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Leisure Time-Use Sequence Analysis

Leisure time-use sequence analysis treats a person's day not as a bundle of activity totals but as an ordered sequence of states, and asks which whole-day patterns of leisure recur across a population. It imports optimal matching -- the alignment technique Andrew Abbott and Angela Tsay reviewed for sociology -- into the study of time-use diaries: each day becomes a string of categorical states (sport, active leisure, passive leisure, work, sleep, and so on) sampled at regular intervals, and the dissimilarity between any two days is the minimum cost of editing one sequence into the other. Clustering the resulting dissimilarity matrix yields a typology of leisure days -- the active morning, the evening screen-leisure pattern, the fragmented weekend -- that preserves the timing and ordering of activity that simple duration tallies discard.

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Kilder

  1. Abbott, A., & Tsay, A. (2000). Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect. Sociological Methods & Research, 29(1), 3-33. DOI: 10.1177/0049124100029001001
  2. Cornwell, B., Gershuny, J., & Sullivan, O. (2019). The Social Structure of Time: Emerging Trends and New Directions. Annual Review of Sociology, 45, 301-320. DOI: 10.1146/annurev-soc-073018-022416

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ScholarGate. (2026, June 23). Leisure Time-Use Sequence Analysis (Optimal Matching of Daily Leisure Episode Sequences). ScholarGate. https://scholargate.app/no/sport-leisure-studies/leisure-time-use-sequence-analysis

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ScholarGateLeisure Time-Use Sequence Analysis (Leisure Time-Use Sequence Analysis (Optimal Matching of Daily Leisure Episode Sequences)). Hentet 2026-06-24 fra https://scholargate.app/no/sport-leisure-studies/leisure-time-use-sequence-analysis · Datasett: https://doi.org/10.5281/zenodo.20539026