Time-use surveys are internationally highly valued methods for capturing daily behaviour. Their combination of questionnaires and paper-and-pencil time-diaries (among others in Europe) or telephone interview yesterdaymethod (in USA) through which respondents (re)construct their daily activities (i.e. what and when) together with contextual information (i.e. with whom and where) is both its strength as well as its weakness. This weakness stems from the high (personnel) costs involved in conducting time-use surveys, costs that can largely be reduced by switching to an online method. However, recent experimenting with online time-use surveys a) jeopardizes the hard work of harmonizing international time-use surveys and b) never truly copied or implemented the methodology of the paper-and-pencil time diaries let alone added additional features that improve the 'old' method. After having received a substantial grant we took the challenge to translate this existing method to an online method a) without loosing its strengths, b) with adding additional features that enrich the data even more, and c) with automated processes that reduce personnel and processing costs. In this contribution we a) reveal our method and its modular design and automated processes, b) provide preliminary results of the quality and response of the population pilot study (n invited ≈40,000), c) evaluate our effort, d) challenge others to comment and collaborate on our methodology in order to end up with a (new) standardized methodology for online diary studies that allows cross-national comparisons, and e) reflect on future possibilities and initiatives that serve the imminent online diary methodology.
The question when people work is almost always reduced to the question how much people work on (non-)standard working hours. In this contribution, we applied optimal matching techniques using Belgian data from a weekly work grid (n ¼ 6330) to identify individuals' work timing patterns, offering a richer analytical approach than most previous studies on (non-)standard work time. Results show that such analysis captures much more and much more relevant variation in the timing of work than simple questions. Three general and 10 more detailed weekly work patterns are identified based on two dimensions of paid work: the number of hours worked and the percentage of hours worked on non-standard periods of time. Additional analyses show that men's work patterns depend only on job characteristics. For women, work patterns are also explained by socioeconomic factors including education, presence of working partner and presence of children.
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