United States 

Department of 

Agriculture

Economic

Research

Service

Karen S. Hamrick

Nonresponse Bias Analysis 

of Body Mass Index Data 

in the Eating and Health 

Module

Technical

Bulletin

Number 1934

August 2012
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Recommended citation format for this publication: 

Hamrick, Karen S. Nonresponse Bias Analysis of Body Mass Index Data

in the Eating and Health Module, TB-1934, U.S. Department of Agriculture, 

Economic Research Service, August 2012.

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A Report from the Economic Research Service

Abstract

The ERS Eating and Health Module, a supplement to the American Time Use Survey 

(ATUS), included questions on height and weight so that respondents’ Body Mass Index 

(BMI—a measure of body fat based on height and weight) could be calculated and 

analyzed with ATUS time-use data in obesity research. Some respondents did not report 

height and/or weight, and BMIs could not be calculated for them. Analyses focusing 

on correlations between BMIs and time use could be biased if respondents who did not 

report height and/or weight differ significantly in other observable characteristics from 

the rest of the survey respondents. However, findings reveal that any nonresponse bias 

associated with the height and weight data appears to be small and would not affect 

future analyses of BMIs and time-use pattern correlations.

Keywords: time use, American Time Use Survey, Eating and Health Module, nonresponse bias, item nonresponse, Body Mass Index, BMI, dissimilarity index, paradata

Acknowledgments

The author wishes to acknowledge the support and assistance of USDA, ERS colleagues 

Jayachandran Variyam, Ephraim Leibtag, and Jeremy Weber. Also, the author wishes 

to thank Rachel Krantz-Kent, Rose Woods, others on the American Time Use Survey 

staff, and an anonymous reviewer for their helpful comments. This report also benefited 

from comments from audience members at the 2012 Federal Committee on Statistical 

Methodology Research Conference. Special thanks are extended to John Weber and 

Wynnice Pointer-Napper for editorial and design assistance.

Nonresponse Bias Analysis of 

Body Mass Index Data in the 

Eating and Health Module

Technical

Bulletin

Number 1934

August 2012

Karen S. Hamrick, khamrick@ers.usda.gov
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Relevant Literature—Item Nonresponse, Time Use, and BMI . . . . . . . 4

Missing BMIs—Characteristics of Respondents . . . . . . . . . . . . . . . . . . . 7

Missing BMIs—Time-Use Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Is a Remedy for Missing BMI Data Needed? . . . . . . . . . . . . . . . . . . . . . 25

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
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Summary

What Is the Issue?

ERS collected data on Americans’ time-use patterns and food-related information in its Eating and Health Module (EH Module), a supplement to the 

American Time Use Survey (ATUS). The EH Module also included questions on height and weight so that respondents’ Body Mass Index (BMI—a 

measure of body fat based on height and weight) could be calculated and 

analyzed with ATUS time-use information. Though the EH Module had a 

high rate of cooperation among respondents, just under 5 percent of respondents did not report height and/or weight, and ERS could not calculate 

BMIs for these individuals. This raises concerns of bias in the data due to 

the missing BMI observations. In this report, ERS examines the BMI data 

to determine if the missing values hinder the ability of researchers to use 

the data in future analyses. If respondents who did not report height and/or 

weight differed significantly in other observable characteristics from the rest 

of the survey respondents, then time-use estimates may be higher or lower 

than they would be if BMIs were available for all respondents.

What Did the Study Find?

•	Respondents who did not report height and/or weight had disproportionately higher indicators of being reluctant or uncooperative survey participants than other respondents. For example, it took more phone calls over 

more weeks to obtain a completed interview from these participants. This 

suggests that for these respondents, the tendency to not report height and/

or weight had less to do with sensitivity to height and weight questions 

and more to do with negative views toward participating in the survey. 

•	The time-use profiles of the total population and of men with missing

BMIs closely resembled the profiles of respondents who were normal 

weight (18.5 ≤ BMI < 25.0). 

•	The time-use profiles of women with missing BMIs closely resembled the

profiles of women who were overweight (25.0 ≤ BMI < 30.0). 

•	These findings suggest that those who did not report height and weight

are unlikely to be at either end of the BMI spectrum—underweight 

(BMI<18.5) or obese (BMI>30.0)—mitigating any bias in the data. 

Based on these findings, any bias in the EH Module height and weight data 

stemming from nonresponse appears to be small and would not affect future 

analyses of correlations between BMI and time use.

How Was the Study Conducted?

This study used data from the Bureau of Labor Statistics American Time Use 

Survey and the ERS Eating and Health Module for 2006-08. Researchers 

analyzed demographic characteristics, such as gender and age, of respondents who did not provide weight or height information. Data quality 

measures (e.g., completeness or incompleteness of diary reports recording 

respondents’ activities) served as indicators of respondents’ reluctance or 
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uncooperativeness toward participating in the ATUS. Multivariate analysis 

was performed on respondents’ demographic and survey characteristics. 

A measure of dissimilarity was used to compare time profiles across BMI 

groups to determine which BMI group most resembled the respondents with 

missing BMIs in terms of activities reported in the time diaries.
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Introduction

ERS collected data on Americans’ time-use patterns and food-related information in its Eating and Health Module (EH Module), a supplement to the 

American Time Use Survey (ATUS). The module included questions on 

height and weight so that respondents’ Body Mass Index (BMI—a measure of 

body fat based on height and weight) could be calculated and analyzed with 

ATUS time-use data in obesity research. Because some survey respondents 

refused to report their height and/or weight, ERS could not calculate BMIs 

for these individuals. Does the lack of BMIs for some respondents create bias 

in the data? Bias would occur if respondents without corresponding BMIs 

had different time-use patterns than other respondents, resulting in under- or 

over-estimates of average time spent in various activities. This report presents 

an examination of the BMI data to determine if the missing values hinder the 

ability to use the data in other analyses. 

In examining the potential for bias, ERS provides technical information in 

this report that will benefit other researchers using the Eating and Health 

Module data. In addition, ERS contributes to the literature by investigating 

nonresponse (i.e., missing information for a survey question) in time use 

surveys, and the ATUS in particular, and by using paradata (i.e., data about 

the process of data collection) in the examination of this nonresponse.

Eating and Health Module

The ATUS, sponsored by the Bureau of Labor Statistics (BLS) and conducted 

by the U.S. Census Bureau, has collected time use data nearly every day since 

the survey was implemented in 2003. In the survey, 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. Survey respondents identify their main activity during any 

time period for which they were engaged in more than one activity at a time. 

They also report where they were and whom they were with when the activities occurred. All ATUS respondents also participated in the BLS Current 

Population Survey (CPS). During the ATUS interview, they were given the 

opportunity to update information on household labor force participation that 

they provided to the CPS. Thus, the ATUS data include time diary, demographic, labor force participation, and household information.

The EH Module, a supplement to the ATUS developed by ERS and funded 

by ERS and the National Cancer Institute, was fielded from January 2006 to 

December 2008, producing 3 full years of data. The ATUS collected data 

from over 12,000 respondents each year. From 2006 to 2008, the ATUS and 

EH Module collected data from 37,832 respondents.1 Weighting factors calculated by the U.S. Census Bureau and applied to the individual respondent data 

enabled the ATUS and EH Module to produce nationally representative estimates for an average day over 2006-08. 

The EH Module contained questions on eating patterns; height, weight, and 

health status; USDA Supplemental Nutrition Assistance Program2 participation and household income; meals obtained at school by household children; 

and grocery shopping and meal preparation (see box, “ATUS Eating and 

1A small number of respondents 

(82, or 0.2 percent of the total sample) 

completed the ATUS survey but did 

not complete the EH Module.

2As of October 1, 2008, the Food 

Stamp Program was renamed the 

Supplemental Nutrition Assistance 

Program.
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Health Module Questions”). Since the ATUS is a time-use survey, it does not 

include food intake information. Nonetheless, the ATUS data provide important information on eating/drinking duration, frequency, and context that 

allow for the characterization of eating patterns of different groups. 

Data on height and weight collected through the EH Module enable 

researchers to examine for correlation between BMI and time-use patterns. 

This may be particularly useful in the design of programs targeted at 

reducing the incidence of obesity, the most common food and nutritionrelated health problem in the United States. Information on patterns of time 

spent in various activities by those who are obese can help researchers understand how behaviors differ among people of different weight status. 

The Eating and Health Module microdata can be downloaded from BLS 

at http://stats.bls.gov/tus/ehdatafiles.htm; estimates and documentation are 

available at www.ers.usda.gov/data-products/eating-and-health-module-

(atus).aspx
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Eating as a secondary activity

Because many Americans eat while engaged in other 

activities, such as driving or watching television, information 

is needed on eating as both a primary and secondary activity. 

This question records when and during which activities the 

respondent was eating or drinking beverages.

Question: 

We’re interested in finding out more about 

how people fit meals and snacks into their 

schedules. Yesterday, you reported eating or 

drinking between [fill times from respondent’s 

time diary]. Were there any other times you 

were eating yesterday—for example while you 

were doing something else? About how long 

would you say you were eating while you were 

[fill activity]? Not including plain water, were 

there any other times yesterday when you were 

drinking any beverages? About how long would 

you say you were drinking while you were [fill 

activity]?

Grocery shopping and meal preparation

Question: 

I’d like to ask a couple of questions about food 

preparation. Are you the person who usually 

does the grocery shopping in your household? 

Are you the person who usually prepares the 

meals in your household?

Food Stamp Program participation

This information allows analysis of the time-use patterns 

of food stamp recipients versus others, and in particular, 

low-income persons who are not participating in the 

program.

Question: 

In the past 30 days, did you or anyone in your 

household get food stamp benefits?

Breakfast and lunch obtained at school 

Question: 

Please think back over the past week starting 

last Monday up to today, Monday. In the past 

week, did [fill names of children in the household 18 or under] eat a BREAKFAST that 

was prepared and served at a school, a paid 

day care or Head Start Center, or a summer 

day program? This question refers to ONLY 

BREAKFASTS prepared at the school or 

center—not meals brought from home. 

What about LUNCH? In the past week, did [fill 

names of children in the household 18 or under] 

eat a LUNCH that was prepared and served at 

a school, a paid day care or Head Start Center, 

or a summer day program? This question refers 

to ONLY LUNCHES prepared at the school or 

center—not meals brought from home. 

Height, weight, and general health

From this self-reported information, Body Mass Index 

(BMI) can be calculated, and time-use patterns, such as 

activity levels and eating patterns, can be analyzed by BMI 

levels.

Question:

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? 

Household income

This question asks if total household income before 

taxes was above or below a certain amount. The ATUS 

Computer Assisted Telephone Interviewing software 

automatically calculated the dollar amount of 185 and 130 

percent of the poverty threshold based on the respondent’s 

household composition. These income thresholds—185 

percent and 130 percent—determine income eligibility 

for food assistance programs.

Question:

Last month, was your total household income 

before taxes more or less than [fill 185 percent 

of poverty threshold] per month?

If answer was LESS:

Was it more or less than [fill 130 percent of 

poverty threshold] per month? 

A text version of the Eating & Health Module questionnaire 

is available at www.bls.gov/tus/ehmquestionnaire.pdf

ATUS Eating and Health Module Questions
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Relevant Literature—Item Nonresponse, 

Time Use, and BMI

Considerable research has been conducted on the effects of unit nonresponse in household surveys. Unit nonresponse refers to sampled individuals 

or households who choose not to participate in a survey. In such cases, the 

concern is that estimates based on survey data affected by unit nonresponse 

may have an increased variance and a bias. A bias would result if those who 

do not participate have different characteristics related to the survey topic 

than those who do respond to the survey, and so an estimate calculated from 

survey data would be different than the actual population value. The literature in this area is extensive (see, for example, Groves (2006), Groves et al. 

(2002), and Bethlehem et al. (2011)). 

Abraham et al. (2006, p. 676) 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.” However, reweighting the estimates to account for this 

effect produced only modest differences in time use. 

Abraham et al. (2008) also looked at unit nonresponse in the ATUS in relation to estimates of time spent in volunteer work. They investigated whether 

the social processes that lead one to participate in a survey also lead one 

to volunteer, which would result in an overrepresentation of volunteers in 

the ATUS. Their findings reveal an association between the factors that 

encourage volunteering and those that encourage survey participation, 

meaning that time-use estimates would contain bias, in this case an overestimate, in time spent in volunteering and other pro-social activities. They do 

not recommend an adjustment for the bias; however, they suggest strategies to 

improve sample weighting to account for this nonresponse. 

O’Neill and Sincavage (2004) conducted a response analysis survey on ATUS 

nonrespondents to better understand the differences between respondents and 

those who refused to respond. One-third of the ATUS nonrespondents cited 

CPS-related survey fatigue—their decision not to participate in the ATUS 

was based on their having previously participated in the CPS, which is an 

8-month survey over a 16-month period. Several nonrespondents stated that 

they were tired of the Census Bureau calling them, and they felt that participating in the CPS more than satisfied their requirement.

Item nonresponse occurs when an individual or household participates in a 

survey but does not provide information for one or more questions. Several 

factors may account for item nonresponse: The respondent may think that 

the information requested by a question is too sensitive to reveal, such as 

income;3 the respondent may think that a question is not directly related to 

the survey topic; or the respondent may not know the answer to a question 

and may not want to spend time researching the answer. It is also possible 

that the respondent 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, thorough overviews exist (see, for example, Bethlehem et al. 

(2011, chapter 14), Dixon (2005), and Mason et al. (2002)). 

3Tourangeau et al. (2000, chapter 9) 

provide a good discussion of sensitive 

questions and nonresponse.
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Fricker and Tourangeau (2010, p. 941) investigated nonresponse, reluctant 

respondents, and data quality in the CPS 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 laborforce questions.” They found 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, they originally 

refused to participate in the ATUS but later agreed to be interviewed. They 

found that data quality for refusal conversion respondents was worse than for 

other nonrespondents.

Other areas of survey-related research include late respondents and data 

quality. Triplett et al. (1996) studied late respondents (converted-refusal 

cases) to the 1993-94 time diary study conducted by the U.S. Environmental 

Protection Agency. They concluded that the converted-refusal respondents 

consistently provided less information than other respondents. Friedman et 

al. (2003, p. 992) investigated whether the characteristics of late respondents 

to the Health Care Survey of Department of Defense Beneficiaries were 

different from those of other respondents. They used the “continuum of resistance” model that posits that “...individuals who require the most contacts 

before participating in a survey are also the most resistant to being interviewed, and the more resistant a respondent, the more similar he or she is to 

the most resistant individuals in the population—the nonrespondents.” They 

studied these late respondents—those who needed the most callbacks before 

participating—as a way of gaining insight into nonrespondents. They found 

that late respondents have different characteristics than nonlate respondents 

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

responses to survey questions. As a consequence, their responses may be less 

reliable than those of nonlate respondents.

Some survey methodologists recommend that researchers adjust the data 

to avoid bias from nonresponse. Graham (2009) and Schafer and Graham 

(2002) provide thorough overviews of dealing with missing data. Both 

discuss the nature of “missingness” and review various approaches avoiding 

bias. One such approach is to re-calculate the sampling weights to account 

for the nonresponse. Estimates using the nonresponse-adjusted weights may 

be used as population estimates or may be used to estimate the extent of any 

nonresponse bias. See Abraham et al. (2006) and Dixon (2008) for examples 

of weight recalculation. Another approach to dealing with missing data is to 

impute values that are missing due to item nonresponse. Kyureghian et al. 

(2011) used a parametric Bayesian model for multiple imputation methods, 

and Andridge and Little (2010) discuss hot deck imputation methods.

The survey methodology literature on BMI focuses on bias of self-reported 

height and weight measures. Representative research includes Danubio et 

al. (2008), Kuczmarski et al. (2001), and Hill and Roberts (1998). Research 

on item nonresponse on BMI is limited, and all of it pertains to children. 

Wagstaff et al. (2009) imputed BMI values for individuals age 2-15 in the 

1999-2000 National Health and Nutrition Examination Survey (NHANES). 

BMI values are missing for 8 percent of the children/youth sampled. Wagstaff 

et al. compared active imputation of BMI (imputing BMI directly) with 
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passive imputation of BMI (imputing height and weight). Using 1999-2000 

NHANES data for non-Hispanic White youths age 2-18, they found little 

difference between the active and passive BMI imputation methods. Nandram 

and Choi (2010) used the NHANES III data to impute missing values for 

those respondents age 2-19 with missing BMI. They modeled population 

means of small domains of age, race, and sex within counties. They focused 

on analysis of BMI of children/youth and created BMI “growth curves” by 

age. Tiggemann (2006) studied missing BMI using data from the Flinders 

University (Australia) survey of teenage boys and girls in South Australia that 

investigated the role of media and adolescent self-image. Over one-fourth of 

the sample had missing BMI values. Tiggemann concludes that nonreporting 

of height and/or weight was not random and consistent with the “motivated 

nonreporting hypothesis,” and, consequently, analysis without the missing 

values or imputation with a mean BMI would produce biased estimates. 

She also concludes that “...the current study provides an illustration of how 

treating missing values as meaningful data can provide some useful information and raise some interesting questions” (p. 349). 
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Missing BMIs—Characteristics 

of Respondents

Obesity is the most common food and nutrition-related health problem in 

America. Data from the ATUS and the EH Module enable researchers to 

identify the types of activities and eating patterns that are associated with 

obesity and those that are associated with healthy weight, overall health, and 

well-being. 

As mentioned earlier, researchers used the height and weight information gathered from EH Module respondents to calculate BMIs.4 They then 

analyzed BMIs in conjunction with time diary, demographic, and labor 

force information to better understand associations among BMI status and 

time-use patterns and demographic and labor force characteristics. Although 

BMIs were calculated from self-reported height and weight in the ATUS, 

researchers found that differences between self-reported BMIs and measured 

BMIs are small and, as a result, self-reported BMIs are acceptable for use 

in analysis of data on nonelderly adults (see Cawley and Burkhauser, 2006; 

Kuczmarski et al., 2001; and Danubio et al., 2008). In addition, the expected 

underreporting of BMIs (through underreporting of weight and overreporting 

of height) in the EH Module data does not appear to be large (Pinkston and 

Stewart, 2009). 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.5

Missing values for height and/or weight

EH Module data collected over 2006-08 reveal that only 1,848 respondents, 

or 4.88 percent of 37,832 completed interviews, did not report height and/or 

weight, and have missing BMI values (tables 1, 2). 6, 7 ERS did not impute 

BMIs for any of the missing values. An additional 347 respondents (0.92 

percent) told interviewers they were pregnant, and these individuals were not 

asked about their weight. As a consequence, these respondents have missing 

BMIs; however, they are not included in this analysis.

Item nonresponse is considered a source of nonsampling error.8 Respondents 

who are willing to participate in the ATUS may be unwilling to answer 

sensitive questions. In response to concerns about sensitivity, ERS placed 

questions about general health, height, weight, and income at the end of 

the survey.9 Respondents with missing BMIs may have declined to report 

height and/or weight, perceiving this information as irrelevant to a time-use 

survey.10 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. Because the ATUS interviews are conducted 

via computer-assisted telephone calls, respondents may be reluctant to pause 

the interview to measure themselves.

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 (see table 3). 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, 

4Height and weight are bottom- and 

top-coded for confidentiality. The EH 

Module included a screening question 

for pregnancy as pregnant women were 

not asked their weight and thus have 

missing BMIs.

5This research does not address 

whether BMI should be adjusted to 

correct for underreporting of weight 

and overreporting of height due to 

self-reporting as the focus here is 

on missing BMIs. See Pinkston and 

Steward (2009) and Danubio et al. 

(2008) for discussions of BMI bias 

correction.

6All estimates presented in 

this report were calculated using 

American Time Use Survey (ATUS) 

data and Eating and Health Module 

(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 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.

7Body 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, the Centers for 

Disease Control (CDC) 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

Continued on page 8
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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.

Demographic characteristics of BMI nonrespondents

In analyzing BMI nonresponse, it is useful to look at the basic demographic 

characteristics of those with missing BMI. As shown in table 1, the majority 

of nonrespondents were women (79 and 75 percent of unweighted and 

weighted counts, respectively). The share of respondents missing BMIs varies 

slightly by age group; however, the estimates are not statistically different 

from each other, so there is essentially no difference in nonresponse across 

age groups (table 4).

Table 1

ATUS/EH Module respondents by Body Mass Index (BMI) status 

and by sex

Item Men Women Total

Number

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

Note: Cell counts (unweighted), age 15 and over, pregnant women excluded.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health (EH) Module. 

Table 2

ATUS/EH Module respondents with missing Body Mass Index (BMI) 

by year

Item 2006 2007 2008 Total

Respondents missing BMI (number) 588 593 667 1,848

Share of total (%) 4.6 4.9 5.3 4.9

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

Note: Cell counts (unweighted), age 15 and over, pregnant women excluded.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health (EH) Module. 

Table 3

Missing height and weight respondents by type of nonresponse

Respondent type Don’t know Refused Total

Number

Missing height 381 478 859

Missing weight 466 1,083 1,549

Note: Cell counts (unweighted), age 15 and over, pregnant women excluded.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health (EH) Module. 

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. For more 

information on BMI, see www.cdc.

gov/healthyweight/assessing/bmi/

index.html

Continued from page 7

8ERS and the Bureau of Labor 

Statistics concluded that including 

height and weight in the EH Module 

would not lead to unit nonresponse 

because the ATUS is not a health 

survey, and individuals would not 

expect questions about height and 

weight. For a discussion of nonresponse and sensitive questions, see 

Tourangeau et al. (2010).

9See Vernon (2005) for a discussion 

of pretesting the EH Module. A text 

version of the questionnaire is at http://

stats.bls.gov/tus/ehmquestionnaire.pdf

10Dixon (2002) finds 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 irrelevant.
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Those who were employed at the time of the ATUS interview were more 

likely to report height and weight, which is reflected by the lower share with 

missing BMIs for that category of respondents (table 5). 

Survey characteristics of BMI nonrespondents

Another way to analyze BMI nonresponse is by looking at ATUS respondent 

characteristics. Perhaps the respondents with missing BMIs are reluctant to 

be interviewed (i.e., they are “uncooperative respondents).” An indicator of an 

uncooperative respondent is the number of phone calls made by the Census 

Bureau to obtain a completed interview.11, 12 Based on the ATUS call history 

data from one of the ATUS survey methodology files, the average number of 

call attempts appears to be higher for those respondents with missing BMIs 

(7.1) than for the total population (6.8); however, these averages are not statistically different at the 90-percent level, so the number of call attempts made 

to those with missing BMIs is about the same as the number of calls made 

to all others (table 6). A related characteristic of ATUS respondents is the 

number of weeks (1-8) that were needed to successfully complete the interview. The number of weeks required for respondents with missing BMIs was 

11Each sampled designated person is 

assigned a diary day of the week, and 

the interview is conducted on the next 

day (e.g., if Monday is the diary day, 

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 8 weeks.

12The number of attempted calls also 

includes times that the respondent’s 

case file was opened. So, an actual 

call may not have been made if the file 

was viewed or queued up for a call. 

Thus, cases with high numbers of call 

attempts likely have fewer actual calls 

made. Information from Mary Dorinda 

Allard, Director, BLS Division of 

Labor Force Statistics, in a discussion 

on March 11, 2011.

Table 4

Respondents with missing Body Mass Index (BMI) by age group

Missing BMI Total population, nonmissing BMI

Unweighted Unweighted Weighted Standard

error

Weighted, 

90% CI

Unweighted Unweighted Weighted Standard

 error

Weighted, 

90% CI

Number ––————— Percent —————–– Number ––————— Percent —————––

Total 

population 1,848 4.9 4.9 0.14 ±0.24 35,637 95.1 95.1 0.14 ±0.24

Age 15-19 129 4.8 4.4 0.46 ±0.75 2,588 95.2 95.6 0.46 ±0.75

Age 20-39 600 5.2 5.1 0.27 ±0.45 11,016 94.8 94.9 0.27 ±0.45

Age 40-64 812 4.9 4.9 0.21 ±0.34 15,736 96.1 95.1 0.21 ±0.34

Age 65+ 307 4.6 4.7 0.35 ±0.57 6,297 95.4 95.3 0.35 ±0.57

Note: Age 15 and over, pregnant women excluded.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use Survey and Eating and Health Module.

Table 5

Missing Body Mass Index (BMI) by employment status, population age 15 and over

Missing BMI Total population, nonmissing BMI

Unweighted Unweighted Weighted Standard

error

Weighted, 

90% CI

Unweighted Unweighted Weighted Standard

 error

Weighted, 

90% CI

Number ––————— Percent —————–– Number ––————— Percent —————––

Employed 1,112 4.6 4.6 0.18 ±0.30 22,988 95.4 95.4 0.18 ±0.30

Not 

employed 736 5.5 5.4 0.26 ±0.43 12,649 94.5 94.6 0.26 ±0.30

Note: Age 15 and over, pregnant women excluded.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use Survey and Eating and Health Module.
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Table 6

Call attempts and weeks called by Body Mass Index (BMI) group

Item Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Number of call attempts

Mean 

(unweighted) 6.7 7.0 6.5 6.8 6.8 6.5

Mean 

(weighted)

6.8

(0.06) 

[±0.09]

7.1

(0.20) 

[±0.33

6.8

(0.44) 

[±0.72

6.9

(0.09) 

[±0.15]

6.8

(0.10) 

[±0.16]

6.6

(0.10) 

[±0.17]

Median 

(unweighted) 4 4 4 4 4 4

Median 

(weighted) 4 5 4 4 4 4

75th percentile 

(unweighted) 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) 

[±0.02]

2.4

(0.05) 

[±0.09]

2.3

(0.12) 

[±0.19]

2.2

(0.02) 

[±0.03]

2.3

(0.02) 

[±0.04]

2.2

(0.02)

[±0.04]

Median 

(unweighted) 1 2 1 1 1 1

Median 

(weighted) 1 2 1 1 1 1

75th percentile 

(unweighted) 3 3 3 3 3 3

75th percentile 

(weighted) 3 3 3 3 3 3

Minimum 1 1 1 1 1 1

Maximum 8 8 8 8 8 8

Note: Age 15 and over, pregnant women excluded. Variables TUATTMPTNO and 

TUATTMWEEK from ATUS call history file used. Standard errors are in parentheses, 

90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health Module
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Economic Research Service/USDA

higher (2.4 weeks—weighted mean) and statistically different from that for 

the total population (2.2 weeks). The median for those with missing BMIs 

was higher as well—2 weeks versus 1 week for the other groups. The higher 

average number of weeks needed to complete an interview may indicate that 

these respondents were reluctant to be interviewed.

Characteristics of late respondents are likely to be similar to those of nonrespondents. Since the 75th percentile for the number of weeks that call 

attempts were made is 3 weeks, late respondents are defined as those who 

needed to be called for 4 or more weeks to participate in the ATUS. About 24 

percent (weighted) of respondents with missing BMIs were late respondents, 

a higher share than for late respondents not missing BMI values (20 percent) 

(table 7). So, the missing BMI group has a higher share of those reluctant to 

be interviewed than the nonmissing BMI group.

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, 

a minimum of 5, and a maximum of 9113 (table 8). Based on the number of 

activities in the time diaries, the missing BMI group has fewer activities as 

measured by mean, median, and maximum value than the total population. 

The figure 1 box plot shows the different distributions of the number of diary 

activities for the BMI groups. The missing BMI group has a lower box than 

the other BMI groups, indicating a distribution with fewer diary activities 

not only for the mean and median but also for the 25th and 75th percentiles. 

Interestingly, men overall have fewer average reported diary activities than 

women. This finding suggests that these respondents may have decided to 

participate in the ATUS but did not report detailed information for their time 

diaries and were perhaps reluctant to provide answers for the questionnaire 

portions of the survey as well. 

Another indicator of respondent cooperation is the degree to which respondents answered other sensitive questions. The final questions in the survey 

instrument ask for information on general health, height, weight, and income. 

General health information is related in content to the height and weight questions. Of the 504 respondents who did not report general health, most (92.5 

percent) did not report height and/or weight and had missing BMIs. This 

13Note that BLS ATUS excludes 

interviews that have fewer than 5 

reported activities and/or reported 

activities that do not cover at least 

21 hours of the diary day. There 

is no constraint on the maximum 

number of diary activities. Email 

correspondence from Rachel KrantzKent, Manager, American Time Use 

Survey, October 7, 2010.

Table 7

Missing Body Mass Index (BMI) by late respondent status—call attempts for 4+ weeks before interview

Missing BMI Total population, nonmissing BMI

Unweighted Unweighted Weighted Standard

error

Weighted, 

90% CI

Unweighted Unweighted Weighted Standard

 error

Weighted, 

90% CI

Number ––————— Percent —————–– Number ––————— Percent —————––

Late 

respondent 

(4+ weeks) 405 21.9 24.1 1.26 ±2.08 6,762 19.0 19.7 0.30 ±0.49

Not late 

respondent 1,443 78.1 75.9 1.26 ±2.08 28,875 81.0 80.3 0.30 ±0.49

Note: Age 15 and over, pregnant women excluded. CI = confidence interval.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use Survey and Eating and Health Module.
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Economic Research Service/USDA

share, however, accounts for only one-fourth of the missing BMI respondents. 

Perhaps some individuals with missing BMIs are in poor health and may be 

reluctant to disclose their height and weight. Some respondents may consider 

this area of questioning to be too intrusive or irrelevant to the survey. The 

three-fourths of respondents with missing BMIs who did report general health 

were found to be less likely to report their health as “Excellent” or “Very 

good,” more likely to report it as “Good “or “Fair,” and equally likely to report 

it as “Poor” than those with a BMI value (table 9). So, the distribution of 

Table 8

Number of diary activities by Body Mass Index (BMI) group

Item Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Number of activities in diary

Mean 

(unweighted) 19.9 18.6 19.8 20.7 19.7 19.3

Mean 

(weighted)

19.7

(0.05) 

[±0.08]

18.7

(0.22) 

[±0.35]

19.2

(0.46) 

[±0.76]

20.3

(0.08) 

[±0.13]

19.5

(0.09) 

[±0.15]

19.3

(0.10) 

[±0.17]

Median 

(unweighted) 19 17 19 19 19 18

Median 

(weighted) 19 17 18 19 18 18

75th percentile 

(unweighted) 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

Number of activities in diary, men only

Mean 

(unweighted) 18.1 15.2 17.0 18.5 18.3 17.8

Mean 

(weighted)

18.0

(0.06) 

[±0.10]

15.4

(0.41) 

[±0.67

17.2

(0.77) 

[±1.26]

18.2

(0.13) 

[±0.21]

18.2

(0.10) 

[±0.17]

17.9

(0.13) 

[±0.22] 

Median 

(unweighted) 17 14 16 18 17 17

Median 

(weighted) 17 14 15 17 17 17

75th percentile 

(unweighted) 22 19 20 22 22 22

75th percentile 

(weighted) 22 19 20 22 22 22

Minimum 5 5 6 5 5 5

Maximum 64 40 44 64 57 54

—continued
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Table 8

Number of diary activities by Body Mass Index (BMI) group—Continued

Item Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Number of activities in diary, women only

Mean 

(unweighted) 21.3 19.5 20.7 21.9 21.4 20.7

Mean 

(weighted)

21.3

(0.07) 

[±0.12]

19.8

(0.24) 

[±0.39]

20.1

(0.47) 

[±0.78]

21.7

(0.10) 

[±0.16]

21.4

(0.16) 

[±0.26]

20.9

(0.15) 

[±0.25]

Median 

(unweighted) 20 18 20 20 20 19

Median 

(weighted) 20 18 19 20 20 20

75th percentile 

(unweighted) 26 24 25 26 26 25

75th percentile 

(weighted) 26 24 24 26 26 25

Minimum 5 5 6 5 5 5

Maximum 91 58 91 75 81 85

Note: Age 15 and over, pregnant women excluded. Standard errors are in parentheses, 

90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey and Eating and Health Module. 

Figure 1

Number of diary activities by Body Mass Index group

Number of activities in diary

Missing BMI Underweight

12.5

15.0

17.5

20.0

22.5

25.0

Normal weight Overweight Obese

Note: Extreme values omitted, 1.5 clip factor used. Age 15 and over, pregnant women

excluded.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey and Eating and Health Module. 
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Economic Research Service/USDA

missing BMI respondents’ health status appears slightly different from that of 

the rest of the respondents.

Questions about income can be problematic in household surveys, as some 

respondents may be highly sensitive to requests for this information. 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.14 The first question asked if household income was above or below 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 a followup question asked whether the household’s income was above 

or below 130 percent of the poverty threshold for the respondent’s household 

size. Among the respondents who did not report household income for the 

185-percent question,15 43 percent were missing BMIs (table 10). This share 

is considerably larger than and statistically different from the 10 percent of 

respondents who reported height and weight but did not report income.

A final indicator is diary quality. Time diary quality, as reported by the 

Census interviewer and accessible through the ATUS case history data, 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?”16

14The 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.

15Used 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 of poverty 

threshold) was not used here.

16See ATUS Questionnaire, June 

2010, http://stats.bls.gov/tus/tuquestionnaire.pdf.

Table 9

Respondents with missing Body Mass Index (BMI) but reported 

general health

Item Reported health status

Excellent Very good Good Fair Poor Total

Missing BMI count 

(number) 138 409 527 247 61 1,382

Total population, 

nonmissing BMI 

count (number) 6,806 12,258 10,566 4,385 1,584 35,599

Missing BMI 

(row percent 

unweighted) 10.0 29.6 38.1 17.9 4.4 100

Total population, 

nonmissing BMI 

(row percent 

unweighted) 19.1 34.4 29.7 12.3 4.5 100

Missing BMI (row 

percent weighted)

10.9

(1.34) 

[±2.20]

29.2

(1.67) 

[±2.75]

38.8

(1.78) 

[±2.93]

17.5

(1.37) 

[±2.26]

3.6

(0.60) 

[±0.99]

100

Total population, 

nonmissing BMI 

(row percent 

weighted)

19.1

(0.28) 

[±0.46]

34.7

(0.33) 

[±0.55]

30.2

(0.37) 

[±0.60]

12.0

(0.22) 

[±0.36]

4.0

(0.13) 

[±0.22]

100

Note: Age 15 and over, pregnant women excluded. A total of 466 respondents had missing 

BMI and missing general health, and an additional 38 respondents had BMI but missing 

general health. Standard errors in parentheses, 90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey and Eating and Health Module.
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Economic Research Service/USDA

In only 275 of the 37,484 completed interviews did the Census interviewer 

think that the respondent’s time diary was not of good quality (table 11). The 

unweighted share of those with missing BMIs and no diary quality issues 

(97.7 percent) is less than the share for those with a BMI value (99.4 percent), 

as expected. The weighted share of those with missing BMIs and no diary 

quality issue is 98.2 percent, statistically different from the 99.3 percent for 

those with a BMI value and no diary quality issue. Although the number and 

share of respondents with diary quality issues is very small, these results 

contribute to understanding the factors accounting for the missing BMIs for 

some respondents. 

Multivariate analysis

A probit model was used to systematically analyze the influence that personal 

characteristics and indicators of uncooperativeness have on the probability 

that an observation will have missing BMIs (see table 12). In the model, the 

dependent variable is missing BMI. The model allows for testing the hypothesis that respondents with missing BMIs are uncooperative respondents. 

Number of diary activities, poor quality time diary, number of weeks that 

calls were made (1-8), and missing income information were used as indicators of uncooperativeness.17 Demographic, economic, geographic, and household controls are included to see if respondents with various characteristics 

are likely to have missing BMIs. 

As expected and consistent with the descriptive analysis presented earlier, 

the probability of missing BMI is higher for female respondents. The probability of missing BMI is also higher for those with less than a high school 

education, noncitizens, employed persons, and those with household income 

less than 185 percent of the poverty threshold. Among all respondents, probability of missing BMI was lower for teenagers, Asians, those of mixed race, 

and those that have any health status except “Good.” Region and household 

composition seem to have little association with missing BMI. Interestingly, 

17Missing general health could not 

be included as it is closely correlated 

with missing income and so resulted 

in a near singular matrix; likewise for 

number of call attempts and weeks call 

was made.

Table 10

Respondents with missing Body Mass Index (BMI) by reporting 

or not reporting income

Item Income reported Income not 

reported

Missing BMI count (number) 1,103 745

Total population, nonmissing BMI count 

(number) 32,718 2,919

Missing BMI (row percent unweighted) 59.7 40.3

Total population, nonmissing BMI (row 

percent unweighted) 91.8 8.2

Missing BMI (row percent weighted) 57.2

(1.67), [±2.75]

42.8

(1.67), [±2.75]

Total population, nonmissing BMI (row 

percent weighted)

89.6

(0.20), [±0.32]

10.4

(0.20), [±0.32]

Note: Age 15 and over, excludes pregnant women. Standard errors are in parentheses, 

90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey and Eating and Health Module.
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Economic Research Service/USDA

in this multivariate analysis that controls for other factors, those who were 

employed have a higher probability of missing BMI than other respondents, 

whereas in the descriptive statistics, the same group had a lower share of 

missing BMI. Employed respondents may have a higher probability of 

missing BMI because those who are overweight but not obese have, on 

average, longer paid work hours than other respondent types.18

The general health variables were all negative and significant, indicating that 

those reporting general health of ”Excellent,” “Very good,” “Fair,” or “Poor” 

had a lower probability of missing BMI than those reporting general health of 

“Good.” However, because missing general health cannot be included in the 

model as it is closely related with missing income, these general health coefficients may also be indicating that these probabilities are less than for those 

with missing general health. 

Among measures of cooperativeness, all coefficients were significant, and 

coefficient signs were in the expected directions. For all respondents, the 

more activities reported in the time diary, the lower the probability of a 

missing BMI. And having a time diary flagged as a poor quality diary 

increases the probability of having a missing BMI, consistent with the possibility that missing BMI observations are from uncooperative respondents. 

The more weeks that call attempts were made to interview a respondent, the 

more likely the respondent’s BMI is missing. Likewise, not reporting household income increased the probability of missing BMI.

18Hamrick et al. (2011), app. table 6.

Table 11

Missing Body Mass Index (BMI) respondents by diary quality

Item No diary 

quality issue

Diary quality problems

Total Intentionally 

wrong

Could not 

remember

Deliberately 

long durations

Other 

reason

Missing BMI count 

(number) 1,806 4 19 11 8 1,848

Total population, 

nonmissing BMI count 

(number) 35,404 32 85 77 39 35,637

Missing BMI (row 

percent unweighted) 97.7 0.2 1.0 0.6 0.5 100

Total population, 

nonmissing BMI (row 

percent unweighted) 99.4 0.1 0.2 0.2 0.1 100

Missing BMI (row 

percent weighted)

98.2

(0.37), [±0.61]

0.2

(0.16), [±0.26]

0.9

(0.26), [±0.43]

0.4

(0.15), [±0.24]

0.3

(0.12), [±0.20] 100

Total population, 

nonmissing BMI (row 

percent weighted)

99.3

(0.06), [±0.10]

0.1

(0.02), [±0.03]

0.2

(0.04), [±0.06]

0.3

(0.04), [±0.07]

0.1

(0.03), [±0.05] 100

Note: Age 15 and over, excludes pregnant women. Standard errors are in parentheses, 90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use Survey and Eating and Health Module.
17

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Table 12

Probit model, dependent variable = missing Body Mass Index (BMI)

Item

Missing 

BMI probit 

coefficient

Marginal 

probability

Standard 

error

Significance 

level 

Intercept -2.0836 -0.1320 0.0824 ***

Demographic variables:

 Female 0.6139 0.0389 0.0375 ***

 Age 15-19 -0.7448 -0.0472 0.0763 ***

 Age 40-64 0.1029 0.0065 0.0378 ***

 Age 65 and over -0.0447 -0.0028 0.0606

 Less than high school 

 education 0.2501 0.0158 0.0502 ***

 Some college 0.0115 0.0007 0.0421

 College degree 0.0704 0.0045 0.0534

 Advanced degree 0.1094 0.0069 0.0589 *

 Noncitizen 0.4787 0.0303 0.0548 ***

 African American -0.0362 -0.0023 0.0476

 Asian -0.3326 -0.0211 0.0950 ***

 Mixed race -0.3200 -0.0203 0.1295 **

Labor force and household income:

 Employed 0.0935 0.0059 0.0378 **

 Income below 185% poverty

 threshold 0.0887 0.0056 0.0465 *

 Income missing (measure of 

 respondent cooperation) 1.1028 0.0698 0.0488 ***

Region:

 Midwest -0.0424 -0.0027 0.0572

 South 0.0164 0.0010 0.0530

 West 0.0265 0.0017 0.0572

 Nonmetro -0.0174 -0.0011 0.0400

Household composition:

 One-adult household -0.0078 -0.0005 0.0378

 No children in household -0.0493 -0.0031 0.0422

General health:

 Excellent health -0.5621 -0.0356 0.0691 ***

 Very good health -0.3638 -0.0230 0.0437 ***

 Fair health -0.2725 -0.0173 0.0535 ***

 Poor health -0.4436 -0.0281 0.0903 ***

Measures of respondent cooperation:

 Number of diary activities -0.0078 -0.0005 0.0020 ***

 Poor quality time diary 0.3116 0.0197 0.1274 **

 Week call made (1-8) 0.0170 0.0011 0.0084 **

Likelihood ratio test, probability 

> ChiSq <.0001

Number of observations 37,485, 4.9% observations have missing BMI.

Note: Population (weighted), age 15 and over, pregnant women excluded. 

*** indicates significance at the 1-percent level, ** indicates significance at the 5-percent 

level, and * indicates significance at the 10-percent level. Excluded group is male, age 20-39, 

high school diploma, citizen, White, not employed, income above 185 percent of the poverty 

threshold, Northeast, Metro, two-adult household with children, and good health.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey and Eating and Health Module.
18

Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

Missing BMIs—Time-Use Profiles

The ATUS and the EH Module facilitate analysis of Americans’ time spent 

in various activities. Having an understanding of the characteristics of survey 

respondents with missing BMIs is useful to time-use research; however, it may 

be more important to understand whether missing BMIs cause bias in timeuse estimates. Are the respondents who did not report height and/or weight 

different with respect to their time-use patterns than other respondents? On 

average, those who did not report height and/or weight reported fewer activities 

in their diaries, but what were the time-duration differences of these activities?

For time spent per day in major activities19 (table 13), the most striking difference between the missing BMI group and the other BMI groups is the long 

amount of time spent by the missing BMI group in Personal Care (which includes 

Sleeping)—586 minutes (9.8 hours). The missing BMI group, of which the 

majority are women, also had a higher average time spent per day in household activities and caring for household members than the other BMI groups. 

Interestingly, the missing BMI group also had the highest average time spent in 

Other Activities. Other Activities include gaps in the time diary that the ATUS 

interviewer was not able to code due to insufficient detail, respondent refusal, 

or the inability of the respondent to recall activity. The higher average Other 

Activities time is consistent with the concept of uncooperative respondents. 

For major time-use activities by gender, the male missing BMI group spent 

about the same amount of time, on average, on Personal Care than underweight males (table 14a). The average time males with missing BMIs spent 

in Eating and Drinking was the shortest among all BMI groups. The female 

missing BMI group had a relatively high average time spent in Personal Care, 

the lowest average time spent in Eating and Drinking, and the most time 

spent in the Other category (table 14b).

The missing BMI group, both men and women, had a long-duration average 

time spent in Personal Care. The two main activities in Personal Care are 

Sleeping and Grooming; the missing BMI groups and the total population 

group differed more in time spent Sleeping than in time spent Grooming (table 

15). The nonresponse bias as a result would be that minutes spent in Sleeping 

are underreported in analysis of BMI groups excluding the missing BMI 

respondents. However, it is unclear whether sleep duration is underreported for 

any specific BMI group, as the underreporting occurs across all BMI groups.

Based on the weighted absolute-deviation index (WADI),20 a measure of dissimilarity of time use “activity profiles” across groups, the difference between the 

missing BMI group and the other BMI groups can be measured systematically 

over the 17 major time-use activities. Stewart (2006) defines WADI as:

= =

− −   + = = ∑ ∑   +   WADI 1 1 2880 2880

k k i i i i i i

i i

i i

a b a b a b

a b

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

19For 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

20Note that weighting with respect 

to the WADI means applying the share 

of total time spent on an activity to 

each difference, whereas weighted 

elsewhere in this report indicates that 

sample weights were used to produce 

national estimates.
19

Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

Table 13

Time spent (minutes) in major activities by Body Mass Index (BMI) group

Item Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Personal care

562.1

(0.86) 

[±1.41]

586.2

(4.86) 

[±7.99]

591.5

(7.33) 

[±12.06]

567.2

(1.66) 

[±2.73]

554.1

(1.48) 

[±2.44]

558.8

(1.99) 

[±3.27]

Household 

 activities

111.6

(0.89) 

[±1.46]

120.9

(4.29) 

[±7.05]

100.1

(6.10) 

[±10.04]

112.8

(1.38) 

[±2.28]

111.8

(1.46) 

[±2.41]

108.8

(1.97) 

[±3.24]

Caring for house-

 hold members

31.2

(0.42) 

[±0.69]

41.8

(2.68) 

[±4.41]

27.5

(4.14) 

[±6.81]

33.0

(0.69) 

[±1.14]

28.2

(0.72) 

[±1.19]

30.8

(1.01) 

[±1.67]

Caring for 

 nonhousehold 

 members

12.8

(0.35) 

[±0.57]

12.7

(1.55) 

[±2.56]

13.5

(3.27) 

[±5.38]

11.6

(0.54) 

[±0.88]

13.6

(0.70) 

[±1.15]

13.4

(0.70) 

[±1.15]

Paid work

226.1

(1.77) 

[±2.91]

198.8

(7.97) 

[±13.12]

153.7

(12.12) 

[±19.93]

209.3

(3.18) 

[±5.24]

245.7

(3.09) 

[±5.08]

233.8

(3.56) 

[±5.86]

Educational

27.8

(0.72) 

[±1.18]

26.0

(3.68) 

[±6.05]

83.6

(9.17) 

[±15.08]

44.2

(1.66) 

[±2.73]

17.0

(1.24) 

[±2.04]

15.7

(1.22) 

[±2.01]

Purchasing goods

38.0

(0.46) 

[±0.76]

40.9

(2.05) 

[±3.38]

43.5

(4.48) 

[±7.37]

38.1

(0.77) 

[±1.26]

37.5

(0.89) 

[±1.46]

37.5

(0.97) 

[±1.60]

Purchasing 

 services

7.3

(0.23) 

[±0.38]

8.4

(0.95) 

[±1.57]

10.7

(2.58) 

[±4.24]

7.0

(0.38) 

[±0.62]

7.4

(0.40) 

[±0.66]

7.3

(0.40) 

[±0.66]

Purchasing house-

 hold services

1.3

(0.08) 

[±0.14]

0.9

(0.25) 

[±0.41]

0.3

(0.22) 

[±0.37]

1.3

(0.15) 

[±0.25]

1.3

(0.15) 

[±0.25]

1.5

(0.20) 

[±0.33] 

Government 

 and civic

0.7

(0.07) 

[±0.12]

0.5

(0.29) 

[±0.47]

1.5

(0.68) 

[±1.12]

0.5

(0.11) 

[±0.18]

0.6

(0.11) 

[±0.18]

0.9

(0.20) 

[±0.33]

Eating and drinking

73.9

(0.40) 

[±0.66]

65.5

(1.63) 

[±2.67]

71.8

(3.03) 

[±4.98]

75.6

(0.75) 

[±1.23]

75.5

(0.74) 

[±1.21]

71.4

(0.72) 

[±1.19]

Leisure

285.7

(1.38) 

[±2.28]

284.1

(5.86) 

[±9.64]

284.2

(11.57) 

[±19.04]

270.3

(2.33) 

[±3.83]

286.2

(2.49) 

[±4.09]

307.3

(2.78) 

[±4.57]

Sports

22.6

(0.46) 

[±0.76]

13.2

(1.39) 

[±2.28]

19.9

(3.34) 

[±5.50]

27.4

(0.86) 

[±1.42]

23.6

(0.92) 

[±1.51]

16.6

(0.84) 

[±1.38]

Religious

9.5

(0.22) 

[±0.36]

10.5

(1.04) 

[±1.71]

7.8

(1.75) 

[±2.89]

9.2

(0.39) 

[±0.63]

9.4

(0.40) 

[±0.65]

10.2

(0.43) 

[±0.71]

Volunteer

10.0

(0.35) 

[±0.57]

7.2

(0.96) 

[±1.58]

6.7

(1.68) 

[±2.76]

10.7

(0.58) 

[±0.96]

10.4

(0.60) 

[±0.99]

9.2

(0.64) 

[±1.05]

Phone and mail

7.1

(0.19) 

[±0.32]

6.4

(0.84) 

[±1.38]

10.0

(1.33) 

[±2.20]

8.3

(0.35) 

[±0.58]

6.3

(0.28) 

[±0.45]

6.5

(0.37) 

[±0.61]

—continued
20

Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

Table 13

Time spent (minutes) in major activities by Body Mass Index (BMI) 

group—Continued

Item Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Other

12.2

(0.34) 

[±0.57]

15.8

(1.51) 

[±2.48]

13.5

(3.26) 

[±5.36]

13.7

(0.66) 

[±1.08]

11.5

(0.63) 

[±1.04]

10.3

(0.45) 

[±0.74]

Total 1,440.0 1,440.0 1,440.0 1,440.0 1,440.0 1,440.0

Note: Population (weighted), age 15 and over, pregnant women excluded. Activities listed 

are ATUS major activity groups. Travel time included with each activity. Standard errors in 

parentheses, 90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health Module.

Table 14a

Time spent (minutes) in major activity groups, men only

Activity Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Personal care

551.5

(1.35) 

[±2.22]

592.4

(10.99) 

[±18.08]

592.8

(11.28) 

[±18.56]

563.9

(2.92) 

[±4.81]

545.3

(2.12) 

[±3.48]

541.8

(2.62) 

[±4.32]

Household 

 activities

85.0

(1.17) 

[±1.93]

60.2

(5.85) 

[±9.63]

61.7

(10.44) 

[±17.17]

81.6

(2.21) 

[±3.63]

91.0

(1.91) 

[±3.14]

82.9

(2.32) 

[±3.82]

Caring for house-

 hold members

20.3

(0.50) 

[±0.82]

18.7

(3.41) 

[±5.60]

12.6

(4.09) 

[±6.72]

17.2

(0.78) 

[±1.29]

20.6

(0.63) 

[±1.04]

23.5

(1.31) 

[±2.16]

Caring for 

 nonhousehold 

 members

10.7

(0.47) 

[±0.77]

6.6

(1.76) 

[±2.90]

10.6

(4.42) 

[±7.28]

 9.9

(0.71) 

[±1.17]

10.9

(0.80) 

[±1.31]

11.5

(0.98) 

[±1.62]

Paid work

271.1

(2.51) 

[±4.13]

254.0

(19.92) 

[±32.77]

159.1

(25.46) 

[±41.87]

244.0

(5.26) 

[±8.65]

285.4

(4.75) 

[±7.81]

284.8

(5.31) 

[±8.74]

Educational

25.5

(1.02) 

[±1.67]

37.3

(9.16) 

[±15.07]

105.3

(19.40) 

[±31.91]

45.8

(2.45) 

[±4.02]

14.6

(0.29) 

[±2.12]

16.1

(1.69) 

[±2.78]

Purchasing goods

30.3

(0.68) 

[±1.12]

26.1

(4.10) 

[±6.74]

29.7

(7.78) 

[±12.80]

27.00

(1.10) 

[±1.81]

31.9

(1.13) 

[±1.85]

32.0

(1.22) 

[±2.01]

Purchasing 

 services

5.2

(0.27) 

[±0.45]

6.2

(1.81) 

[±2.98]

 8.8

(3.79) 

[±6.23]

5.0

(0.56) 

[±0.93]

5.3

(0.44) 

[±0.72]

4.9

(0.46) 

[±0.76]

Purchasing house-

 hold services

1.6

(0.14) 

[±0.24]

0.4

(0.20) 

[±0.33]

0

(0)

 [0]

1.4

(0.31) 

[±0.51]

1.7

(0.24) 

[±0.39]

1.8

(0.34) 

[±0.56]

Government 

 and civic

0.7

(0.12) 

[±0.20]

1.3

(1.06) 

[±1.75]

0.6

(0.44) 

[±0.72]

0.5

(0.14), 

[±0.24]

0.6

(0.16) 

[±0.26]

0.9

(0.32) 

[±0.53]

—continued
21

Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

Table 14a

Time spent (minutes) in major activity groups, men only—Continued

Activity Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Eating and drinking

76.1

(0.58) 

[±0.96]

59.8

(2.39) 

[±3.94]

67.5

(5.41) 

[±8.90]

77.7

(1.20) 

[±1.98]

77.3

(0.98) 

[±1.61]

74.2

(1.06) 

[±1.74]

Leisure

299.8

(2.20) 

[±3.62]

331.4

(15.09) 

[±24.82]

343.7

(23.90) 

[±39.31]

295.5

(3.98) 

[±6.54]

291.3

(3.60) 

[±5.93]

312.9

(4.06) 

[±6.68]

Sports

29.8

(0.78) 

[±1.29]

15.1

(3.08) 

[±5.06]

19.1

(5.50) 

[±9.04]

35.8

(1.49) 

[±2.45]

30.4

(1.44) 

[±2.36]

24.2

(1.47) 

[±2.42]

Religious

7.9

(0.31) 

[±0.50]

11.5

(2.64) 

[±4.35]

6.2

(2.32) 

[±3.82]

7.7

(0.56) 

[±0.93]

7.8

(0.44) 

[±0.73]

7.8

(0.53) 

[±0.87]

Volunteer

9.2

(0.49) 

[±0.81]

3.5

(1.17) 

[±1.93]

5.7

(3.14) 

[±5.17]

8.7

(0.76) 

[±1.25]

10.3

(0.85) 

[±1.40]

9.0

(0.99) 

[±1.62]

Phone and mail

3.9

(0.20) 

[±0.32]

3.7

(1.18) 

[±1.94]

6.4

(2.42), 

[±3.98]

5.0

(0.43), 

[±0.70]

3.8

(0.32) 

[±0.52]

2.8

(0.27) 

[±0.45]

Other

11.4

(0.51) 

[±0.84]

12.0

(2.69) 

[±4.42]

10.2

(4.54) 

[±7.47]

13.2

(1.11) 

[±1.83]

11.8

(0.97) 

[±1.59]

8.8

(0.59) 

[±0.97]

Total 1,440.00 1,440.00 1,440.00 1,440.00 1,440.00 1,440.00

Note: BMI = Body Mass Index. Population (weighted), age 15 and over. Activities listed 

are ATUS major activity groups. Travel time included with each activity. Standard errors in 

parentheses, 90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health Module.

Table 14b

Time spent (minutes) in major activity groups, women only

Activity Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Personal care

572.2

(1.25) 

[±2.05]

584.1

(5.11) 

[±8.41]

591.0

(8.93) 

[±14.69]

569.4

(1.81) 

[±2.97]

566.8

(2.48) 

[±4.08]

578.0

(2.95) 

[±4.86]

Household 

 activities

137.1

(1.36) 

[±2.24]

141.4

(5.31) 

[±8.74]

115.2

(7.71) 

[±12.69]

133.9

(1.93) 

[±3.17]

142.2

(2.63) 

[±4.33]

138.1

(2.72) 

[±4.48]

Caring for house-

 hold members

41.6

(0.63) 

[±1.04]

49.6

(3.39) 

[±5.57]

33.5

(5.26) 

[±8.65]

43.7

(1.01) 

[±1.66]

39.2

(1.38) 

[±2.27]

39.1

(1.54) 

[±2.53]

Caring for 

 nonhousehold 

 members

14.8

(0.54) 

[±0.88]

14.7

(2.02) 

[±3.32]

14.7

(4.10) 

[±6.74]

12.8

(0.80) 

[±1.32]

17.5

(1.17) 

[±1.93]

15.5

(1.00) 

[±1.64]

—continued
22

Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

Table 14b

Time spent (minutes) in major activity groups, women only—Continued

Activity Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Paid work

182.9

(2.28) 

[±3.75]

180.3

(8.78) 

[±14.44]

151.6

(16.02) 

[±26.35]

185.8

(3.71) 

[±6.11]

187.6

(4.51) 

[±7.41]

176.3

(4.69) 

[±7.72]

Educational

30.0

(1.03) 

[±1.69]

22.2

(4.15) 

[±6.82]

75.0

(10.65) 

[±17.51]

43.0

(2.22) 

[±3.65]

20.6

(2.30) 

[±3.78]

15.2

(1.83) 

[±3.02]

Purchasing goods

45.3

(0.66) 

[±1.09]

45.8

(2.49) 

[±4.09]

48.9

(5.50) 

[±9.05]

45.6

(1.08) 

[±1.78]

45.7

(1.29) 

[±2.13]

43.8

(1.48) 

[±2.44]

Purchasing 

 services

9.4

(0.37) 

[±0.61]

9.2

(1.10) 

[±1.81]

11.5

(3.30) 

[±5.43]

8.4

(0.51) 

[±0.84]

10.5

(0.77) 

[±1.27]

10.0

(0.73) 

[±1.20]

Purchasing house-

 hold services

1.1

(0.08) 

[±0.13]

1.1

(0.32) 

[±0.53]

0.4

(0.31) 

[±0.51]

1.3

(0.13) 

[±0.22]

0.7

(0.10) 

[±0.17]

1.2

(0.20) 

[±0.34]

Government 

 and civic

0.6

(0.09) 

[±0.15]

0.3

(0.14) 

[±0.23]

1.8

(0.92) 

[±1.51]

0.5

(0.14) 

[±0.23]

0.5

(0.15) 

[±0.25]

1.0

(0.25) 

[±0.42]

Eating and drinking

71.9

(0.54) 

[±0.89]

67.4

(2.01) 

[±3.30]

73.5

(3.52) 

[±5.80]

74.1

(0.90) 

[±1.48]

72.8

(1.12) 

[±1.84]

68.1

(1.01) 

[±1.66]

Leisure

272.1

(1.81) 

[±2.98]

268.2

(6.24) 

[±10.27]

260.8

(13.11) 

[±21.57]

253.2

(2.84) 

[±4.67]

278.7

(3.40) 

[±5.60]

300.9

(3.97) 

[±6.52]

Sports

15.8

(0.50) 

[±0.82]

12.6

(1.57) 

[±2.58]

20.2

(4.06) 

[±6.68]

21.7

(0.92) 

[±1.52]

13.8

(0.95) 

[±1.56]

8.0

(0.57) 

[±0.93]

Religious

11.1

(0.33) 

[±0.55]

10.2

(1.08) 

[±1.78]

8.5

(2.11) 

[±3.46]

10.2

(0.51) 

[±0.84]

11.7

(0.68) 

[±1.13]

12.9

(0.68) 

[±1.11]

Volunteer

10.7

(0.47) 

[±0.77]

8.5

(1.18) 

[±1.94]

7.1

(1.87) 

[±3.08]

12.1

(0.82) 

[±1.34]

10.5

(0.84) 

[±1.38]

9.5

(0.85) 

[±1.40]

Phone and mail

10.2

(0.31) 

[±0.52]

7.3

(1.06) 

[±1.74]

11.5

(1.65) 

[±2.71]

10.5

(0.50) 

[±0.83]

10.1

(0.51) 

[±0.84]

10.7

(0.69) 

[±1.14]

Other

13.0

(0.43) 

[±0.70]

17.2

(1.84) 

[±3.03]

14.8

(3.58) 

[±5.89]

14.0

(0.83) 

[±1.36]

11.0

(0.66) 

[±1.08]

11.9

(0.73) 

[±1.21]

Total 1,440.00 1,440.00 1,440.00 1,440.00 1,440.00 1,440.00

Note: BMI = Body Mass Index. Population (weighted), age 15 and over, pregnant women 

excluded. Activities listed are ATUS major activity groups. Travel time included with each 

activity. Standard errors in parentheses.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health Module.
23

Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

The absolute difference in time 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 (1,440 + 1,440 = 2,880, 1,440 is 

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

The smaller the WADI, the smaller the difference between the two groups, 

and a WADI=0 would indicate no difference in activity profiles. Stewart 

(2006) recommends using a WADI index over other dissimilarity indices as 

it is not sensitive to the level of aggregation of activities (e.g., ATUS major 

groups of two-digit activity codes versus ATUS four-digit activity code 

groups) and short-duration activities receive little weight. The index value is 

“equal to the average proportional difference in the time spent in all activities” (Stewart, 2006, p. 59). Looking specifically at Personal Care (ATUS 

codes 01xxxx), an index of the absolute deviation (ADI), not the WADI, can 

be used as it is only one activity so weighting is not needed. 

Overall, the WADIs are small, indicating little difference between the 

activity profiles of the missing BMI group and those of other BMI groups21

(table 16). Among the BMI groups, the missing BMI group had the lowest 

WADI with the normal-weight group for the total population and for men. 

This means the time use profile of those with missing BMIs most resembled 

that of normal-weight individuals. For women, the lowest WADI was with 

the overweight group, which indicates that the activity profile of women 

with missing BMIs is closest to that of overweight women. Looking only at 

the absolute deviation index for Personal Care activities, those with missing 

BMI had Personal Care durations most like the underweight group for the 

total population and for men and the obese group for women. 

21The mean BMI for those who 

reported height and weight age 15 and 

older is 27.13; for men, it is 27.60, and 

for women, it is 26.66.

Table 15

Time spent (minutes) in Personal Care activities by Body Mass Index 

(BMI) group

Activity Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Men

 Sleeping

509.7

(1.25) 

[±2.05]

553.9

(11.29) 

[±18.58]

557.3

(12.08) 

[±19.88]

522.9

(2.75) 

[±4.53]

502.1

(1.99) 

[±3.27]

500.7

(2.55) 

[±4.20]

 Grooming

32.9

(0.31) 

[±0.50]

31.3

(2.13) 

[±3.51]

28.5

(3.70) 

[±6.09]

33.1

(0.50) 

[±0.83]

33.2

(0.49) 

[±0.81]

32.7

(0.54) 

[±0.88]

Women

 Sleeping

513.4

(1.22) 

[±2.00]

528.1

(4.58) 

[±7.53]

531.8

(9.64) 

[±15.86]

510.9

(1.67) 

[±2.76]

507.2

(2.39) 

[±3.93]

518.5

(2.83) 

[±4.66]

 Grooming

47.0

(0.32) 

[±0.53]

43.3

(1.06) 

[±1.75]

51.3

(2.95) 

[±4.85]

48.7

(0.53) 

[±0.88]

47.1

(0.64) 

[±1.06]

44.5

(0.72) 

[±1.18]

Note: Population (weighted), age 15 and over. Sleeping is ATUS activity code 010101, and 

Grooming is 010201. Travel time included. Standard errors in parentheses, 90-percent confidence intervals in brackets.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey (ATUS) and Eating and Health Module.
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Economic Research Service/USDA

Perhaps the long sleep times are due to unobserved characteristics more 

specific than general health. For example, the missing BMI respondents 

could be more likely to be suffering from depression or other illnesses at the 

time of the ATUS interview, which could result in longer sleep times, poorer 

health, and fewer activities in the time diary. It does appear that the missing 

BMI individuals may be in slightly worse health than others and, as a consequence, may sleep more. 

However, the longer average times engaged in sleeping may also indicate that 

those in the missing BMI group are uncooperative respondents; that is, they 

cannot remember; they do not want to make the effort to remember; or they 

do not want to disclose their activities in detail. Reporting longer sleep times 

allows respondents to cover a large block of time with one activity. This is 

consistent with the earlier analysis 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. 

Table 16

Measures of dissimilarity by Body Mass Index (BMI) group

Item Total

population

Missing

BMI

Underweight Normal

weight

Overweight Obese

Total population

 WADI 0.0356 na 0.0600 0.0407 0.0513 0.0494

 ADI, personal 

 care only 0.0205 na 0.0045 0.0165 0.0282 0.0239

Men

 WADI 0.0626 na 0.0758 0.0566 0.0801 0.0692

 ADI, personal 

 care only 0.0358 na 0.0003 0.0246 0.0413 0.0446

Women

 WADI 0.0190 na 0.0586 0.0343 0.0250 0.0298

 ADI, personal 

 care only 0.0095 na 0.0059 0.0128 0.0150 0.0053

Note: na = not applicable WADI = weighted absolute deviation index. Population (weighted), 

age 15 and over. Shading indicates smallest index among the BMI groups.

Source: USDA, Economic Research Service using data from 2006-08 American Time Use 

Survey and Eating and Health Module.
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Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

Economic Research Service/USDA

Is a Remedy for Missing BMI Data Needed?

Several factors make it unnecessary to devise a remedy for missing BMI 

data in the EH Module: less than 5 percent of respondents are missing BMIs; 

respondents with missing BMIs have indicators of being uncooperative 

respondents with poor data quality; and the index of dissimilarity shows that 

the missing BMI group has time use patterns similar to normal-weight men 

and overweight women. The BMI data in the EH Module are suitable for 

research with the ATUS time diary data. Researchers can use case deletion 

in their analysis to exclude respondents with missing BMIs and calculate estimates using the responses with BMI values.

Researchers who want to increase the number of observations, or who want 

to fully account for any possible bias, may undertake one of two approaches. 

First, sample weights could be recalculated for BMI nonresponse (see 

Abraham et al. (2006) for analysis of unit nonresponse in the ATUS). The 

ATUS final sample weights control for a variety of factors, including unit 

nonresponse and interview day of week. A large number of technical adjustments are included as well.22 A researcher considering this remedy may want 

to consider the extensive computational requirement of recalculating weights 

with the expected reduction of bias.

The other approach would be to impute BMI, or impute height and weight, 

for the missing BMI values. One difficulty in imputing BMI from the ATUS 

and EH Module data is that there are no anthropomorphic or medical information available other than the self-reported general health. The studies cited 

above that imputed BMI used NHANES data and had medical history and 

waist circumference information for the respondents with missing BMIs. 

Having anthropomorphic and/or medical information would make for more 

informed imputations of BMI. Without this information, BMI imputations 

would essentially be cell averages of the demographic and labor force participation group of missing BMI respondents. Another possibility is to use the 

NHANES data and probabilistic matching to match the missing BMI respondents with NHANES respondents and then apply the NHANES BMI value to 

the ATUS/EH Module respondents.

22See ATUS User’s Guide section 

7.2, and BLS and Census (2006), 

Current Population Survey: Design 

and Methodology, chapter 10.
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Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module / TB-1934

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Conclusions

The 4.9 percent of EH Module respondents who did not report height and/or 

weight had disproportionately higher indicators of being reluctant or uncooperative respondents. It took more call attempts over more weeks to interview 

these respondents, indicating that they were reluctant to participate in the 

survey. They were also 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 other sensitive 

questions on the survey. These findings indicate that the respondents’ lack of 

reporting height and/or weight had less to do with the height and weight questions and more to do with the respondents’ views 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, they are 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 the analysis 

is unlikely to produce bias in time-use estimates. As a consequence of these 

findings, any item nonresponse bias in the EH Module height and weight data 

appears to be small, allowing for future analysis of time use by BMI.
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Websites

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Eating and Health Module: ers.usda.gov/Data/ATUS/

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