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Research Methods for Studying Daily Life: Experience Sampling and a Multilevel Approach to Study Time and Mood at Work

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The Temporal Structure of Multimodal Communication

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Abstract

The Experience Sampling Method (ESM) allows the examination of ongoing thoughts, feelings and actions as they occur in the course of everyday life. A prime benefit is that it captures events in their natural context, thereby complementing information obtained by more traditional techniques. We used ESM to study time and mood at work. Our data were collected by sending 30 text messages over 10 working days to each of 168 part-time workers. On each occasion, respondents assessed their mood. We explored the joint effects of three sets of variables: activities in which people are engaged; individual differences; and time (i.e., when mood is measured). Since the data in our study can be thought of as being collected at two levels, we applied techniques of hierarchical linear models. The results indicated that activities were significant but no systematic individual differences were detected. There were some small diurnal effects as well as an overall “Friday effect.” Lastly, the weather had little or no influence on self-reported mood state. We discuss the results in terms of their methodological implications for studying daily life.

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Notes

  1. 1.

    Seasonal effects of weather on moods and behavior have been documented (see, e.g., Smith 1979; Harmatz 2000).

  2. 2.

    The reference category used as a base for coding the dummy variables for different activities was “Housework, personal time organization, and managing funds.”

  3. 3.

    We calculated z-scores for each individual respondent such that the mean of each person’s mood judgments was 0 with a standard deviation of 1. This allowed us to categorize all observations/occasions as being positive or negative, i.e., whether they were above or below each individual’s mean mood score.

  4. 4.

    These results are robust to analyses assuming fixed or random coefficients.

  5. 5.

    These included: daily average temperature (C); precipitation (liters per square meter); rain (dummy variable, 1:yes; 0:not); daily sunshine (total number of hours); relative daily sunshine (percentage out of expected total hours); degree of cloudy at 7am (scale from 0 to 8); degree of cloudy at 1pm (scale from 0 to 8); daily solar radiation (watts per square meter); daily average of relative humidity (%); and daily average of barometric pressure (in hectoPascals, hPa). The data were obtained from the Servei Meteorolgic de Catalunya, Xarxa d’Estacions Meteorolgiques Automtiques (XEMA) del Valls Occidental and Observatori Fabra (Barcelona).

  6. 6.

    We use the term “reactivity” to mean a phenomenon that occurs when individuals alter their performance or behavior due to the awareness that they are being observed.

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Acknowledgements

This research was partially supported by Spanish Government grant [SEJ2006-27587-E/SOCI, DEP2015- 66069-P, and PSI2015-71947-REDT], as well as was partially supported by Generalitat de Catalunya [2014 SGR 971].

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Authors and Affiliations

  1. Universitat Autònoma de Barcelona, Campus de Bellaterra, 08193, Cerdanyola del Vallés, Spain

    Mariona Portell

  2. Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005, Barcelona, Spain

    Robin M. Hogarth

  3. Universitat Pompeu Fabra (retired professor), Ramon Trias Fargas 25-27, 08005, Barcelona, Spain

    Anna Cuxart

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  1. Mariona Portell
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  2. Robin M. Hogarth
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Correspondence to Mariona Portell .

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Editors and Affiliations

  1. Department of General and Applied Linguistics, University of Debrecen, Debrecen, Hungary

    Laszlo Hunyadi

  2. Institute of Philosophy, University of Debrecen, Debrecen, Hungary

    István Szekrényes

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Portell, M., Hogarth, R.M., Cuxart, A. (2020). Research Methods for Studying Daily Life: Experience Sampling and a Multilevel Approach to Study Time and Mood at Work. In: Hunyadi, L., Szekrényes, I. (eds) The Temporal Structure of Multimodal Communication. Intelligent Systems Reference Library, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-030-22895-8_4

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