3 The Time-Diary Method Structure and Uses John P. Robinson
Many of the vital issues facing societies today revolve around time, particularly social trends related to people's work, leisure, and other daily experiences. Many of these issues and trends have been covered incorrectly by different authors and mass media, particularly if they use the traditional method of asking respondents to estimate time. This is especially relevant in examining claims of " time famines" and overworked employees over the last three decades (Linder, 1970; Schor, 1991), where there is now new behavior-linked evidence that challenges the validity of such assumptions and propositions (e.g., Robinson & Godbey 1997). The source of this new evidence is a comprehensive set of data on how people spend their time. These data on activities are recorded by survey respondents in the form of time diaries. These diary data, which have been collected on irregular bases in more than 20 Western countries since 1965 (with some measurements extending back to the 1920s), provide unique scientific insights into how daily life is and has been changing. The data represent an important scientific innovation, something akin to a social microscope, that allows researchers to examine facets and details of societal life that are otherwise unobservable. Like early discoveries with the microscope, they challenge widely accepted accounts of how John P. Robinson Maryland 20742.
â€˘ Department of Sociology, University of Maryland, College Park,
Time UseResearch in theSocialSciences,editedbyWendyE. Pentland,AndrewS. Harvey,M. PowellLawton, andMaryAnn McColl.KluwerAcademic/PlenumPublishers,NewYork, 1999. 47
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life is structured, and provide the basis for more refined theories about the nature of social change in postmodern and less modern societies. In these diary accounts, cross-section samples of the public have provided complete accounts of what they do on a particular dayâ€”and for the full 24 hours of that day. Respondents in these surveys take one stepby-step through their day by describing when they went to bed, when they got up and started a new day and all the things they did throughout that day. In many of these accounts, the analyst also learns about where these people spent their day, who they were with, what other activities they were doing to accompany these activities, and how they felt about these activities. A sample diary form covering the morning hours from midnight to 9:00 A.M. is shown in Figure 3.1.
USES OF DIARY DATA Because they represent complete accounts of daily activity, diary data collected from cross-section samples allow one to generate estimates of how much societal time is spent on the complete range of human behaviorâ€” from work to free time, from travel to time spent at home. For example, Harvey (1988) distinguished six different uses of nationally aggregated diary data: upgraded economic accounts, improved labor force analysis, evaluation of social change, study of gender issues, improved quality of life, and systematic analysis of leisure. In terms of the major categories of activity that are reported in diaries (see Figure 3.2, p. 54), it is possible to identify four major types of time.
Work One of the most interesting recent uses of national diary data has been to compare the paid work respondents report in them to the traditional work-hour estimate data regularly collected by government labor departments in most countries. In each of the 10 countries they examined, Robinson and Gershuny (1994) have found respondent work estimates to be significantly higher than the hours reported in their time diaries. The gap was particularly high for respondents estimating long work hours and, surprisingly, was notably higher among women with longest estimated workweeks. At the other end of the workweek spectrum, the diary records contained significant amounts of paid work time by respondents classified by traditional questions as " unemployed." Since the diary-estimate gap seems to be growing across time as more workers are employed in temporary and time-elastic service jobs, the diary should become an essential
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Figure 3.1. Time diary structure and layout.
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outside source of verifying trends in real work time. The need is particularly great given that economic figures on a society's productivity must be derived using work hours in the denominator.
Family Care Another economically relevant calculation from the diaries is in terms of hours per week spent in unpaid work, particularly related to the family. Diary figures show that total weekly hours of family-care time are very close to paid work time in most societies. Goldschmidt-Clermont and Pagnossin-Aligisakis (1995) have now provided 14-nation comparisons of such activity for men and women based on diary data, showing surprisingly little difference in the "productive" work hours of men and women when paid work and family-care hours are combined in most countries. As with paid work, estimate data on hours of family care seem subject to respondent overreportingâ€”by closer to 50% according to the analyses of Marini and Shelton (1993); in contrast, the overall overestimate for paid work is closer to 10%. On the other hand, Paille (1994) found that a single housework estimate question (rather than the component questions used by Marini and Shelton) asked in the most recent Statistics Canada diary study underestimated housework diary time by about 20%. Within these family-care figures, the analyst can generate separate figures for home care, child care, and shopping activities, each an important set of activities in its own right. Of particular interest here are the hours spent on child care, although the hours of child care as a primary activity (recorded in the " What Did You Do" column in Figure 3.1) represent a very conservative estimate of how having children affects life. Child care as a secondary activity adds nearly 50% more time, while the time with children in the " With Whom" column can be 5â€“10 times higher. That may account for Paille's finding that child-care estimate time in Canada was almost four times that reported in diaries. There is little question that time spent on these family-care activities will take on increased quality-oflife concern in the years ahead.
Personal Care and Travel Although seemingly of minimal policy interest, diary time spent on personal care (sleeping, eating, and grooming) also reflect on the quality of life in a society. Citizens of other countries may well try to emulate the lifestyles of the leading "eat and sleep" cultures of France and Germany. Differences in personal care times among the aged or less healthy seg-
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ments of the population can reflect important changes and differences in the quality of their lives. Of more policy concern is the time that people spend in travel: Here, a properly detailed diary will reveal the times that people spent not only in automobile versus mass transit, but also additional travel accomplished by other means, such as walking or biking. Experience in some countries has been that complete time diaries pick more such trips than " travel diaries" that focus only on trips during the day (Robinson & Godbey, 1994).
Free Time Perhaps the most central single quality-of-life parameter emerging from the diary, however, is the time free from, or not spent, on these productive and maintenance activities; that is, how much free time do people have in society, and how does that vary by gender, age, or social class (Robinson, 1995)? The diary not only captures the total amount of such free time but also its disparate components as well. Using the Table 3.1 activity scheme described on page 70, at a minimum, one can distinguish the following diverse aspects of free time: 1. Adult Education: includes both in-class and homework time, as well as trips to the library. 2. Organizational Activity: now being described by social scientists as the most basic of "social capital" in a society (Coleman, 1982; Putnam, 1995). Included here as well is religious practice, an activity that Presser and Stinson (1998) show is overestimated by 50% by traditional survey estimate questions. Paille (1994) found a parallel 50% overestimate for volunteer activity in comparison to the diary in Canada, and even larger overestimates have been found in U.S. surveys of volunteer work. 3. Cultural Events: include attending sports events (usually amateur rather than professional events as recorded in diaries), art and craft fairs, festivals, movies, and live arts performances and museum attendance (the latter examples of " high culture" being most infrequent of these events). 4. Social Life: includes visiting, parties, pubs, and other social gatherings. In the U.S., this is the second most prevalent of free time activities, but it has been declining since the advent of television, raising concern over the loss of social capital and personal bonding in modern and postmodern societies. 5. Fitness and Sports Activity: includes team sports, walkingâ€“hiking and huntingâ€“fishing. Changes in these activities obviously reflect on health and physical development concerns. 6. Hobbies: include traditional collections and needlework activ-
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ities, but artistic expression and computer usage (nonwork) as well. As use of the Internet and home computers grow, how does that affect other aspects of daily life? 7. Mass Media: include reading and radio-recordings, as well as television. Unlike media ratings, the diary separates "primary" from "secondary" usage, thus putting media usage in clearer overall perspective. What proportion of free time goes to television? How has television affected reading and stereo use? 8. Home Communication: includes family discussion, telephone use, written correspondence, and personal contemplation. Again, how have these been affected by television or other mass media? Most policy concern, however, centers on the sum of all these free-time activities, which have been found to vary from under 25 hours per week in 1960s Hungary to nearly 50 hours per week in 1980s Holland for the 18â€“64 age segment of the population. One of the important advantages of these separate accounts for various free-time activities is that survey respondents seem to have a particularly difficult time accounting for them when asked to estimate how much free time they have. Thus, when American and Japanese respondents are asked to estimate their weekly free time, their esimates are less than half that reported in diaries (Robinson & Godbey, 1997; Harris & Associates, 1988). This more than 100% underestimate (18 hours vs. 40 hours in diaries) is even more pronounced than the overestimates for work and housework. At the same time, when asked to estimate their daily television hours, respondent estimates of 21 hours per week are very close to diary reportsâ€”being actually higher than their estimates of weekly free time. Nonetheless, it is very clear that the extra time required of respondents to complete diaries rather than provide time estimates pays off in more consistent and reliable figures. Certain features of the diary method that act to ensure that this is the case are discussed next.
FEATURES OF THE DIARY METHOD One of the most valuable features of the diary accounts is that they are usually reported in respondents' own words. Respondents structure their day as they experience it or recall it, without using prearranged or constraining time/activity categories that researchers have devised. That allows one to be sensitive to changes across time in the language and context in which people describe their lives. What these diary accounts provide on a national basis, then, is a
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unique historical documentation of "A Day in the Life" of that country in quantitative terms. At the same time, that reporting frame can be the source of limitations in these accounts. One knows no more than what people are able or willing to reveal in this reporting framework. If they want to distort their reports, the researcher has only limited ability to control or correct them. Thus, very few respondents report engaging in sexual or other biological activity in their diary accounts. These are limitations one must expect given the interest in the broader structure of people's daily lives; they realize that there are better ways to get at such underground and sensitive activities than the diary approach, for example, specialized surveys such as the detailed study of sexual activity of Michael, Gagnon, Laumann, & Kolata (1994). Respondents also vary in the detail of their accounts across the day. In the sample diary account shown in Figure 3.3 (p. 62), one U.S. respondent describes the 27 activities of her day. While some respondents describe more than 40 activities, others report less than 10â€”even when probed for additional details. Some diary accounts provide detailed and graphic accounts of personal care or meal episodes; others say nothing at all about these activities during the day. These accounts are usually collected for only a single day. For the respondent taking an occasional day off or responding to some family emergency, the reporting day can be quite atypical for that person. Nor does one have much insight into how events on prior or subsequent days may be affected as a result, such as whether a long workday may have forced the respondent to cram needed home and family care into another day. Nor is the diary analyst able to say much about how a spouse's or other person's activities may have resulted in changes in what the respondent might otherwise have done on that day if no parallel diaries are collected from these spouses. It is even rather difficult to tell whether families are eating meals together, and unless analysts have asked specific follow-up questions, they do not know what television programs respondents watched, what kind of food they ate, or even whether it was a good day for them. Most of these limitations are not inherent in the diary method and could be overcome by more ambitious formats (and more costly research funding). For example, diary researchers in the Netherlands and England have carried out weekly diary studies, in which respondents kept them for a full 7 days. At the same time, these single-day diary accounts, when cumulated across fairly large representative samples, do provide a rather impressive and solid base for measuring long-term societal changes in how people spend their time. Statistically, at least, one can expect in large samples that those respondents who spend more time at work or at home than usual
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should be balanced by others who are spending less time there than usualâ€”and that seems to be borne out in the data. Evidence presented at the end of this chapter indicates that these diary accounts seem both valid (in the sense of being corroborated by observational data) and reliable (in the sense of producing consistent results from one sample to another). This enhances confidence in the accuracy of these diary " time-andmotion" studies of a country's daily life and the often counterintuitive results they produce. Given the increasingly fragmented and diverse composition of today's societal populations and the bewildering proliferation of lifestyles, the diary seems an ideally suited instrument for capturing and reflecting these trends. In order to further understand the complex of factors that relate paid work and free time, time-diary researchers have found it convenient to distinguish the four basic types of time use, shown in Figure 3.2; a fifth category of time can be added here to include all the travel that connects these other activities. These four basic types of time are strongly related to people's social roles, such as spouse or parent. The inclusion of the two other types of daily activity (family care and personal care) besides work and free time also is a reminder us that there are more complicated connections in the ways in which work and free-time activities interact. Thus, with four time categories, we can see how an increase (decrease)
Figure 3.2. Interrelations between four basic types of time.
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in paid work time may not necessarily translate into a decrease (increase) in free time. One could also reduce time in committed activities or personal care as a way to maintain desired or acceptable levels of free time. The quality of that free time might thus be affected, but not the amount of it. Figure 3.2 thus reminds us of how these four types of time can relate to one another. At the left are the " productive" activities of paid work and family care, and the main components of each type of work. In the middle are the " maintenance" or personal care activities of sleeping, eating and groomingâ€”with travel shown as a separate aspect of such adjunct activity. Finally, at the right are the more " expressive" activities in which people can engage during free time. For many people, particularly those who do not enjoy their work or family care, such activities presumably allow them the maximum opportunity to express their personal attitudes and personalities, or to experience true leisure. At the same time, diary analysts may need to remind themselves that the convenience and elegance of the quantitative diary measures may not translate straightforwardly into conclusions about human behavior, either in terms of what they mean subjectively to individuals, or objectively in terms of what is produced as a result. For example, an increase in such a simple activity as television watching can represent a shift toward increased laziness, a reaction to the dangers that lie outside the confines of one's home, an improvement in television's ability to meet audience needs, the use of a more efficient mode of learning about human behavior, or the only activity left to individuals after the more exhausting aspects of their lives. Bell (1976) has described how Americans can act to increase the " yield" even on their free time, not just on productive activities. Thus we see our diary records as something akin to the physical artifacts (like bones and tools) available to anthropologists. In their patterns and traces, they invite several insightful speculations about the nature of human behavior. For example, the finding that early human settlements contained tools and animal bones in close proximity was consistent with the model of early man as an aggressive hunter of animals. More recently, however, these same patterns have supported a rather different model of early man as a scavenger, who instead used these tools not to kill, but to extract food that other animals could not access.
THE ZERO-SUM CHARACTER OF TIME What we do find from diary studies that can be more revealing and persuasive comes from the " zero-sum" property of time. Simply put, if one increases time on some new activity, such as computer usage, yoga classes, or rollerblading, time on some other activity must show a decrease. Some-
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times these exchanges are rather straightforward, such as when one could show the decreases in the " functionally equivalent" activities of moviegoing, radio listening, and fiction reading that accompanied the arrival of television (Robinson, 1972). However, other post-television changes in daily activity followed less clearly from the functional equivalence argument, such as the decreases in post-television times devoted to sleep or gardening. In the same way, the declines in women's housework found since 1965 do follow from the increased time they spend at work, but they also arise because of the of the decreases in marriage and parenthoodâ€“ and also as a response to societal changes in norms and expectations. This highlights another important feature of diary data, namely, that they are complete. When all 24 hours of the day are represented, all human behavior is potentially captured and represented. Anything that people can do, they must do in time. Since "everybody has to be somewhere," the diary should allow one to compare activities directly in terms of the time that is devoted to them. Such interpretations are subject to multiple interpretations, but there can be little argument over the common yardstick applied to each activity.
MEASURING HOW PEOPLE SPEND TIME At first glance, the matter of measuring how people spend time seems straightforward. Since our modern lifestyles encourage us to think of time as money, envisioning time as linear, a line with a beginning and an end ticked off in uniform seconds, minutes, and hours, it should be an easily measurable commodity. Time has become a medium by which our daily activities are bound together. Thus, time is such an all-encompassing variable with rich implications about the nature of human and social behavior. Most survey efforts at measuring time expenditures assume that people can accurately recall their own time usage, as in the diverse bodies of historical time-estimate data from national samples that rely solely on the time-estimate approachâ€” on time spent working, doing volunteer work, doing housework, traveling, watching television, and other media usage. A central question concerning such research is whether we will get an accurate answer when we ask respondents simple time-estimate questions. There is mounting evidence that we will not.
Problems with Time Estimates Problems arise from the various steps involved in how respondents to surveys deal with the task of providing an accurate answer. Implicitly,
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asking someone " How many hours do you work?" or " How many hours do you watch television?" assumes that respondents do the following: 1. Interpret " work" or "TV" in the same way. What about work done at home, the commute to work, lunch breaks, unexpected overtime, and the like? What about television listened to when the respondent is in another room? 2. Separate the most important activity (primary) from other activities that are taking place simultaneously but are ancillary or less important (secondary), such as television viewing while either ironing, reading, or at work, or that done simply to monitor the viewing of young children. 3. Undertake the work of searching memory for all episodes of work or television yesterday or the last week. Will they remember the holiday or the day they were sick and could not go to work, or the unusually long football game or movie they watched? 4. Give estimates that properly include all the episode lengths across the day for yesterday or across days in the last week. But how well can they recall these episodes and add them together accurately in the few seconds they are given in a typical survey? 5. Feel comfortable describing this duration to an interviewer, when it may not be a typical day or week. Will it reflect badly on respondents' image of themselves that they wish to portray to an interviewer or the research organization, if the day or week in question contains too little work or too much television? 6. Avoid resorting to personal memory and instead resort to social norms, stereotypes or images of themselves in terms of how much time a " normal" person ought to work, like the normal 40-hour work week. Any or all of these obstacles may be problematic in obtaining accurate responses in regard to time use. This is particularly true in the survey context in which respondents are expected to provide on-the-spot answers in a few seconds. What seems at first to be a simple estimate task turns out to involve several steps that are quite difficult to perform, even for a respondent with regular and clear work hours or viewing patterns and a repetitive daily routine. One consequence is that when asked to give daily and weekly estimates of several activities, survey respondents give estimates that add up to considerably more than the 168 hours of time that each of us has available each week. In the studies of Verbrugge and Gruber-Baldine (1993), average estimated weekly times totaled 187 hours, and did not include churchgoing, shopping for durable goods, or professional services and adult education. In the study of Hawes, Telardzyk, and Blackwell
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(1975), estimated weekly hours averaged 238 hours, and in our own studies of college students, the totals are closer to 268 hours. Thus, the estimate approach has a built-in bias toward overreporting, much as we described in our analyses of hours at work (Robinson & Bostrom, 1994) and housework (Marini & Shelton, 1993). Basically, time-estimate questions encounter the same types of problems that arise from expecting respondents in surveys to answer other "simple" questions that are put to them. Survey researchers have fallen into the trap of accepting answers from respondents on almost any type of question. Often, these answers provide quite misleading or inaccurate results, as in the case of the question, " Where do you get most of your information?" When we look at more careful studies of actual information acquisition, it turns out that television viewers are less likely than users of other media to have picked up that information (Robinson & Levy, 1986). The simple question, when broken into the constituent information expected of respondents, is beyond the ability of most respondents to answer accurately. Much the same problem arises in time-estimate questions that extend much beyond a day or two. For example, Chase and Godbey asked respondents how often they played tennis during the last 12 months at a particular club, or went swimming at a particular pool. They then validated the actual number of visits using the required member sign-in sheets and found that, in both cases, about half of their respondents gave estimates that were double their actual visits as revealed on the sheets. Because of such estimation problems, the diary employs what we call a "microbehavioral" approach to such questions, one that breaks each part of the question into its easiest and most answerable components and then asks about that microlevel behavior. Rather than ask people about a vague reference period such as an average week or a typical day, we ask specifically about "yesterday," the complete day that is freshest in their memory. As an example, take the common survey question: " How many hours of television do you watch on a average day?" The usual estimate response to this question may be too high, because respondents translate the average day question into "the average day that you watch television," and not the occasional day when none is seen. These subtle respondent strategies can work to hinder their abilities to provide the accurate estimates that are expected of them and are why we put more faith in the microbehavioral method of the diary.
Alternatives to Time Estimates and Time Diaries There are several alternative ways of estimating time durations that are likely to produce more accurate figures than the estimate approach.
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This is because, like the diary, they also are sensitive to the equal property of time across individualsâ€”the recognition that at any instant of time "everyone has to be somewhere," with only one place/activity occurring at a given time. These alternative methods include the following: 1. The Experience Sampling Method (ESM) of Czikszentmihalyi (1991), in which respondents write down what they are doing when an electronic beeper goes off at random points during the day. This method was also used by Robinson (1985) to validate activities reported in diaries. 2. Direct observation studies, such as those done by anthropologists in third-world countries (e.g., McSweeney 1980), in which observers rather than respondents keep time records of what natives in a particular country do across the day. In the same way, we have recently employed American college students to "shadow" a person they know across the day; we then verify later, retrospective diary reports against these observations. 3. Electronic trackers, such as those used by parole officers to verify whether those out on parole stay within certain locations. More recently, media rating services have developed electronic badges that audience members wear, which record when they are in receiving range of an operating television or radio. 4. On-site verification, in which an observer can count the numbers of people at a particular site (e.g., a church, theater, or school) at a particular time and project that to the larger population under study (e.g., Hadaway, Marler, & Chaves, 1993). 5. Telephone coincidental studies, in which respondents report what they were doing when the telephone rang. 6. The random-hour technique, in which respondents report on a smaller segment of the day and not the full day (Robinson, 1985); by so reducing the descriptive task, respondents can focus more carefully on these smaller periods of behavior. Each of these techniques requires minimal memory work or recall on the part of the respondent, and, as a result, is usually considered to provide more "objective" measures of what people do. At the same time, none of these approaches outside of the shadow technique covers a very long period of time nor gives much dynamic insight into where these various activities fit into the overall lifestyles of the individuals being observed. To some extent, that limitation applies to our one-day diary approach as well. People may be involved in an unusual day as far as their normal activities are concerned. Thus, nearly 40% of respondents in our first
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national diary studies claimed that the day was unusual in some way for them. The "usual day" often turns out to be unusual. One way around this problem is to have respondents keep weeklong diaries instead, an approach that has been used in national studies in England and Holland. The problem is one of cooperation rates, however, with the cooperation rate in both countries being only about 40%. At the same time, Gershuny and his colleagues (1986) in England report that those who kept diaries differed little from those who did not in terms of their estimates of how they spent time. That has not been the experience with American samples. Indeed, we have come up with the rather counterintuitive result that respondents who agree to cooperate in the single-day diary are more likely to lead highly active lives than those who refuse. Thus, respondents in the 1975 national survey who agreed to be reinterviewed 3 months after the initial data collection were more likely to report longer work and housework times in their initial diaries, and were less likely to watch television and sleep. That result was replicated in our 1985 study, even though the design of the study was different in that respondents were first interviewed by telephone, mailed another diary to keep on a day for the following week, and asked to return the completed diary. Again, those who returned the diaries (for a cash reward) differed from those who did not in terms of their busier lifestyles. These results are consistent with " he more, the more" principle of time allocation described in Robinson et al. (1985), in which already busy people are more likely to participate in a given activity (except television) than those who are initially less active. Such results also counter the criticism made by Hochschild (1989) that busier women have been underrepresented in our time-diary studies.
Features of the Time Diary and a Sample Diary The measurement logic behind our approach to time studies follows from that employed in the most extensive and well-known of diary studiesâ€”the Multinational Time Budget Study of Szalai (1972). In that study roughly 2,000 respondents from each of 12 different counties kept a diary account for a single day. The same diary procedures and activity codes were employed in each country in 1965. Respondents were chosen in such a way that each day of the week was equally represented. That equalday allocation was very important for several European countries in the study because many workers in these countries were on 5Â˝- to 6-workday schedules, so that their weekend activities were very different from those
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in America. In our subsequent studies, we have taken great care to ensure that each day of the week is equally represented. Figure 3.3 shows how the diary was filled out by one American respondent in the study. It can be seen that this respondent was watching television at midnight as the new day began, and that she went to pick up her daughter between 12:15 and 12:30 A.M. She then got ready for bed and got to sleep at 12:50. She then woke up to make breakfast and lunches for her son and husband from 4:00 to 4:30 A.M. She then got ready for work and left at 4:55, arriving at 5:00 A.M. She took a work break at 8:00 A.M . for 15 minutes with a friend who worked nearby. She returned to work and took 15 minutes to eat lunch and then continued to work until 1:30 P.M. , at which time she drove home, arriving home at 1:35. Here, she visited with a neighbor in the back yard for 25 minutes, before doing a marathon 3-hour and 30-minute house cleaning. She then went out to pick up her daughter from school, returning home to serve and eat supper until 8:00 P.M. and spent the next hour washing dishes and doing laundry. She watched television for 75 minutes and then went out to pick up her daughter from work. Returning at 10:30 P.M. , she got ready for bed and was asleep by 10:45. Figure 3.3 thus includes 27 separate activities and shows not only the duration but also the time when each of these activities began and ended, and the codes describing the activity (e.g., sleep = code 45, working = code 1, and talking on the phone = code 96) and where it took place (e.g., home bedroom = code 5, office = code 21). Totaling activities across the day, we see that she spent 4.4 hours (265 = 190 + 75 minutes) sleeping, 8 hours (480 = 180 + 225 + 75 minutes) working, and 1 hour (60 minutes) eating meals. To calculate her free time during the day, we add the 90 minutes of television to the minutes for the visit with the neighbor to arrive, for a total of 115 minutes, or 1.9 hours. While one might prefer to classify her trips with her daughter or her work break as a social activity, that is not consistent with the coding scheme described here. Nonetheless, it is possible to accommodate any such recodings within that coding scheme. It can be seen that the task of keeping the diary, while presenting some recall difficulties, is fundamentally different from the task of making estimates. The diary keeper's task is to recall all of the day's activities in sequence. This is probably similar to the way the day was structured chronologically for the respondent and to the way most people store their activities in, and recall them from, memory. Rather than having to consider a long time period, the respondent need only focus attention on a single day. Rather than working from some list of activities whose meanings vary from respondent to respondent, the diary keepers simply describe their day's activities in their own words.
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The diary procedure thus avoids most of the pitfalls of the estimate approach described earlier. There are still problems of memory, as when respondents have trouble piecing together a particular period during the day, but once begun, the task becomes rather clear to both respondents and interviewers and proceeds with few structural problems. The diary technique also presents respondents with a task that gives them minimal opportunity to distort activities in order to present themselves in a particular light. They are given no clue about our interest in one activity or another, because we are simply interested in all activity. Some respondents may wish to portray themselves as hard workers or light television viewers, but in order to do so they must fabricate not only these activities but also the ones coming before and after them, making their accounts of events later in the day more difficult. Besides, we expect that respondents realize that this is only a one-day account, and that on any given day, they may work less or watch television more than usual. Moreover, respondents are not pressured to report an activity if they cannot recall it or do not wish to report it. Automatic procedures have been built into our recent diary recording procedures that are now conducted by telephone CATI (Computer Assisted Telephone Interviewing) to ensure accurate reporting. Anytime respondents report consecutive activities that involve different locations, they are reminded that there needs to be some travel episode to connect them. Activity periods that last more than 2 hours automatically involve the probe, " Were you doing anything else during that time, or were you (activity) for the entire time?" And as is apparent in Figure 3.3, all periods across the day must be accounted for in order that the diary accounts total to 1,440 minutes.
PREVIOUS TIME-DIARY STUDIES Time use surveys evolved from early studies of living conditions of the working class in response to pressures generated by the rise of industrialization in the late 19th century. These studies were concerned with the paid work, housework, personal care, and leisure activities of workers on a daily, weekly, or yearly time basis for the population. There was also interest in how time expenditures varied across such population segments as workers, students, and housewives, and in what use was made of free time. Most often respondents were asked, through direct questions, to estimate the amounts of time they allocated to various activities. The bulk of preâ€“World War II diaries originated in Great Britain, the Soviet Union
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and the United States, with other studies being conducted in France and Germany. The earliest systematic diary study was that of Strumlin in the Soviet Union in 1924, which was undertaken for use in governmental and communal planning; that study was replicated by his student Prudensky 35 years later, as reported in Szalai (1965). In the early 1930s, the Westchester County survey by Lundberg and associates launched a whole new era of studies of leisure, and Sorokin and Berger's (1939) Time Budgets of Human Behavior provided some fascinating insights into the psychological and sociological dynamics of daily life in a time-diary data context. Time-diary studies have been increasingly adopted since the 1960s, with national studies conducted in most Eastern and Western European countries. Japan, Holland, Finland, and Norway (among others) conduct studies every 5 years or so. Of particular note are the time use studies of the television network NHK in Japan, which have been carried out every 5 years since 1960, with samples exceeding 50,000 respondents, and by the statistical office in Japan, with more than 200,000 respondents. Unfortunately, these data have not been made available for secondary analysis to permit direct comparisons with diary data from other countries. The most ambitious and landmark study of time was the Multinational Time Use Study conducted in 12 different countries and 15 different survey sites under the direction of the Hungarian sociologist Alexander Szalai in the mid-1960s. It was a pioneering effort in cross-national collaborative survey research. Interest in repeating it was expressed by members of the International Association for Time Use Research, but on further reflection, it was concluded that it would be virtually impossible to repeat the project today. Alternatively, many countries are contributing data to the Multinational Time Budget Data Archive being developed by Professor Jonathan Gershuny, now at Essex University in England. Since 1985, national time use studies have been carried out, or are being planned, by central statistical agencies in over 15 Western countries. Often, these studies are initiating or continuating social indicator-type longitudinal studies, with a regular schedule of time use data collection. Australia and Italy have recently completed studies. Among the countries carrying out or planning studies are Israel, New Zealand, Sweden, and Germany. In addition to government studies, the collection of time use data for various academic research purposes has also found growing favor. Several diary surveys since 1965 have introduced important new dimensions of activity. Harvey and Procos (1974) and Chapin (1974) examined the important spatial aspect of time. Panel studies were conducted to examine how the same respondents changed their time-use patterns
JOHN P. ROBINSON
across time (Harvey, 1988; Juster & Stafford, 1985). Week-long diaries have been collected in Holland and England.
U.S. Studies Prior to the 1985 national study with 5,358 total respondents aged 12 and older, three national time-diary studies had been conducted using this general approach. These three studies and the organizations involved are as follows: 1. Mutual Broadcasting Corporation (1954) study, in which more than 8,000 American adults ages 15â€“59 kept time diaries for a 2-day period (more exact details are given in de Grazia,.1962). Unfortunately, only a few general data tables survive from this study for comparison with later studies, so that we can only briefly compare its results with later studies. However, many of the time figures from that study are surprisingly close to our 1965 results (Robinson & Converse, 1972). 2. Survey Research Center, University of Michigan (1965) study, in which 1,244 adult respondents ages 18â€“64 kept a single-day diary of activities, mainly in the fall of that year; respondents living in rural and nonemployed households were excluded (Robinson, 1977). Supplementary data were collected from a community sample in Jackson, Michigan (n = 788). We have made use only of the national part of the 1965-1966 data, adjusting in later years for its urban and employed focus. We also make reference to a 1986 replication of the diary data collection in Jackson. 3. Survey Research Center, University of Michigan (1975) study, in which 1,519 adult respondents ages 18 and over kept diaries for a single day in the fall of that year (Robinson, 1976); in addition, diaries were obtained from 887 spouses of these designated respondents. These respondents became part of a panel and were subsequently reinterviewed mainly by telephone in the winter, spring, and summer months of 1976; about 1,500 respondents remained in this four-wave panel. Some 677 of these respondents were reinterviewed in 1981, again, across all four seasons of the year (Juster & Stafford, 1985). Because of the difference in activities between those who stayed or dropped out of the panel, we have made use only of the original sample of 2,406 respondents and spouses interviewed in the fall of 1975. In the 1985 study conducted by the Survey Research Center, University of Maryland, single-day diaries were collected from 4,939 respondents ages 18 and over; unlike the 1965 and 1975 data, these data were collected across the entire calendar year of 1985. Further details on the 1965,1975 and 1985 studies are contrasted in the Appendix. All three studies were based on strict probability sampling methods across the nation. Only the 1985 study was spread across the
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entire year. Moreover, the 1985 national data were mainly collected by prospective mail-back diaries, while the 1975 study employed the retrospective recall of activities done " yesterday." The 1965 and 1975 studies had somewhat higher overall response rates (72%, 72%), although not much higher than the telephone portion of the national study (67%). The 1985 study had more than twice the number of adult respondents over age 18 than the 1975 study (n = 4,939 vs. 2,409). The 1985 national study had more spread across the year and across days of the week, while the 1975 study oversampled Sundays and undersampled Saturdays. All studies used open-end diary entries across the full 24 hours of a single day and the same basic code for diary activities, although the 1975 and 1985 studies employed more than twice as many activity codes, still collapsible to the Table 3.1 scheme. In addition to these U.S. national studies, 1965 and 1986 diary studies were conducted in Jackson, Michigan (Robinson, Andreenkov, & Patruchev, 1989); much the same trends emerged from this community study as in the national surveys. There is also the 1987â€“1988 statewide study of California, in which 1,762 respondents ages 12 and older gave retrospective diary accounts of what they did "yesterday" (Wiley et al., 1991). As described in the Appendix, the 1985 U.S. national study used three different modes of diary collection for methodological comparison: personal, mail-back and telephone. As in earlier diary studies (e.g., Juster & Stafford, 1985; Robinson, 1977,1985) using the basic diary recording framework in Figure 3.3 and Table 3.1, there was little difference in obtained time estimates, as shown in Robinson and Godbey (1997). That bears on the basic reliability of the diary method as described here. The telephone method does result in less " Not ascertained" time (code 48 in Table 3.1), and also less time in such shorter activities as radio listening and meal cleanup.
DIARY STUDIES IN CANADA The first diary study was conducted in Halifax, Nova Scotia, in 1971, and the latest two surveys were national in scope in 1986 and 1992. A 1981 study provided the first nationwide picture of time use in Canada, as well as an opportunity to follow a panel of 1971 Halifax respondents. In general, the same activity codes have been used as in Table 3.1 below. Further details and features of these four studies are as follows: 1. Halifax Metropolitan Survey (1972â€“1972). In this first Canadian study, 2,141 respondents ages 18â€“65 living in this area of nearly 300,000 population completed a single-day diary following the general procedures
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in Szalai et al. (1972). In addition, detailed geographic coding of locations on a one-tenth kilometer grid allowed a uniquely precise spatialâ€“temporal analysis of that community's activity (Elliot, Harvey & Procos, 1976). As part of the 1981 nationwide data collection, 453 of these original respondents were reinterviewed a decade later on the same day of the week. This allowed the analysts to simultaneously analyze stability and change in daily activity. 2. Canadian Pilot Nationwide Survey (1981). In this study, respondents from 14 diverse urban, rural, and suburban areas across Canada were interviewed in late fall (Harvey & Elliot, 1983; Kinsley & O'Donnell, 1983). The final sample of 2,685 people ages 15 and older provided up to four simultaneous primary activities (and 6% of all diary entries used all four of them) that were coded into 173 categories, along with location and social contact. 3. General Social Survey Time Use Module (1986). More than 9,000 respondents ages 15 and older were interviewed by Statistics Canada using RDD telephone methods (Harvey, Marshall, & Frederick, 1991). Activity codes generally mirrored those in Table 3.1 and the 1971 code. 4. General Social Survey Time Use Module (1992). Again more than 9,000 respondents randomly selected across the country provided singleday diaries of primary activity, location, and social contact by telephone (Frederick, 1995). Unlike the 1986 survey diaries were collected across the entire year of 1992 instead of a few months.
ACTIVITY CODING As in earlier diary surveys, we have coded the open-ended diary reports using the basic activity coding scheme developed for the 1965 Multinational Time Budget Research Project (as described in Szalai et al., 1972). As shown in outline form in Table 3.1, the Szalai et al. code first divides activities into non-free-time activities (codes 00â€“49) and free-time activities (codes 50â€“99). Non-free-time activities are further subdivided into paid work, family care, and personal care, with free-time activities being further subdivided under the five general headings of adult education, organizational activity, social life, recreation, and communication. More fine-grained distinctions within these categories were captured in the more than 250 categories developed in the 1985 study that reveal further distinctions under these broader headings. Nonetheless, the main value of the open-ended diary approach is that activities can be recorded or recombined depending on the analyst's unique assumptions or purposes.
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The Table 3.1 code has several attractive features. First, it has been tested and found to be reliable in several countries around the world. Second, and because of this, extensive prior national normative data are available for comparison purposes. Third, it can be easily adapted to include new code categories of interest to researchers looking into different scientific questions from different scientific disciplines. Location, as described in the " where" category of the diary, was coded into one of the basic location categories shown in Table 3.2, as developed for the environmentally oriented 1985 study. Proper location coding can be arranged in estimated aggregate time spent in travel, outdoors, or at home, all important parameters for analyzing trends in use of time. Unfortunately, these distinctions were not employed in earlier studies so crosstime location comparisons are not as exact as for activities. Activity diary data, when aggregated, have been shown to provide generalizable national estimates of the full range of alternative daily activities in a society: from "contracted" time to "committed" time, to personal care, and to all the types of activities that occur in free time. The multiple uses and perspectives afforded by time-diary data have led to a recent proliferation of research and literature in this field. Comparable national time-diary data have been collected in over 25 countries over the last two decades, including virtually all Eastern and Western European countries.
PROCEDURES TO ANALYZE TIME-DIARY DATA For the most part, interest in time-diary data has focused on the primary activity as coded into the scheme in Table 3.1. But the diary records are much richer than that, with appeal to researchers in a variety of disciplines. Location: Data on the location of activities are of great interest to market researchers, media analysts, geographers, and urban planners. Marketers can learn when and how long people are at home or in their cars, in order to reach them with advertising messages; media analysts can know when people are watching television; urban planners can determine when people use public places; and environmental health researchers can know when people are outdoors and exposed to unhealthy air (one main purpose of the detailed code in Table 3.2). One can use these data to simulate the consequences of public policies affecting locations, such as restricting auto traffic or banning smoking in certain public locations. With Whom: Data on social partners during activities are of interest to mental health specialists and sociologists. They can learn how many
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Table 3.1. Activity Codes for Time-Diary National Studies 00-49Nonfree Time
50–99 Free Time
00-09 Paid Work
00 01 02 03 04 05 06 07 08 09
(Not used) Main job Unemployment (Notused) (Notused) Second job Eating at work Before/after work Breaks Travel/to-from work
10–19 Household Work 10 11 12 13 14 15 16 17 18 19
Food preparation Meal cleanup Cleaning house Outdoor cleaning Clothes care (Not used) Repairs (by R) Plant, pet care (Not used) Other household
20–29 Child Care 20 21 22 23 24 25 26 27 28 29
Baby care Child care Helping/ teaching Talking/reading Indoor playing Outdoor playing Medical care—child Other child care (Not used) Travel/child care
30–39 Obtaining Goods/Services
30 31 32 33 34 35 36 37 38 39
Everyday shopping Durable/house shop Personal services Medical appointments Govt/financial services Repair services (Not used) Other services Errands Travel/goods and services
50 51 52 53 54 55 56 57 58 59
Students' classes Other classes (Not used) Internet use Homework Librarian use Other education Computer use Other computer use Travel/education
60–69 Organizational 60 61 62 63 64 65 66 67 68 69
Professional/union Special interest Political/civic Volunteer helping Religious groups Religious practice Fraternal Child / youth /family Other organizations Travel/organizational
70–79 Entertainment/Social 70 71 72 73 74 75 76 77 78 79
Sports events Entertainment Movies Theater Museums Visiting Parties Bars/lounges Other social Travel/social
80 81 82 83 84 85 86 87 88 89
Active sports Outdoor Walking/hiking Hobbies Domestic crafts Art Music/drama/dance Games Computer use games Trave/recreation
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Table3.1. (Continued) 00–49 Nonfree Time 40–49 Personal Needs and Care 40 41 42 43 44 45 46 47 48 49
Washing, hygiene, etc. Medical care Help and care Meals at home Meals out Night sleep Naps/day sleep (Not used) Dressing/grooming, etc. Travel/personal care
50-99Free Time 90–99 Communications 90 91 92 93 94 95 96 97 98 99
Radio TV Records/ tapes Read books Magazines/etc. Reading newspaper Conversations Writing Think/relax Travel/communication
Americans are part of the " lonely crowd," when children are in contact with their parents, and when families spend time as a unit. They can also find out how these partnership patterns vary across activities or locations, or by the person's marital status. Secondary Activities: It is speculated that Americans are continually combining more and more activities into the same time period, engaging in a form of " time deepening" (Burns, 1993; Robinson & Godbey & Robinson, 1997). This is seen as symptomatic of the society's " time famine," as described by Linder (1970). Media analysts find that much television view-
Table 3.2. 1985 National Study Location Codes A. At Respondent's Home (00–19) 00 Respondent's home/yard (general) 01 Basement/cellar 02 Bathroom 03 Bedroom 04 Dining room 05 Computer room 06 Den 07 Family room/front room/living room 08 Game room/recreation room 09 Garage 10 Kitchen 11 Laundry/utility room 12 Office 13 Porch 14 Hall 19 Other home
B. Travel 20 Transit (NA mode) 21 Car transit 22 Other transit C. Other 30 Work 40 Friends/relative home 50 Restaurant/bar/fast food 60 Indoor place of leisure 70 Outdoor place of leisure 80 School 81 Church 82 Store, etc. 83 Banks/office/library 89 Other 99 NA/Ref
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ing and reading, and almost all radio listening, occur as secondary activities. Communication researchers will also find here most of the interpersonal conversation that occurs during the day. The same scheme as in Table 3.1 can be used to code secondary activities. Day and Time of Day: Many subtle shifts occur regarding when activities are performed, and these can have important consequences for societal behavior. Thus, Robinson (1994) finds more work being done on Sunday and less during the week as more "blending" of days of the week occurs. In more fine-grained analyses, Robinson (1993) reports that since the 1960s, both similarities and differences in the time when people go to bed and get up, since there are notable day-of-the-week differences in these patterns as well. An example of time-of-day differences in activity is shown in Figure 3.4. Other Aspects of Activity: There is an almost limitless number of activity aspects that could be considered when one examines the flexible features of the diary format. The following are among those that have been tried most prominently to date: 1. Psychological States: Differences in how much people enjoy various activities or feel time pressured or under others' control have been examined in Cullen and Godson (1972) and Robinson (1994), with a general review provided in Michelson (1986).
Persons Aged 18 and Older Weighted byTlMEWT (n = 1579) Figure 3.4. Percentage of 1579 California adults reporting "smokers present," by time of day.
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2. Media Usage: Differences in "tertiary" media use ("Was the television set (radio) on during that activity?") have been examined in Robinson (1990). 3. Environmental Tobacco Smoke (ETS) Exposure: Differences in exposure to secondhand tobacco smoke across activities have been analyzed in Robinson, Ott, and Switzer (1994). Figure 3.4 shows how this ETS exposure varies across the 24 hours of the day for both smokers and nonsmokers. Again, these aspects only suggest the facets of activities that remain unexplored in the usual diary studiesâ€“the time Americans spend in sickness and in health, dazed and confused, on-line or in love, or in ecstasy or quiet desperation. Indeed, it could be argued that this specific focus is an essential component of any proper investigation of an activity. Only so much of an activity's full context can be captured in the basic diary format usually employed; work, television, or personal care can take place at any point during the day and not be picked up in the diary. Unfortunately, there are limits to how many details respondents can be expected to report on a particular day. Once one asks more than two or three questions about each activity during the day, the reporting task becomes very burdensome, and the quality of respondent reporting can be expected to be adversely affected.
Computer File Formats Time-diary data are mainly analyzed in two types of formats, by activity (variable field) and by summary totals (fixed field). The activity file format is structured using the same activity-by-activity format as the raw diary entries described in Figure 3.3. It is described as a variable-field format because the number of activities is variable across respondents, some reporting 35 activities, others only 10â€“15 lines of activity descriptions. As shown in Figure 3.5A, the first line of the activity file (equivalent to the episode file, Chapter 2) contains the code for the first activity, the times it began and ended (in "military" time, e.g., 8:00 A.M. = 0800 and 8:00 P.M. = 2000), the duration of that activity (in minutes), the code for the secondary activity (if any), the code for the location of the activity, the code for social partners during the activity, and the code for any other feature of the activity (such as the enjoyment level during the activity or the presence of smokers) included in the diary format. The second line of the file contains the same information for the second activity in the diary.
JOHN P. ROBINSON
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In order to link these activity characteristics to the social background variables (see Figure 3.6, p. 78), demographic descriptors need to be added to each line of this activity file. Thus, alongside these digital activity summaries, we need codes for that respondent's gender, age, education, and so on. In that way, we can tell whether more time in each activity is spent by men or women, or older versus younger people. But that is not the most efficient way to conduct these analyses, because the averages are obtained on an activity basis and not duration during the day. For example, if a person works from 8:00 A.M. to 12:00 P.M. and from 1:00 P.M . to 3:00 P.M. on the diary day the average duration per activity is 3 hours. It is much more meaningful to describe the day in terms of the total of 6 hours that was worked. That is what is provided in the summary total file. Figure 3.5B is in "fixed-field" format because each respondent's representation in the file is fixed equally (at one line for each respondent in the study/file). Here, a three-digit space is provided in the file for the minutes per day spent on each of the possible 96 activities in the overall activity code shown in Table 3.1. In the slot for work activity, the person's 360 minutes of work would be totaled; if another person did no work, the entry would be 000 minutes. Averaging these two respondents, we obtain a mean of 180 minutes. In the same way, we can use these summary totals to arrive at average daily times that men, women, older people, and so on, spent on each of the 96 possible activities. Assuming all days of the week are equivalently represented, we can convert these daily minute figures into weekly hours devoted to these activities using simple arithmetic. We first multiply the daily figures by 7 (days of the week) and divide the total by 60 (minutes). If the number of respondent days of the week varies from day to day, the data need to be weighted toward equality; thus, if we have 100 diaries from all days of the week except Monday, for which we have only 50 diaries, the Monday diaries need to be multiplied by 2. The same approach is needed to compute average hours for the other aspects of time use reported in the diaryâ€”average hours spent at home or in an automobile, average time spent with children, mean hours watching television as a secondary activity, and the like. For many such calculations, the analyst may be more interested in a different kind of average, namely, the average time per participant (see Harvey's discussion in Chapter 2). For example, to calculate the average length of one's workday only those who worked should be considered in the denominator and all those who do not work should be excluded. A figure of 6.7 hours for a workday across all respondents makes less sense as an analytic statistic than a figure of 8.6 hours for those who actually worked. Similarly it makes more sense to report the average of 2.3 hours of those who went to a movie on the diary day than the .04 hours of
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moviegoing for the entire sample. Both sets of calculations can be easily done on a personal computer with the summary total file in Figure 3.5B. The activity file, on the other hand, is needed for more fine-tuned calculations of aspects of activities, such as the secondary activities that accompany work or housework, or the social partners who are present. Of particular interest here are activity differences by time of day. By confining the activity to those that occur at 8:00 A.M. or 5:00 P.M., one can obtain snapshots of what the sample is doing at different times of the day, as in the picture of exposure to secondhand tobacco smoke across the day, shown in Figure 3.4. Similar graphs could also be constructed to show when during the day the public goes to sleep or watches television, or is at home, or in their cars.
Multivariate Analyses In cross-sectional and cross-time analyses, several steps need to be taken to ensure comparability of diary data across the three decades of analyses reported in Robinson and Godbey (1997). First, we mainly confined our analyses to the age 18â€“64 segment of the population, that is, the segment most likely to be in the labor force. Then, we examined the rural segment of the population (who were excluded in the 1965 data collection) in 1975 and 1985 to see whether they are different from the nonrural population. We find they are not (that is, the diary evidence shows little difference between urban and rural people), so we treat them much the same as more urban people in aggregating data across each of the three main study years. Multiple Classification Analysis Techniques: More conveniently, we have employed the technique of Multiple classification analysis (MCA) to control for this and other unwanted sources of differences in the data across studies. In other words, when comparing housework across decades, we need to adjust for the fact that more women are employed and fewer are married or have children. That is necessary to ensure that we are comparing 1965 apples with 1985 apples in such analyses. Similarly, when we compare cross-decade data on child care, we need to adjust for the fact that fewer people have children in more recent years. MCA was specifically designed by Andrews, Morgan, Sonquist, and Klem (1973) to provide these types of statistical controls on large-scale data. What it does is effectively to ensure that "other things are equal" in our diary analyses. An example of how the procedure works in practice is provided in Robinson et al. (1985). In effect, then, MCA acts as a corrective for all the demographic ways in which a "standard 1965 adult" would behave in 1985. Since respondents report on activities only for a single day,
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MCA is particularly useful in ensuring that respondents interviewed on a Monday are made comparable to those interviewed on other days of the week. In more straightforward terms, we have mainly presented our basic unadjusted 1965 to 1985 results in terms of four major subgroups: employed women, women not in the paid labor force, employed men, and men not in the paid labor force. To simplify comparisons further, we show these data in terms of implicit hours per weekâ€”even though respondents generally only kept diaries for a single day. Here, again, we can use the MCA procedures to ensure that day-of-the-week differences are taken into account in the trends that are identified.
A LARGER MULTIVARIATE MODEL Thus far, we have described several demographic variables that are important to understanding time use. Most prominent is the variable of gender, and that has been a major focus of many of our analyses of time. This is not only because of the large gender differences found in the uses of time (which seem to be becoming less pronounced across time), but also because of the sociopolitical sensitivities that are now raised about these gender differences. Thus, gender plays a central role in the diagram in Figure 3.6, showing the various demographic and background factors that affect how time is spent. There are other " biological" factors, factors that mark us at birth, in the left-hand side of Figure 3.6 that lead to interesting and important differences in how people spend time. First is the factor of age. There are clear and familiar differences prescribed by most societies in how time use changes across the life cycle. Early life in Western societies is set aside as a period of learning and socialization, largely in preparation for the "work" expected of individuals in the middle part of their lives. Past a certain age, people move into the retirement phase of life, in which they are freed of the necessity of doing most forms of work. If for no other reason than these societal expectations about appropriate activity across the life cycle, age differences play a large role in how time is spent. Another ascribed status is nationality or race. While there are large differences across peoples of various ethnic backgrounds in America, the most visible and troublesome differences are found between blacks, or African Americans, and the predominant white population. In examining these racial differences in time use, it is important that the analyst recognize that they are confounded by a host of other demographic differencesâ€” such as parenthood, marriage, urbanicity, and most importantly for time
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Figure 3.6. Basic factors in the model underlying activity participation.
use, level of education. Thus, one must take care to separate differences by race from these other factors. Nonetheless, there is a further problem in analyzing racial differences in these diary studies, namely, the relatively small sample sizes in a data collection in which behavior is examined only for a single day. Thus in a cross-section sample of 1,500 people, we will find only 150â€“200 blacks, representing little more than 20 blacks per day. The problem becomes even more acute for other important minorities, such as Hispanics, Native Americans, or Asian Americans. Demographic variables of more interest to sociologists involve the three main indicators of a person's social status: education, income, and occupation. Having higher levels of such indicators of status means increased access to skills and resources. This is most apparent in the case of income, since it allows direct ability to purchase technology or services to do things one wants to do. But the factors of education and occupation also provide important resources of "cultural capital" that allow individuals
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access to more specialized and varied ways of spending timeâ€”such as being able to derive meaning, insight, and satisfaction from reading a book, enjoying a gourmet meal, or attending an opera. The three status factors are, of course, highly intercorrelated, so that not only do more highly educated people have the skills and vocabulary to find and enjoy these activities, but also the income to pay for them and the occupational co-workers with whom to share these experiences and to reinforce their value. While the factors of education and income have been regularly measured in diary studies done to date, the factor of occupational status is more difficult to measure in a standardized way and cannot be analyzed in as systematic a fashion. In the studies that have measured the factor well, however, differences in type of occupation in time use are largely explained by education and income. Nonetheless, that may not be the case in future studies in which larger sample sizes would allow for more fine-grained categories of occupation to be examined. Another important set of demographic factors can be described as role factors. Three role factors noted in Figure 3.6 are work, marriage, and parenthood. Role factors usually imply strong commitments of timeâ€”to the job, to the marriage, and to one's children. Thus, they are extremely important factors to control and adjust for in examining differences and trends in different ways of spending time. Their function in predicting time use varies widely depending on the activity in question. Thus, an hour devoted to paid work does not necessarily result in an hour of lost free time, and marriage and parenthood affect not only work and housework differently but also the things people do in their free time. In general, these role factors are the most important factors affecting how time is spent. Temporal factors also greatly affect how time is spent. Obviously, time use also varies with many activities done only on certain days of the week. While work is usually done Mondays through Fridays, there is increasing evidence of work time being more equal across days of the week, especially Sundays. Activities also vary by month or season of the year, largely a function of more moderate temperatures in the spring and fall months that allow people to spend more time outdoors. Activities also vary across years, which has also been a major focus of analyses, although this seems more a function of irregular social trends than the predictable weekly or daily cycles of activities regularly expected in society. There are interesting cross-time trends as well in another temporal way in which activities differ, namely, by time of day as in Figure 3.4. Another set of variables that can affect time use involes more locational or geographic factors. Three types of locational factors can be distinguished: region of the country, size of place or urban area, and type of
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housing. Region of the country can reflect distinct lifestyles, such as in the South or on the West Coast, but regional differences can also arise from different types of inhabitants of that region or different climate and weather conditions. Except for weather, much the same is true for differences between residents of urban, suburban, and rural locations, although these differences can arise because of different access to facilities as well. Type of housing can also affect behavior, with residents of single-family houses having more internal space in which to spread out technology and activities, but entailing more space to clean and more outdoor space to maintain as well. A related factor here is the permanence of the space, in the sense that renters of a housing unit will invest less time and effort to its upkeep than will an owner. Of the myriad other factors that can affect time, we have examined home technology in particular. Technology is often cited as an important factor because of the presumed time-saving features of new household appliances related to production. While detailed analyses provide little evidence of these features of technology being realized, it is clear that at least one piece of household technology has truly revolutionized life in America and the rest of the world as well. Television's impact on society is evident not only in the sense of taking up close to half of free time but also in the ways that it is making continued inroads into the way we spend time. Its effects have been seen to spill over to non-free-time activities as well. While some of its functions and roles have now been joined with the computer, it will be interesting to see whether the computer will have as clear and monumental impact on daily behavior as television. Other pieces of technology that have been studied in this way include cable television, VCRs, home laundry equipment, dishwashers, vacuum cleaners, and the latest technology, microwave ovens. In no case are there anywhere near the differences in time associated with these technologies as those found for television. Figure 3.6 thus represents an attempt to bring all of these factors together into a single multivariate model. This model serves to remind us of the many cross-cutting variables that can affect time use, and the need to take them into account in describing activity patterns and trends.
METHODOLOGICAL PROPERTIES OF TIME DIARIES Two most important measurement properties of social science measures are reliability and validity. Reliability refers to the ability of a measurement instrument to provide consistent results from study to study or under different conditions. Do we get similar results using the same
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method? Validity refers to the ability of an instrument to provide accurate or valid data, in the sense that it agrees with estimates provided by other methods (such as observation or beepers, as described earlier).
Reliability In the 1965 and 1975 studies, estimates from time diaries produced rather reliable and replicable results at the aggregate level. For example, Robinson (1977) found a .95 correlation between the time use patterns in the 1965-1966national time diaries (n = 1,244) and the aggregate figures for the single site of Jackson, Michigan (n = 788). Similar high correspondence was found for the American data and for time-diary data from Canada, both in 1971 and in 1982 (Harvey & Elliot, 1983). Reliability was also noted using different diary approaches. Thus, a correlation of .85 was found between time expenditure patterns found in the U.S.â€“Jackson time study using the "day after" approach and time expenditures for a random one-tenth of the samples who also filled out a "day before diary." 1 In a smaller replication study in Jackson in 1973, an aggregate correlation of .88 was obtained (Robinson, 1977). Further support for the reliability of the diaries comes from the rather convergent results from the telephone, mail-back, and personal interviews in the 1985 national study and from the overall national results and those obtained in 1986 in Jackson in 1987â€“1988, in California, and in 1987 in Canada.
Validity Almost all diary studies depend on the self-report method rather than on some form of observation. This may be seen as an unfortunate situation, since it leaves these self-reported data open to basic questions of their being verifiable by some independent method of observation or report. But there are encouraging signs from those observational studies that have been conducted. Several studies bear directly on the validity of the time diary in the sense of there being an independent source or quasi-observer of reported 1
These results provided the rationale for using the much less expensive day "yesterday" diary approach in the 1975 study rather than the more expensive "tomorrow" diary approach, in which the respondentfills out the diary for the following day, and which requires a separate, second visit to the respondent's home. The tomorrow approach did pick up less detailed activities, but only about 10% less detail. At the same time, telephone methodsused in 1985 involved much less missing data.
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behavior. The first of these studies did not involve the time diary directly but rather the low viewing figure from the time diaries relative to standard television rating-service figures that we found in our initial 1965 study. In this small-scale study (Bechtel, Achepohl, & Akers, 1972), the television viewing behavior of a sample of 20 households was monitored over a week's time by means of a video camera; the camera was mounted on top of that set, thus allowing the video camera/microphone to record all of the behavior that took place in front of the television screen. The results of this study, as in the earlier camera monitoring of television audiences by Allen (1968), indicated that both rating-service methods of television exposure (the audiometers and the viewing diaries) produced estimates of viewing that were 20â€“50% higher than primary or secondary activities reported in time diaries. In brief, the study provided considerable support for an explanation of the lower viewing times reported in time diaries than by commercial rating services. It also illustrated the need for a complete open-end diary rather than one focused on a specific set of activities. Three more general validity studies that have been published subsequent to Bechtel et al. (1972) provided further evidence on the validity of time-diary data. These examined the full range of activities and not just television viewing, and they employed larger and more representative samples. However, none involved the independent observation of behavior utilized in the Bechtel et al. study. In the first study (Robinson, 1985), a 1973 random sample of 60 residents of Ann Arbor and Jackson, Michigan kept beepers for a one-day period and reported their activity whenever the beeper was activated (some 30 to 40 times across the day). Averaged across all 60 respondents, the correlation of activity durations from the beeper and from the diaries was .81 for the Ann Arbor sample and .68 for the Jackson sample (across the nonsleep periods of the day). In a second study, a telephone sample of 249 respondents interviewed as part of a 1973 national panel survey (Robinson, 1985), respondents were asked to report their activities for a particular " random hour" during which they were awake that day, with no hint from the interviewer about what they had previously reported for that hour in their diary. An overall correlation of .81 was found between the two aggregate sets of data, that is, between the activities reported in the random hours and in the diary entries for those same random hours. In a more recent study, Juster (1985) compared the "with whom" reports in the 1975â€“1976 diaries of respondents with those of their spouses across the same day. Juster found that over 80% of these independently obtained husband and wife diaries agreed that their spouses were present
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or absent. In a separate analysis of these 1975 data, Hill (1985) found a .93 correlation between diary time spent on various home, energy-related activities and aggregate time-of-day patterns of energy use derived from utility meters. More recently, we have conducted some preliminary studies using the "shadow" technique described earlier with student samples. Each student shadows someone he or she knows for 8â€“12 hours during the waking day, recording all the things that person is doing during that observation period. The next day, that student then asks the shadowed person for an unrehearsed account of those same activities. Although the samples are thus far are very small and highly unrepresentative, there is general agreement on over 90% of the activities across the day. In conjunction with the reliability studies, then, the data from these studies provide a considerable degree of assurance about the basic generalizability of time-diary data. This has been the case as well in methodological studies conducted in other countries (e.g., Gershuny et al., 1986; Michelson, 1978). For example, Ziegler and Michelson (1981) found correlations above .75 between observed and diary times parents spent on child care. Nonetheless, a definitive well-controlled study needs to be conducted to update and extend these results.
SUMMARY AND CONCLUSIONS The time diary, then, is a microbehavioral technique for collecting selfreports of an individual's daily behavior in an open-ended fashion on an activity-by-activity basis. Individual respondents keep or report these activity accounts for a short, manageable period such as a day or a weekâ€” usually across the full 24 hours of a single day. In that way, the technique capitalizes on the most attractive measurement properties of the time variable, namely, 1. All daily activity is potentially recorded (including that which occurs in early morning hours when most people may be asleep); thus, the diary accounts are by definition complete across the 1,440 minutes of the day. 2. All 1,440 minutes of the day are equally disturbed across respondents (thus preserving the "zero-sum" property of time that allows various "trade-offs" between activities to be examined). 3. Respondents are allowed to use a time frame and accounting variable that is maximally understandable to them and accessible to the way they probably store their daily events in memory.
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The open-ended nature of activity reporting means these activity reports are automatically geared to detecting new and unanticipated activities (e.g., aerobic exercises, use of new communications technologies), as well as capturing the full temporal context of how daily life is experienced (when it occurred or the activities that preceded or followed it). In these diary accounts, respondents report on each activity in which they engage across the full 24 hours of the day, as well as where they were and various other aspects of each activity. The multiple and diverse insights into the quality of life provided by the diary have led to its use in more than 25 countries around the world, and that should increase as societies become more conscious of the limits and value of the resource of time.
APPENDIX METHODOLOGY OF THE 1965–1985 AMERICANS' USE OF TIME PROJECTS I. The 1985 Study The 1985 Americans' Use of Time study employed the same basic, open-ended diary approach as the 1965 and 1975 national studies. An important innovation in the 1985 study was the explicit attempt to spread the collection of diary days across the entire calendar year—from January through December of 1985. Mail-back sample: The data for the main (mail-back) study were collected from a sample of Americans who were first contacted by telephone, using the random-digit-dial (RDD) method of selecting telephone numbers. All calls were made from the central telephone facility at the Survey Research Center of the University of Maryland, College Park. Once a working telephone household was contacted, one respondent aged 18 and older in each household was selected at random. That person was given a brief (2–5 minute) orientation interview, followed by an invitation to participate in the diary/mailout part of the study. If that respondent agreed, diaries were then mailed out for each member of the participating household aged 12 and above to complete for a particular day for the subsequent week. Over 80% of respondents agreed to accept the mailout. Brief Call 2 and Call 3 interviews were made 4–6 days later to ensure that respondents had received these materials and understood how to complete them. After respondents had completed these diaries, they then mailed all their completed forms back to College Park for coding and analysis. Some 3,349 diaries from 997 households were returned using this mailout procedure during the 12 months of 1985, over 70% of those originally contacted. It is the diaries obtained from adults aged 18 and over,
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however, that form the database for our main analyses. Other 1985 data included parallel diary data from 809 additional respondents interviewed in a separate personal interview sample in the summer and fall of 1985, and from an additional 1,210 "yesterday"diaries obtained by telephone as part of the initial contact for the mail-back diaries. Telephone sample: The telephone sample consisted of the random Sample of the population who were contacted in the first phase of the RDD sample. This consisted of the randomly selected adults (aged 18 or older) who responded to the first interview. Some 67% of respondents contacted by telephone, however, did complete a day-before diary over the telephone. This was the highest response rate for any of the three data collection modes. Personal sample: In addition to the mail-back and telephone diaries, a separate national sample of 809 diaries were collected by personal, inhome interviews. Respondents in this sample followed much the same procedures for the initial telephone sample. One adult selected at random was to complete a retrospective diary from memory for the previous day. The interviewer then left diaries for all adult respondents in the household to complete for the following day. The University of Maryland diary coders were extensively trained on the activity code category system and used the same complete document of coding conventions that were developed by the Survey Research Center at the University of Michigan for its 1975 time-diary project. Each activity in the diary was coded descriptively as a separate block 17 digits in length. This was comprised of the primary activity (a three-digit code) during the period, the time the activity began and ended (each coded in four-digit military time, e.g., 8:00 A.M. = 0800; 8:00 P.M. = 2000), location (one digit), social partners (two digits), and secondary activity (three digits). When this 17-digit entry for all activities in the diary was data entered and computed, the totals were programmed into the machine to ensure that each day's diary entries added to exactly 1,440 minutes (24.0 hours). These " variable-field" data (i.e., varying depending on the number of activities reported) were then processed by a special computer program that generated "fixed-field" compilations of diary time for each of the Table 3.1 activities across the day (i.e., total daily minutes spent working, cooking, watching television, etc., for that respondent for that day).
II. The 1975 Study The 1975 study was designed to facilitate development of a fully articulated system of national economic and social accounts. Particular emphasis was placed on obtaining accurate estimates of yearly productive uses of time on a household basis for analysis using a microdata perspective.
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The data for the study were collected from a sample of Americans first interviewed from October–November 1975, as part of the 1975 fall omnibus study conducted by the University of Michigan's Institute for Social Research. The respondents in the 1975 omnibus were chosen to form a representative sample of American adults 18 years of age and older (including age 65+) living in the coterminous United States. As part of the time-use measurement effort, spouses of the respondents were interviewed as well. The original respondents and their spouses were then reinterviewed three times during 1976 (in February, May, and September of 1976), mainly by telephone. Only the first wave personal-interview data (n = 2,405) were usually used in our analyses, due to the difference in activity patterns of those who stayed in this panel, compared to those who dropped out.
III. The 1965 Study The 1965 study interviewed a sample of over 2,000 American adults aged 18–65, who kept complete diaries of their activities for a single day, mainly between November 1 and December 15,1965, but also in the winter and spring of 1966. The sample was deliberately chosen to be an urban and employed one in order to conform to the guidelines of the multinational study of which it was a part (Szalai et al., 1972). Thus residents of nonSMSAs (areas with no city greater than 50,000 population) were excluded, as well as residents of households in which no member aged 18–65 was part of the labor force; farmers were also excluded. Respondents were randomly assigned to fill out diaries on a weekday or on a weekend. Of the total sample, 1,244 adults were part of the national urban sample; another 788 adults came from the city of Jackson, Michigan, and its suburbs. Although showing basically the same patterns as the national study, the Jackson data have not been included in the national results in order to make the trend comparisons more precise. The field procedures involved the " tomorrow" approach; that is, the interviewer contacted the respondent and conducted a brief " warm-up" interview on the first day and left the diary for the respondent to enter the next day's activities. The interviewer returned to the respondent’s home on the subsequent day (i.e., the day after " tomorrow") to ensure that the diary had been filled out correctly and to fill in any missing parts if it had not.
IV. Comparison of the Three Studies All three studies were based on strict probability sampling methods across the nation. Only the 1985 study was spread across the entire year.
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Moreover, the 1985 national data were mainly collected by prospective mail-back diaries, while the 1975 study employed the retrospective recall of activities done " yesterday." The 1965 and 1975 studies had somewhat higher overall response rates (72%, 72%), although not much higher than the telephone portion of the national study (67%). The 1985 study had more than twice the number of adult respondents over age 18 than the 1975 study (n = 5,358 vs. 2,409). The 1985 national study had more spread across the year and across days of the week, while the 1975 study oversampled Sundays and undersampled Saturdays. All studies used open-end diary entries across the full 24 hours of a single day and the same basic code for diary activities, although the 1975 and 1985 studies employed more than twice as many activity codes. In addition to these U.S. national studies and the two studies from Jackson, Michigan, we also have made use of two other recent diary studies from rather large representative samples. The first is the 1987-1988 statewide study of California, in which 1,762 respondents aged 12 and older gave retrospective diary accounts of what they did "yesterday" (Wiley & Robinson, 1991). The second was the national diary studies conducted in Canada with nearly 10,000 respondents, as described in the text. Like the 1985 University of Maryland study, both of these studies were conducted by telephone and were done across the year. The comparative results generally show remarkably similar ways of spending time in the two countries.
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