Time Use, Response Rates, and Data Quality by Time of Day 

Rose Woods and Laura Wronski 

Bureau of Labor Statistics 

2 Massachusetts Ave. NE, 

Washington, DC 20212 

Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference 

Abstract 

The American Time Use Survey (ATUS) collects a detailed 24-hour time diary of all the activities respondents 

participated in the previous day. Interviews are conducted via Computer Assisted Telephone Interviewing (CATI) 

throughout the day, with most interviews being conducted between 9 a.m. to 9 p.m. (respondent time). Respondents 

may call in or set up an appointment to complete the survey outside of these hours. This may result in some 

interviews beginning as early as 6 a.m. and some ending as late as midnight. Given the level of detail of time-use 

diaries, we are interested in whether data quality suffers later in the day. 

We examine survey response, data quality measures, and characteristics of people responding to the ATUS by the 

time of day when respondents were interviewed. These characteristics include respondents’ demographic

composition as well as their time use. 

Introduction 

The American Time Use Survey (ATUS) is a nationally-representative survey that collects data on how, where, and 

with whom Americans spend their time. It is the only federal survey providing data on the full range of nonmarket 

activities, from childcare to volunteering. Sponsored by the Bureau of Labor Statistics (BLS) and conducted by the 

U.S. Census Bureau since 2003, it is the first federally-funded, continuous survey of time use in the United States. 

Data are collected nearly every day of the year via Computer Assisted Telephone Interviewing (CATI). The core 

part of the interview is the time diary, in which respondents are asked to report a full 24 hours of activities starting at 

4 a.m. on the day before the interview (“yesterday” or the “diary day”) and ending at 4 a.m. on the day of the 

interview. These responses are aggregated to produce estimates for the average hours spent doing all sorts of 

activities, such as working, cleaning, sleeping, eating, and spending time with friends and family. The survey also 

collects demographic and labor force data, which means that these estimates can be produced for the full civilian 

non-institutional population age 15 and older as well as for subpopulations such as parents of young children, fulltime employed persons, or the elderly. 

Time use surveys conducted in other countries follow similar methodologies, with a few important differences. 

Studies conducted in Australia, New Zealand, and Japan have used paper diaries that ask respondents to record all 

their activities for 2 consecutive days. European countries that conduct time use studies—including France and the 

United Kingdom—are encouraged by Eurostat to collect 2 days (1 weekday and 1 weekend day) of data via a paper 

diary. The Canadian time use survey is probably the most similar to the ATUS, as it also collects data for a single 

24-hour period via CATI. Significantly, though, the Canadian time use survey allows for a longer recall period; if a 

respondent cannot be reached on his interview day, he will be called again on the following day and still interviewed 

about his original “diary day” even though it is by that time two days prior. The ATUS practice of only asking 

respondents about yesterday was put in place to avoid overburdening the respondents: they only have to remember 

activities that occurred over the past day. However, relaxing the ATUS methodology to match the Canadian 

methodology may have some benefits. Increasing the recall period from 1 to 2 days (or 24 to 48 hours) might allow 

greater contactability—and therefore help achieve higher response rates—yet preserve the random day assignment 

of the sample design. 

Compared to most surveys, the ATUS has a rigid data collection design. As described below, this design ensures 

that the sample data will provide estimates representative of the national population; however, it may introduce 
some complications to the survey. In particular, the strict design parameters may hinder response among the 

population in general or among certain segments of the population. The average annual ATUS response rate is low 

for a federal survey, ranging between 52.5 and 57.8 percent annually from 2003 to 2012 (See Table 1). 

This paper examines the survey design recall period. In theory, allowing sample members more time to respond to 

the survey would convert some of the noncontacts and refusals to completed interviews, as a longer response time 

means more call attempts and more opportunities to reach busy sample members. The downside to this increased 

response time is a potential negative effect on data quality. At this time, we are unable to compare the quality of data 

collected using a one-day versus a two-day recall period because these data are not available to us; however, we can 

use the ATUS to examine data quality over the course of one day. That is, we can examine ATUS diary data by the 

time of day when they were collected. Because the ATUS asks participants to recall every activity they took part in 

the previous day, and memory tends to fade over time, increased recall periods may cause respondents to remember 

fewer of their activities, or to have other problems with recalling “yesterday’s” events. This potential tradeoff 

between improved response rates and diminished data quality is the central concern of this paper. 

Table 1. ATUS Annual Response Rates* 

Year Response Rate 

2003 57.8% 

2004 57.3% 

2005 56.6% 

2006 55.1% 

2007 52.5% 

2008 54.6% 

2009 56.6% 

2010 56.9% 

2011 54.6% 

2012 53.2% 

*Note: These are unweighted, pre-processing response rates, calculated according to the American Association for 

Public Opinion Research’s Response Rate 2 formula: 

http://www.aapor.org/AM/Template.cfm?Section=Standard_Definitions2&Template=/CM/ContentDisplay.cfm&Co

ntentID=3156. 

Data 

The ATUS follows strict statistical sampling principles to ensure representation by population demographics and 

day of the week. The stratified random sample oversamples Blacks and Non-Black Hispanics, households with 

children under age 18, and weekend days in order to have a large enough sample size to produce estimates for these 

subpopulations and for weekdays and weekend days. Statistical weights are applied to adjust for this oversampling 

and for nonresponse. The ATUS sample is drawn from the population of households that have completed the eighth 

month of interviewing for the Current Population Survey (CPS). Because of this, information on household

composition and demographics is already known before the survey is fielded and it is used to select respondents. 

From each household in the sample, one person (the “designated person” or “DP”) is selected for participation in the 

ATUS and randomly assigned a day of the week about which he will be interviewed (the “diary day”). The DP is 

called on the day of the week following the diary day—and only that day of the week—for up to eight weeks, until a 

single interview is completed. For example, if a DP’s diary day is a Thursday, he would be called every Friday for 

up to 8 weeks until one interview is completed. The sample allocation of diary days is split evenly between 

weekdays and weekend days (50% of the sample to each, meaning 25% each for Saturdays and Sundays, and 10% 

for each of the weekdays). ATUS interviews are conducted seven days a week, on nearly every day of the year. 

The core component of the ATUS interview is the time use “diary”: a series of questions in which the interviewer 

collects information about every activity the respondent did on the previous day. However, while we refer to the 

core component of the ATUS as a time use diary, there is no paper component; all interviews are conducted over the 
telephone (CATI) by Census interviewers. This means that respondents must recall yesterday’s events from memory 

while on the phone with an interviewer. 

 

Data used in this analysis come from ATUS interviews conducted from January 2008 through December 2010.

Information from about 37,500 interviews conducted during this time period is available in the public data files 

published by the BLS. As the purpose of this analysis is to examine data quality, we also include an additional 900 

interviews or “cases” that were excluded from the public use files because they do not meet ATUS publication 

criteria. For example, ATUS publication criteria require that cases must have a time diary with at most 180 minutes 

of unaccounted-for time (i.e., time that is assigned either the data code 500105 “Respondent refused to provide 

information/‘none of your business’” or 500106 “Gap/can’t remember”). As described in detail below, however, the 

measure of this unaccounted-for time is an important indicator of data quality, and excluding those cases that are 

outliers for that measure would bias our analysis. Throughout this paper, we refer to these two data codes (500105 

and 500106) collectively as “Refused/Gap.” 

This analysis compiles data contained in several of the ATUS public use files, along with the unpublished records of 

cases that do not meet data quality standards. The ATUS Case History file and Call History file provide information 

needed to create data quality measures, such as information about the final case outcome code, the call attempt 

number, and the date of the call. Additional data not publicly available include the time each call attempt was made 

in both the interviewers’ and respondents’ time zones and many other call-level variables. For this analysis, we use 

call-level information about the final call that resulted in a completed interview. Additional data—such as 

demographic information and the number of activity episodes—come from the ATUS Respondent, Roster, and 

Activity files. 

All estimates presented in this analysis are unweighted; that is, adjustments have not been made to account for the 

sample’s stratification, nor have they been made to account for survey nonresponse. Therefore, these results are not 

representative of the U.S. population as a whole, and should not be confused with official time use estimates 

produced by the ATUS. 

Methods 

The ATUS Call History File contains information on the time of day that all interviews were completed. (Unless 

otherwise specified, all mentions of time of day in this paper refer to the respondent’s time zone.) Outgoing calls are 

conducted between 9 a.m. and 9 p.m., but respondents who voluntarily call in to complete their interview or 

schedule an interview time on their designated day may be interviewed before or after the official call hours. For 

ease of analysis, we divided the interview day into six call blocks: 

 Call block 1 – 6:00 to 8:59 a.m. 

 Call block 2 – 9:00 to 11:59 a.m. 

 Call block 3 – 12:00 to 2:59 p.m. 

 Call block 4 – 3:00 to 5:59 p.m. 

 Call block 5 – 6:00 to 8:59 p.m. 

 Call block 6 – 9:00 to 11:59 p.m. 

Our analysis begins by creating descriptive statistics by call block. We are interested in the characteristics of sample 

members who respond at different times of the day; in particular, we’d like to know whether people of a given sex, 

race, age, and employment status are more likely to respond earlier rather than later in the day or vice versa. This 

background information will help us to determine whether differences in data quality are due to the types of 

respondents who participate at different hours, rather than the difficulty or ease in recall. 

We then go on to examine measures of data quality. While there is no “gold standard” of comparison for time use 

statistics, there are certain data quality measures that have been used in past research (Fricker and Tourangeau). 

Specifically, we analyze: 

1. Percent of publishable cases. The percent of publishable cases is the total number of cases included in 

the public use files divided by the total number of completed cases. During the data editing process, a
small number of completed cases are removed from the ATUS each year for data quality reasons. 

Virtually all removals occur for one of two reasons: if the respondent reports fewer than 5 activities in 

the diary, or if he reports more than 180 minutes of time as “Refused/Gap.” In any instance where the 

case is determined to not be publishable, it is removed from the public use data files and excluded from

official ATUS estimates. 

2. Percent of cases with fewer than 5 activities in the diary. Cases with fewer than 5 activities do not 

meet ATUS publication criteria, and therefore they do not appear on the ATUS public use files. The 

percent of cases with fewer than 5 activities in the diary is small as a percent of all completed cases, 

and is also a small share of the cases that are removed for any reason. 

3. Percent of cases with more than 180 minutes of “Refused/Gap” time in the diary. Most of the 

completed cases that are removed from the data files are done so because they do not meet this data 

quality standard. While it is a relatively small share of all the completed interviews, these cases 

account for a majority of the eliminated cases. 

4. Average number of activities per case. The number of activities reported in the diary is frequently used 

to assess data quality in time-use surveys. We include this measure in our analysis to determine 

whether and how the timing of the interview may be related to it. 

Because there are some differences between weekdays and weekend days in terms of the types of activities reported 

and potentially the ability to recall these activities, we look at these data quality measures for weekdays and 

weekend days in addition to the overall averages for all days of the week. 

As a final step, we conduct some simple regression analyses to measure the relationship between the interview time 

and these data quality measures, while controlling for demographic characteristics mentioned above. 

Results 

Demographics 

Overall, 94 percent of all interviews were conducted between 9 a.m. and 9 p.m., with 3.3 percent occurring before 9 

a.m. and 2.7 percent occurring after 9 p.m. (See Table 2.) The largest share of interviews occurred between 9 a.m. 

and noon for both men and women, followed by the 6 to 9 p.m. call block. The distribution of call times was similar 

for men and women, although women were more likely than men to complete the interview between 9 a.m. and 

noon, while men were more likely to complete the interview between 6 and 9 p.m.

Table 2. Percent of interviews conducted by sex and time of day 

Call Block Time Call Began Total Men Women 

1 6 - 9 am 3.3 3.2 3.4 

2 9 am - noon 28.4 26.8 29.7 

3 Noon – 3 pm 19.1 18.4 19.6 

4 3 – 6 pm 22.2 22.3 22.2 

5 6 – 9 pm 24.2 26.3 22.6 

6 9 pm - midnight 2.7 2.9 2.6 

Total 100 % 100 % 100 % 

By age group, men ages 61 and older were more likely to complete the interview between 9 a.m. and noon than 

during other call blocks—41.1 percent of interviews with men in this age group occurred during the 9 – noon call 
block. (See table 3.) Younger men ages 15 to 19 were most likely to complete the interview later in the day: 31.8 

percent completed the interview between 3 and 6 p.m. and 30.4 percent completed it between 6 and 9 p.m. Men ages 

20 to 60 tended to respond between 9 a.m. and noon (23.9 percent) or 6 to 9 p.m. (29.3 percent). These response 

patterns likely reflect the contact rates at different times of the day: teenagers are likely to be in school during the 

earlier call blocks, and working-age men are likely to be at work during the middle of the day. Response patterns by 

age were similar for women. The most popular call block for women age 61 and older was also from 9 a.m. to noon 

(41.8 percent of all interviews with women age 61 and older), with response rates falling as the day went on. Like 

the younger men, younger women were more likely to complete the interview between 3 and 6 p.m. (32.9 percent) 

or 6 and 9 p.m. (27.5 percent). Women ages 20 to 60 were about equally likely to complete the interview between 9 

a.m. and noon (26.1 percent) or 6 and 9 p.m. (26.0 percent). 

Table 3. Percent of interviews conducted by age, sex, and time of day 

Call 

Block 

Time Ages 15-19 Ages 20-60 Age 61 and older 

Men Women Men Women Men Women 

1 6 - 9 am 1.1 0.8 2.9 2.7 5.1 5.8 

2 9 am - noon 13.2 15.2 23.9 26.1 41.1 41.8 

3 Noon – 3 pm 20.4 19.2 17.7 19.1 20.1 21.0 

4 3 – 6 pm 31.8 32.9 22.8 23.1 17.7 17.4 

5 6 – 9 pm 30.4 27.5 29.3 26.0 14.8 12.8 

6 9 pm - 

midnight 

3.0 4.4 3.4 3.0 1.3 1.1 

Total 100 % 100 % 100% 100% 100% 100%

By household composition, those living alone were more likely to complete the interview between 9 a.m. and noon 

(36.1 percent), than were those living only with a spouse or unmarried partner (32.4 percent). (See table 4.) Those 

who lived with a spouse or partner in addition to other household members were more likely to complete the 

interview between 6 and 9 p.m. (29.5 percent) or 9 a.m. and noon (24.5 percent). Those living in households with 

multiple members that did not include a spouse or unmarried partner were more likely to complete the interview 

between 6 and 9 p.m. (26.2 percent) or 3 and 6 p.m. (25.4 percent). 

Table 4. Percent of interviews conducted by household composition and time of day 

Call Block Time Call Began Living alone Living with a 

spouse or 

unmarried 

partner only 

 Living 

with a 

spouse or 

unmarried 

partner and 

others

Living in 

other multiperson 

households 

1 6 - 9 am 4.6 4.0 2.8 2.4 

2 9 am - noon 36.1 32.4 24.5 23.2 

3 Noon – 3 pm 21.1 18.6 17.3 19.7 

4 3 – 6 pm 20.0 20.2 22.4 25.4 

5 6 – 9 pm 16.5 22.5 29.5 26.2 

6 9 pm - midnight 1.7 2.3 3.5 3.1 

Total 100 % 100 % 100 % 100 % 

The median age (unweighted) of the ATUS respondents whose data were used in this analysis was 46 years. (See 

table 5.) The median age was the highest for the first call block (54 years) and declined throughout the day. 

Employed persons were more likely to complete the interview later in the day; the percent of respondents who were 

employed increased for each call block. White respondents comprised 80.2 percent of all interviews, with little 
variation across the call blocks. The largest share of interviews conducted with Hispanics was between 3 and 6 p.m. 

(15.7 percent); overall 13.9 percent of the interviews were with Hispanic respondents. 

Table 5. Selected demographic characteristics by time of day

Call Block Time Call Began Median Age Percent 

Employed 

Percent 

White 

Percent 

Hispanic 

1 6 - 9 am 54 44.1 80.4 11.1 

2 9 am - noon 52 50.0 79.9 11.9 

3 Noon – 3 pm 46 56.2 80.2 14.0 

4 3 – 6 pm 43 65.0 80.3 15.7 

5 6 – 9 pm 42 76.8 80.8 14.6 

6 9 pm - midnight 42 77.5 77.5 14.5 

Total 46 61.6 80.2 13.9 

Data quality 

Tables 6 through 9 present data quality measures by call block. Table 6 shows that the percentage of publishable 

cases increased throughout the day, although the differences by call block are relatively small. As shown in table 7, 

the percentage of cases with more than 180 minutes assigned to data codes (for responses of “Refused/Gap”) 

declined throughout the day. These results were true for both weekdays and weekend days. The percentage of cases 

reporting fewer than 5 activity episodes in the diary had a similar pattern of having higher quality measures 

associated with interviews that occurred later in the day (see table 8). Whether cases were publishable or not 

depends on whether they had more than 180 minutes of “Refused/Gap” time and whether they reported fewer than 5 

activities, so we would expect these three data quality measures to covary. The number of activities reported in the 

diary averaged 19.6 overall, with weekday diaries averaging 2 more per day than weekend days – 20.6 compared 

with 18.6 (see table 9). There did not appear to be any trends or patterns in the average number of activities reported 

per diary across the different call blocks. 

Table 6. Percent of publishable cases by diary day and time of day 

Call Block Time Call Began Total Weekday Weekend days 

1 6 - 9 am 97.9 98.2 97.3 

2 9 am - noon 97.1 97.3 97.0 

3 Noon – 3 pm 97.2 97.4 97.1 

4 3 – 6 pm 98.0 98.4 97.6 

5 6 – 9 pm 98.4 98.6 98.2 

6 9 pm - midnight 99.0 99.2 98.7 

Total 97.7 97.9 97.5 

Table 7. Percent of cases with more than 180 minutes of “Refused/Gap” time by diary day and time of day

Call Block Time Call Began Total Weekday Weekend days 

1 6 - 9 am 2.0 1.6 2.7 

2 9 am - noon 2.3 2.3 2.3 

3 Noon – 3 pm 2.2 2.1 2.2 

4 3 – 6 pm 1.7 1.4 2.0 

5 6 – 9 pm 1.2 1.0 1.4 

6 9 pm - midnight 0.6 0.6 0.6 

Total 1.8 % 1.6 % 2.0% 

Table 8. Percent of cases reporting fewer than 5 activities by diary day and time of day 

Call Block Time Call Began Total Weekday Weekend days 

1 6 - 9 am 0.2 0.2 0.0 
2 9 am - noon 0.6 0.5 0.7 

3 Noon – 3 pm 0.6 0.5 0.7 

4 3 – 6 pm 0.3 0.3 0.4 

5 6 – 9 pm 0.4 0.4 0.4 

6 9 pm - midnight 0.4 0.2 0.6 

Total 0.5% 0.5% 0.5% 

Table 9. Number of activities by diary day and time of day 

Call Block Time Call Began Total Weekday Weekend days

1 6 - 9 am 19.8 20.5 18.3 

2 9 am - noon 19.7 20.9 18.4 

3 Noon – 3 pm 19.2 20.3 18.5 

4 3 – 6 pm 19.5 20.8 18.4 

5 6 – 9 pm 19.6 20.4 18.8 

6 9 pm - midnight 20.0 20.5 19.5 

Total 19.6 20.6 18.6 

Regression analysis 

The data quality measures in general appear to improve throughout the day. Specifically, we found that the percent 

of publishable cases increased and the share of cases with more than 180 minutes of time coded as “Refused/Gap” 

decreased as the call blocks progressed from early morning to late at night. This was a surprising result, because we 

expected to observe a deterioration in data quality associated with interviews conducted further from the reference 

period. Earlier, we observed that the characteristics of respondents varied by the time of their interview; these 

differences might provide an explanation for the improvement in data quality as the day progressed. To understand 

better the relationship between interview times and data quality, we used simple regression analysis and controlled 

for respondents’ demographic characteristics and other factors. 

First we look at the number of activities reported in the diary (data quality measure 4) by the call block in which the 

interview was conducted, while controlling for age, sex, weekday or weekend diary day, and the year in which the 

interview was conducted (see Table 10). The results indicate that at a 90 percent confidence level, sex, age, and 

weekday versus weekend day were all significantly correlated with the number of activities reported in the diary, 

while the call blocks were not. Age was negatively correlated with the number of activities reported in the diary, 

which is consistent with the idea that older people may have more difficulty recalling detailed activities throughout 

the diary day. Controlling for other factors, women reported more activities in their diary, and interviews about 

weekday diaries had more activities listed. 

Table 10. Regression Results – Number of Activities in the diary 

Variable Estimate Standard Error T-Value 

Intercept 18.312* 0.267 68.64 

Women 3.289* 0.081 40.63 

Age -0.029* 0.002 -12.62 

Weekday 2.106* 0.081 26.11 

y2009 -0.088 0.097 -0.89 

y2010 -0.555* 0.098 -5.64 

Call Block 2 0.093 0.233 0.40 

Call Block 3 -0.231 0.240 -0.96 

Call Block 4 -0.103 0.237 -0.43 

Call Block 5 0.002 0.236 -0.01 

Call Block 6 0.233 0.329 0.71 

*Estimates that are significant at a 90 percent confidence level (α=0.10).
The second analysis examines the average number of minutes that were assigned a data code per diary (data quality 

measure 3) by the same characteristics as the previous regression (see Table 11). The majority of activities assigned 

a data code in the ATUS were times when respondents provided answers of “Refused/Gap,” although there are also 

data codes that indicate interviewer errors or uncodeable activities; this analysis focuses specifically on interviews 

with “Refused/Gap” codes. At a 90 percent confidence level, the call blocks were not significantly correlated with 

the average minutes per diary that were assigned data codes, while sex, age, whether it was a weekday versus a 

weekend day, and the year in which the interview was conducted were significant factors. Women and older

respondents were associated with more minutes assigned to data codes. Weekday diaries had fewer minutes of data 

codes on average than weekend day diaries. 

Table 11. Regression Results –Average minutes of data codes in the diary 

Variable Estimate Standard Error T-Value 

Intercept -8.436* 1.955 -4.31 

Women 2.030* 0.593 3.42 

Age 0.377* 0.017 22.28 

Weekday -1.276 * 0.591 -2.16 

y2009 -0.416 0.723 -0.58 

y2010 -0.480 0.721 -0.67 

Call Block 2 1.282 1.704 0.75 

Call Block 3 3.047 1.755 1.74 

Call Block 4 1.018 1.738 0.59 

Call Block 5 -0.034 1.730 -0.02 

Call Block 6 -1.604 2.408 -0.67 

*Estimates that are significant at a 90 percent confidence level (α=0.10).

In the final regression analysis, we look at the average amount of time spent in the diary section of the interview per 

activity to get a sense of how long the respondent took to report each activity (see Table 12). This measure provides 

insight about whether respondents’ ability to remember the previous days’ activities varies by the timing of the 

interview. We use the same set of predictors as in the previous two regressions (see Tables 10 and 11). Unlike the 

regressions presented in Tables 10 and 11, three of the call blocks were significant in this third regression: the later 

call blocks (4, 5, and 6) were all positively related to the time spent in the diary section of the interview for each 

activity, which supports the idea that respondents’ ability to recall their previous days’ activities is more difficult 

when the interview is conducted further from the reference period. The age of the respondent also had a positive and 

significant effect on this measure, meaning that older respondents took longer to respond to the diary than younger 

persons. It also took longer to report activities that occurred on weekend days than on weekdays. 

Table 12. Regression Results –Average minutes spent reporting the diary per activity 

Variable Estimate Standard Error T-Value 

Intercept 0.437* 0.008 54.41 

Women 0.001 0.002 0.31 

Age 0.001* 0.000 17.55 

Weekday -0.008* 0.002 -3.20 

y2009 0.005* 0.003 1.65 

y2010 0.052* 0.003 17.70 

Call Block 2 0.001 0.007 0.17 

Call Block 3 0.013 0.007 1.77 

Call Block 4 0.037* 0.007 5.16 

Call Block 5 0.044* 0.007 5.96 

Call Block 6 0.052* 0.010 5.25 

*Estimates that are significant at a 90 percent confidence level (α=0.10).

As a quality check, we ran each of the regressions above a second time, including a binary variable (“Publishable”) 

that indicated whether a case met the ATUS publication criteria. The “Publishable” variable was statistically 

significant in each regression—as would be expected given its relationship with each of the data quality measures. 

The effects on the coefficients for other variables were minimal. 
 

Discussion 

Results from the three regression analyses finds mixed evidence that memory of yesterday’s events decays over the 

course of one day. After controlling for age, sex, and weekday versus weekend day, our data show that respondents 

take a little longer to recall their activities when interviewed later in the day, but the quality of the data they report is 

just as good at 9 p.m. as at 9 a.m. In fact, our data quality measures improved from the earlier call blocks to the later 

call blocks, although this seemed to be related to the characteristics of respondents who are available to be 

interviewed at different times of the day rather than the time of day itself. The analyses above therefore reveal a 

more complicated relationship between recall and time of day than originally expected. Interviewers attempt to 

make contact with respondents throughout the day, calling multiple times until either a response is secured or time 

runs out. Therefore, the time of day that an interview is completed depends upon the respondent’s traits and his time 

use that make him available and unavailable at different points throughout the day. As our analysis has shown, 

respondents who tend to be available earlier in the day are more likely to be older and women. While we find that 

our data quality measures improve throughout the course of the day, the driving force behind this improvement 

seems to be the types of respondents who are available in the afternoon and evening hours compared with the 

morning hours. 

Women and older people are more likely to be available earlier in the day, probably because they are less likely to 

be employed. Overall, women report more activities and have more time assigned to data codes (“Refused/Gap”) 

compared to men. Similarly, older respondents report fewer activities, have more time assigned to data codes 

(“Refused/Gap”), and spend longer in the diary portion of the interview compared to younger respondents. 

Respondents reporting about weekdays report more activities and spend less time in the diary portion of the 

interview compared to those reporting about weekend days. These findings may indicate a real difference in data 

quality by sex, age, and diary day, or they may indicate a flaw in the available data quality measures. Respondents 

interviewed later in the day take longer to respond to the diary portion of the interview, indicating that it may be 

harder to remember yesterday’s events; however, there is no difference in the average number of activities reported 

or the percent of cases with data codes by time of day. The differences in our data quality measures may not truly 

indicate data quality errors; instead, they may simply (and accurately) reflect the differences in time use for different 

segments of the population. For example, while older respondents are more likely to have some memory loss 

compared to younger respondents (leading to poorer data quality), they also tend to have less structured days that are 

more difficult to recall, and they may also partake in fewer activities than younger respondents on a given day. It 

makes sense, then, that the age variable is significant in each of the regression analyses, but we cannot say for sure 

whether this indicates poor data quality. Similarly, it is certainly possible that women have less structured days than 

men and partake in more activities than men on a given day, and that weekend days are more difficult than 

weekdays for respondents to remember. 

While we cannot extrapolate this analysis to say that “yesterday’s” time use could be just as easily remembered 

tomorrow as today, it does support the survey design that the ATUS has in place. Future research will be needed to 

determine whether and to what extent respondents’ ability to recall their activities is affected when they are 

interviewed more than 24 hours after the day’s activities. 
References 

Fricker, Scott, and Tourangeau, Roger. Examining the Relationship Between Nonresponse Propensity and Data 

Quality in Two National Household Surveys. Public Opinion Quarterly 74(5), December 2010.