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4 Analysis and Exploration of Meaning and Outcomes in Connection with Time Use Data William Michelson

INTRODUCTION To collect data is not thereby to predetermine its use. The same data set may be used in a number of ways. One approach in data usage is that data are seen to represent directly a phenomenon of interest in its own right. A contrasting approach is for data to be used in an explanatory scheme on a topic that is separate from its explicit content or is at a more inclusive level of consideration. In the first approach, the data are used descriptively; in the latter, analytically. Many kinds of data are used both ways. Scales, for example, are used descriptively to assess the extent and types of prejudice against a group and analytically to learn the impact of education and social mobility on attitudes. In another example, ecological measures of housing distribution can show how segregated a group might be and at the same time be found in analyses of how housing patterns vary among groups with different

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William Michelson Centre for Urban and Community Studies, University of Toronto, Toronto, Ontario, Canada M5S 2G9. Time Use Research in the Social Sciences, edited by Wendy E. Pentland, Andrew S. Harvey, M. Powell Lawton, and Mary Ann McColl. Kluwer Academic/Plenum Publishers, New York, 1999.

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economic strategies. A third kind of data, social network data, can on the one hand be of interest in and of itself, in terms of illustrating the forms and implications of human contact, and also as a dependent variable indicating certain aspects of community thought to vary according to the nature of the built environment. This chapter takes as its foundation that time use techniques may be employed both ways.

TWO PURPOSES OF TIME USE RESEARCH: DESCRIPTIVE PATTERNS AND INDICATORS OF MEANING Conventional employment of time use data has been to describe how people spend their time, often viewed according to country of residence or by sex (e.g., Szalai, 1972). Some trends over time have been from singlegroup studies to comparative national studies, and, more recently, to longitudinal studies. But in each case, the focus was primarily on the pattern of daily activity. In contrast, the subjective meaning of activities found in time use data, as well as the implications of the component aspects of time use data for outcomes derived from specific substantive hypotheses, are secondary (cf. Michelson, 1986). Despite this emphasis on the descriptive employment of time use data, there are precedents for more explicitly analytic use of these data. M책rtensson (1979), for example, notes how the round of life that people lead is in large part a function of the established opening and closing hours, and accessibility in space of the activities involved. In this view, while people's activity patterns can be viewed descriptively, they cannot be fully understood outside the framework of a larger set of factors. Cullen and Phelps (1978) made the point that to know what people have done is not to know the outcomes of these activities for them. They found, for example, that stress and annoyance are a function of unpleasant or difficult conditions, disruptions, and time conflicts. My own approach has been similarly unconventional. None of my studies has involved description of the time use of a group or aggregate for its own sake. In each case, time use has been considered a strategic indicator of behavior relevant to specific substantive concerns. Furthermore, the subjective aspects of such behavior have been critical for the understanding of these concerns and as a basis for eventually taking steps to improve difficult situations. I have come to see that time use data are valuable for more than the assessment of what people do every day. The several dimensions of time use files, variously including what people do (when and for how long), with whom, where, and with what subjective feelings


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or outcomes, are useful as measures of behavior and behavioral outcomes in a much wider range of applications. My experiences have given me confidence in the potential of time use techniques for the assessment of meaning and outcomes, not just patterns.

FOUR WAYS TO STUDY MEANING AND OUTCOMES In this chapter, I would like to discuss four ways I have used in which meaning and outcomes may be pursued with time use data for analytic inquiry. These are (1) sampling time with analytic intent, (2) contextual analysis of episodes, (3) direct measurement of subjective aspects, and (4) triangulation.

Sampling with Analytic Intent The central thread of my research has been on how people's everyday contexts impact upon certain aspects of their everyday lives. Of particular interest in this regard is physical environment: housing, neighborhoods, and urban infrastructure. Time use data are helpful in this kind of analysis as a systematic but normatively neutral measure of a number of complementary dimensions of what people do (cf. Hanson & Hanson, 1993). These data are typically seen as indicators of people's habits under the particular conditions of opportunity presented by the contexts of interest. They are in this sense dependent variables in a chain of causality that involves physical and/or social contexts. Even when people select a context in which to live or work in order to optimize their own current or latent behavior patterns, a process known as self-selection, desired behavior still requires the opportunity presented by the given context for realization, making observed in situ behavior at least in partly dependent on the context in which observed (cf. Michelson, 1991). In analytic research that tests whether behavioral expectations held by people, found in the professional literature and/or held by practitioners, are well-founded, sampling procedures that differ from conventional practices are pursued. A representative sample of a city or nation is inefficient because it does not focus on the particular variations among settings that underlie the analysis; typically, one has little a priori knowledge of exactly how the people sampled live. Many of them have home environments that are irrelevant to the analysis. Furthermore, at no point is there any reason for the purpose of the study why the time-use of the entire sample should be studied descriptively. Thus, one samples according to the independent variables that will be tested.


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I have done this in two studies of housing. One (Michelson, 1977) was an analysis of congruence between housing type (contrasting singlefamily homes and high-rise apartments) and daily activities among families with children (controlling also for central city and suburban locations).1 My colleagues and I attempted to assess both the amount of selfselection that underlay choice of housing type and location as well as the patterns of behavior that emerged in specific settings. Instead of doing some kind of random sampling, we made arrangements with a large network of appropriate landlords and real estate agents to make a 100% sample of appropriate households that at that time were signing agreements to rent or buy apartments and houses within particular geographic zones. In a longitudinal design, members of households were interviewed while in their previous homes and at three successive periods after settling into their new homes. Hence, we were able to assess the presence and extent of a number of behaviors hypothesized as relevant to selection of, and living in, particular combinations of housing location in a way that facilitated inferences, in the absence of the many irrelevancies that would have accompanied a fully representative sample. Thus, the sample consisted entirely of families moving to the types of environments that were pertinent to the study's hypotheses: downtown high-rise apartments (109 families), downtown single-family houses (94), suburban high-rise apartments (286), and suburban single-family houses (272) (Michelson, 1977, p. 81). With this sampling technique, it was possible to examine people's behavior in their premove residences, to see if there were preexisting differences linked to the contexts chosen. Then, under the longitudinal perspective, it was possible to note postmove changes, to see if particular behavior patterns strengthened or emerged under specific environmental conditions. Thus, we could show that not only did those choosing to live downtown have much more compact daily travel patterns than those choosing suburban housing, but also that the settings conveyed remarkably different implications for daily travel for those newly moving to them. Among those newly moving to downtown locations, about 60% of husbands and wives alike decreased their commuting time. The reverse was true for the new suburbanites, with 60% of wives and 50% of husbands encountering increases. Though these results are not particularly surprising, what we could establish as a consequence of sampling procedures that anticipated the analysis was that what people were doing was a reflection of their contexts, not just their established habits. 1

This research was carried out with funding from Canada Mortgage and Housing Corporation and the Canada Council.


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In a second project, I was interested to assess the extent that behavioral objectives behind the design and organization of a growing number of new housing projects in Sweden were realized as a consequence of the innovations involved.2 It is obvious that the sampling in this case had to focus on buildings that exemplified these innovations. But in order to have some degree of inference, it was desirable also to match the "experimental" housing with other areas that were similar in important respects (e.g., location, cost, year of construction) but lacking the innovations hypothesized as critical to the behavioral objectives—control groups. In this case, samples were drawn from four experimental housing areas and from four conventional (control) areas, matched on a one-to-one basis with the experimental areas. Some of the experimentation had to do with physical design. Not every experiment had the same components. Therefore, our small sample of areas was set to include some of the different physical design hypotheses, all intended to facilitate the same kinds of outcomes: enhanced social contact with neighbors, easier child care, lessened household work, and greater intergenerational interaction. These included a combination of trading off marginal dwelling unit space for shared spaces. Some of the shared spaces were relatively undefined; others were clearly designed for meeting and lounging, and still others were for specific uses, such as for crafts or sports. Varying these attributes over different areas enabled some assessment of which attributes appear to foster the hypothesized outcomes, as measured through time use indicators. Other forms of experimentation were with social organization. Two forms of organization besides ordinary tenancy were employed for this purpose: cooperative ownership, and what has been called collective housing or cohousing (McCamant & Durrett, 1989). The sampling thus gave the potential to explore the extent that physical and/or social innovations (and which ones of these) help create conditions favorable for the desired outcomes. In time, and because these attributes varied across the areas sampled, it became clear that these types of innovation had the greatest effects in combination (i.e., where the physical and the social innovations reinforced each other) (cf. Michelson, 1992,1993). Some analytic techniques by which these were teased out of time use data are subsequently examined. At present, I am working with a research team concerned with the dynamics and impacts of telecommuting (conducting paid employment at 2Funding

for this research was provided by Sweden's Building Research Council, in conjunction with Professor Birgit Krantz and the Department of Building Functions Research, University of Lund, and from the Social Sciences and Humanities Research Committee of the University of Toronto.


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home, with the support of electronic forms of communication). Reanalyzing the results of prior time use studies undertaken for entirely other purposes led to a constant finding of approximately 5% or fewer adult workers spending any substantial time at home doing paid work. Once again, it appears crucial that sampling be conducted to identify those who are actually involved in the phenomenon, variations in this practice, and even variations in the geographic areas in which telecommuting is practiced. The representative sample may be useful for describing a population, but this is not necessarily the case at all when analyses of specific contexts or situations are necessary. In short, innovative sampling can play an essential role in how time use data can help to indicate outcomes.

Episode Analysis The dynamics of behaviors assessed as hypothesized outcomes can be examined more closely by focusing on the interaction among the different dimensions of time use at the episode level. Again, this is different from conventional use of these data. In assessing the time use of national aggregates, for example, it is customary to add up the time devoted by an individual to each given activity over the period of time assessed (Day week, year). These individual totals are then aggregated according to the categories of variables for which breakdowns are desired: sex, age, education, rural-urban location, and so on (Kirkjavainen, et al, 1992; Koslov, 1994). Although the activity is the single, most pursued dimension, such aggregated duration totals have been calculated also for the " with whom" and "where" dimensions (Michelson, 1992). These last two dimensions give some knowledge of how much time people in a given country (or subcategories of people) spend in certain kinds of company or in categories of place or location—social indicators of value. When viewed longitudinally these provide potentially pertinent measures of change. Some of the great societal implications of the enormous trend in North America and Europe toward paid employment by women within the childbearing years come from changes in where and with whom the great majority of women spend the bulk of their waking day (Michelson, 1994). However, if the goal is to examine the impact of specific contexts, then it is desirable to assess the extent that particular attributes of the context can be related to the presence or absence of the behavior(s) hypothesized to take place there. For example, in the Swedish study noted in the preceding section, we could assess through straightforward analysis whether people in the experimental housing areas spent more daily time in the


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company of their neighbors than did those living in the control areas. It is of more analytic value, though, to take the relationship to a greater degree of complexity, to ask to what extent differences in neighbor contact are a function of physical and/or social features intended to foster such contact, and, if so, which one(s). Such more detailed assessment can be achieved by looking at the interaction of time use dimensions at the level of the individual episode of activity. For example, we examined not just how much time people spent with their neighbors, but also how much time was spent at the episode level with whom and where, and with whom doing what. Only once we had calculated duration for the linkages binding two or more dimensions of time use was it desirable to aggregate each of these figures into a daily total and eventually take mean values of those totals among people in our analytic categories (e.g. experimental VS. control areas). With measures thereby based on the joint analysis of episode components, it was possible to show that among the several organizational and spatial innovations, those that eventuated in communal dining (i.e., providing the opportunity to eat weekday dinners cooked in advance by rotating teams in a collective dining room) accounted for a major part of the increased neighbor contact among adults. This system of activities was based on innovations in both social organization and physical design. In contrast, simply covering what would otherwise be outside space with a glass roof does not account for any great amount of contact among adults (Michelson, 1993). Another example of the merit of episode analysis is related to the subject of telecommuting. Without the advantages of a dedicated sample of telecommuters, it is necessary to identify those who work at paid employment at home from among more diverse samples. Analysis of time use data at the episode level for this purpose involves the identification of people who conduct paid employment (i.e., the activity) at home (i.e., the place). If a certain minimum of time per day is required to be considered as telecommuting (rather than more traditional homework), then the aggregate total of time devoted to such episodes per day becomes pertinent. In one such analysis of an existing, diverse sample of time use data I recently conducted, nearly 30 women were identified as doing at least an hour per day of paid employment at home. But the same kind of episode analysis that made it possible to find such people also showed that most of them spent even more time in addition at a formal place of employment. Such attention to the contingency of activity and place suggests caution in our interpretation of the extent that the trend to telecommuting has gone to date. Everybody who has a home office does not work exclusively or even predominantly at home.


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An approach proposed by Harvey (1984) has some potential for episode analysis. He suggests taking a combined code, made up from the codes from all the dimensions of an episode at once, as a potentially useful variable. He calls this long numerical code a hypercode. The analytic approach is to analyze which are the modal hypercodes in samples representing differing contexts, conditions, or times. If the qualitative nature of activity, made up as it is from combinations of behavior, place, and persons present (at the least), is expected to be different under different circumstances, then this might be an efficient way of assessing the more prominent examples of these differences. It does not substitute for assessment of the validity of more subtle factors that are part of substantive hypotheses. But the two approaches are useful for different purposes, as well as in combination. By whatever approach, the analysis of interaction among time use dimensions at the episode level enables the fine-tuning of hypothesized outcomes.

Direct Measurement of Subjective Aspects The preceding approaches to assessment of outcomes may be steps to greater analytic power, but they nevertheless offer little or no more insight as to how respondents feel about their behavior than is gleaned from conventional analyses. Feelings can be addressed explicitly by adding subjective dimensions to the more common objective ones for each episode of activity. As noted earlier, there are some precedents for such procedures, but these are nonetheless done more as the exception than as the rule. Although it is speculative to guess why not, my own hunch is that such dimensions are felt to be less essential to agency mandates, more timeconsuming and hence costly, less easy to justify in traditional practice, and with more potential for sensationalism in the public press. Nonetheless, understanding how people experience their daily activity patterns in general and how they evaluate those contexts that are of immediate pragmatic concern should be directly relevant to all users of time use data for applied purposes, points made and illustrated cogently most recently by Gershuny (1994). Accounting for what people do is by no means equivalent to learning how people regard what they do. In addition, a full picture of the impact of a context on people requires one or more subjective dimensions. The use of subjective dimensions in a time use study can be illustrated in a study of maternal employment in Toronto (Michelson, 1985). This study examined the extent and ways that the everyday lives of women and their families are impacted by paid outside employment; and it included


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the perspectives of the community contexts in which employment and family life occur, on the one hand, and subjective outcomes, on the other. A random sample of mothers of children ages 14 and younger, which was stratified by whether the women did or did not live with a partner, and according to what child-care facilities were used to care for younger children, was utilized.3 Two additional dimensions were added to the time use protocol. Women were asked to rate each behavioral episode on a scale of 1–7 to the extent that they felt tense or relaxed when doing it, and also to the extent that they felt they had anywhere from no choice to free choice in doing it. This enabled assessment of women's subjective evaluations of particular episodes (and the contexts in which they occurred), as well as durationweighted, aggregated measures of subjective sides of both particular kinds of activities and the waking hours of the weekday concerned. Including this subjective dimension led to a number of observations that would otherwise not have been possible (Michelson, 1985,1986). 1. In which daily activities do respondents feel relatively tense? Both men and women experience their employment activities as relatively tense, but women experience a greater range of daily activities as tense than do men. 2. Why is travel noted as sensitive and potentially tense for women? It falls between two kinds of activity that are viewed as both involuntary and of high salience for employed mothers: child care and paid employment. While the activities on both sides of the commuting trip demand punctuality and engagement, the conditions and timing of the trip itself are often outside the control of the commuter and can add to whatever tension is associated with work and home responsibilities. To add to this, the female head of household is often forced to deal with this situation in second place regarding the allocation of family transportation resources. 3. As women increasingly undertake paid employment, do their daily life patterns increasingly approximate those of men? If one were to look strictly at the objective aspects of time use, this appears to be the case. Women with full-time jobs resemble their husbands in terms of how the length of their workdays is positively related to the amount of time spent traveling, but related negatively to every other kind of everyday activity. Nonetheless, the continued existence of traditional gender roles, which This project was made possible by a contribution from the Welfare Grants Directorate of Health and Welfare Canada, as well as a contract for analysis from the U.S. Department of Transportation.

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may be narrowing but not as yet disappearing, puts these daily activities into a context in which women experience the same pattern of activities very differently than do men. Using the subjective dimensions in time use studies helps get out of the trap of potentially viewing what people do as positive simply because we can document that they do it. As Cullen (1978) long ago pointed out, people form habits as a suboptimization response to the contexts that constrain behavioral possibilities. 4. How do people's perception of tension relate to the use of different travel modes? Putting the commute into the context of other daily responsibilities helps us understand why people view the automobile favorably. However, fixed rail transit involves far less tension than bus travel, due to its relative speed, comfort, and reliability. 5. If we instead view tension in terms of a weighted daily aggregate, how does this vary by gender? Men and women have nearly identical weighted daily aggregates. Although women have more activities that contribute to high tension, their husbands typically spend more time at work, which is their major source of tension. Hence, the overall outcomes appear equal (by this measure!). 6. Do single parents (in this study, females) report greater feelings of tension than parents with partners? This was our hypothesis. The data presented a consistent pattern that was exactly opposite. On another occasion, a time use inquiry was employed in connection with the programming of a new building for an educational institution. It was not sufficient to find out simply what people did in their existing building, because this would have precluded consideration of activities that were constrained or even precluded by the inadequacies of the status quo. Even when such activities could be documented as frequent and of long duration, this would not necessarily mean that the spaces in which they occurred were supportive, or that the activity pursued there was considered enjoyable or efficient. Therefore, with respect to each episode of on-site behavior, we inquired as to whether the space in which it occurred was adequate for the task, and, if not, what might be. What we found was that the academics in this institution spent the greater part of their time not in classes or doing solo preparation, writing, or research, but in meetings. Furthermore, they indicated that the existing spaces, in the form largely of individual offices and a small number of heavily booked classrooms, were dysfunctional for the kinds of small-group meetings they found necessary. Creation of many rooms for small groups and seminars was found desirable. Documentation of this need might not have been done as explicitly without the extra subjective or evaluative dimension of the time use protocol.


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Triangulation Another technique to optimize understanding of meaning and outcomes is triangulation. The term triangulation comes from the points of a triangle, in which two points converge onto a third: two measures bearing on a single phenomenon. The literal derivation of triangulation, however, does not restrict the number of measures involved to two. Two or more is more accurate. ln the current discussion, triangulation refers to the use of complementary data or data-gathering techniques to clarify and validate such inferences as might be made with time use data. While by no means excluded from conventional time use inquiries, triangulation is nonetheless more difficult to employ in descriptive studies because the additional items and/or techniques would have to cover the entire spectrum of everyday behavior. Furthermore, insofar as time use data are already time-consuming and expensive both to collect and to analyze, triangulation across the board leads to dramatic increases in costs of all kinds, to researcher and respondent alike. In contrast, when time use is an indicator in an otherwise focused analytic study, the points on which triangulation might be desirable and even critical are fewer and more evident. Let me provide a few more examples from my own research experiences. In the study on implications of maternal employment (Michelson, 1985), not only were such differences in everyday behavior patterns as accompanied different degrees of paid employment of interest, but the outcomes of such patterns were considered of particular importance for social policy. The use of subjective dimensions of time use episodes described in the previous section was intended to assess at least one kind of outcome. An aggregate measure of outcome took the form of daily mean tension, with weights assigned to the tension levels of specific episodes directly according to their durations. Hence, if one activity took twice as long as another, its tension level would be weighted twice that of the second activity in the daily mean. Although this measure appeared logical and appropriate, it gives more confidence to examine its contribution by means of additional empirical measures rather than to leave this as an assumption. One such attempt was the application of what we later called busy scales.4 We asked respondents to indicate the extent that they felt they had just enough, not enough, or too much time for what they needed or wanted to do during specific periods (eg., day, week, lifetime) and for Linda Hagarty and Susan Hodgson helped develop them, and Linda Naiditch suggested the name.

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particular persons and purposes (e.g., children, husband, friends, self; employment, home, medical care). Zero was the point of equilibrium between supply of and demand for time, while +10 was the extreme for an excess of demand over supply (i.e., highest time pressure) and –10 represented the less common extreme of " time on my hands." The results of these scales were in fact extremely helpful in both validating and clarifying the subjective side of the time use data. Correlations were calculated between each person's mean tension score and all of the busy scale scores, one by one. The correlations were positive and significantly high in most cases, indicating that our mean tension values were indeed tapping the intended phenomenon. However, the relative level of the correlations helped to specify more clearly the meaning of our aggregate tension measure. The highest correlations with mean tension were generated by the busy scale scores dealing with the weekly time frame and for the time that women felt they had available for themselves. The pattern of results indicated that women (in general, and employed women in particular) willingly give the time needed to their husbands, their children, and their jobs; what gets most squeezed in the whole picture is whatever time is left for themselves. Triangulation was particularly helpful in making this interpretation. A similar form of triangulation was helpful in validating the unexpected finding that single mothers experienced less daily tension than did their married counterparts. Once our initial findings were tabulated, we invited respondents to a choice of focus groups to hear and comment upon these preliminary results. I specifically asked about the validity of this particular finding, in the eyes of the respondents. In return, I received a spontaneous, consistent, and apparently definite round of support for the subjective time use data. The man of the house was perceived as a major additional person with whom to deal in the course of the day. While it should be noted that the results from another question in the protocol indicated that the married women professed to be happier with their lives, the men still created the conditions for a higher level of procedural tension during the typical weekday. Still another example of triangulation may be drawn from the study of Swedish experimental housing. In this study, samples of families living in the experimental and control areas were given time use diaries and conventional, self-administered questionnaires on complementary information. One feature of the time use diaries was the provision of precodes for responses, which included specification of on-site spaces where activities might take place as part of the "where" dimension. Nonetheless, time use information, while extremely informative in many ways, did not give much of a qualitative feeling for the nature of social contact in public spaces within the respective residential areas.


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To provide an additional spotlight on this phenomenon, as well as to sharpen what was learned from the time use diaries, we conducted systematic observation of the public spaces in these residential complexes (Michelson, 1994). The observations captured what people were doing when together, the sex and approximate age groupings of those observed, the location where found, and the size and composition of the groups observed. This research technique typically validated what was learned from the time diaries. But it also made several findings much more clear. For example, the one residential area with the glassed-over public space produced time use data (taken in midwinter) showing that children had more contact with their neighbors than did those in the nearby control area but that this was not so among adults. The observations made clear from the start that despite providing protection from darkness and the elements, the glass roof did not alter the fact of winter chill. It was cold under the roof! Parents sent their children out to play whenever this was possible. With the roof, parents could send out the children in inclement weather and after dark, if only because what was outside the dwelling unit could still be inside the overall structure. In the absence of well-defined activity spaces, the children ran around and played noisily in various places, often making what adults considered as trouble. This was a situation that visibly annoyed adults, who then had even less incentive to stand or sit outside their own homes in the cold. The observations produced some feeling for the dynamics behind the more parsimonious yet objective and systematic time use data. A second example relates to an experimental area featuring an extensive ground-floor service area for residents, run by the local municipality. This included a convenience store, concierge desk, tuck shop, library, party hall, sauna, playroom, and laundry facility. The time use data showed very little social contact among residents. The observations showed intensive contact, but of short duration, as residents shopped at the convenience store. Unlike other bases of contact (e.g., breaking bread together) that appear to widen contact among residents into other spheres, it appears that contact in this kind of commercial context was extremely segmented and did not foster continuing association.

CONCLUDING COMMENTS There are undoubtedly many more ways to facilitate the exploration of meaning and outcomes in connection with time use data. I have presented just a few that have recurred in my own research. But beyond the literal examples of such techniques is the message that this pursuit is certainly possible but requires a different outlook on potential objectives


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for the collection of time use data. These techniques may take different directions than the conventional approaches of the past. But as the potential gains to be made with time use data become more understood, these directions may point to the paths we might wish to take more frequently.

REFERENCES Cullen, I. (1978). The treatment of time in the explanation of spatial behavior. In T. Carlstein, D. Parkes & N. Thrift (Eds.), Human activity and time geography (pp. 27–38). New York Halstead Press. Cullen, I., & Phelps, E. (1978). Patterns of behavioral responses to the urban environment. In W. Michelson (Ed.), Public policy in temporal perspective (pp. 105–181). The Hague: Mouton. Gershuny, J. (1994). Tie use, quality of life and process benefits. In N. Kalfs & A. S. Harvey (Eds.), Fifteenth reunion of the international association for time use research (pp. 170–186). Amsterdam: NIMMO. Hanson, S., & Hanson P. (1993). The geography of everyday life. In T. Gaerling & R. G. Golledge (Eds.), Behavior and environment: psychological and geographical approaches (pp. 249–269). New York North-Holland. Harvey, A. S. (1984). Special time budget measures. In A. S. Harvey, A. Szalai, D. Elliott, P. Stone, & S. Clark (Eds.), Time budget research (pp. 116-135). Frankfurt: Campus Verlag. Kujavainen, L. et al. (1992). Housework time in Bulgaria and Finland. Helsinki: Statistics Finland. Koslov, G. (1994). Aspects of paid and unpaid work as reflected in the provisional findings of Israel's time budget survey—l991/92. In N. Kalfs & A. S. Harvey (Eds.), Fifteenth reunion of the international association for time use research (pp. 89–102). Amsterdam: NIMMO. Mårtensson, S. (1979). On the formation of biographies. Lund Studies in Geography No. 47. Lund: C. W. K. Gleerup. McCamant, K., & Durrett, C. (1989). Cohousing in Denmark. In K. Franck & S. Ahrentzen (Eds.), New households, new housing (pp. 95–126). New York Van Nostrand Reinhold. Michelson, W. (1977). Environmental choice, human behavior, and residential satisfaction. New York Oxford University Press. Michelson, W. (1985). From sun to sun: Daily obligations and community structure in the lives of employed women and their families. Totowa, NJ: Rowman & Allenheld. Michelson, W. (1986). The empirical merger of objective and subjective aspects of daily life. In D. Aas et al. (Eds.), Time use studies: dimensions and applications (pp. 176–188). Helsinki: Statistics Finland. Michelson, W. (1991). Built environment as a mediator of human intentions. In T. Niit, M. Raudsep, & K. Liik (Eds.), Environment and social development (pp. 98–107). Tallinn: Tallinn Pedagogical Institute. Michelson, W. (1992). Measuring the experience of everyday life in the comparison of large cities. In B. Hamm (Ed.), Progress in social ecology (pp. 245–259). New Delhi: Mittal. Michelson, W. (1993). Grounding time-use in microspace: Empirical results. Social Indicators Research, 30, 121–137. Michelson, W. (1994). Measuring new objectives in suburban housing. Research in Community Sociology,4, 253–269. Patrushev, V. D. (1982). Satisfaction with free time as a social category and an indicator of way of life. In Z. Staikov (Ed.), It's about time (Ch. 17). Sofia, Bulgaria: Jusautor. Szalai, A. (1972). The use of time. The Hague: Mouton.

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William Michelson INTRODUCTION Toronto, Ontario, Canada M5S 2G9. Time Use Research in the Social Sciences, edited by Wendy E. Pentland, Andr...

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