2

Guidelines for Time Use DataCollection and Analysis

Andrew S. Harvey

INTRODUCTION

Time diaries provide an ideal approach to the collection of activity data.Activity data collected by means of stylized questions or activity lists,taken out of the context of daily life, miss many of the objective andsubjective circumstances about participation in activities. Yet often theseare the circumstances that, with personal characteristics, determine actualbehavior. A time diary places activities in their natural temporal context.By its nature, the diary provides a record of all activities during a specifiedperiod (day, week), along with a potentially rich array of contextual infor-mation. This chapter explores the collection and analysis of diary data andspecific opportunities and problems they pose for the researcher.

As indicated in Chapter 1, even the simplest time use studies providecrucial measures of involvement in a broad range of activities engaged inby individuals—such as paid work, housework and child care, education,sleep, eating, socializing, games, sports, media use. If supplementary dataare collected about the location of activities, and whom individuals arewith, many more measures can be generated. These additional data pro-

Andrew S. Harvey • Department of Economics, St. Mary's University, Halifax, Nova

Scotia, Canada B3H 3C3.

Time Use Research in the Social Sciences, edited by Wendy E. Pentland, Andrew S. Harvey, M.

Powell Lawton, and Mary Ann McColl. Kluwer Academic/Plenum Publishers, New York,

1999.19

20 ANDREW S. HARVEY

vide an opportunity to develop measures of mobility, infrastructure use,sociability, and other diverse social phenomena. If subjective informationhas also been collected, construction of affective measures of the quality oflife are also possible. Many different, rich measures of the texture ofeveryday life can be developed.

COLLECTION GUIDELINES

In many ways, the collection of time use data differs little from thecollection of other social and economic data. There are, however, a numberof issues that should be addressed to optimize the value and accuracy ofthe final data. While the diary is the preferred data collection method.there are alternatives. Activity lists, logs, continuous or random observa-tion, and beeper studies have all been used at one time or another to collectactivity data (United Nations International Research and Training Institutefor the Advancement for Women [INSTRAW], 1995). The actual approachchosen will depend on a number of issues that can be evaluated in terms ofboth input and output criteria (Harvey & MacDonald, 1976). The sug-gested input criteria are respondent knowledge, respondent cooperation,time and money resources, and processability. Output criteria are validity,reliability, usability, and flexibility. Once an activity capture approach hasbeen chosen, questions regarding data collection remain. Collection meth-odology issues can be classified in terms of sampling, collection, diarycontent, and background variable content (Harvey, 1993b).

Sampling of Respondents

Sampling issues relate to the choice of the respondent population, thesample size, geography, and survey timing. Typically, national statisticalagencies collect data that are nationally representative. The major issuesstatistical agencies face in terms of population are whether to collectdiaries for individuals only or for several or all household members.Additionally, they must set the ages of the respondent population. There isno clear choice. The ages of populations covered have ranged from age 2years in Bulgaria to age 15 in Canada. The current Statistical Office of theEuropean Communities (EUROSTAT) project guidelines are to collect di-aries for all household members aged 10 years and over (EUROSTAT,1996).

Many time use studies have been carried out for particular sub-populations of substantive value to the research design. Michelson (1988) collected data on complete families in Toronto to study the effects of

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS

maternal employment. Several of the editors of this volume are collecting data for a sample of individuals with spinal-cord injuries (McCall, Pent-land, Harvey, Walker, & Comis, 1993). At a minimum, the sample must be chosen in such a manner as to provide unbiased data for the population it purports to represent.

Sample size must be considered in terms of coverage of both popula-tion and diary days, since both population and behavior are being sam-pled. Consequently, the amount of data collected on particular behavior (e.g., meal preparation) is a function of both how many persons do it and how frequently. The sampling will be particularly affected by the nature of the issues motivating the survey. If one is interested in particular behavior, it is important that the sampling take both the propensity for doing the targeted activity and its frequency of occurrence into account. Eating, sleeping, and television viewing are not a problem, since they are done virtually daily. Sewing and mending, use of services (bank, doctor, etc.) and concert going are done by sufficiently few individuals and with sufficient infrequency that either extremely large, or extremely focused samples, would be required to provide useful analytical data. The geogra-phy of the sample will depend to a great extent on the purpose of the study.Gershuny (1991) suggests that time use estimates are somewhat insensitive to gross locational differences. This is understandable, since measured behavior is a function of the role and context of an individual (Harvey, 1983). If the geographic area is sufficiently large to be representative of a broad range of individual and microareal differences, the time use esti-mates should be relatively stable.

The final sampling issue relates to the time of year for data collection. Practice has varied, ranging from drawing a full sample in only 3–6 days (Nippon Hoso Kyokai [NHK], 1995) to sampling for over a full year (Niemi, Pääkkönen, Rajaniemi, Laaksonen, & Lauri, 1991; Statistics Can-ada, 1995). The choice of period is not just of academic concern. To the extent that behavior varies by time of the week, month, or season, it is necessary to ensure that the survey period appropriately reflects the gen-eral or particular behavior of interest. Niemi (1983) showed that time use during October–November, typically used for short-duration studies, was close to the annual average. Other work, however, found time-of-year did lead to substantial variation in the data (Hill, 1985). One must be sensitive to the interaction of population and season. If the sample includes young children, choice of a school period as representative of an annual average can be misleading both with respect to education and to the behavior of child caregivers. The results may well overestimate time allocated to edu-cation by the students and underestimate parental time spent caring for children.

21

22 ANDREW S. HARVEY

Diary Design

There are a number of diary design issues involving the interviewmode, the focus on "yesterday" versus "tomorrow" diaries, and the choiceof day. Two major options are the choice of precoded versus open-responsecategories and the choice of fixed time versus open intervals.

Open versus Coded Category

Most diary researchers shun the precoded format, opting instead foran open-response diary in which individuals respond in their own words.Precoding, usually limited to relatively few codes, forces excessive and ir-reversible data reduction at too early a stage in the survey process. How-ever, the extreme data reduction accompanying precoding is not absolute.An ongoing study in the Netherlands (Knulst & Schoonderwoerd, 1983)has used a broadly based precoding scheme incorporating a large numberof codes, which is somewhat more flexible later in the process.

Closed versus Open Interval

The option relates to the closed versus open time intervals. The Multi-national Time Use Study used an open-interval approach (Szalai, 1972),meaning that the respondent reports starting and ending times of eachactivity as part of the diary entry. This approach has been followed by allthe major North American studies mentioned in Chapter 1. However, mostof the European national surveys have opted for fixed-interval diaries,with intervals rangingfrom5 to20 minutes. Workof Lingsom (1979) andofNiemi (1983) suggested little difference between the two approaches. Un-published pilot testing for the 1986 Canadian Time Use Study concludedthat there were no cost savings from fixed time slots. Some work, however,suggests that there may be hidden problems. There is evidence that the useof, and size of, time slots differentially affect various activities (HarveyElliott, & Stone, 1977). The cooperative European time use survey beingfacilitated by EUROSTAT has adopted a 10-minute fixed-interval diary.

Yesterday versus Tomorrow Basis

Time-diary data can be collected on either a yesterday or tomorrowbasis. Yesterday diaries are typically collected by personal or phone inter-view, while tomorrow diaries are left behind by interviewers ("leave-behind diaries") or mailed to respondents. Although tomorrow diariesyield more events, research suggests that the difference in the number of events (an increase on the order of 10%) does not justify the additional cost

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 23

of obtaining tomorrow diaries (Juster, 1985b; Robinson, 1977,1985; Szalai,1972). Can diaries be collected for days further back than yesterday? Thereis no clear answer to this question. Research on it (Juster, 1985b; Keller,Kempter, Timmer, & Young-Demarco, 1982; Klevemarken, 1982) givesmixed results. Juster (1985b) argues that people appear to be able to recallFridays through Sundays better than other days. The general view ofexperienced time-diary researchers is, however, that recall should not beattempted for more than 2 days in arrears.

Number of Days

There are choices in the number of days to capture per respondent.While many diary studies collect only 1 day per respondent, it has becomemore common to collect at least 2 days per respondent. It is argued that atleast 2 days provide for greater reliability (Kalton, 1985; Pas, 1986; Sanik,1983). Kalton, however, argues for 2 weekdays, leaving aside the issue oftwo Saturdays or Sundays (Kalton, 1985). The EUROSTAT pilot surveydesign calls for collecting two diary days per respondent, one weekdayand a Saturday or Sunday (EUROSTAT, 1996).

Random versus Convenient Days

The actual days may be designated by random selection or chosen atthe convenience of the interviewer or respondent. While Kinsley andO'Donnell (1983) found no strong argument for either approach, they didfind that designated-day diaries were more likely to contain time spent athome. Juster (1985b) believes that the designated-day approach will en-hance representativeness. Although administrative and cost considera-tions may favor the convenience approach, it is preferable to use adesignated-day approach in order to reliably capture the several dimen-sions of behavior. To reduce sample loss if a respondent is unavailable onthe designated day, the diary day may be set for the same day, one or twoweeks later. Lyberg (1989), following tests with Swedish data, suggestedthat there was little difference in diaries collected " on time" and those"delayed" to the same day the following week or two.

Personal versus Telephone Interview

There are several ways in which the diaries may be administered, including a personal interview, phone interview, drop-off and pick-up, or drop-off and mail-back of time-diary protocols. Research suggests that there is little difference between a yesterday diary completed over the

24 ANDREW S. HARVEY

phone and one completed by personal interview (Kinsley & O'Donnell,1983; Klevemarken, 1982). There is, however, no reported research I knowof that can provide guidance on the choice between drop-off /pick-up anddrop-off /mail-back. However, without considerable follow-up, it wouldappear that the drop-off/mail-back approach is subject to much greaternonresponse, and experience with drop-off /pick-up diaries indicates thata review at the time of pick-up usually leads to revisions and additions tothe diary.

Diary Content

Diary content is driven by three factors: the need for relevant informa-tion in line with the objectives of the study, the need for validity andreliability, and concern for respondent burden. Typically, researchers areinterested in a variety of dimensions of each activity. The vast majority ofnational time-diary surveys collect or report information on what is beingdone (primary activity), what else is being done (secondary activity),where it is being done (location), and with whom it is being done (socialcontact). Collecting such information is important not only for the data,but also because it can add to the validity and reliability of the activitydata. Recalling changes in the several dimensions as one reports the un-folding day serves as a memory jog for other dimensions and adds rela-tively little time to the interview process. Other objective information hasalso been sought. For example, studies focusing on household productionhave sought information on appliances used; other studies have soughtinformation on smokers present (Robinson, Ott, & Switzer, 1996).

Subjective Dimensions

Several researchers have shown the efficacy of, and argue for, thecollection of subjective data (Clark, Harvey & Shaw, 1990; Cullen, Godson,& Major, 1972; Michelson, 1986; Robinson, 1983). The subjective data can beused both to define activities and provide perceptions of activities. Forexample, respondents have provided their own information on whichactivities they view as work and leisure (Shaw, 1986). Alternatively, thesubjective data may be used to measure the respondent’s feelings aboutactivities ouster, 1985a; Robinson, 1983,1984b). Subjective dimensions ex-plored include satisfaction (Robinson, l977,1983,1984b), liking (Moss &Lawton, 1982), tension (Michelson, 1988), and material benefit from activity(Harvey, 1993a). Recently, attention has turned to gathering motivationalinformation related to "for whom" activities are being done (Blânke, 1994).Such information is being sought in the EUROSTAT pilot survey. This

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 25

information takes on primary relevance in studies focused on upgradingeconomic accounts or in studies on volunteer activity.

Background Data

Interpretation of time-diary data is highly dependent on the nature ofthe attendant background data. An individual's role or sociodemographiccircumstances is of central importance in determining time use. The im-portance of sociodemographic characteristics was noted in reporting onthe Multinational Time-Use Study where it was found that individualsoccupying roles defined in terms of sex and employment were more alikeacross sites than they were like individuals occupying other, different basicroles in their own site (Converse, 1972). Aas (1982) argues for the impor-tance of role in the household (child, spouse, parent, other). If diaries andattendant background information are not collected from all members ofthe household, it is important that, at least, employment status of thespouse be obtained, since it can significantly affect the household divisionof labor and other time use as well. Additionally data on socioeconomicstatus, income, life-cycle state, age, education, number and ages of chil-dren, number of other household members, and employment status andurbanization level of household community should also be collected(Harvey,1993).

DATA-FILE EDITING AND CREATION

One of the most challenging aspects of time-diary data analysis is the preparation and organization of the diary data. It is this process, more than any other, that separates the collection and analysis of time-diary data from similar processes in traditional social surveys. At the heart of the editing and coding of the diary data is the coding scheme used to record the reported behavior. There is no standard activity coding scheme. The multinational study established a de facto standard (Szalai, 1972). Most national studies have maintained some comparability to the multinational coding scheme. There are, however, a number of problems with it (Harvey, 1996b). A coding scheme addressing some of these problems was recently proposed (Harvey & Niemi, 1994). The current EUROSTAT time use pro-ject may well establish a new referent.

As with any survey, once completed, the forms need to be edited for accuracy and completeness. The major difference in a diary survey is in the editing and checking of the diary form. It is not sufficient to simply browse for nonresponse to items that should be completed. The diary form itself

26 ANDREW S. HARVEY

must be checked for consistency and completeness by following it throughthe day to ensure that there are no time gaps, that all activities and theirseveral dimensions have been reported, and that several competing activ-ities (7:00–7:20 A.M., ate breakfast and took daughter to school) have notbeen recorded in one time slot. Often, in the editing process, it will bepossible for the editor to make corrections from the information provided.However, since there may be a need to recontact the respondent and con-firm information, this process should be done immediately followingcompletion of the diary to ensure accurate recall on the part of the respondent.

File Creation

Processing and analysis of the diary data can be facilitated with the construction of three different files: a respondent summa ry file, an episode file equivalent to the activity file (Chapter 3), and a time-points file. Once the questionnaire and diary have been edited and the data entered, it is useful to construct at least two data files, one containing respondent-level infor-mation, and one containing episode-level data (Fraire, 1993; Harvey, 1984). A third file, a time-points file, is also useful for further analyzing episodes and the temporal location of activities (Faire, 1993; Stone, 1984). Because the initial data extracted from diaries are typically time allocation by activity, it is necessary to summarize for each diary day the time allocated to all episodes of a given activity; that is, the total time spent eating at various times of the day must be consolidated into total daily time spent eating. Typically, such aggregations are preformed and a respondent sum-may file is created, with one variable for each activity code, which contains the number of minutes allocated to that activity during the day. These time aggregation variables are then appended to the respondent information. If there is only one diary day per respondent, this approach is the most efficient, and the number of cases in the file equals the number of respon-dents in the study. Similarly, time devoted to various locations and social contacts should be summed over the day, and a variable should be created containing the duration of time on the diary day allocated to the given location (time at home) or social contact (time with spouse). If there is more than one day per respondent, it is probably best to create separate files, one for each day, containing the respondent ID and summarized durations for each day separately. The number of cases per file would equal the number of diaries (respondents) for the given day (i.e., day 1, day 2, etc.). The several files can be merged for analysis using the IDS in virtually any standard statistical package.

Analysis at the episode level requires the construction of an episode file. The episode file contains one case for each episode. The episode is equiva-

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 27

lent to a line on the diary. The case contains, minimally, a respondent ID, anactivity code indicating what was done, the time it was started, and theamount of time spent on that episode. If a secondary activity, location,social contact, and other dimensions were captured with the episode onthe diary, appropriate codes for these would also be included as part of theepisode-based case. The number of cases is equal to the total number ofepisodes of all respondents across all days. The episode file often posesparticular problems to the researcher, since it is what is called a "raggedfile," with a variable number of cases (episodes) per respondent, per day.

An additional file, a time-points file, is useful for analyzing temporallocation. It is typically constructed using 96 time points, one for each 15minutes of the day (Fraire, 1993). The value of the time-point variable is thecode for the activity being performed at that time. If one wishes to trackwho individuals are with at each time point, or where they are located,another 96 time points would be created for each, with one code showingthe social contact and another showing location at each of the 96 points.This facilitates the construction of graphs showing the timing of activitiesand how activities are distributed over the day. Temporal analysis capital-izes on the strength of the time-diary approach.

ANALYSIS ISSUES

Dimensions

In time-diary surveys, the basic unit is the episode. This is defined bythe activity engaged in by the respondent at a specified place and timeunder certain conditions. For example, the episode might be eating lunch,at home, alone, from 12:15 to 12:35 P.M. as shown in Figure 1 in Chapter 3,this volume. A diary might yield, for example, at least the following sixuntransformed activity dimensions for an episode, all of which would beprovided on a line in the diary:

• Primary activity (what was done?)• Temporal location (time it began and ended?)• Secondary activity (what else was being done?)• Location of activity (where it was being done?)• Social contacts, that is, persons present (with whom it was being

• Additional items (remarks) that can elaborate the primary activitydone?)

(e.g., type of television show, reading material, etc.)

28 ANDREW S. HARVEY

From the diary, other derived dimensions can be calculated. These includeduration, order in a sequence, and daily frequency of occurrence.

Subjective dimensions can also be attached to each activity episode.Thus, it becomes possible to collect data on perceptions and preferencessimultaneously with objective episodes. However, perceptual data neednot be asked for every episode. Shaw (1986) effectively selected someepisodes from completed diaries and asked several perceptual questionson each. These data across all diaries offer considerable scope for analysis.Another approach, used very effectively on completion of the diary, askedrespondents which activity listed on the diary they most enjoyed (StatisticsCanada, 1995).

Typically, time use studies have focused on hours and days. Weeklytime estimates can be calculated from these using synthetic combinationsacross respondents. Months and seasons have seldom been calculated intime use studies. However, for some activities, month and season can beimportant. For this reason, at least one study in the United States collectedtime diaries, three or four per respondent, in a manner that would providediaries over the entire year (Hill, 1985). Other countries such as Finland, in1987–1988, and Canada, in 1992, have spread their sample across the yearand collected diaries for all seasons and virtually all days (Frederick, 1995;Niemi, Pääkkönen, Rajaniemi, Laaksonen, & Lauri, 1991). However, therehas been a tendency to avoid holidays, leading to a dearth of time use datafor them.

The full diary format enables one to account easily for the dimensionsof people's lives beyond actual activities. For example, the use of the diaryapproach in the Halifax study made possible the examination of the extentof daily social contact of various groups, as well as the extent to whichindividuals made use of alternative locations within the city (Elliott,Harvey, & Procos, 1976). Examining social contacts, it was found that'suburban dwellers had greater family contact than did those living in amore urban setting, averaging over an hour more with family each day(Harvey & Procos, 1974). Beyond these simple observations, it is possibleto identify more complex events such as what the individuals were doingwith their families at what time of day.

Descriptive Measures

Harvey (1984) and INSTRAW (1995) present overviews of the descrip-tive measures provided by time use studies. The following draws heavily on those overviews. The primary measures shown in Figure 2.1 and Table 2.1 are as follows:

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 29

Figure 2.1. Primary and derived activity measures, meal preparation.

I. P Population, the completed sample populationII. D Doers, participants who did a given activity

III. E Episodes—Lines on a diaryIV. T Time (duration)

Given these four basic measures, six descriptive values can be calcu-lated, thus providing considerable insight into behavior, Figure 2.1 andTable 2.2. The addition of each dimension adds both to the cost and the

30 ANDREW S. HARVEY

Table 2.1. Primary Measures, Canadian Time Use Study, 1986

I II III IV

P D E T

All persons Doers Episodes Time

Work for pay 9,744 4,002 10,264 1,705,970

Extra to work/overtime/looking for 9,744 127 184 16,725

Travel during work 9,744 192 430 26,407

Waiting, delays at work 9,744 84 95 4,602

Meals-snacks at work 9,744 1,814 1,962 81,875

Idle time before or after work 9,744 579 662 15,440

Coffee, other breaks 9,744 1,217 1,936 36,254

Uncodable work activities 9,744 250 351 25,914

Travel: to/from work 9,744 3,714 7,768 172,236Meal preparation 9,744 5,478 10,352 351,555

Meal clean-up (dishes/clearing table) 9,744 3,701 5,437 136,839

Indoor cleaning (dusting, vacuuming) 9,744 3,013 3,897 289,432

Outdoor cleaning (sidewalks/garbage) 9,744 441 521 44,144

Laundry, ironing, folding 9,744 1,219 1,657 99,511

Mending 9,744 85 98 9,005

Home repairs, maintenance 9,744 539 745 90,286

Gardening, pet care 9,744 443 576 21,018

871 1,943 56,166Other uncodable housework (bills) 9,744

Travel: domestic work 9,744 140 241 5,343

Baby care 9,744 579 1,832 63,228

Child care 9,744 1,386 2,802 87,320

20,690Helping, teaching, reprimanding 9,744 372 425

Reading, talking, conversation with 9,744 310 358 15,340

work

children

childrenPlay with children 9,744 498 638 42,900

Medical care—child 9,744 57 80 5,645

Missing time (gaps) 9,744 95 107 12,520

Other child care (unpaid babysitting) 9,744 128 182 13,547

20,048Travel: child care 9,744 560 1,271

Everyday shopping (food, clothing, gas) 9,744 2,893 3,732 303,319

Shopping for durable household goods 9,744 142 166 12,040

Personal care services (hairdresser) 9,744 120 124 8,723

Government and financial services 9,744 443 498 10,168

Adult medical and dental care (outside 9,744 280 343 18,894

Other professional services (lawyer) 9,744 35 38 1,878

Repair services (cleaning, auto, 9,744 134 161 8,195

Waiting, queuing for purchase 9,744 137 146 6,566Other uncodable services 9,744 193 219 11,815

Travel: goods or services 9,744 3,331 7,315 133,118

(house/car)

home)

appliance)

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 31

Table 2.1. (Continued )

I II III IV

P D E T

All persons Doers Episodes Time

Washing, dressing, packing 9,744 8,260 14,824 388,549Adult medical care (at home) 9,744 154 207 26,171Help and personal care to adults 9,744 164 228 18,167Meals at home/snacks/coffee 9,744 9,078 20,008 689,494Restaurant meals 9,744 1,845 2,229 128,995Night sleep/essential sleep 9,744 9,722 19,318 4,777,428Incidental sleep, naps 9,744 1,022 1,144 119,605Relaxing, thinking, resting 9,744 1,789 2,258 178,954Other personal care or private activities 9,744 655 806 33,079Travel: personal care 9,744 1,858 3,651 81,031Full-time classes 9,744 542 1,200 157,172Other classes—part-time 9,744 108 138 18,726Special lectures: occasional 9,744 19 23 2,835Homework: course, career, self- 9,744 745 1,230 141,408

Meals–snacks, coffee at school 9,744 340 406 17,302

Breaks or waiting for class to begin 9,744 276 426 9,687Leisure and special interest class 9,744 69 77 7,805Other uncodable study 9,744 99 146 9,055Travel: education 9,744 770 1,783 37,845Professional, union, general 9,744 31 55 5,070

Child, youth, family organization 9,744 46 63 6,615

Religious meetings, organizations 9,744 502 638 41,687Religious services/prayer/read bible 9,744 502 638 41,687Fraternal, social organizations 9,744 55 76 10,590Volunteer work, helping 9,744 154 242 30,584Other uncodable organizations 9,744 46 59 4,855Travel: organizations 9,744 626 1,195 20,821Sports episodes 9,744 189 2321 24,257Pop music, fairs, concerts 9,744 91 105 13,553Movies, films 9,744 107 122 14,712Opera, ballet, drama 9,744 27 31 3,380Museums and art galleries 9,744 19 21 2,075

Visits, entertaining friends, relatives 9,744 2,911 4,405 469,599

Other social gatherings 9,744 184 219 35,151Travel: entertainment 9,744 2,524 5,378 112,493

Sports, physical exercise, coaching 9,744 818 989 89,804

Hunt, fish, camp 9,744 64 106 18,463Walk,hike 9,744 595 750 42,951

Hobbies 9,744 219 287 32,760Domestic home crafts 9,744 668 974 109,770

(continued)

development

Political, civic activity 9,744 27 42 5,445

Socializing at bars, clubs 9,744 347 419 57,995

32 ANDREW S. HARVEY

Table 2.1 (Continued )

I II III IV

P D E T

All persons Doers Episodes Time

Music, theater, dance 9,744 140 182 16,630Games, cards, arcade 9,744 583 702 85,783Pleasure drives, sightseeing 9,744 123 139 13,755Other uncodable sport or active leisure 9,744 296 358 30,830Travel: sports, hobbies 9,744 835 1,597 32,996Radio 9,744 385 452 34,839Television, rented movies 9,744 7,237 11,844 1,345,341Records, tapes, listening 9,744 218 248 18,853Reading books, magazines 9,744 1,621 2,019 162,698Reading newspapers 9,744 1,660 1,872 96,209Talking, conversation, phone 9,744 2,039 2,714 136,124Letters and mail 9,744 416 490 36,429Activity not stated 9,744 174 245 21,654Other uncodable (media or 9,744 37 44 2,950

Travel: media or communication 9,744 106 157 3,125

SOURCE: Statistics Canada, General Social Survey, 1992.

communication)

usefulness of the time use data gathered; that is, mere participation is lesscostly to collect than is the number of times an activity is done, and bothare less costly than collecting time allocation. However, the increased costbuys both more detail and greater accuracy, since diaries provide bothgreater accuracy in measuring time and the opportunity to elicit additionaldimensions for each diary episode.

Participation

Knowing no information other than members of a given population perform, or do not perform, a given activity, one can calculate the partici-pation rate R in activity i.

——Dj Doers

Ri = P = All persons

This is shown as Di/P in Figure 2.1 and Table 2.2, which indicates that on an average day, 56.2% of all persons engaged in at least one meal-preparation episode.

Ri is, in fact, a composite of two factors: one indicating the propensity of individuals to participate or engage in activity i, and the other indicating

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 33

Table 2.2. Derived Measures, Canadian Time Use Study, 1986

(1) (2) (3) (4) (5) (6)D/P E/P E/D T/E T/P T/D

Work for pay 41.1% 1.95 2.56 166.2 175.1 426.3Extra to work/overtime/looking for 1.3% 0.02 1.45 90.9 1.7 131.7

Travel during work 2.0% 0.04 2.24 61.4 2.7 137.5Waiting, delays at work 0.9% 0.01 1.13 48.4 0.5 54.8

Idle time before or after work 5.9% 0.07 1.14 23.3 1.6 26.6Coffee, other breaks 12.5% 0.2 1.59 18.7 3.7 29.8

Travel: to/from work 38.1% 0.8 2.09 22.2 17.7 46.4Meal preparation 56.2% 1.06 1.89 34 36.1 64.2

work

Meals–snacks at work 18.6% 0.2 1.08 41.4 8.3 44.7

Uncodable work activities 2.6% 0.04 1.4 73.8 2.7 103.7

Meal clean-up (dishes/clearing table) 38.0% 0.56 1.47 25.2 14 37Indoor cleaning (dusting, vacuuming) 30.9% 0.4 1.29 74.3 29.7 96.1Outdoor cleaning (sidewalks/garbage) 4.5% 0.05 1.18 84.7 4.5 100.1Laundry, ironing, folding 12.5% 0.17 1.36 60.1 10.2 81.6Mending 0.9% 0.01 1.15 91.9 0.9 105.9Home repairs, maintenance 5.5% 0.08 1.38 121.2 9.3 187.5Gardening, pet care 45.% 0.08 1.3 36.5 2.2 47.4Other uncodable housework (bills) 8.9% 0.11 1.2 53.9 5.8 64.5Travel domestic work 1.4% 0.02 1.72 22.2 0.5 38.2Babycare 5.9% 0.19 3.16 34.5 6.5 109.2Child care 14.2% 0.29 2.02 31.2 9 63Helping, teaching, reprimanding 3.8% 0.04 1.12 49.9 2.1 55.6

Reading, talking, conversation with 3.2% 0.04 1.15 42.8 1.6 49.5

Play with children 5.1% 0.07 1.28 67.2 4.4 86.1Medical care—child 0.6% 0.01 1.4 70.6 0.6 99Missing time (gaps) 1.0% 0.01 1.13 117 1.3 131.8Other child care (unpaid babysitting) 1.3% 0.02 1.42 74.4 1.4 105.8Travel: child care 6.0% 0.13 2.19 15.8 2.1 34.6Everyday shopping (food, clothing, gas) 29.7% 0.38 1.29 81.3 31.1 104.8Shopping for durable household goods 1.5% 0.02 1.17 72.5 1.2 84.8

children

children

(house /car)Personal care service 1.2% 0.01 1.03 70.3 0.9 72.7Government and financial services 4.5% 0.05 1.12 20.4 1 23Adult medical and dental care (outside 2.9% 0.04 1.23 55.1 1.9 67.5

home)

Other professional services (lawyer) 0.4% 0 1.09 49.4 0.2 53.7

Repair services (cleaning, auto, 1.4% 0.02 1.2 50.9 0.8 61.2

Waiting, queuing for purchase 1.4% 0.01 1.07 45 0.7 47.9

appliance)

Other uncodable services 2.0% 0.02 1.13 53.9 1.2 61.2Travel: goods or services 34.2% 0.75 2.2 18.2 13.7 40

(continued)

34 ANDREW S. HARVEY

Table 2.2. ( Continued )

(1) (2) (3) (4) (5) (6) D/P E/P E/D T/E T/P T/D

Washing, dressing, packing 84.8% 1.52 1.79 26.2 39.9 47

Help and personal care to adults 1.7% 0.02 1.39 79.7 1.9 110.8 Meals at home/snacks/coffee 93.2% 2.05 2.2 34.5 70.8 76Restaurant meals 18.9% 0.23 1.21 57.9 13.2 69.9 Night sleep/essential sleep 99.8% 1.98 1.99 247.3 490.3 491.4Incidental sleep, naps 10.5% 0.12 1.12 104.5 12.3 117Relaxing, thinking, resting 18.4% 0.23 1.26 79.3 18.4 100Other personal care or private activities 6.7% 0.08 1.23 41 3.4 50.5 Travel: personal care 19.1% 0.37 1.97 22.2 8.3 43.6 Full-time classes 5.6% 0.12 2.21 131.0 16.1 290.0Other classes—part-time 1.1% 0.01 1.28 135.7 1.99 173.4Special lectures: occasional 0.2% 0.00 1.21 123.3 0.3 149.2Homework: course, career, self- 7.6% 0.13 1.65 115.0 14.5 189.8

Adult medical care (at home) 1.6% 0.02 1.34 126.4 2.7 169.9

development

Meals–snacks, coffee at school 3.5% 0.04 1.19 42.6 1.8 50.9 Breaks or waiting for class to begin 2.8% 0.04 1.54 22.7 1.0 35.1 Leisure and special interest class 0.7% 0.01 1.12 101.4 0.8 113.1 Other uncodable study 1.0% 0.01 1.47 62.0 0.9 91.5 Travel: education 7.9% 0.18 2.32 21.2 3.9 49.1 Professional, union, general 0.3% 0.01 1.77 92.2 0.5 163.5 Political, civic activity 0.3% 0.00 1.56 129.6 0.6 201.7 Child, youth, family organization 0.5% 0.01 1.37 105.0 0.7 143.8 Religious meetings, organizations 1.0% 0.01 1.44 96.2 1.4 138.2 Religious services /prayer /read bible 5.2% 0.07 1.27 65.3 4.3 83.0 Fraternal, social organizations 0.6% 0.01 1.38 139.3 1.1 192.5 Volunteer work, helping 1.6% 0.02 1.67 126.4 3.1 198.6 Other uncodable organizations 0.5% 0.01 1.28 82.3 0.5 105.3 Travel: organizations 6.4% 0.12 1.91 17.4 2.1 33.3Sports episodes 1.9% 0.02 1.22 105.0 2.5 128.3 Pop music, fairs, concerts 0.9% 0.01 1.15 129.1 1.4 148.9 Movies, films 1.1% 0.01 1.14 120.6 1.5 137.5 Opera, ballet, drama 0.3% 0.00 1.15 109.0 0.3 125.2

Visits, entertaining friends, relatives 29.9% 0.45 1.51 106.6 48.2 161.3 Socializing at bars, clubs 3.6% 0.04 1.212 138.4 6.0 167.1 Other social gatherings 1.9% 0.02 1.19 160.5 3.6 191.0 Travel: entertainment 25.9% 0.55 2.13 20.9 11.5 44.6 Sports, physical exercise, coaching 8.4% 0.10 1.21 90.8 9.2 109.8 Hunt, fish, camp 0.7% 0.01 1.66 174.2 1.9 288.5

Hobbies 2.2% 0.03 1.31 114.1 3.4 149.8Domestic home crafts 6.9% 0.10 1.46 112.7 11.3 164.3Music, theater, dance 1.4% 0.02 1.30 91.4 1.7 118.8

Museums and art galleries 0.2% 0.00 1.11 98.8 0.2 109.2

Walk, hike 6.1% 0.08 1.26 57.3 4.4 72.2

Games, cards, arcade 6.0% 0.07 1.20 122.2 8.8 147.1

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 35

Table2.2. (Continued)

(1) (2) (3) (4) (5) (6)D/P E/P E/D T/E T/P T/D

Pleasuredrives, sightseeing 1.3% 0.01 1.13 99.0 1.4 111.8Other uncodable sport or active leisure 3.0% 0.04 1.21 86.1 3.2 104.2Travel: sports, hobbies 8.6% 0.16 1.91 20.7 3.4 39.5Radio 4.0% 0.05 1.17 77.1 3.6 90.5Television, rented movies 74.3% 1.22 1.64 113.6 138.1 185.9Records, tapes, listening 2.2% 0.03 1.14 76.0 1.9 86.5

Reading newspapers 17.0% 0.19 1.13 51.4 9.9 58.0Talking, conversation, phone 20.9% 0.28 1.33 50.2 14.0 66.8

Letters and mail 4.3% 0.05 1.18 74.3 3.7 87.6

Readingbooks, magazines 16.6% 0.21 1.25 80.6 16.7 100.4

Activitynot stated 1.8% 0.03 1.41 88.4 2.2 124.4

Other uncodable (media or 0.4% 0.00 1.19 87.0 0.3 79.7communication)

Travel: media orcommunication 1.1% 0.02 1.48 19.9 0.3 29.5

SOURCE: Derived from Statistics Canada, General Social Survey, 1992.

the probability of the occurrence of i on diary day, assuming a one-daydiary. Thus,

Ri = ai * bi

where

ai = population participation rate 0 ai 1;

bi = periodicity, probability of occurrence on diary day

where

bi = 1, if activity occurs daily

and

bi < 1, if activity occurs less often than daily.

For example, if we assumed that everyone who prepares meals doesso every day, then Ri is equal to ai, the population participation rate, that is,

Ri = ai * bi

then

Ri = 56.2% = ai * 1; thus ai = 56.2%.

However, if it is assumed that people who prepare meals do so only six days a week and let someone else do it the other day, then on any given

36 ANDREW S. HARVEY

day only 85%—bi—of the persons who prepare meals will be doing so(6/7). With Pi and this information—bi—one can calculate what propor-tion of the population ever prepares meals. It is given by

=pi .562

bi .85.661.ai = — = ——-

Frequency

Frequency refers to the number of episodes of a given activity occur-ring during a specified period of time. Examples are the number of mealseaten per day, or the number of movies attended per month. It is the kindof information typically collected by means of activity lists and is usuallyused as a surrogate measure of time allocation. However, it is of limitedvalue in comparing activities that are likely to differ significantly in theamount of time devoted to each episode. Examples are the number ofmeals prepared per day, averaged over the whole population, 1.06; thenumber of meals prepared per day by those who do prepare them, 1.89(see Figure 2.1).

Duration

The foregoing measures do not involve time spent at the activity.When time is introduced, durations can be calculated. Duration refers tothe quantity of time, typically denoted by minutes or hours per day orweek devoted to a particular activity or situation. It is the major temporalindicator. Any positive value indicates the extent of participation duringthe period being monitored. A zero value indicates nonparticipation. Asan indicator, duration can serve to quantify an endless number of items ofinterest depending on the collateral information capture. These include the

• Time spent on a meal preparation episode (i.e., T/E = 34 minutes,

• Time spent per day by doers preparing meals (i.e., T/D = 64.2

• Time spent per person per day over the whole population (i.e., T/p

• Time spent in various locations, such as home, workplace, stores,

• Time spent alone or with family, neighbors, co-workers, and so on• Time exposed to stress• Time spent in automobiles, on public transit, walking, and so on• Time spent in routine, planned, or unexpected activities

following:

Figure 2.1)

minutes, Figure 2.1)

= 36.1 minutes, Figure 2.1)

and so on

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 37

The range of indicators that can be quantified in this manner is limitedprimarily by practical data collection considerations. The key value ofduration is that it provides a metric that can be used to relate informationthat has been collected in disparate ways and at different times. Forexample, an accounting of the number of club meetings and their lengthand attendance has been used to estimate, for a small community, percapita time devoted to such activities. These estimates were found tocorrespond closely to similar estimates obtained via time-diary studies.

The formulas shown in Figure 2.1 are only some of the ways-of calcu-lating the various measures. Given any two of the measures, a third can becalculated.

Temporal locution refers to the time of day, week, month, or year anactivity is undertaken. Examples include the time of day persons departfor work or the time school lets out. At another level, it may refer tolaundry day grocery day or the time of year when vacations are sched-uled. While temporal location has not frequently been used in the past asan indicator, it is highly significant to the rhythm of society and is receivinggrowing attention (Hammermesh, 1995; Harvey, 1996a). Of more particularconcern may be the variation in time when an activity can be performed.Low variation often means little freedom in exercising a given activity(e.g., leaving for work) and thus, possible system overload (e.g., trafficcongestion).

Activity sequence is another temporal measure that can only be ob-tained from time-diary studies. Sequence differs from temporal location inthat it relates the undertaking of a given activity to the activities thatprecede and follow it. It takes one closer to understanding how individ-uals organize their day. It also helps to increase our understanding ofactivity participation (Stone, 1972b). Thus, work out of the home increasesthe probability that an individual will engage in out-of-home discretionaryactivity in the next period. Housework considerably reduces, relative toother activities, the probability that one will engage in out-of-home discre-tionary activity in the next period.

Contextual Analysis

The real strength of the diary approach emerges when the analystturns to contextual analysis, which incorporates the richness of the diary-episode data (Harvey, 1982; Michelson, 1991). At this level, any of theattendant information captured on the line of a diary can be utilized. Itthus becomes possible to examine "activity settings" as well as activities(Harvey, 1982).

The concept of an activity setting is akin to Roger Barker's concept ofa "behavior setting" (Barker, 1968). Behavior settings are units of the envi-

38 ANDREW S. HARVEY

ronment that have relevance for behavior. They coerce people and things to conform to their temporal spatial pattern. The components of a behavior setting are the physical parameters, sets of rules (formal and informal), symbols, and other props, participants, and behavior. Behavior is regu-larized in behavior settings because the physical parameters make it pos-sible, the rules and props make it expected, and the participants are at-tracted or forced to appear. This formulation is fruitful because it presents a way of understanding how regularities of behavior can be facilitated by context. The spatial dimensions make it possible. Cultural and institu-tional factors provide orientation and reinforcement. And sufficient num-bers of individuals provide motivation and personnel to make it happen.

aspects within a community presents a way of understanding the contex-tual basis for how and why behavior among communities may differ. The kinds of small towns studied by Barker and his colleagues were found to have several hundred behavior settings, and they differed according totheir contexts, and hence behaviors. Barker's behavior settings, however,were all public. Lunch with one's spouse at home would not constitute abehavior setting, while lunch in a restaurant with one's spouse would.

Activity settings, like Barker's behavior settings, are based on themultidimensionality of activities. They occur in time, over time, in place, with others, or alone. They require certain skills or capabilities and, in some instances, certain powers or permissions. Each dimension impinges

sent the totality of observable human behavior, not just that portion which is public. Harvey (1982) defined "activity settings" incorporating spatiallocation (home, away from home), temporal location (morning, afternoon, evening, night), duration (short, medium, long) and social contact (alone, family, friends, others). Work to date confirms the usefulness of the ap-proach for cross-national comparative work (Harvey & Grønmo, 1984).

Activity settings can be operationalized by means of "hypercodes"that concatenate their several dimensions, expressing them in a single code (Clark, Elliott, & Harvey, 1982). Table 2.3 shows the approach followed indefining activity settings using hypercodes. Time use data from the 1992 Canadian General Social Survey conducted by Statistics Canada provide an opportunity to explore the nature of activity settings.

The first step in creating the hypercode is to aggregate the several dimensions into desired aggregates. Thus, for example, location (LOC) that was captured in some detail, including mode of travel, is collapsed into a binary variable home (1) or away (2) (see Table 2.3). The temporal location (TIME) was collapsed into four codes (1–4), duration (DUR) into three (1–3), and social contact (SC) into four (1-4) (see Table 2.3). The hypercode is formed as

Locating and inventorying the settings that have these complementing

on, or facilitates, given activities. However, when aggregated, they repre-

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 39

Table 2.3. Activity Setting Hypercodes

Location (LOC) H 1 HomeA 2 Other (away)

A 2 Afternoon 12noon–6P.M.

N 4 Night 0–6 A.M.

Time (TIME) M 1 Morning 6A.M.–12noon

E 3 Evening 6P.M.–midnight

Duration S 1 Short 0–15 minutesDUR) M 2 Medium 15–-60minutes

L 3 Long 60 minutes +

Contact (SC) A 1 AloneFa 2 Family Family onlyFr 3 Friends Friends (maybe family)O 4 Others Others (maybe also friends and family)

Alone

Examplesetting:1123 H__M__M__Fr Home, morning, medium, friends

hypercode = LOC*1000 +TIME *100+DUR *10 + SC

Thus, an activity at home in the morning, lasting 15-60 minutes,withfriends, is coded "1123" or "H__M__M__Fr " (see Table 2.3).

The extent to which the settings vary is illustrated with data from the 1992 Canadian Time-Use Study by identifying the various settings used for the main activity groups. There is both considerable similarity and consid-erable diversity and fit between settings and activities (see Table 2.4).

The top setting for each of the activities is defined as the setting with

Table2.4. Activities and Activity Settings, Canada, 1992

Settings needed to account for

Top setting 25% 50% 75% top setting Proportion in

0 Work A__M__L__O 2 6 12 13.9

1 Housework H__A__M__A 2 5 12 14.62 Child care H__E__M__Fa 2 5 12 19.0

3 Shop A__A__S__A 2 6 12 13.44 Personal H__N__L__A 2 5 10 17.9

5 Education A__M__L__O 4 10 19 8.8

6 Organizations A__A__M__Fa 4 16 26 4.87 Entertainment H__E__L__Fr 5 11 21 6.68 Hobbies H__A__L__A 7 17 28 4.39 Media H__E__L__Fa 2 59 18.6

40 ANDREW S. HARVEY

the maximum number of episodes for the given activity. Work, shopping,education, organizations, and entertainment had a dominant setting away(A__) from home (see Table 2.4). Housework, child care, personal, hobbies,and media were home based (H__). One of the most interesting observa-tions is that for the activity groups usually deemed nondiscretionary workthrough personal care, the two top settings account for one-fourth of allepisodes devoted to them, and 12 or fewer settings, from a possible 96, arerequired to account for 75% of all related episodes. In contrast, for theactivity groups usually denoted free time or discretionary, there is muchgreater setting diversity. From four to seven settings are required to ac-count for one-fourth of all discretionary episodes and from 19 to 28 toaccount for 75% of them. The media setting, however, appears to be themost constrained of all, with only nine activity settings accounting for 75%of all related episodes. The major work setting is, not surprisingly, "away-morning-long-others." The dominant setting for entertainment is "away-evening-long-friends"; for media, it is "home-evening-long-family." Thisindicates how the difference in setting can affect activity content or viceversa. Thus, the presence of friends both diminishes the likelihood ofwatching television, the key media activity, and gives rise to the likelihoodof socializing, a key element in entertainment.

Sequence Analysis

Rydenstam (1994), applying event history analysis to time use data, provided insight into another significant analytical prospect for under-standing time allocation. Using data from the 1990-1991 Swedish Time Use Survey, he explored transitions to household activities, focusing on tran-sitions that occur after coming home, following at least 3 hours of paid work. Using data on 1,298 men and 1,203 women, he found, as expected, a significant difference between women and men for household work events on returning home. The analysis further showed that intensities varied with employment status of spouse, time of arrival home, and hours worked. Event history analysis provides for the analysis of interactions among the independent variables. In exploring these Rydenstam found that coming home late lowered the intensity of household work for both men and women, but lowered it more for men than for women. He further found that the intensities for second and third household work events were higher for women than for men, regardless of paid work hours.

A final analytical approach that offers considerable promise in the analysis of time use data is the use of DNA sequencing methodology for understanding the structure of activities implicit in the diaries (Wilson, 1998). Using a 19-letter activity classification and 7-day diaries for three

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS 41

housewives drawn from the Reading Diary Survey Wilson mapped multi-day short-form activity sequences that showed periods of identical activityacross several days for a respondent. Looking at a given respondent, hefound that he could identify clearly typical daily events with routinemornings and relative variability in the afternoons and evenings. Havingidentified daily patterns, Wilson proceeded to join days using consensusalignment, finding for the given respondent Monday/Tuesday and Satur-day/Sunday to be the most similar days. Wilson's work is promising andcan play a crucial role in helping capture the full value of time-diary data.Time diaries are data rich, and there is a great need to extract relevantinformation from them through the application of techniques such assequence analysis. Wilson and I have been discussing the prospects forintegrating the concept of "activity setting" into the sequence approach.While Wilson used the single dimension of activity content (housework,paid work, sleep, etc.), one can define activities in terms of settings incor-porating dimensions such as with whom and where the act is done. Thiswill yield much greater insight into differential behavior and even morefully utilize the diary data.

Episode Sampling

Often a researcher is interested in either particularized activities or people who engage in particularized activities. Time-diary data provide an opportunity to identify both activities of interest and/or individuals en-gaging in those activities. Traditional activity surveys provide the oppor-tunity to identify and study participants in generalized activities such as television viewing or moviegoing. However, they rarely provide the op-portunity to identify particularized activity such as television viewing with children, drinking in a pub, or doing paid work at home. Time diaries, however, as indicated earlier in the discussion of activity context, provide the opportunity to identify and study particular instances of behavior. For example, researchers have used time-diary data to explore drinking be-havior (Cosper & Elliott, 1983; Cosper, Elliott, & Harvey, 1986). In one study, researchers identified instances of drinking behavior through sam-pling of pub/bar use, showing that 5.5% of the respondents had at least one public drinking activity on their diary day (Cosper & Elliott, 1983). Using that data, they were able to examine the timing of public drinking, travel related to drinking, and other dimensions of it (Cosper & Elliott, 1983). In another study, they were able to examine not only with whom and where drinking took place, but also what else the respondent was doing (e.g., drinking at home with friends watching sports on television) (Cosper et al., 1986). More recently, Michelson (1996) used time-diary data to iden-

42 ANDREW S. HARVEY

tify telecommuters by finding in the diaries individuals engaging in paidwork at home. Michelson found that even though only small subsampleswere identified, comparisons with conventional workers were consistentwith hypotheses in the literature.

CONCLUSIONS

Time diaries provide the opportunity to carry out a wide range ofstudies, explore a wide variety of issues, and present temporal and activityinformation in many different ways. This chapter has only touched onsome of the many interesting ways the time use data can be analyzed andpresented. Above all, in the collection and storing of time-diary data, it isimportant to preserve as far as possible the precise detail attendant withthe activity as recorded in a diary. While the aggregate times and participa-tion rates are interesting and useful, the real value of time-diary studies isin their ability to provide insight into the very fine grain of human activityand to link objective and subjective states. There is no area of humanbehavior for which time use studies cannot provide valuable and interest-ing data. As such, they provide any researcher a complex and fascinatingopportunity and challenge.

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