Cap 2

2 Guidelines for Time Use Data Collection 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 and subjective circumstances about participation in activities. Yet often these are the circumstances that, with personal characteristics, determine actual behavior. A time diary places activities in their natural temporal context.

By its nature, the diary provides a record of all activities during a specified period (day, week), along with a potentially rich array of contextual information. This chapter explores the collection and analysis of diary data and specific opportunities and problems they pose for the researcher.

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

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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.

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ANDREW S. HARVEY

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

COLLECTION GUIDELINES In many ways, the collection of time use data differs little from the collection of other social and economic data. There are, however, a number of issues that should be addressed to optimize the value and accuracy of the final data. While the diary is the preferred data collection method. there are alternatives. Activity lists, logs, continuous or random observation, and beeper studies have all been used at one time or another to collect activity data (United Nations International Research and Training Institute for the Advancement for Women [INSTRAW], 1995). The actual approach chosen will depend on a number of issues that can be evaluated in terms of both input and output criteria (Harvey & MacDonald, 1976). The suggested 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 has been chosen, questions regarding data collection remain. Collection methodology issues can be classified in terms of sampling, collection, diary content, and background variable content (Harvey, 1993b).

Sampling of Respondents Sampling issues relate to the choice of the respondent population, the sample size, geography, and survey timing. Typically, national statistical agencies collect data that are nationally representative. The major issues statistical agencies face in terms of population are whether to collect diaries for individuals only or for several or all household members.

Additionally, they must set the ages of the respondent population. There is no clear choice. The ages of populations covered have ranged from age 2 years in Bulgaria to age 15 in Canada. The current Statistical Office of the European Communities (EUROSTAT) project guidelines are to collect diaries for all household members aged 10 years and over (EUROSTAT, 1996).

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

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maternal employment. Several of the editors of this volume are collecting data for a sample of individuals with spinal-cord injuries (McCall, Pentland, 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 population and diary days, since both population and behavior are being sampled. 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 geography 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 estimates 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 Canada, 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 general 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 education by the students and underestimate parental time spent caring for children.

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Diary Design There are a number of diary design issues involving the interview mode, the focus on "yesterday" versus "tomorrow" diaries, and the choice of day. Two major options are the choice of precoded versus open-response categories and the choice of fixed time versus open intervals.

Open versus Coded Category Most diary researchers shun the precoded format, opting instead for an open-response diary in which individuals respond in their own words.

Precoding, usually limited to relatively few codes, forces excessive and irreversible data reduction at too early a stage in the survey process. However, 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 number of 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 Multinational Time Use Study used an open-interval approach (Szalai, 1972), meaning that the respondent reports starting and ending times of each activity as part of the diary entry. This approach has been followed by all the major North American studies mentioned in Chapter 1. However, most of the European national surveys have opted for fixed-interval diaries, with intervals ranging from5 to 20 minutes. Workof Lingsom (1979) and of Niemi (1983) suggested little difference between the two approaches. Unpublished pilot testing for the 1986 Canadian Time Use Study concluded that there were no cost savings from fixed time slots. Some work, however, suggests that there may be hidden problems. There is evidence that the use of, and size of, time slots differentially affect various activities (Harvey Elliott, & Stone, 1977). The cooperative European time use survey being facilitated 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 tomorrow basis. Yesterday diaries are typically collected by personal or phone interview, while tomorrow diaries are left behind by interviewers ("leavebehind diaries") or mailed to respondents. Although tomorrow diaries yield 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

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of obtaining tomorrow diaries (Juster, 1985b; Robinson, 1977,1985; Szalai, 1972). Can diaries be collected for days further back than yesterday? There is no clear answer to this question. Research on it (Juster, 1985b; Keller, Kempter, Timmer, & Young-Demarco, 1982; Klevemarken, 1982) gives mixed results. Juster (1985b) argues that people appear to be able to recall Fridays through Sundays better than other days. The general view of experienced time-diary researchers is, however, that recall should not be attempted 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 become more common to collect at least 2 days per respondent. It is argued that at least 2 days provide for greater reliability (Kalton, 1985; Pas, 1986; Sanik, 1983). Kalton, however, argues for 2 weekdays, leaving aside the issue of two Saturdays or Sundays (Kalton, 1985). The EUROSTAT pilot survey design calls for collecting two diary days per respondent, one weekday and a Saturday or Sunday (EUROSTAT, 1996).

Random versus Convenient Days The actual days may be designated by random selection or chosen at the convenience of the interviewer or respondent. While Kinsley and O'Donnell (1983) found no strong argument for either approach, they did find that designated-day diaries were more likely to contain time spent at home. Juster (1985b) believes that the designated-day approach will enhance representativeness. Although administrative and cost considerations may favor the convenience approach, it is preferable to use a designated-day approach in order to reliably capture the several dimensions of behavior. To reduce sample loss if a respondent is unavailable on the designated day, the diary day may be set for the same day, one or two weeks later. Lyberg (1989), following tests with Swedish data, suggested that 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

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

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

Subjective Dimensions Several researchers have shown the efficacy of, and argue for, the collection of subjective data (Clark, Harvey & Shaw, 1990; Cullen, Godson, & Major, 1972; Michelson, 1986; Robinson, 1983). The subjective data can be used both to define activities and provide perceptions of activities. For example, respondents have provided their own information on which activities they view as work and leisure (Shaw, 1986). Alternatively, the subjective data may be used to measure the respondent’s feelings about activities ouster, 1985a; Robinson, 1983,1984b). Subjective dimensions explored 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 motivational information 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

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

Background Data Interpretation of time-diary data is highly dependent on the nature of the attendant background data. An individual's role or sociodemographic circumstances is of central importance in determining time use. The importance of sociodemographic characteristics was noted in reporting on the Multinational Time-Use Study where it was found that individuals occupying roles defined in terms of sex and employment were more alike across sites than they were like individuals occupying other, different basic roles in their own site (Converse, 1972). Aas (1982) argues for the importance of role in the household (child, spouse, parent, other). If diaries and attendant background information are not collected from all members of the household, it is important that, at least, employment status of the spouse be obtained, since it can significantly affect the household division of labor and other time use as well. Additionally data on socioeconomic status, income, life-cycle state, age, education, number and ages of children, number of other household members, and employment status and urbanization 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 project 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

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must be checked for consistency and completeness by following it through the day to ensure that there are no time gaps, that all activities and their several dimensions have been reported, and that several competing activities (7:00–7:20 A.M., ate breakfast and took daughter to school) have not been recorded in one time slot. Often, in the editing process, it will be possible for the editor to make corrections from the information provided.

However, since there may be a need to recontact the respondent and confirm information, this process should be done immediately following completion 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 summary 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 information, 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 summay 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 respondents 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

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

An additional file, a time-points file, is useful for analyzing temporal location. It is typically constructed using 96 time points, one for each 15 minutes of the day (Fraire, 1993). The value of the time-point variable is the code for the activity being performed at that time. If one wishes to track who individuals are with at each time point, or where they are located, another 96 time points would be created for each, with one code showing the social contact and another showing location at each of the 96 points.

This facilitates the construction of graphs showing the timing of activities and how activities are distributed over the day. Temporal analysis capitalizes on the strength of the time-diary approach.

ANALYSIS ISSUES Dimensions In time-diary surveys, the basic unit is the episode. This is defined by the activity engaged in by the respondent at a specified place and time under 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 six untransformed activity dimensions for an episode, all of which would be provided 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 done?)

Additional items (remarks) that can elaborate the primary activity (e.g., type of television show, reading material, etc.)

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ANDREW S. HARVEY

From the diary, other derived dimensions can be calculated. These include duration, 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 preferences simultaneously with objective episodes. However, perceptual data need not be asked for every episode. Shaw (1986) effectively selected some episodes from completed diaries and asked several perceptual questions on each. These data across all diaries offer considerable scope for analysis.

Another approach, used very effectively on completion of the diary, asked respondents which activity listed on the diary they most enjoyed (Statistics Canada, 1995).

Typically, time use studies have focused on hours and days. Weekly time estimates can be calculated from these using synthetic combinations across respondents. Months and seasons have seldom been calculated in time use studies. However, for some activities, month and season can be important. For this reason, at least one study in the United States collected time diaries, three or four per respondent, in a manner that would provide diaries over the entire year (Hill, 1985). Other countries such as Finland, in 1987–1988, and Canada, in 1992, have spread their sample across the year and collected diaries for all seasons and virtually all days (Frederick, 1995; Niemi, Pääkkönen, Rajaniemi, Laaksonen, & Lauri, 1991). However, there has been a tendency to avoid holidays, leading to a dearth of time use data for them.

The full diary format enables one to account easily for the dimensions of people's lives beyond actual activities. For example, the use of the diary approach in the Halifax study made possible the examination of the extent of daily social contact of various groups, as well as the extent to which individuals 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 a more urban setting, averaging over an hour more with family each day (Harvey & Procos, 1974). Beyond these simple observations, it is possible to identify more complex events such as what the individuals were doing with their families at what time of day.

Descriptive Measures Harvey (1984) and INSTRAW (1995) present overviews of the descriptive 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:

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

I. II. III. IV.

P Population, the completed sample population D Doers, participants who did a given activity E Episodes—Lines on a diary T Time (duration)

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

ANDREW S. HARVEY

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

All persons P

Work for pay Extra to work/overtime/looking for work Travel during work Waiting, delays at work Meals-snacks at work Idle time before or after work Coffee, other breaks Uncodable work activities Travel: to/from work Meal preparation Meal clean-up (dishes/clearing table) Indoor cleaning (dusting, vacuuming) Outdoor cleaning (sidewalks/garbage) Laundry, ironing, folding Mending Home repairs, maintenance Gardening, pet care Other uncodable housework (bills) Travel: domestic work Baby care Child care Helping, teaching, reprimanding children Reading, talking, conversation with children Play with children Medical care—child Missing time (gaps) Other child care (unpaid babysitting) Travel: child care Everyday shopping (food, clothing, gas) Shopping for durable household goods (house/car) Personal care services (hairdresser) Government and financial services Adult medical and dental care (outside home) Other professional services (lawyer) Repair services (cleaning, auto, appliance) Waiting, queuing for purchase Other uncodable services Travel: goods or services

II

Doers D

III

Episodes E

IV

Time T

9,744 9,744

4,002 127

10,264 184

1,705,970 16,725

9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744

192 84 1,814 579 1,217 250 3,714 5,478 3,701 3,013 441 1,219 85 539 443 871 140 579 1,386 372

430 95 1,962 662 1,936 351 7,768 10,352 5,437 3,897 521 1,657 98 745 576 1,943 241 1,832 2,802 425

26,407 4,602 81,875 15,440 36,254 25,914 172,236 351,555 136,839 289,432 44,144 99,511 9,005 90,286 21,018 56,166 5,343 63,228 87,320

9,744

310

358

15,340

9,744 9,744 9,744 9,744 9,744 9,744 9,744

498 57 95 128 560 2,893 142

638 80 107 182 1,271 3,732 166

42,900 5,645 12,520 13,547 20,048 303,319 12,040

9,744 9,744 9,744

120 443 280

124 498 343

8,723 10,168 18,894

9,744 9,744

35 134

38 161

1,878 8,195

9,744 9,744 9,744

137 193 3,331

146 219 7,315

20,690

6,566 11,815 133,118

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Table 2.1. (Continued )

Washing, dressing, packing Adult medical care (at home) Help and personal care to adults Meals at home/snacks/coffee Restaurant meals Night sleep/essential sleep Incidental sleep, naps Relaxing, thinking, resting Other personal care or private activities Travel: personal care Full-time classes Other classes—part-time Special lectures: occasional Homework: course, career, selfdevelopment Meals–snacks, coffee at school Breaks or waiting for class to begin Leisure and special interest class Other uncodable study Travel: education Professional, union, general Political, civic activity Child, youth, family organization Religious meetings, organizations Religious services/prayer/read bible Fraternal, social organizations Volunteer work, helping Other uncodable organizations Travel: organizations Sports episodes Pop music, fairs, concerts Movies, films Opera, ballet, drama Museums and art galleries Visits, entertaining friends, relatives Socializing at bars, clubs Other social gatherings Travel: entertainment Sports, physical exercise, coaching Hunt, fish, camp Walk,hike Hobbies Domestic home crafts

I

II

All persons

Doers

III

Episodes

IV

Time

P

D

9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744

8,260 154 164 9,078 1,845 9,722 1,022 1,789 655 1,858 542 108 19 745

14,824 207 228 20,008 2,229 19,318 1,144 2,258 806 3,651 1,200 138 23 1,230

388,549 26,171 18,167 689,494 128,995 4,777,428 119,605 178,954 33,079 81,031 157,172 18,726 2,835 141,408

9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744

340 276 69 99 770 31 27 46 502 502 55 154 46 626 189 91 107 27 19 2,911 347 184 2,524 818 64 595 219 668

406 426 77 146 1,783 55 42 63 638 638 76 242 59 1,195 2321 105 122 31 21 4,405 419 219 5,378 989 106 750 287 974

17,302 9,687 7,805 9,055 37,845 5,070 5,445 6,615 41,687 41,687 10,590 30,584 4,855 20,821 24,257 13,553 14,712 3,380 2,075 469,599 57,995 35,151 112,493 89,804 18,463 42,951 32,760 109,770

E

T

(continued)

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ANDREW S. HARVEY

Table 2.1 (Continued )

Music, theater, dance Games, cards, arcade Pleasure drives, sightseeing Other uncodable sport or active leisure Travel: sports, hobbies Radio Television, rented movies Records, tapes, listening Reading books, magazines Reading newspapers Talking, conversation, phone Letters and mail Activity not stated Other uncodable (media or communication) Travel: media or communication

I All persons P

II Doers D

III Episodes E

IV Time T

9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744 9,744

140 583 123 296 835 385 7,237 218 1,621 1,660 2,039 416 174 37

182 702 139 358 1,597 452 11,844 248 2,019 1,872 2,714 490 245 44

16,630 85,783 13,755 30,830 32,996 34,839 1,345,341 18,853 162,698 96,209 136,124 36,429 21,654 2,950

9,744

106

157

3,125

SOURCE: Statistics Canada, General Social Survey, 1992.

usefulness of the time use data gathered; that is, mere participation is less costly to collect than is the number of times an activity is done, and both are less costly than collecting time allocation. However, the increased cost buys both more detail and greater accuracy, since diaries provide both greater accuracy in measuring time and the opportunity to elicit additional dimensions 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 participation rate R in activity i.

Dj Doers —— R i = 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 mealpreparation 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

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Table 2.2. Derived Measures, Canadian Time Use Study, 1986 (1) D/P Work for pay Extra to work/overtime/looking for work Travel during work Waiting, delays at work Meals–snacks at work Idle time before or after work Coffee, other breaks Uncodable work activities Travel: to/from work Meal preparation Meal clean-up (dishes/clearing table) Indoor cleaning (dusting, vacuuming) Outdoor cleaning (sidewalks/garbage) Laundry, ironing, folding Mending Home repairs, maintenance Gardening, pet care Other uncodable housework (bills) Travel domestic work Baby care Child care Helping, teaching, reprimanding children Reading, talking, conversation with children Play with children Medical care—child Missing time (gaps) Other child care (unpaid babysitting) Travel: child care Everyday shopping (food, clothing, gas) Shopping for durable household goods (house /car) Personal care service Government and financial services Adult medical and dental care (outside home) Other professional services (lawyer) Repair services (cleaning, auto, appliance) Waiting, queuing for purchase Other uncodable services Travel: goods or services

(2) E/P

(3) E/D

(4) T/E

(5) T/P

(6) T/D

41.1% 1.3%

1.95 0.02

2.56 1.45

166.2 90.9

175.1 1.7

426.3 131.7

2.0% 0.9% 18.6% 5.9% 12.5% 2.6% 38.1% 56.2% 38.0% 30.9% 4.5% 12.5% 0.9% 5.5% 45.% 8.9% 1.4% 5.9% 14.2% 3.8%

0.04 0.01 0.2 0.07 0.2 0.04 0.8 1.06 0.56 0.4 0.05 0.17 0.01 0.08 0.08 0.11 0.02 0.19 0.29 0.04

2.24 1.13 1.08 1.14 1.59 1.4 2.09 1.89 1.47 1.29 1.18 1.36 1.15 1.38 1.3 1.2 1.72 3.16 2.02 1.12

61.4 48.4 41.4 23.3 18.7 73.8 22.2 34 25.2 74.3 84.7 60.1 91.9 121.2 36.5 53.9 22.2 34.5 31.2 49.9

2.7 0.5 8.3 1.6 3.7 2.7 17.7 36.1 14 29.7 4.5 10.2 0.9 9.3 2.2 5.8 0.5 6.5 9 2.1

137.5 54.8 44.7 26.6 29.8 103.7 46.4 64.2 37 96.1 100.1 81.6 105.9 187.5 47.4 64.5 38.2 109.2 63 55.6

3.2%

0.04

1.15

42.8

1.6

49.5

5.1% 0.6% 1.0% 1.3% 6.0% 29.7% 1.5%

0.07 0.01 0.01 0.02 0.13 0.38 0.02

1.28 1.4 1.13 1.42 2.19 1.29 1.17

67.2 70.6 117 74.4 15.8 81.3 72.5

1.2% 4.5% 2.9%

0.01 0.05 0.04

1.03 1.12 1.23

70.3 20.4 55.1

0.9 1 1.9

72.7 23 67.5

0.4% 1.4%

0 0.02

1.09 1.2

49.4 50.9

0.2 0.8

53.7 61.2

1.4% 2.0% 34.2%

0.01 0.02 0.75

1.07 1.13 2.2

45 53.9 18.2

0.7 1.2 13.7

47.9 61.2 40

4.4 86.1 0.6 99 1.3 131.8 1.4 105.8 2.1 34.6 31.1 104.8 1.2 84.8

(continued)

34

ANDREW S. HARVEY

Table 2.2. ( Continued )

Washing, dressing, packing Adult medical care (at home) Help and personal care to adults Meals at home/snacks/coffee Restaurant meals Night sleep/essential sleep Incidental sleep, naps Relaxing, thinking, resting Other personal care or private activities Travel: personal care Full-time classes Other classes—part-time Special lectures: occasional Homework: course, career, selfdevelopment Meals–snacks, coffee at school Breaks or waiting for class to begin Leisure and special interest class Other uncodable study Travel: education Professional, union, general Political, civic activity Child, youth, family organization Religious meetings, organizations Religious services /prayer /read bible Fraternal, social organizations Volunteer work, helping Other uncodable organizations Travel: organizations Sports episodes Pop music, fairs, concerts Movies, films Opera, ballet, drama Museums and art galleries Visits, entertaining friends, relatives Socializing at bars, clubs Other social gatherings Travel: entertainment Sports, physical exercise, coaching Hunt, fish, camp Walk, hike Hobbies Domestic home crafts Music, theater, dance Games, cards, arcade

(1) D/P

(2) E/P

(3) E/D

(4) T/E

(5) T/P

(6) T/D

84.8% 1.6% 1.7% 93.2% 18.9% 99.8% 10.5% 18.4% 6.7% 19.1% 5.6% 1.1% 0.2% 7.6%

1.52 0.02 0.02 2.05 0.23 1.98 0.12 0.23 0.08 0.37 0.12 0.01 0.00 0.13

1.79 1.34 1.39 2.2 1.21 1.99 1.12 1.26 1.23 1.97 2.21 1.28 1.21 1.65

26.2 126.4 79.7 34.5 57.9 247.3 104.5 79.3 41 22.2 131.0 135.7 123.3 115.0

39.9 2.7 1.9 70.8 13.2 490.3 12.3 18.4 3.4 8.3 16.1 1.99 0.3 14.5

47 169.9 110.8 76 69.9 491.4 117 100 50.5 43.6 290.0 173.4 149.2 189.8

3.5% 2.8% 0.7% 1.0% 7.9% 0.3% 0.3% 0.5% 1.0% 5.2% 0.6% 1.6% 0.5% 6.4% 1.9% 0.9% 1.1% 0.3% 0.2% 29.9% 3.6% 1.9% 25.9% 8.4% 0.7% 6.1% 2.2% 6.9% 1.4% 6.0%

0.04 0.04 0.01 0.01 0.18 0.01 0.00 0.01 0.01 0.07 0.01 0.02 0.01 0.12 0.02 0.01 0.01 0.00 0.00 0.45 0.04 0.02 0.55 0.10 0.01 0.08 0.03 0.10 0.02 0.07

1.19 1.54 1.12 1.47 2.32 1.77 1.56 1.37 1.44 1.27 1.38 1.67 1.28 1.91 1.22 1.15 1.14 1.15 1.11 1.51 1.212 1.19 2.13 1.21 1.66 1.26 1.31 1.46 1.30 1.20

42.6 22.7 101.4 62.0 21.2 92.2 129.6 105.0 96.2 65.3 139.3 126.4 82.3 17.4 105.0 129.1 120.6 109.0 98.8 106.6 138.4 160.5 20.9 90.8 174.2 57.3 114.1 112.7 91.4 122.2

1.8 1.0 0.8 0.9 3.9 0.5 0.6 0.7 1.4 4.3 1.1 3.1 0.5 2.1 2.5 1.4 1.5 0.3 0.2 48.2 6.0 3.6 11.5 9.2 1.9 4.4 3.4 11.3 1.7 8.8

50.9 35.1 113.1 91.5 49.1 163.5 201.7 143.8 138.2 83.0 192.5 198.6 105.3 33.3 128.3 148.9 137.5 125.2 109.2 161.3 167.1 191.0 44.6 109.8 288.5 72.2 149.8 164.3 118.8 147.1

35

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS Table2.2. (Continued)

Pleasure drives, sightseeing Other uncodable sport or active leisure Travel: sports, hobbies Radio Television, rented movies Records, tapes, listening Readingbooks, magazines Reading newspapers Talking, conversation, phone Letters and mail Activity not stated Other uncodable (media or communication) Travel: media or communication

(1) D/P

(2) E/P

(3) E/D

(4) T/E

(5) T/P

1.3% 3.0% 8.6% 4.0% 74.3% 2.2% 16.6% 17.0% 20.9% 4.3% 1.8% 0.4%

0.01 0.04 0.16 0.05 1.22 0.03 0.21 0.19 0.28 0.05 0.03 0.00

1.13 1.21 1.91 1.17 1.64 1.14 1.25 1.13 1.33 1.18 1.41 1.19

99.0 86.1 20.7 77.1 113.6 76.0 80.6 51.4 50.2 74.3 88.4 87.0

1.4 111.8 3.2 104.2 3.4 39.5 3.6 90.5 138.1 185.9 1.9 86.5 16.7 100.4 9.9 58.0 66.8 14.0 3.7 87.6 2.2 124.4 0.3 79.7

1.1%

0.02

1.48

19.9

0.3

(6) T/D

29.5

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

the probability of the occurrence of i on diary day, assuming a one-day diary. Thus, Ri = ai * bi where ai 1; ai = population participation rate 0 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 does so every day, then Ri is equal to ai, the population participation rate, that is, Ri = ai * bi then Ri = 56.2% = ai * 1;

thus a i = 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 proportion of the population ever prepares meals. It is given by pi .562 ai = —b = ——- = .661. .85 i

Frequency Frequency refers to the number of episodes of a given activity occurring during a specified period of time. Examples are the number of meals eaten per day, or the number of movies attended per month. It is the kind of information typically collected by means of activity lists and is usually used as a surrogate measure of time allocation. However, it is of limited value in comparing activities that are likely to differ significantly in the amount of time devoted to each episode. Examples are the number of meals prepared per day, averaged over the whole population, 1.06; the number 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 to the quantity of time, typically denoted by minutes or hours per day or week devoted to a particular activity or situation. It is the major temporal indicator. Any positive value indicates the extent of participation during the period being monitored. A zero value indicates nonparticipation. As an indicator, duration can serve to quantify an endless number of items of interest depending on the collateral information capture. These include the following:

• • • • • • • •

Time spent on a meal preparation episode (i.e., T/E = 34 minutes, Figure 2.1) Time spent per day by doers preparing meals (i.e., T/D = 64.2 minutes, Figure 2.1) Time spent per person per day over the whole population (i.e., T/p = 36.1 minutes, Figure 2.1) Time spent in various locations, such as home, workplace, stores, and so on 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

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS

37

The range of indicators that can be quantified in this manner is limited primarily by practical data collection considerations. The key value of duration is that it provides a metric that can be used to relate information that has been collected in disparate ways and at different times. For example, an accounting of the number of club meetings and their length and attendance has been used to estimate, for a small community, per capita time devoted to such activities. These estimates were found to correspond closely to similar estimates obtained via time-diary studies.

The formulas shown in Figure 2.1 are only some of the ways-of calculating the various measures. Given any two of the measures, a third can be calculated.

Temporal locution refers to the time of day, week, month, or year an activity is undertaken. Examples include the time of day persons depart for work or the time school lets out. At another level, it may refer to laundry day grocery day or the time of year when vacations are scheduled. While temporal location has not frequently been used in the past as an indicator, it is highly significant to the rhythm of society and is receiving growing attention (Hammermesh, 1995; Harvey, 1996a). Of more particular concern 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., traffic congestion).

Activity sequence is another temporal measure that can only be obtained from time-diary studies. Sequence differs from temporal location in that it relates the undertaking of a given activity to the activities that precede and follow it. It takes one closer to understanding how individuals organize their day. It also helps to increase our understanding of activity participation (Stone, 1972b). Thus, work out of the home increases the probability that an individual will engage in out-of-home discretionary activity in the next period. Housework considerably reduces, relative to other activities, the probability that one will engage in out-of-home discretionary activity in the next period.

Contextual Analysis The real strength of the diary approach emerges when the analyst turns to contextual analysis, which incorporates the richness of the diaryepisode data (Harvey, 1982; Michelson, 1991). At this level, any of the attendant information captured on the line of a diary can be utilized. It thus 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 of a " 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 regularized in behavior settings because the physical parameters make it possible, the rules and props make it expected, and the participants are attracted 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 institutional factors provide orientation and reinforcement. And sufficient numbers of individuals provide motivation and personnel to make it happen.

Locating and inventorying the settings that have these complementing aspects within a community presents a way of understanding the contextual 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 to their contexts, and hence behaviors. Barker's behavior settings, however, were all public. Lunch with one's spouse at home would not constitute a behavior setting, while lunch in a restaurant with one's spouse would.

Activity settings, like Barker's behavior settings, are based on the multidimensionality 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 on, or facilitates, given activities. However, when aggregated, they represent the totality of observable human behavior, not just that portion which is public. Harvey (1982) defined "activity settings" incorporating spatial location (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 approach 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 in defining 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 (1â&#x20AC;&#x201C;4), duration (DUR) into three (1â&#x20AC;&#x201C;3), and social contact (SC) into four (1-4) (see Table 2.3). The hypercode is formed as

39

GUIDELINES FOR TIME USE DATA COLLECTION AND ANALYSIS

Table 2.3. Activity Setting Hypercodes Location (LOC) Time (TIME)

Duration DUR) Contact (SC)

Examplesetting: 1123

H A M A E N S M L A Fa Fr O

1 2 1 2 3 4 1 2 3 1 2 3 4

Home Other (away) Morning Afternoon Evening Night Short Medium Long Alone Family Friends Others

H__M__M__Fr

6 A.M.–12 noon 12noon–6P.M. 6 P.M.–midnight 0–6 A .M .

0–15 minutes 15–- 60minutes 60 minutes + Alone Family only Friends (maybe family) Others (maybe also friends and family) Home, morning, medium, friends

hypercode = LOC*1000 + TIME * 100 +DUR * 10 + SC Thus, an activity at home in the morning, lasting 15-60 minutes,with friends, 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 considerable 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

Table 2.4. Activities and Activity Settings, Canada, 1992 Settings needed to account for

0 Work 1 Housework 2 Child care 3 Shop 4 Personal 5 Education 6 Organizations 7 Entertainment 8 Hobbies 9 Media

Top setting

25%

50%

75%

Proportion in top setting

A__M__L__O H__A__M__A H__E__M__Fa A__A__S__A H__N__L__A A__M__L__O A__A__M__Fa H__E__L__Fr H__A__L__A H__E__L__Fa

2 2 2 2 2 4 4 5 7 2

6 5 5 6 5 10 16 11 17

12 12 12 12 10 19 26 21 28 59

13.9 14.6 19.0 13.4 17.9 8.8 4.8 6.6 4.3 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 observations is that for the activity groups usually deemed nondiscretionary work through personal care, the two top settings account for one-fourth of all episodes devoted to them, and 12 or fewer settings, from a possible 96, are required to account for 75% of all related episodes. In contrast, for the activity groups usually denoted free time or discretionary, there is much greater setting diversity. From four to seven settings are required to account for one-fourth of all discretionary episodes and from 19 to 28 to account for 75% of them. The media setting, however, appears to be the most constrained of all, with only nine activity settings accounting for 75% of all related episodes. The major work setting is, not surprisingly, "awaymorning-long-others." The dominant setting for entertainment is "awayevening-long-friends"; for media, it is " home-evening-long-family." This indicates how the difference in setting can affect activity content or vice versa. Thus, the presence of friends both diminishes the likelihood of watching television, the key media activity, and gives rise to the likelihood of 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 understanding time allocation. Using data from the 1990-1991 Swedish Time Use Survey, he explored transitions to household activities, focusing on transitions 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 multiday short-form activity sequences that showed periods of identical activity across several days for a respondent. Looking at a given respondent, he found that he could identify clearly typical daily events with routine mornings and relative variability in the afternoons and evenings. Having identified daily patterns, Wilson proceeded to join days using consensus alignment, finding for the given respondent Monday/Tuesday and Saturday/Sunday to be the most similar days. Wilson's work is promising and can 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 relevant information from them through the application of techniques such as sequence analysis. Wilson and I have been discussing the prospects for integrating 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 incorporating dimensions such as with whom and where the act is done. This will yield much greater insight into differential behavior and even more fully 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 engaging in those activities. Traditional activity surveys provide the opportunity to identify and study participants in generalized activities such as television viewing or moviegoing. However, they rarely provide the opportunity 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 behavior (Cosper & Elliott, 1983; Cosper, Elliott, & Harvey, 1986). In one study, researchers identified instances of drinking behavior through sampling 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 paid work at home. Michelson found that even though only small subsamples were identified, comparisons with conventional workers were consistent with hypotheses in the literature.

CONCLUSIONS Time diaries provide the opportunity to carry out a wide range of studies, explore a wide variety of issues, and present temporal and activity information in many different ways. This chapter has only touched on some of the many interesting ways the time use data can be analyzed and presented. Above all, in the collection and storing of time-diary data, it is important to preserve as far as possible the precise detail attendant with the activity as recorded in a diary. While the aggregate times and participation rates are interesting and useful, the real value of time-diary studies is in their ability to provide insight into the very fine grain of human activity and to link objective and subjective states. There is no area of human behavior for which time use studies cannot provide valuable and interesting data. As such, they provide any researcher a complex and fascinating opportunity and challenge.

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Blânke, K. (1994, June 15–18). The “with whom” coding. Paper presented at the 15th reunion of the International Association for Tie Use Research, (pp. 211–222). Amsterdam, Holland. NIMMO. Clark, S. M., Elliott, D. H., & Harvey, A. S. (1982). Hypercodes and composite variables: Simple techniques for the reduction and analysis of time budget data. In It’s about time 7th Reunion of Research Group on Time Budgets and Social Activities. 1980, 66–92.

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Michelson, W. (1988). Divergent convergence: The daily routines of employed spouses as a public affairs agenda. In C. Andrew & 8. M. Milroy (Eds.), Life spaces: Gender, household, and employment (pp. 81–101). Vancouver: University of British Columbia Press.

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Michelson, W. (1996, September 2–4). Sampling through episodal data: Telecommuting. Paper presented at the International Association for Time Use Research Conference, Vienna, Austria.

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