TY - CHAP
T1 - Research Methods for Studying Daily Life: Experience Sampling and a Multilevel Approach to Study Time and Mood at Work
T2 - Experience Sampling and a Multilevel Approach to Study Time and Mood at Work
AU - Portell, Mariona
AU - Hogarth, Robin M.
AU - Cuxart, Anna
N1 - Funding Information:
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].
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85070107514
U2 - 10.1007/978-3-030-22895-8_4
DO - 10.1007/978-3-030-22895-8_4
M3 - Chapter
SN - 1868-4394
VL - 164
T3 - Intelligent Systems Reference Library
SP - 69
EP - 94
BT - Intelligent Systems Reference Library
PB - Springer Science and Business Media Deutschland GmbH
ER -