Social Indicators Research

, Volume 119, Issue 3, pp 1517–1534 | Cite as

Extracting More Knowledge from Time Diaries?

  • Mattias HellgrenEmail author


Time-use diary data convey information about the activities an individual was engaged in, when and for how long, and the order of these activities throughout the day. The data are usually analyzed by summarizing the time used per activity category. The aggregates are then used to determine the mean time use of a mean individual on an average day. However, this approach discards information about the duration of activities, the order in which they are undertaken, and the time of day each activity is carried out. This paper outlines an alternative approach grounded in the time-geographic theoretical framework, which takes the duration, order, and timing of activities into consideration and thus yields new knowledge. The two approaches to analyzing diary data are compared using a simple empirical example of gender differences in time use for paid work. The focus is on the effects of methodological differences rather than on the empirical outcomes. The argument is made that using an approach that takes the sequence of activities into account deepens our understanding of how people organize their daily activities in the context of a whole day at an aggregate level.


Time-geography Time-use Methodology Daily life Sequence analysis 



The author would like to thank Kajsa Ellegård and the TEVS seminar group at the Department of Thematic Studies—Technology and social change at Linköping University as well as the anonymous referees for valuable comments and suggestions, which have greatly improved the article.


  1. Abbott, A. (1990). A primer on sequence methods. Organization Science, 1, 375–392.CrossRefGoogle Scholar
  2. Abbott, A. (1995). Sequence analysis: New methods for old ideas. Annual Review of Sociology, 21, 93–113.CrossRefGoogle Scholar
  3. Abbott, A., & Tsay, A. (2000). Sequence analysis and optimal matching in sociology: Review and prospects. Sociological Methods & Research, 29, 3–33.CrossRefGoogle Scholar
  4. Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  5. Ås, D. (1978). Studies of time use: Problems and prospects. Acta Sociologica, 21, 125–141.CrossRefGoogle Scholar
  6. Baxter, J. (2011). Flexible work hours and other job factors in parental time with children. Social Indicators Research, 101, 239–242.CrossRefGoogle Scholar
  7. Beck, M. E., & Arnold, J. E. (2009). Gendered time use at home: an Ethographic examination of leisure time in middle-class families. Leisure Studies, 28, 121–142.CrossRefGoogle Scholar
  8. Bejerholm, U., & Eklund, M. (2006). Engagement in occupations among men and women with schizophrenia. Occupational Therapy International, 13, 100–121.CrossRefGoogle Scholar
  9. Bendixen, H. J., & Ellegård K. (2013). Occupational therapists’ job satisfaction in a changing hospital organisation: A time-geography-based study. Work. doi: 10.3233/WOR-121572.
  10. Bianchi, S., & Robinson, J. (1997). What did you do today? Children’s use of time, family composition, and the acquisition of social capital. Journal of Marriage and Family, 59, 332–344.CrossRefGoogle Scholar
  11. Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. The Annual Review of Psychology, 54, 579–616.CrossRefGoogle Scholar
  12. Bonke, J. (2005). Paid work and unpaid work: Diary information versus questionnaire information. Social Indicators Research, 70, 349–368.CrossRefGoogle Scholar
  13. Boone, J., Sadreih, A., & van Ours, J. (2009). Experiments on unemployment benefit sanctions and job search behaviour. European Economic Review, 53, 937–951.CrossRefGoogle Scholar
  14. Brown, J., Broom, D., Nicholson, J., & Bittman, M. (2010). Do working mothers raise couch potato kids? Maternal employment and children’s lifestyle behaviours and weight in early childhood. Social Science and Medicine, 70, 1816–1824.CrossRefGoogle Scholar
  15. Brown, J., & Dunn, P. K. (2011). Comparisons of tobit, linear and Poisson-gamma regression models: An application of time use data. Sociological Methods & Research, 40, 511–535.CrossRefGoogle Scholar
  16. Claessens, B. J. C., van Eered, W., Rutte, C. G., & Roe, R. A. (2010). Things to do today…: A daily diary study on task completion at work. Applied Psychology, 59, 273–295.CrossRefGoogle Scholar
  17. Cohen, D. J., White, S., & Cohen, S. B. (2011). A time use diary study of adult everyday writing behavior. Written Communication, 28, 3–33.CrossRefGoogle Scholar
  18. Dribe, M., & Stanfors, M. (2009). Does parenthood strengthen a traditional household division of labour? Evidence from Sweden. Journal of Marriage and Family, 71, 33–45.CrossRefGoogle Scholar
  19. Dunn, P. K. (2013). tweedie: Tweedie exponential family models. R package version, 2(1), 7.Google Scholar
  20. Ellegård, K. (1994). Att fånga det förgängliga: Utveckling av en metod för studier av vardagslivets skeenden [Capturing the perishable: Development of a method for studies of the course of events of everyday life]. Occasional Papers/Department of Human and Economic Geography, School of Economics and Commercial Law 1994:1. Gothenburg, Sweden: Gothenburg University.Google Scholar
  21. Ellegård, K. (2006). The power of categorisation in the study of everyday life. Journal of Occupational Science, 13, 37–48.CrossRefGoogle Scholar
  22. Eurostat. (2009). Harmonised European time use surveys 2008 guidelines. Luxembourg: Office for Official Publications of the European Communities.Google Scholar
  23. Fisher, K., Bennett, M., Tucker, J., Altintas, E., Jahandar, A., Jun, J., & Other members of the Time Use Team (2009). Time Use studies. Accessed 12 December 2012.
  24. Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37.Google Scholar
  25. Gershuny, J. (2011). Time-use surveys and the measurement of national well-being. Newport, UK: Office for National Statistics.Google Scholar
  26. Gershuny, J. (2012). Too many zeros: A method for estimating long-term time-use from short diaries. Annals of Economics and Statistics, 105(106), 247–270.Google Scholar
  27. Hägerstrand, T. (1970a). Tidsanvändning och omgivningsstruktur [Time use and context]. In SOU 1970:14 Urbaniseringen i Sverige, en geografisk samhällsanalys [The urbanization in Sweden, a geographic social analysis]. Sweden: Offentliga Förlaget.Google Scholar
  28. Hägerstrand, T. (1970b). What about people in regional science? Papers in Regional Science, 24(1), 6–21.CrossRefGoogle Scholar
  29. Hägerstrand, T. (1973). The domain of human geography. In Richard J. Chorley (Ed.), Directions in geography (pp. 67–87). London: Methuen.Google Scholar
  30. Hägerstrand, T. (1974). On socio-technical ecology and the study of innovations. Rapporter och notiser nr 10. Lund, Sweden: Lund University, Kulturgeografiska institution.Google Scholar
  31. Hägerstrand, T. (1989). Reflections on “What about people in regional science?”. Papers in Regional Science, 66(1), 1–6.CrossRefGoogle Scholar
  32. Juster, F. T., Ono, H., & Stafford, F. P. (2003). An assessment of alternative measures of time use. Sociological Methodology, 33, 19–54.CrossRefGoogle Scholar
  33. Kan, M. Y. (2008). Measuring housework participation: The gap between “stylised” questionnaire estimates and diary-based estimates. Social Indicators Research, 86, 381–400.CrossRefGoogle Scholar
  34. Kan, M. Y., & Pudney, S. (2008). Measurement error in stylized and diary data on time use. Sociological Methodology, 38, 101–132.CrossRefGoogle Scholar
  35. Krueger, A. B., & Mueller, A. (2010). Job search and unemployment insurance: New evidence from time use data. Journal of Public Economics, 94, 298–307.CrossRefGoogle Scholar
  36. Lesnard, L. (2010). Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns. Sociological Methods & Research, 38, 389–419.CrossRefGoogle Scholar
  37. Lindmark, A. (2010). Reliabilitet hos metoder för mätning av avstånd mellan sekvenser. Master’s thesis, Umeå Universitet, Umeå, Sweden.Google Scholar
  38. Millward, H., & Spinney, J. (2011). “Active living” related to the rural–urban continuum: A time-use perspective. The Journal of Rural Health, 27, 141–150.CrossRefGoogle Scholar
  39. Motiram, S., & Osberg, L. (2009). Gender inequalities in tasks and instruction opportunities within Indian families. Feminist Economics, 16(3), 141–167.CrossRefGoogle Scholar
  40. Otterbach, S., & Sousa-Poza, A. (2010). How accurate are German work-time data? A comparison of time-diary reports and stylized estimates. Social Indicators Research, 97, 325–339.CrossRefGoogle Scholar
  41. R Core Team (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Google Scholar
  42. Ricci, J., Jerome, N., Megally, N., Galal, O., Harrison, G., & Kirksey, A. (1995). Assessing the validity of informant recall: Results of a time use pilot study in peri-urban Egypt. Human Organization, 54, 304–308.Google Scholar
  43. Robinson, J., Martin, S., Glorieux, I., & Minnen, J. (June 2011). The overestimated workweek revisited. Monthly Labor Review, 134(6), 43–53. Google Scholar
  44. Schneider, D. (2011). Market earnings and household work: New tests of gender performance theory. Journal of Marriage and Family, 73, 845–860.CrossRefGoogle Scholar
  45. Smyth, G. (2013). statmod Statistical modeling. R package version, 1(4), 17.Google Scholar
  46. Spinney, J. E. L., Millward, H., & Scott, D. M. (2011). Measuring active living in Canada: A time-use perspective. Social Science Research, 40, 685–694.CrossRefGoogle Scholar
  47. Statistics Sweden. (2013). Nu för tiden En undersökning om svenska folkets tidsanvändning år 2010/11. Stockholm: Statistics Sweden.Google Scholar
  48. Stone, P. J. (1972). The analysis of time-budget data. In A. Szalai (Ed.), The use of time: Daily activities of urban and suburban populations in twelve countries. Den Haag, Netherlands: Mouton & Co.Google Scholar
  49. Studer, M., Ritschard, G., Gabadinho, A., & Müller, N. S. (2011). Discrepancy analysis of state sequences. Sociological Methods & Research, 40, 471–510.CrossRefGoogle Scholar
  50. Sullivan, O., & Gershuny, J. (2001). Cross-national changes in time-use: Some sociological (hi)stories re-examined. British Journal of Sociology, 52, 331–347.CrossRefGoogle Scholar
  51. Szalai, A. (1972). The use of time: Daily activities of urban and suburban populations in twelve countries. Den Haag, Netherlands: Mouton & Co.Google Scholar
  52. US Bureau of Labor Statistics. (2012). American Time Use Survey user’s guide: Understanding ATUS 2003 to 2011. Accessed 15 March 2013.
  53. Vrotsou, K. (2010). Everyday mining: Exploring sequences in event-based data. PhD thesis, Linköping University, Linköping, Sweden.Google Scholar
  54. Widén, J., & Wäckelgård, E. (2010). A high-resolution stochastic model of domestic activity patterns and electricity demand. Applied Energy, 87, 1880–1992.CrossRefGoogle Scholar
  55. Wu, L. (2011). Some comments on “Sequence analysis and optimal matching methods in sociology: review and prospects”. Sociological Methods & Research, 29, 41–64.CrossRefGoogle Scholar
  56. Yanos, P., West, M., & Smith, S. (2010). Coping, productive time use, and negative mood among adults with severe mental illness: A daily diary study. Schizophrenia Research, 124, 54–59.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Thematic Studies, Technology and Social ChangeLinköping UniversityLinköpingSweden

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