Abstract
A fundamental issue pursued by researchers in positive psychology involves defining what constitutes a good life and understanding how individuals can create one. From the perspective of flow theory, “a good life is one that is characterized by complete absorption in what one does” (Nakamura and Csikszentmihalyi in Handbook of positive psychology. Oxford, New York, 2002). Born out of a desire to understand intrinsically motivated activity, flow refers to a state of optimal experience characterized by total absorption in the task at hand: a merging of action and awareness in which the individual loses track of both time and self, The flow state is experientially positive, and out of the flow experience emerges a desire to replicate the experience. Over the past three decades, Csikszentmihalyi and colleagues have developed theoretical constructs and empirical research tools to better understand the nature, origins, and consequences of this state of optimal experience called flow. In this chapter, we describe the flow model and then present data analyses in which we explore the personal traits and contextual conditions associated with the experience of flow among adolescents in the United States. We demonstrate the utility of hierarchical linear modeling (HLM) for exploring flow using a complex data set characterized by repeated measures.
Copyright © 2007 Oxford University Press. Republished with permission in A.D. Ong & M.H.M. van Dulmen (Eds.) Oxford Handbook of Methods in Positive Psychology. New York: Oxford University Press, pp. 542–548, 2007, http://www.oup.com.
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Notes
- 1.
Interview studies suggest that perceived alteration of time is also characteristic of flow, but it is difficult to assess this element with ESM.
- 2.
By definition, instances of flow simultaneously pull together frequently uncorrelated aspects of experience (e.g., concentration and enjoyment); therefore, we would not necessarily expect this measure to yield a high level of reliability.
- 3.
These particular day and time distinctions were made because they capture significant shifts in adolescents’ physical location and activities and because exploratory analyses indicated corresponding shifts in students’ experience of flow.
- 4.
Assessing the effects of companionship is difficult to achieve with a high degree of confidence because very often adolescents are with people who fall into a variety of different categories. For example, adolescents are very often in the same room as parents and friends. Thus, companionship distinctions are somewhat crude.
- 5.
A further implication of this analysis is that it specifies the total amount of variation to be explained at both levels. The variance component in subsequent models with predictors indicates the residual variance, or variance left unexplained.
- 6.
Correlations between all independent variables in the model were low enough to suggest that multicolinearity was not a problem. Hence, all variables were included in the model.
- 7.
While Raudenbush and Bryk (2002) demonstrated “variance explained” as the reduction in residual variance after including predictors compared to a fully unconditional model, we wish to acknowledge that several experts have offered alternative formulas to compute variance explained (e.g., Hox 2002; Snijders and Bosker 1999).
- 8.
No random effects are significant with the exception of the slope for school hours on weekdays (u1 = 0.31, p < 0.05). This indicates that while the effect of flow does not generally vary among participants at particular times of day, there is significant between-person variation during school hours.
- 9.
Correlations between all independent variables in the model were low enough to suggest that colinearity was not a problem. Hence, all variables were included in the model.
- 10.
To avoid over specification of the model, some coefficients were modeled as fixed (not varying among participants) rather than random. We fixed variables for which random variation among participants had no meaningful interpretation (e.g., maintenance or other activities), as well those shown not to vary among participants in previous analyses (e.g., schoolwork, focus).
- 11.
We lacked a reliable measure for distortion of time in this study.
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Schmidt, J.A., Shernoff, D.J., Csikszentmihalyi, M. (2014). Individual and Situational Factors Related to the Experience of Flow in Adolescence. In: Applications of Flow in Human Development and Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9094-9_20
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