module data does not appear to be large.8

 So while 

the EH Module BMIs should not be used to obtain an official measure of obesity in the United States, the data are 

suitable for analyzing time use behavior as it relates to BMI.9

Missing Values for Height and/or Weight

Over the three years of Eating & Health Module data, only 1,848 respondents, 4.88 percent of 37,832 completed 

interviews, did not report height and/or weight, and so have a missing BMI value. ERS did not impute BMI for any 

of the missing values. An additional 347 respondents (0.92 percent) reported being pregnant when the interviewer 

stated, “Since pregnancy effects weight, please let me know if you are currently pregnant,” and so the EH Module 

survey did not ask their weight as including BMI for pregnant women would confound BMI analysis. These 

respondents who reported being pregnant and so have missing BMIs are not included in the analysis presented here. 

Item nonresponse is considered a source of nonsampling error.10

 Respondents who are willing to participate in the 

American Time Use Survey may be unwilling to answer sensitive questions. Because of concern about sensitivity, 

the general health, height, weight, and income questions were placed at the end of the survey instrument.11

 

Respondents with missing BMI may have declined reporting height and/or weight perceiving them as not relevant to 

a time use survey.12

 It is also possible that some individuals, such as growing teenagers and elderly individuals with 

age-related weight loss, may not know their current height and/or weight.

It is useful to start with a basic description of BMI non-response in the ATUS. Table 113 shows the cell counts of 

the missing BMIs,

14 and Table 2 summarizes cell counts by year. The breakout by type of nonresponse is shown in 

 

6 Height and weight are bottom- and top-coded for confidentiality. The EH Module includes a screening question for pregnancy 

as pregnant women were not asked their weight and so have missing BMIs.

7 Cawley and Burkhauser, 2006. Kuczmarski, Kuczmarski, and Najjar, 2001. Danubio et al., 2008.

8

Pinkston and Steward, 2009.

9 This research does not address whether BMI should be adjusted to correct for underreporting of weight and overreporting of 

height due to self-reporting. See Pinkston and Steward (2009) and Danubio et al. (2008) for discussions of BMI bias correction. 

10 ERS and BLS concluded that including height and weight in the EH Module would not lead to unit nonresponse, that is, that 

sampled individuals would refuse to be interviewed for the ATUS because they did not want to be asked their height and weight. 

This is because the ATUS is not a health survey, and so individuals would not expect that height and weight questions would be 

asked. For a discussion of how the topic of a survey can lead to nonresponse of individuals concerned about sensitive questions, 

see Tourangeau, Groves, and Redline (2010).

11 See Vernon (2005) for discussion of pretest of EH Module. A text version of the questionnaire is at 

http://stats.bls.gov/tus/ehmquestionnaire.pdf

12 Dixon (2002) found that some respondents in the Current Population Survey respond to labor force questions, indicating that 

they agree with the purpose of the survey, but not to demographic questions that they may perceive as not relevant.

13 All estimates presented in this report were calculated using the ATUS data and the EH Module data for 2006-08. The ATUS 

Respondent, Activity, Activity Summary, Roster, Who, and ATUS-Current Population Survey files were used, along with the 
Table 3. Because some respondents did not report height, some did not report weight, and some did not report 

either, the sum of missing height and missing weight is greater than the total number of missing BMI. For height 

and especially for weight, more respondents refused to report values than those who answered that they did not 

know their height and/or weight. However, whereas a “refusal” is a refusal to answer the question, a “don’t know” 

response may be a soft refusal, so it is unclear how to interpret the “don’t know” responses.

Table 1—Cell counts of respondents by BMI status and by gender. 

2006-08 ATUS and EH Module data, cell counts (unweighted), age 15 and over, pregnant women excluded. 

Men Women Total

Missing BMI 389 1,459 1,848

Underweight (BMI<18.5) 148 471 619

Normal weight (18.5≤BMI<25.0) 4,669 8,498 13,167

Overweight (25.0≤BMI<30.0) 6,814 5,649 12,463

Obese (30.0≤BMI) 4,407 4,981 9,388

Total 16,427 21,058 37,485

Table 2—Cell counts of missing BMI respondents by year.

2006-08 ATUS and EH Module data, cell counts (unweighted), age 15 and over, pregnant women excluded. 

2006 2007 2008 Total

Missing BMI

% of Total

588

4.6%

593

4.9% 

667

5.3% 

1848

4.9% 

Total respondents 12,764 12,108 12,613 37,485

 

Table 3—Missing height and weight respondents by type of nonresponse.

2006-08 ATUS and EH Module data, cell counts (unweighted), age 15 and over, pregnant women excluded. 

Don’t Know Refused Total

Missing height 381 478 859

Missing weight 466 1,083 1,549

Survey characteristics of BMI nonrespondents

A way to describe Missing BMI respondents is by their characteristics as an ATUS respondent. Perhaps the Missing 

BMI respondents are reluctant to be interviewed and so are “uncooperative respondents.” An indicator of a reluctant 

respondent is the number of call attempts the Census Bureau made in order to obtain a completed interview.15, 16

 

ATUS Methodology Call History and Case History files, and the EH Module Respondent and Replicate Weights files. The

dataset used contains 37,832 respondents, with a total of 753,604 activities. Excluding respondents who reported being pregnant 

results in a total of 37,485 respondents. All estimates and unweighted counts are for individuals age 15 and over. All 

calculations were done using SAS 9.2. Estimation procedures outlined in the ATUS User’s Guide

(http://stats.bls.gov/tus/atususersguide.pdf) and the EH Module User’s Guide (http://ers.usda.gov/Publications/AP/AP047/) were 

followed. Averages were calculated as the mean unless otherwise stated. Standard errors were calculated according to Section 

7.5 of the ATUS User’s Guide. The EH Module Replicate Weights were used to calculate standard errors.

14 Body Mass Index is calculated as: weight (lb) / [height (in)]2

x 703. Adult BMI groups are Underweight (BMI < 18.5), 

Normal weight (18.5 ≤ BMI < 25), Overweight (25 ≤ BMI < 30), and Obese (30 ≤ BMI). For purposes of interpreting BMI, 

Centers for Disease Control defines adults as those age 20 and over, and uses a different interpretation for youth and teens. 

However, here these adult groupings are, for convenience of exposition, used for all respondents age 15 and older as the focus is 

on the missing BMIs for the entire dataset, and not on analyzing time use patterns of those who are, say, overweight. In analysis 

by BMI, ERS uses the CDC adult and youth/teen definitions.

15 Each sampled designated person is assigned a diary day of the week, and so the interview would be the next day. (E.g., 

Monday is the diary day so Tuesday is the interview day.) If the designated person is not reached or the interview cannot be 

completed, call attempts are made on the interview day for up to eight weeks.

16 The number of attempted calls also includes times that the respondent’s case file was opened. So, an actual call may not have 

been made as the file may have been viewed or queued up for a call. The cases with high maximum call attempts, such as the 

case of 94 call attempts, does not actually represent 94 calls being made to the respondent. Information from Mary Dorinda 

Allard, Director, BLS Division of Labor Force Statistics, in a discussion on March 11, 2011.
Using the ATUS Call History data, one of the ATUS survey methodology files, I find that the average number of 

call attempts appear to be higher for those respondents with missing BMI, 7.1 (weighted mean) call attempts versus 

6.8 for the total population, however these averages are not statistically different at the 90 percent level, so the 

number of call attempts for those with missing BMI is about the same as for others. (Table 4) Related to call 

attempts is the number of weeks (1-8) that were needed to successfully complete the interview. On average, the 

Missing BMI respondents took longer, 2.4 weeks (weighted mean) and is statistically different from the total 

population mean of 2.2 weeks weeks. Their median was higher as well, 2 weeks versus one week for the other 

groups. The higher average number of weeks to interview may indicate that these respondents were reluctant to 

participate in the survey. 

Table 4—Call attempts and weeks called by BMI group.

2006-08 ATUS and EH Module data, age 15 and over, pregnant women excluded. 

Total

population

Missing

BMI

UnderweightNormal

weight

OverweightObese

Number of call attempts

 mean (unweighted) 6.7 7.0 6.5 6.8 6.8 6.5 

 mean (weighted) 6.8

(0.06)

7.1

(0.20)

6.8

(0.44)

6.9 

(0.09)

6.8

(0.10)

6.6

(0.10)

 median (unweighted) 4 4 4 4 4 4 

 median (weighted) 4 5 4 4 4 4 

 75th percentile (unwgtd) 9 9 8 9 9 8 

 75th percentile (weighted) 9 10 9 9 9 9 

 minimum 1 1 1 1 1 1 

 maximum 94 72 42 94 78 86

Number of weeks call attempts made

 mean (unweighted) 2.2 2.4 2.2 2.2 2.2 2.2 

 mean (weighted) 2.2

(0.01)

2.4

(0.05)

2.3 

(0.12)

2.2

(0.02)

2.3 

(0.02)

2.2

(0.02)

 median (unweighted) 1 2 1 1 1 1 

 median (weighted) 1 2 1 1 1 1 

 75th percentile (unwgtd) 3 3 3 3 3 3 

 75th percentile (wgtd) 3 3 3 3 3 3 

 minimum 1 1 1 1 1 1 

 maximum 8 8 8 8 8 8 

Note: Variables TUATTMPTNO and TUATTMWEEK from ATUS Call History file used. Standard errors are in parenthesis.

I looked at late respondents since those are likely to be similar to nonrespondents. Since the 75th percentile number 

of weeks call attempts were made is 3 weeks, late respondents are defined as those who needed to be called 4 or 

more weeks to participate in the ATUS. Table 5 shows that 24.1 percent (weighted percent) of missing BMI 

respondents were late respondents, a higher share than late respondents with a BMI value (19.7 percent). So, the 

missing-BMI group has a higher share of those reluctant to be interviewed.

Table 5—Missing BMI by late respondent status—call attempts for 4+ weeks before interview. 

2006-08 ATUS and EH Module data, age 15 and over, pregnant women excluded. 

Missing BMI Total Population, non-missing BMI

Unweighted Unweighted Weighted Unweighted Unweighted Weighted

N percent N percent

Late Respondent (4+ weeks) 405 21.9 24.1

(1.26) 6,762 19.0 19.7(0.30)

Not late respondent 1443 78.1 75.9

(1.26) 28,875 81.0 80.3(0.30)

Note: Standard errors are in parenthesis.
Another indicator of an uncooperative respondent is the number of activities that the respondent reported in the time 

diary. The mean number of activities reported across the total population was 19.7 (weighted), with a median of 19 

activities, a minimum of 5 and a maximum of 91.17 (Table 6) Looking at the number of activities in the time diaries, 

the Missing BMI group does indeed have fewer activities as measured by mean, median, and by maximum value. 

Table 6—Number of diary activities by BMI group.

2006-08 ATUS and EH Module data, age 15 and over, pregnant women excluded.

Total

population

Missing

BMI

UnderweightNormal

weight

OverweightObese

Mean (unweighted) 19.9 18.6 19.8 20.7 19.7 19.3

Mean (weighted) 19.7

(0.05)

18.7

(0.22)

19.2

(0.46)

20.3

(0.08)

19.5

(0.09)

19.3

(0.10)

Median (unweighted) 19 17 19 19 19 18

Median (weighted) 19 17 18 19 18 18

75th percentile (unwgtd) 24 23 24 25 24 23

75th percentile (weighted) 24 23 23 24 24 24

Minimum 5 5 6 5 5 5 

Maximum 91 58 91 75 81 85

Note: Standard errors are in parenthesis.

Another indicator of lack of respondent cooperation is whether the respondent answered other sensitive questions. 

Income is well documented as a sensitive question that can be problematic in household surveys. The EH Module 

benefited from several rounds of cognitive pre-testing, in which ERS was able to craft income questions that 

produced a high response rate.

18

 The first question asked if household income is above or below the dollar amount 

of 185 percent of the poverty threshold for the respondent’s household size. If the respondent answered “below” or 

gave a “don’t know” or “refused,” answer, then the second question was asked, whether the household’s income was 

above or below the dollar amount of 130 percent of the poverty threshold for the respondent’s household size. 

Looking at respondents who did not report household income for the 185 percent question,

19 43 percent (weighted) 

of those who did not report BMI also did not report income. (Table 7) This is considerably larger than and 

statistically different from the 10 percent of respondents who reported height and weight but did not report income.

Table 7—Respondents with missing BMI by reporting or not reporting income.

2006-08 ATUS and EH Module data, age 15 and over, pregnant women excluded. 

Income reported Income missing

Missing BMI count 1,103 745

Total population, non-missing BMI count 32,718 2,919

Missing BMI row % unweighted 59.7% 40.3% 

Total population, non-missing BMI row %, unweighted 91.8% 8.2%

Missing BMI row %,

weighted

57.2% 

(1.67%)

42.8% 

(1.67%)

Total population, non-missing BMI row %,

weighted

89.6% 

(0.20%)

10.4% 

(0.20%)

Note: Standard errors are in parenthesis.

 

17 Note that BLS ATUS excludes interviews that have fewer than 5 reported activities, and/or where reported activities do not 

cover at least 21 hours of the day. There is no constraint on the maximum number of diary activities. Email correspondence 

from Rachel Krantz-Kent, Manager, American Time Use Survey, October 7, 2010. 

18 The EH Module income questions had a nonresponse rate of 10 percent, which is lower than the CPS income nonresponse rate 

of 13 percent. Using household earnings, ERS and BLS imputed income for some respondents, yielding only 6 percent with 

missing income in the released EH Module data. See Hamrick (2010) for more information. 

19 Used here is the original missing values for variable EEINCOME1, that is, EEINCOME1 without the values that were imputed 

for those who did not report income. The flag EXINCOME1 was used to remove imputed values. EUINCOME2 (more/less than 

130 percent poverty threshold) was not used here.
As a final indicator, I looked at time diary quality. Using the ATUS Case History data, an ATUS survey 

methodology file, time diary quality as reported by the Census interviewer can be used to evaluate whether the 

missing BMI observations were from uncooperative respondents. After each ATUS interview is completed, the 

Census interviewer answers two data quality questions: “Is there any reason the information from this interview 

should NOT be used?” and “Why do you think the data should not be used?”20

In only 275 cases of the 37,484 completed interviews, the Census interviewer thought that the respondent’s time 

diary was not of good quality. (Table 8) The unweighted share of those with missing BMI and no diary quality 

issues (97.7 percent) is less than for those with a BMI value (99.4 percent) as expected. In addition, the weighted 

share of those with missing BMI and no diary quality issue is 98.2 percent, lower and statistically different from the 

99.3 percent for those with a BMI value and no diary quality issue. Although the number and percent of 

respondents with diary quality issues is very small, these results contribute to understanding the missing BMI 

respondents. 

Table 8—Missing BMI respondents by diary quality.

2006-08 ATUS and EH Module data, age 15 and over, pregnant women excluded. 

No diary 

quality issue

Diary quality problems Total

intentionally 

wrong

could not 

remember

deliberately long 

durations

other 

reason

Missing BMI count 1,806 4 19 11 8 1,848

Total population, nonmissing BMI count 35,404 32 85 77 39 35,637

Missing BMI row % 

unweighted 97.7% 0.2% 1.0% 0.6% 0.5% 100%

Total population, nonmissing BMI

row %, unweighted

99.4% 0.1% 0.2% 0.2% 0.1% 100%

Missing BMI 

row %, weighted

98.2%

(0.37) 

0.2% 

(0.16)

0.9% 

(0.26)

0.4% 

(0.15)

0.3% 

(0.12) 100%

Total population, nonmissing BMI

row %, weighted

99.3%

(0.06)

0.1% 

(0.02)

0.2% 

(0.04)

0.3% 

(0.04)

0.1% 

(0.03) 100%

Note: Variable TUDQUAL2 from ATUS Case History data file. Standard errors are in parenthesis.

Missing BMI—Time Use Profiles

Having an understanding of the characteristics of those with missing BMI is useful, however more important to time 

use research is whether these missing BMIs cause bias in time use estimates. The ATUS and EH Module data exist

in order to analyze Americans’ time spent in various activities. Are the respondents who did not report height 

and/or weight different with respect to their time use patterns than others? Results above found that on average 

those who do not report height and/or weight reported fewer activities in their diaries, but what are the time duration 

differences?

In looking at time use by the ATUS major activity groups,21 (Table 9) the most striking difference between the 

Missing BMI group and the other BMI groups is the long amount of time those who did not report height and/or 

weight spend in Personal care, 586 minutes (9.8 hours), which includes sleeping. The Missing BMI group also had 

a high average time spent in household activities and caring for household members on an average day, however 

since the majority of the Missing BMI group is female this is not surprising. Interestingly, the Missing BMI group 

had the highest average time spent in Other activities. Other activities include gaps in the time diary that the ATUS 

 

20 See American Time Use Survey Questionnaire, June 2010, http://stats.bls.gov/tus/tuquestionnaire.pdf. 

21 For definitions of the major activity groups, see ATUS User’s Guide Appendix H: Bridge between published tables activity 

categories and ATUS coding lexicon activity categories, http://stats.bls.gov/tus/atususersguide.pdf
interviewer was not able to code due to insufficient detail, respondent refusal, or respondent unable to recall activity. 

The higher average Other Activities time is consistent with the concept of uncooperative respondents. 

Looking at the major activity groups by gender, the Male Missing BMI group has an average Personal Care time 

about the same as Underweight males. (Table 10) The average time males with Missing BMI spent in Eating & 

drinking is the shortest of the BMI groups, and the time spent in Other activities, that is, diary gaps, is the longest, 

although not statistically different from the total population or BMI group means. The Female Missing BMI group 

has a high average time spent in Personal care, the lowest average time spent in Eating and drinking, and the most 

time in the Other category, just as the case with the Missing BMI Males.

Table 9—Time spent in major activities by BMI group.

2006-08 ATUS and EH Module data, population (weighted), age 15 and over, pregnant women excluded. 

minutes

Total

population

Missing

BMI

UnderweightNormal

weight

OverweightObese

Personal care 562.1

(0.86)

586.2

(4.86)

591.5

(7.33)

567.2

(1.66)

554.1

(1.48)

558.8

(1.99)

Household activities 111.6

(0.89)

120.9

(4.29)

100.1

(6.10)

112.8

(1.38)

111.8

(1.46)

108.8

(1.97)

Caring for household members 31.2

(0.42)

41.8

(2.68)

27.5

(4.14)

33.0

(0.69)

28.2

(0.72)

30.8

(1.01)

Caring for non-household members 12.8

(0.35)

12.7

(1.55)

13.5

(3.27)

11.6

(0.54)

13.6

(0.70)

13.4

(0.70)

Paid Work 226.1

(1.77)

198.8

(7.97)

153.7

(12.12)

209.3

(3.18)

245.7

(3.09)

233.8

(3.56)

Educational 27.8

(0.72)

26.0

(3.68)

83.6

(9.17)

44.2

(1.66)

17.0

(1.24)

15.7

(1.22)

Purchasing goods 38.0

(0.46)

40.9

(2.05)

43.5

(4.48)

38.1

(0.77)

37.5

(0.89)

37.5

(0.97)

Purchasing services 7.3

(0.23)

8.4

(0.95)

10.7

(2.58)

7.0

(0.38)

7.4

(0.40)

7.3

(0.40)

Purchasing household services 1.3

(0.08)

0.9

(0.25)

0.3

(0.22)

1.3

(0.15)

1.3

(0.15)

1.5

(0.20)

Government & civic 0.7

(0.07)

0.5

(0.29)

1.5

(0.68)

0.5

(0.11)

0.6

(0.11)

0.9

(0.20)

Eating & drinking 73.9

(0.40)

65.5

(1.63)

71.8

(3.03)

75.6

(0.75)

75.5

(0.74)

71.4

(0.72)

Leisure 285.7

(1.38)

284.1

(5.86)

284.2

(11.57)

270.3

(2.33)

286.2

(2.49)

307.3

(2.78)

Sports 22.6

(0.46)

13.2

(1.39)

19.9

(3.34)

27.4

(0.86)

23.6

(0.92)

16.6

(0.84)

Religious 9.5

(0.22)

10.5

(1.04)

7.8

(1.75)

9.2

(0.39)

9.4

(0.40)

10.2

(0.43)

Volunteer 10.0

(0.35)

7.2

(0.96)

6.7

(1.68)

10.7

(0.58)

10.4

(0.60)

9.2

(0.64)

Phone & Mail 7.1

(0.19)

6.4

(0.84)

10.0

(1.33)

8.3

(0.35)

6.3

(0.28)

6.5

(0.37)

Other 12.2

(0.34)

15.8

(1.51)

13.5

(3.26)

13.7

(0.66)

11.5

(0.63)

10.3

(0.45)

Total 1440.0 1440.0 1440.0 1440.0 1440.0 1440.0

Note: Activities listed are ATUS major activity groups. Travel time included with each activity. Standard errors in parenthesis.
Table 10—Time spent in major activity groups, by gender.

2006-08 ATUS and EH Module data, population (weighted), age 15 and over, pregnant women excluded.

minutes

Men Women

Total

population

Missing

BMI

UnderweightNormal

weight

OverweightObese Total

population

Missing

BMI

UnderweightNormal

weight

OverweightObese

Personal care 551.5

(1.35)

592.4

(10.99)

592.8

(11.28)

563.9

(2.92)

545.3

(2.12)

541.8

(2.62)

572.2

(1.25)

584.1

(5.11)

591.0

(8.93)

569.4

(1.81)

566.8

(2.48)

578.0

(2.95)

Household activities 85.0

(1.17)

60.2

(5.85)

61.7

(10.44)

81.6

(2.21)

91.0

(1.91)

82.9

(2.32)

137.1

(1.36)

141.4

(5.31)

115.2

(7.71)

133.9

(1.93)

142.2

(2.63)

138.1

(2.72)

Caring for household

members

20.3

(0.50)

18.7

(3.41)

12.6

(4.09)

17.2

(0.78)

20.6

(0.63)

23.5

(1.31)

41.6

(0.63)

49.6

(3.39)

33.5

(5.26)

43.7

(1.01)

39.2

(1.38)

39.1

(1.54)

Caring for non-household

members

10.7

(0.47)

6.6

(1.76)

10.6

(4.42)

9.9

(0.71)

10.9

(0.80)

11.5

(0.98)

14.8

(0.54)

14.7

(2.02)

14.7

(4.10)

12.8

(0.80)

17.5

(1.17)

15.5

(1.00)

Paid Work 271.1

(2.51)

254.0

(19.92)

159.1

(25.46)

244.0

(5.26)

285.4

(4.75)

284.8

(5.31)

182.9

(2.28)

180.3

(8.78)

151.6

(16.02)

185.8

(3.71)

187.6

(4.51)

176.3

(4.69)

Educational 25.5

(1.02)

37.3

(9.16)

105.3

(19.40)

45.8

(2.45)

14.6

(0.29)

16.1

(1.69)

30.0

(1.03)

22.2

(4.15)

75.0

(10.65)

43.0

(2.22)

20.6

(2.30)

15.2

(1.83)

Purchasing goods 30.3

(0.68)

26.1

(4.10)

29.7

(7.78)

27.00

(1.10)

31.9

(1.13)

32.0

(1.22)

45.3

(0.66)

45.8

(2.49)

48.9

(5.50)

45.6

(1.08)

45.7

(1.29)

43.8

(1.48)

Purchasing services 5.2

(0.27)

6.2

(1.81)

8.8

(3.79)

5.0

(0.56)

5.3

(0.44)

4.9

(0.46)

9.4

(0.37)

9.2

(1.10)

11.5

(3.30)

8.4

(0.51)

10.5

(0.77)

10.0

(0.73)

Purchasing household

services

1.6

(0.14)

0.4

(0.20)

0 

(0)

1.4

(0.31)

1.7

(0.24)

1.8

(0.34)

1.1

(0.08)

1.1

(0.32)

0.4

(0.31)

1.3

(0.13)

0.7

(0.10)

1.2

(0.20)

Government & civic 0.7

(0.12)

1.3

(1.06)

0.6

(0.44)

0.5

(0.14)

0.6

(0.16)

0.9

(0.32)

0.6

(0.09)

0.3

(0.14)

1.8

(0.92)

0.5

(0.14)

0.5

(0.15)

1.0

(0.25)

Eating & drinking 76.1

(0.58)

59.8

(2.39)

67.5

(5.41)

77.7

(1.20)

77.3

(0.98)

74.2

(1.06)

71.9

(0.54)

67.4

(2.01)

73.5

(3.52)

74.1

(0.90)

72.8

(1.12)

68.1

(1.01)

Leisure 299.8

(2.20)

331.4

(15.09)

343.7

(23.90)

295.5

(3.98)

291.3

(3.60)

312.9

(4.06)

272.1

(1.81)

268.2

(6.24)

260.8

(13.11)

253.2

(2.84)

278.7

(3.40)

300.9

(3.97)

Sports 29.8

(0.78)

15.1

(3.08)

19.1

(5.50)

35.8

(1.49)

30.4

(1.44)

24.2

(1.47)

15.8

(0.50)

12.6

(1.57)

20.2

(4.06)

21.7

(0.92)

13.8

(0.95)

8.0

(0.57)

Religious 7.9

(0.31)

11.5

(2.64)

6.2

(2.32)

7.7

(0.56)

7.8

(0.44)

7.8

(0.53)

11.1

(0.33)

10.2

(1.08)

8.5

(2.11)

10.2

(0.51)

11.7

(0.68)

12.9

(0.68)

Volunteer 9.2

(0.49)

3.5

(1.17)

5.7

(3.14)

8.7

(0.76)

10.3

(0.85)

9.0

(0.99)

10.7

(0.47)

8.5

(1.18)

7.1

(1.87)

12.1

(0.82)

10.5

(0.84)

9.5

(0.85)

Phone & Mail 3.9

(0.20)

3.7

(1.18)

6.4

(2.42)

5.0

(0.43)

3.8

(0.32)

2.8

(0.27)

10.2

(0.31)

7.3

(1.06)

11.5

(1.65)

10.5

(0.50)

10.1

(0.51)

10.7

(0.69)

Other 11.4

(0.51)

12.0

(2.69)

10.2

(4.54)

13.2

(1.11)

11.8

(0.97)

8.8

(0.59)

13.0

(0.43)

17.2

(1.84)

14.8

(3.58)

14.0

(0.83)

11.0

(0.66)

11.9

(0.73)

Total 1440.00 1440.00 1440.00 1440.00 1440.00 1440.00 1440.0 1440.0 1440.0 1440.0 1440.0 1440.0

Note: Activities listed are ATUS major activity groups. Travel time included with each activity. Standard errors in parenthesis.
Measure of Dissimilarity

The difference between the Missing BMI group and the other BMI groups can be measured systematically over the 17 

major time use activities by using the weighted absolute-deviation index (WADI),22 a measure of dissimilarity of time 

use “activity profiles” across groups. The WADI is defined as:23

WADI = 

where i = activity

 k = number of activities

 ai = time in minutes spent in activity i by group a

 bi = time in minutes spent in activity i by group b

The absolute difference in times spent is divided by the total time spent in activity i by the two groups, then that is 

weighted by the fraction of activity i’s time by total available time to groups a and b (1440 + 1440 = 2880, 1440 is the 

total minutes in a day). These terms are then summed over all activities.

The smaller the WADI is, the smaller the difference between the two groups, and a WADI=0 would indicate no 

difference in activity profiles. Stewart (2006) recommends using this index over other dissimilarity indices as it is not 

sensitive to the level of aggregation of activities (e.g., ATUS major groups of 2-digit activity codes versus ATUS 4-digit 

activity code groups) and also because short-duration activities receive little weight. The index value is “equal to the 

average proportional difference in the time spent in all activities.” (p. 59) 

WADIs by BMI group are in Table 11. Overall, the WADIs are small, indicating little difference between the activity 

profiles of the Missing BMI group and those of other BMI groups.24

 Among the BMI groups, the Missing BMI group 

had the lowest WADI with the Normal weight group both for the total population and for men. This means that looking 

at the time use profile of those with Missing BMI, their time use resembled that of Normal weight individuals the most. 

For women, the lowest WADI was with the Overweight group, which indicates that the activity profile of women with 

missing BMI is closest to that of overweight women.

Table 11—Measure of dissimilarity by BMI group.

2006-08 ATUS and EH Module data, population (weighted), age 15 and over, pregnant women excluded. 

Total Missing

BMI

UnderweightNormal

weight

Overweight Obese

Total Population WADI 0.0356 na 0.0600 0.0407 0.0513 0.0494

Men WADI 0.0626 na 0.0758 0.0566 0.0801 0.0692

Women WADI 0.0190 na 0.0586 0.0343 0.0250 0.0298

Note: Shading indicates smallest index among the BMI groups.

There is always the possibility that the long Personal Care times are due to unobserved characteristics. For example, the 

Missing BMI respondents could be more likely to suffer from depression or other illnesses at the time of the ATUS 

interview, which could result in longer sleep times and fewer activities in the time diary. However, the longer average 

times engaged in Personal Care may also indicate that the Missing BMI group are uncooperative respondents, that is, 

those who cannot remember, do not want to make the effort to remember, or do not want to disclose their activities in 

detail. Reporting longer sleep times allows the respondent to cover a large block of time with one activity. This is 

consistent with the analysis above on indicators of uncooperativeness. As a consequence, any nonresponse bias of 

underreported sleep time may be one of sleep time as reported, and not actual sleep time, since the Missing BMI group

has a disproportionate share of uncooperative respondents. 

 

22 Note that weighting with respect to the WADI means applying the share of time spent on an activity of total time to each 

difference, whereas weighted elsewhere in this report indicates that sample weights were used to produce national estimates.

23 From Stewart (2006).

24 The mean BMI for those who reported height and weight age 15 and older is 27.13; for men, 27.60; and for women 26.66.
Conclusions

The 4.9 percent of Eating & Health Module respondents who did not report height and/or weight had disproportionately

higher indicators of being reluctant or uncooperative respondents. It took more weeks to interview these respondents, 

indicating that they were reluctant to participate in the survey. They were more likely to have time diaries with little 

detail as measured by the number of activities in the diary, and they were more likely to have poor quality time diaries. 

They were less likely to answer another sensitive question on the survey, income. These findings indicate that the 

respondent’s lack of reporting height and/or weight had less to do with the height and weight questions, but was more 

about the respondent’s view of participating in the survey.

The time use profiles of the total population and of men with missing BMI closely resembled those of respondents with 

normal weight. For women, the missing BMI time profile closely resembled that of women who were overweight. This 

suggests that those who did not report height and weight are unlikely to be at either end of the BMI spectrum, that is, 

unlikely to be severely underweight or morbidly obese, mitigating any bias. Since the missing BMI respondents have 

time profiles close to those in the middle BMI groups (normal weight and overweight), excluding their time diaries in 

analysis is unlikely to produce bias in time use estimates. As a consequence of these findings, any item nonresponse 

bias in the Eating & Health Module height and weight data appears to be small, allowing for analysis of time use by 

BMI.

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Websites

American Time Use Survey: http://www.bls.gov/tus/

Eating & Health Module: http://ers.usda.gov/Data/ATUS/

Centers for Disease Control BMI information: http://www.cdc.gov/healthyweight/assessing/bmi/index.html