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Emotion. Author manuscript; available in PMC 2017 October 01. Published in final edited form as: Emotion. 2016 October ; 16(7): 941–944. doi:10.1037/emo0000187.
Happy All the Time? Affect, Resources, and Time Use Suzanne C. Segerstrom and Daniel R. Evans University of Kentucky
Abstract Author Manuscript
When examined at the level of activities, people spend more time in activities associated with more negative affect (NA), suggesting that affect may not influence time use. However, when the normal time frames of activities such as work or eating are considered, people may spend relatively more time in activities they find more enjoyable. The present study examined time use between and within activities, using multilevel models, to further explain time use. Working women (N = 98) reported on time use, affect, and resources associated with 18 different activities using the day reconstruction method. Across activities, higher NA was associated with more time spent in that activity, an effect driven partially by work. However, within activities, higher NA but especially higher positive affect and more resource growth was associated with more time spent in that activity by a particular woman. Individuals who derive more affective and resource value from an activity devote more time to it.
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Keywords time use; affect; resources; Simpson’s paradox; ecological fallacy
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One surprising result of relating affect to time use using the day reconstruction method (DRM) is that people report spending more time in activities that they associate with less positive affect (PA) and more negative affect (NA). For example, although prayer and meditation were reported by working women as instilling much more PA than using the Internet, those same women spent almost 5-fold the amount of time using the Internet as praying or meditating (Kahneman et al., 2004). This finding violates longstanding proposals in philosophy and psychology that people are motivated to maximize pleasure and minimize pain. One possible explanation is that unpleasant activities offer other value. In one DRM study, long-duration activities were less pleasurable but more rewarding, that is, they were reported as meaningful, useful, or related to achieving important goals (White & Dolan, 2009). If an activity builds resources such as status or health, NA may be perceived as a necessary evil. Furthermore, eudaemonic consequences of activity, such as mastery, growth, and social connection, can be relatively independent of affective consequences (Ryff, 1989). Another possibility is that comparing affect across activity types is misleading with regard to time use. Simpson’s paradox (Kievet, Frankenhuis, Waldorp, & Borsboom, 2013) arises
Correspondence concerning this article should be addressed to Suzanne C. Segerstrom, Department of Psychology, University of Kentucky, 125 Kastle Hall, Lexington, KY 40506-0044. [email protected]. Suzanne C. Segerstrom and Daniel R. Evans, Department of Psychology, University of Kentucky. Daniel R. Evans is now at the Department of Primary Care, Warren Alpert Medical School, Brown University.
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when an effect that occurs between entities fails to occur or even reverses direction within entities. Heart attacks provide a classic example: People who exercise are less likely to have heart attacks than those who do not, but a person is more likely to have a heart attack when he or she is exercising than not exercising. Likewise, a correlation between NA and time use across activities might not be true across people within activities. Across different activities, time use may represent realities other than maximizing pleasure. For example, a “perfect day” optimized for affect had unrealistic elements, such as a 36-minute workday (Kroll & Pokutta, 2013). Average time spent in activities such as work, commuting, eating, and intimate relations may represent inherent time frame of the activity. For example, an 8-hour workday would be normal, but an 8-hour meal would not. Within a particular activity, however, people who find that activity less pleasing or valuable may invest less time in that activity than people who find the activity more pleasing or valuable. The average person might have an 8-hour workday, but some people may elect to work slightly less and others, more. Some people may elect to spend more time in prayer or meditation, and others, less.
Author Manuscript
The present study examined time use both between and within activities to further explain time use relationships revealed by the DRM. Our primary hypotheses were that (1) replicating other work, when examined across activities, the experiences of more NA, less PA, or both would associate with more time spent in the activity and (2) that when examined across people (within activity types), the experiences of more PA, less NA, and more resource growth would associate with more time spent in the activity.
Method Participants
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Participants were 98 working women recruited via email invitation from the Kentucky Women’s Health Registry (representing 51% of packets mailed to respondents). This population was chosen to parallel the population in the original DRM study (Kahneman et al., 2004). Mean age of the sample was 48.71 years (SD = 10.19). The women were 94% Caucasian, 3% African-American, and 3% multiracial or other. The majority were married (66%), with others divorced (25%), single (7%), or widowed (2%). Of the households with children living in them (40%), the mean number of children was 1.87 (SD = 0.89). Median household income range was $70–80 thousand, with a large range and flat distribution (at least 10 women in each income category from $30–40 thousand to over $100 thousand). For multilevel modeling, given a medium effect size (r = .3), data from N = 100 women (target sample size) would yield a priori power of .80 if the intraclass correlation (ICC) for time use (women within activities) was less than or equal to .21 (Snijders & Bosker, 1999). The post hoc, empirical ICC for raw hours was .20 and for log hours, .13.
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Method Women were mailed three packets. The first packet included consent forms, instructions, and a demographic questionnaire. They were instructed to open and complete the other packets at the end of a workday and a non-workday, which were not required to be contiguous. A non-workday was included to increase the variability of activities and time use. Half of the women completed the workday packet first, and the other half completed the non-workday
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packet first. Each packet included a standard DRM questionnaire (Kahneman et al., 2004). At the end of each study day, women were asked to reconstruct the day as a series of episodes and report on each episode’s qualities (e.g., activity type, resources) and mood during the episode. Women returned all questionnaires in a self-addressed, stamped envelope provided. They were paid $20 as thanks for participation. All study procedures were approved by the University of Kentucky institutional review board. Measures Activity types—For each daily episode, women reported the start and end time of the episode and checked off activities during that episode. Examination of an “other” category suggested a high frequency of activities related to grooming (e.g., showering, dressing, grooming) and reading, which were added for analysis. A full list of activity types and descriptive statistics is found in the Supplemental Online Material.
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Affect—For each daily episode, women were asked “How did you feel during this episode?” and responded on a 7-point scale (0 = not at all, 6 = very much) for 4 PA items (joyful, happy, competent/capable, and warm/friendly/loving) and 4 NA items (depressed/ blue, angry/hostile, worried/anxious, and tired). Reliability of the PA items across women and episodes was .98; between episodes within women, .79 (Cranford et al., 2006, equations for Rkf and Rc); of the NA items, .98 and .44. Note that scale reliability for difference or change is generally lower (Cranford et al., 2006), and floor effects may have additionally contributed to lower reliability for NA. However, aggregation across episodes (see below) may have mitigated this problem.
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Affect ICCs calculated for each activity type suggest that women’s affect during each activity (across episodes) was reasonably consistent, with the median ICC for PA = .65 and the median ICC for NA = .61. Therefore, for each woman, PA and NA during each activity was averaged across episodes. Although other studies using the DRM have operationalized affect as affect balance (the difference between PA and NA; Kahneman et al., 2004; Kroll & Pokutta, 2013; White & Dolan, 2009), PA and NA were analyzed individually given that they are often not inversely related to each other (Barrett & Russell, 1999). Resources—Within each episode, women were asked “Did you develop something during this episode?” and responded on an 7 point scale (0 = not at all, 6 = a great deal) for 4 resources: Your body (that is, your physical strength, health, energy); your self (that is, your
Author Manuscript
status, skill, achievement, independence, influence, success); your relationships (that is, your acceptance by others, intimacy, closeness, love, communication); beyond yourself (that is, connecting or deepening your relationship with a higher power, contributing to others, creating something permanent, making the world a better place). These questions reflected physical, status, social, and existential resources, respectively. Reliability of the resource items across women and episodes was .98; between episodes within women, .64. Each woman’s resource appraisal during that activity across episodes was reasonably consistent, with the median ICC for physical resources = .49; status, .55, social, .43, and existential, .59. Therefore, resources during each activity were averaged across episodes.
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Data analysis
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For each activity, time spent in that activity was aggregated across the two days for each woman. Time was operationalized as first, the sum of the raw number of minutes spent in the activity across all episodes of that activity and second, the sum of the log minutes spent. It has been argued that time spent in a particular activity has diminishing hedonic value and that log time may be a better functional form for examining the relationship between affect and time use (Kroll & Pokutta, 2013).
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The substantive hypotheses were addressed with multilevel mixed models (SAS [9.3] PROC MIXED) using restricted maximum likelihood estimation and Kenward-Roger correction and degrees of freedom. The models included crossed random effects (e.g., Locker, Hoffman, & Bovaird, 2007) of activity types and individuals. Total time or total log time were predicted by PA, NA, or total resources. These predictors were separated into Level 1 (within activity) and Level 2 (between activity) by taking the activity-level mean across women as the Level 2 predictor and each woman’s deviation from that mean as the Level 1 predictor (Enders & Tofighi, 2007). The Level 1 predictor reflects differences between women within activities. The Level 2 predictor, subsequently centered around 0, reflects differences across activities. Estimates, which are similar to unstandardized beta weights in regression, are reported with their 95% confidence intervals.
Results
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Table 1 shows the results of the multi-level models that separated between-activity and within-activity relationships for raw hours and log hours, respectively. The first column shows results from the null model with no predictors; for raw hours, 20.6% of the variance in time use was due to differences across activity types, 22.6% to differences across people, and 56.8% to individual differences within activity types. For log hours, variance was 12.2% due to activity types, 25.1% due to people, and 62.7% due to individual differences. Effects in the following columns reflect the number of raw or log hours’ difference in time spent associated with a 1 point difference in the affect or resources measure (scale range = 0 – 6). Between activity types, the only notable effect was the relationship between NA and time use in raw hours, with more time spent in activities associated with higher NA (p = . 07). One possibility is that this relationship was primarily being driven by work. This possibility was partially confirmed by a model not including work: The relationship between NA and time use in raw hours was attenuated (effect = 4.13, 95%CI = −3.07 – 11.32) and not statistically significant (p = .24). Neither PA nor resources distinguished time use between activity types.
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As hypothesized, individual differences between women within activity types had different relationships with time use. More time in raw hours was statistically significantly associated with higher resources and also tended to be associated with more NA (p = .06). More time in log hours was statistically significantly associated with more PA and higher resources. Figure 1 shows the relationships between positive affect and resources and time use with model estimates converted to raw hours.
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Examination of individual resources revealed that women spent more time in log hours (which yielded a more robust effect of total resources) in an activity if they perceived more gains in status resources (effect = 0.13, 95%CI = 0.05 – 0.21) and existential resources (effect = 0.18, 95%CI = 0.10 – 0.25), and, to a lesser degree, social resources (effect = 0.07, 95%CI = −0.004 – 0.14). Gains in physical resources had little effect on time use (effect = 0.03, 95%CI = −0.06 – 0.11).
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When PA, NA, and resources were included in the same model, there was little change in the results. Between activities, more time use in raw hours was associated with more NA (estimate = 11.17, 95%CI = 2.69 – 19.65). Within activities, more time use in raw hours was associated with both more NA (estimate = 0.37, 95%CI = 0.04 – 0.70) and higher resources (estimate = 0.24, 95%CI = −0.02 – 0.50) Likewise, more time use in log hours was associated with more PA (estimate = 0.12, 95%CI = −0.003 – 0.25), more NA (estimate = 0.16, 95%CI = −0.0002 – 0.32), and higher resources (estimate = 0.14, 95%CI = 0.02 – 0.27).
Discussion
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Across different activities, women spent the most time in activities associated with the highest NA, replicating previous reports (e.g., Kahneman et al., 2004) and apparently contradicting the proposal that people are motivated to maximize PA and minimize NA. Further examination, however, suggested that this relationship does not fully represent daily time use. First, this effect may have been driven in part by work. Women spent the most time in work (7.3 hours) and reported the highest NA while working. Second, comparing activities may not be the most informative level of analysis. Taking into account time use associated with each activity, women spent more time in an activity if they reported higher PA and, less reliably, NA and if they found it to yield more resources. Therefore, there was evidence for a relationship between affect or resources and time use at the level of the person rather than the activity, and when PA and NA were examined separately. These findings give a different picture from those at the level of the activity. In contrast with the conclusion that people spend more time in less pleasant activities, the within-activity relationships lead to the conclusion that people spend more time in activities that yield more affective engagement and more resources.
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These findings support both of the propositions to explain DRM results. First, as described above, there was evidence that relationships across activities did not necessarily generalize to relationships across people. Second, the evidence also supported the idea that NA might be a necessary cost of pursuing important goals or growing resources (White & Dolan, 2009), because NA tended to be related to time use within as well as between activities, as was resource growth. NA related to time use after accounting for PA and resources, however, which raises the possibility that people who find an activity more emotionally evocative tend to spend more time in that activity. Because the main purpose of the present investigation was to further explore a finding generated by a particular method (Kahneman et al., 2004), that methodology was adopted. However, these results represent only a starting point. First, paper diaries limited ability to
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check compliance. Future use of ecological momentary assessment (EMA) could increase confidence in the results, although DRM has been validated against EMA results (Dockray et al., 2010). Second, the results can be generalized to Caucasian working women, although the sample represented a wide range of ages, incomes, and marital status and included women with and without children in the home. These findings await replication in more diverse samples. Third, these findings were generated across two days, but assessment across more days in future would be desirable. Fourth, although we adopted a theoretical framework in which activity influences affect, the reverse is also possible. For example, women had high NA when napping or resting; the most likely explanation is that they rested when they felt tired (an NA item). Despite these limitations, these results contradict the surprising finding that people spend time in aversive activities: The finding is partially a function of Simpson’s paradox, and time use is in fact influenced by experiences of positive affect and resource growth.
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Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments This research was supported by the National Institute of Aging (K02-AG033629) and the Templeton Foundation.
References
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Barrett LF, Russell JA. The structure of current affect controversies and emerging consensus. Current Directions in Psychological Science. 1999; 8:10–14. Cranford JA, Shrout PE, Iida M, Rafaeli E, Yip T, Bolger N. A procedure for evaluating sensitivity to within-person change: can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin. 2006; 32:917–929. [PubMed: 16738025] Dockray S, Grant N, Stone AA, Kahneman D, Wardle J, Steptoe A. A comparison of affect ratings obtained with ecological momentary assessment and the day reconstruction method. Social Indicators Research. 2010; 99:269–283. [PubMed: 21113328] Enders CK, Tofighi D. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods. 2007; 12:121–138. [PubMed: 17563168] Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: The day reconstruction method. Science. 2004; 306(5702):1776–1780. [PubMed: 15576620] Kievit RA, Frankenhuis WE, Waldorp LJ, Borsboom D. Simpson's paradox in psychological science: a practical guide. Frontiers in Psychology. 2013; 4 Article 513. Kroll C, Pokutta S. Just a perfect day? Developing a happiness optimised day schedule. Journal of Economic Psychology. 2013; 34:210–217. Locker L, Hoffman L, Bovaird JA. On the use of multilevel modeling as an alternative to items analysis in psycholinguistic research. Behavior Research Methods. 2007; 39:723–730. [PubMed: 18183884] Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology. 1989; 57:1069–1081. Snijders, TAB.; Bosker, RJ. Multilevel analysis. Thousand Oaks, CA: Sage; 1999. White MP, Dolan P. Accounting for the richness of daily activities. Psychological Science. 2009; 20:1000–1008. [PubMed: 19549079]
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Author Manuscript Author Manuscript Author Manuscript Figure 1.
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Relationships between positive affect and resources and time use between and within activities, converted from log hours to raw hours.
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Table 1
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Affect, resources, and time use between and within activities. Effects reflect the number of hours difference in time use for each 1-point change in the predictor (range = 0–6) and are reported with their 95% confidence interval. Predictor None
Positive affect
Negative affect
Resources
Raw hours Intercept
4.66 [3.54 – 5.77]
Between-activity effect Within-activity effect Between-activity variance Between-person variance
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Person*activity variance AIC
4.19 [2.30 – 9.89]
4.74 [3.51 – 5.81]
4.75 [3.69 – 5.81]
4.64 [3.49 – 5.79]
−0.06 [−2.93 – 2.81]
5.96 [−0.66 – 12.59]
0.28 [−2.12 – 2.68]
0.09 [−0.13 – 0.31]
0.30 [−0.01 – 0.62]
0.23 [0.007 – 0.45]
4.46 [2.42 – 10.84]
3.62 [1.96 – 8.82]
4.46 [2.41 – 10.85]
4.59 [3.37 – 6.64]
4.50 [3.28 – 6.55]
4.59 [3.36 – 6.64]
4.39 [3.21 – 6.38]
11.54 [10.65 – 12.55]
11.56 [10.67 – 12.58]
11.52 [10.63 – 12.53]
11.54 [10.65 – 12.56]
6842.5
6849.9
6843.3
6846.4
0.03 [−1.00 – 1.06]
0.67 [−1.92 – 3.26]
0.35 [−0.50 – 1.19]
0.16 [0.06 – 0.27]
0.08 [−0.07 – 0.23]
0.19 [0.08 – 0.30]
Log hours Between-activity effect Within-activity effect Between-activity variance
0.52 [0.28 – 1.24]
0.56 [0.30 – 1.37]
0.54 [0.29 – 1.33]
0.53 [0.28 – 1.31]
Between-person variance
1.07 [0.79 – 1.55]
1.01 [0.74 – 1.47]
1.08 [0.79 – 1.57]
1.05 [0.76 – 1.52]
Person*activity variance
2.67 [2.47 – 2.91]
2.67 [2.46 – 2.90]
2.67 [2.47 – 2.91]
2.65 [2.45 – 2.88]
5004.3
5007.2
5011.0
4999.0
AIC
AIC = Aikake’s information criterion. Lower values represent a more informative model. Maximum likelihood estimation was used to derive AIC.
Author Manuscript Author Manuscript Emotion. Author manuscript; available in PMC 2017 October 01.
Emotion. Author manuscript; available in PMC 2017 October 01. Published in final edited form as: Emotion. 2016 October ; 16(7): 941–944. doi:10.1037/emo0000187.
Happy All the Time? Affect, Resources, and Time Use Suzanne C. Segerstrom and Daniel R. Evans University of Kentucky
Abstract Author Manuscript
When examined at the level of activities, people spend more time in activities associated with more negative affect (NA), suggesting that affect may not influence time use. However, when the normal time frames of activities such as work or eating are considered, people may spend relatively more time in activities they find more enjoyable. The present study examined time use between and within activities, using multilevel models, to further explain time use. Working women (N = 98) reported on time use, affect, and resources associated with 18 different activities using the day reconstruction method. Across activities, higher NA was associated with more time spent in that activity, an effect driven partially by work. However, within activities, higher NA but especially higher positive affect and more resource growth was associated with more time spent in that activity by a particular woman. Individuals who derive more affective and resource value from an activity devote more time to it.
Author Manuscript
Keywords time use; affect; resources; Simpson’s paradox; ecological fallacy
Author Manuscript
One surprising result of relating affect to time use using the day reconstruction method (DRM) is that people report spending more time in activities that they associate with less positive affect (PA) and more negative affect (NA). For example, although prayer and meditation were reported by working women as instilling much more PA than using the Internet, those same women spent almost 5-fold the amount of time using the Internet as praying or meditating (Kahneman et al., 2004). This finding violates longstanding proposals in philosophy and psychology that people are motivated to maximize pleasure and minimize pain. One possible explanation is that unpleasant activities offer other value. In one DRM study, long-duration activities were less pleasurable but more rewarding, that is, they were reported as meaningful, useful, or related to achieving important goals (White & Dolan, 2009). If an activity builds resources such as status or health, NA may be perceived as a necessary evil. Furthermore, eudaemonic consequences of activity, such as mastery, growth, and social connection, can be relatively independent of affective consequences (Ryff, 1989). Another possibility is that comparing affect across activity types is misleading with regard to time use. Simpson’s paradox (Kievet, Frankenhuis, Waldorp, & Borsboom, 2013) arises
Correspondence concerning this article should be addressed to Suzanne C. Segerstrom, Department of Psychology, University of Kentucky, 125 Kastle Hall, Lexington, KY 40506-0044. [email protected]. Suzanne C. Segerstrom and Daniel R. Evans, Department of Psychology, University of Kentucky. Daniel R. Evans is now at the Department of Primary Care, Warren Alpert Medical School, Brown University.
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when an effect that occurs between entities fails to occur or even reverses direction within entities. Heart attacks provide a classic example: People who exercise are less likely to have heart attacks than those who do not, but a person is more likely to have a heart attack when he or she is exercising than not exercising. Likewise, a correlation between NA and time use across activities might not be true across people within activities. Across different activities, time use may represent realities other than maximizing pleasure. For example, a “perfect day” optimized for affect had unrealistic elements, such as a 36-minute workday (Kroll & Pokutta, 2013). Average time spent in activities such as work, commuting, eating, and intimate relations may represent inherent time frame of the activity. For example, an 8-hour workday would be normal, but an 8-hour meal would not. Within a particular activity, however, people who find that activity less pleasing or valuable may invest less time in that activity than people who find the activity more pleasing or valuable. The average person might have an 8-hour workday, but some people may elect to work slightly less and others, more. Some people may elect to spend more time in prayer or meditation, and others, less.
Author Manuscript
The present study examined time use both between and within activities to further explain time use relationships revealed by the DRM. Our primary hypotheses were that (1) replicating other work, when examined across activities, the experiences of more NA, less PA, or both would associate with more time spent in the activity and (2) that when examined across people (within activity types), the experiences of more PA, less NA, and more resource growth would associate with more time spent in the activity.
Method Participants
Author Manuscript
Participants were 98 working women recruited via email invitation from the Kentucky Women’s Health Registry (representing 51% of packets mailed to respondents). This population was chosen to parallel the population in the original DRM study (Kahneman et al., 2004). Mean age of the sample was 48.71 years (SD = 10.19). The women were 94% Caucasian, 3% African-American, and 3% multiracial or other. The majority were married (66%), with others divorced (25%), single (7%), or widowed (2%). Of the households with children living in them (40%), the mean number of children was 1.87 (SD = 0.89). Median household income range was $70–80 thousand, with a large range and flat distribution (at least 10 women in each income category from $30–40 thousand to over $100 thousand). For multilevel modeling, given a medium effect size (r = .3), data from N = 100 women (target sample size) would yield a priori power of .80 if the intraclass correlation (ICC) for time use (women within activities) was less than or equal to .21 (Snijders & Bosker, 1999). The post hoc, empirical ICC for raw hours was .20 and for log hours, .13.
Author Manuscript
Method Women were mailed three packets. The first packet included consent forms, instructions, and a demographic questionnaire. They were instructed to open and complete the other packets at the end of a workday and a non-workday, which were not required to be contiguous. A non-workday was included to increase the variability of activities and time use. Half of the women completed the workday packet first, and the other half completed the non-workday
Emotion. Author manuscript; available in PMC 2017 October 01.
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packet first. Each packet included a standard DRM questionnaire (Kahneman et al., 2004). At the end of each study day, women were asked to reconstruct the day as a series of episodes and report on each episode’s qualities (e.g., activity type, resources) and mood during the episode. Women returned all questionnaires in a self-addressed, stamped envelope provided. They were paid $20 as thanks for participation. All study procedures were approved by the University of Kentucky institutional review board. Measures Activity types—For each daily episode, women reported the start and end time of the episode and checked off activities during that episode. Examination of an “other” category suggested a high frequency of activities related to grooming (e.g., showering, dressing, grooming) and reading, which were added for analysis. A full list of activity types and descriptive statistics is found in the Supplemental Online Material.
Author Manuscript
Affect—For each daily episode, women were asked “How did you feel during this episode?” and responded on a 7-point scale (0 = not at all, 6 = very much) for 4 PA items (joyful, happy, competent/capable, and warm/friendly/loving) and 4 NA items (depressed/ blue, angry/hostile, worried/anxious, and tired). Reliability of the PA items across women and episodes was .98; between episodes within women, .79 (Cranford et al., 2006, equations for Rkf and Rc); of the NA items, .98 and .44. Note that scale reliability for difference or change is generally lower (Cranford et al., 2006), and floor effects may have additionally contributed to lower reliability for NA. However, aggregation across episodes (see below) may have mitigated this problem.
Author Manuscript
Affect ICCs calculated for each activity type suggest that women’s affect during each activity (across episodes) was reasonably consistent, with the median ICC for PA = .65 and the median ICC for NA = .61. Therefore, for each woman, PA and NA during each activity was averaged across episodes. Although other studies using the DRM have operationalized affect as affect balance (the difference between PA and NA; Kahneman et al., 2004; Kroll & Pokutta, 2013; White & Dolan, 2009), PA and NA were analyzed individually given that they are often not inversely related to each other (Barrett & Russell, 1999). Resources—Within each episode, women were asked “Did you develop something during this episode?” and responded on an 7 point scale (0 = not at all, 6 = a great deal) for 4 resources: Your body (that is, your physical strength, health, energy); your self (that is, your
Author Manuscript
status, skill, achievement, independence, influence, success); your relationships (that is, your acceptance by others, intimacy, closeness, love, communication); beyond yourself (that is, connecting or deepening your relationship with a higher power, contributing to others, creating something permanent, making the world a better place). These questions reflected physical, status, social, and existential resources, respectively. Reliability of the resource items across women and episodes was .98; between episodes within women, .64. Each woman’s resource appraisal during that activity across episodes was reasonably consistent, with the median ICC for physical resources = .49; status, .55, social, .43, and existential, .59. Therefore, resources during each activity were averaged across episodes.
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Data analysis
Author Manuscript
For each activity, time spent in that activity was aggregated across the two days for each woman. Time was operationalized as first, the sum of the raw number of minutes spent in the activity across all episodes of that activity and second, the sum of the log minutes spent. It has been argued that time spent in a particular activity has diminishing hedonic value and that log time may be a better functional form for examining the relationship between affect and time use (Kroll & Pokutta, 2013).
Author Manuscript
The substantive hypotheses were addressed with multilevel mixed models (SAS [9.3] PROC MIXED) using restricted maximum likelihood estimation and Kenward-Roger correction and degrees of freedom. The models included crossed random effects (e.g., Locker, Hoffman, & Bovaird, 2007) of activity types and individuals. Total time or total log time were predicted by PA, NA, or total resources. These predictors were separated into Level 1 (within activity) and Level 2 (between activity) by taking the activity-level mean across women as the Level 2 predictor and each woman’s deviation from that mean as the Level 1 predictor (Enders & Tofighi, 2007). The Level 1 predictor reflects differences between women within activities. The Level 2 predictor, subsequently centered around 0, reflects differences across activities. Estimates, which are similar to unstandardized beta weights in regression, are reported with their 95% confidence intervals.
Results
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Table 1 shows the results of the multi-level models that separated between-activity and within-activity relationships for raw hours and log hours, respectively. The first column shows results from the null model with no predictors; for raw hours, 20.6% of the variance in time use was due to differences across activity types, 22.6% to differences across people, and 56.8% to individual differences within activity types. For log hours, variance was 12.2% due to activity types, 25.1% due to people, and 62.7% due to individual differences. Effects in the following columns reflect the number of raw or log hours’ difference in time spent associated with a 1 point difference in the affect or resources measure (scale range = 0 – 6). Between activity types, the only notable effect was the relationship between NA and time use in raw hours, with more time spent in activities associated with higher NA (p = . 07). One possibility is that this relationship was primarily being driven by work. This possibility was partially confirmed by a model not including work: The relationship between NA and time use in raw hours was attenuated (effect = 4.13, 95%CI = −3.07 – 11.32) and not statistically significant (p = .24). Neither PA nor resources distinguished time use between activity types.
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As hypothesized, individual differences between women within activity types had different relationships with time use. More time in raw hours was statistically significantly associated with higher resources and also tended to be associated with more NA (p = .06). More time in log hours was statistically significantly associated with more PA and higher resources. Figure 1 shows the relationships between positive affect and resources and time use with model estimates converted to raw hours.
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Examination of individual resources revealed that women spent more time in log hours (which yielded a more robust effect of total resources) in an activity if they perceived more gains in status resources (effect = 0.13, 95%CI = 0.05 – 0.21) and existential resources (effect = 0.18, 95%CI = 0.10 – 0.25), and, to a lesser degree, social resources (effect = 0.07, 95%CI = −0.004 – 0.14). Gains in physical resources had little effect on time use (effect = 0.03, 95%CI = −0.06 – 0.11).
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When PA, NA, and resources were included in the same model, there was little change in the results. Between activities, more time use in raw hours was associated with more NA (estimate = 11.17, 95%CI = 2.69 – 19.65). Within activities, more time use in raw hours was associated with both more NA (estimate = 0.37, 95%CI = 0.04 – 0.70) and higher resources (estimate = 0.24, 95%CI = −0.02 – 0.50) Likewise, more time use in log hours was associated with more PA (estimate = 0.12, 95%CI = −0.003 – 0.25), more NA (estimate = 0.16, 95%CI = −0.0002 – 0.32), and higher resources (estimate = 0.14, 95%CI = 0.02 – 0.27).
Discussion
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Across different activities, women spent the most time in activities associated with the highest NA, replicating previous reports (e.g., Kahneman et al., 2004) and apparently contradicting the proposal that people are motivated to maximize PA and minimize NA. Further examination, however, suggested that this relationship does not fully represent daily time use. First, this effect may have been driven in part by work. Women spent the most time in work (7.3 hours) and reported the highest NA while working. Second, comparing activities may not be the most informative level of analysis. Taking into account time use associated with each activity, women spent more time in an activity if they reported higher PA and, less reliably, NA and if they found it to yield more resources. Therefore, there was evidence for a relationship between affect or resources and time use at the level of the person rather than the activity, and when PA and NA were examined separately. These findings give a different picture from those at the level of the activity. In contrast with the conclusion that people spend more time in less pleasant activities, the within-activity relationships lead to the conclusion that people spend more time in activities that yield more affective engagement and more resources.
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These findings support both of the propositions to explain DRM results. First, as described above, there was evidence that relationships across activities did not necessarily generalize to relationships across people. Second, the evidence also supported the idea that NA might be a necessary cost of pursuing important goals or growing resources (White & Dolan, 2009), because NA tended to be related to time use within as well as between activities, as was resource growth. NA related to time use after accounting for PA and resources, however, which raises the possibility that people who find an activity more emotionally evocative tend to spend more time in that activity. Because the main purpose of the present investigation was to further explore a finding generated by a particular method (Kahneman et al., 2004), that methodology was adopted. However, these results represent only a starting point. First, paper diaries limited ability to
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check compliance. Future use of ecological momentary assessment (EMA) could increase confidence in the results, although DRM has been validated against EMA results (Dockray et al., 2010). Second, the results can be generalized to Caucasian working women, although the sample represented a wide range of ages, incomes, and marital status and included women with and without children in the home. These findings await replication in more diverse samples. Third, these findings were generated across two days, but assessment across more days in future would be desirable. Fourth, although we adopted a theoretical framework in which activity influences affect, the reverse is also possible. For example, women had high NA when napping or resting; the most likely explanation is that they rested when they felt tired (an NA item). Despite these limitations, these results contradict the surprising finding that people spend time in aversive activities: The finding is partially a function of Simpson’s paradox, and time use is in fact influenced by experiences of positive affect and resource growth.
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Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments This research was supported by the National Institute of Aging (K02-AG033629) and the Templeton Foundation.
References
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Barrett LF, Russell JA. The structure of current affect controversies and emerging consensus. Current Directions in Psychological Science. 1999; 8:10–14. Cranford JA, Shrout PE, Iida M, Rafaeli E, Yip T, Bolger N. A procedure for evaluating sensitivity to within-person change: can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin. 2006; 32:917–929. [PubMed: 16738025] Dockray S, Grant N, Stone AA, Kahneman D, Wardle J, Steptoe A. A comparison of affect ratings obtained with ecological momentary assessment and the day reconstruction method. Social Indicators Research. 2010; 99:269–283. [PubMed: 21113328] Enders CK, Tofighi D. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods. 2007; 12:121–138. [PubMed: 17563168] Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: The day reconstruction method. Science. 2004; 306(5702):1776–1780. [PubMed: 15576620] Kievit RA, Frankenhuis WE, Waldorp LJ, Borsboom D. Simpson's paradox in psychological science: a practical guide. Frontiers in Psychology. 2013; 4 Article 513. Kroll C, Pokutta S. Just a perfect day? Developing a happiness optimised day schedule. Journal of Economic Psychology. 2013; 34:210–217. Locker L, Hoffman L, Bovaird JA. On the use of multilevel modeling as an alternative to items analysis in psycholinguistic research. Behavior Research Methods. 2007; 39:723–730. [PubMed: 18183884] Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology. 1989; 57:1069–1081. Snijders, TAB.; Bosker, RJ. Multilevel analysis. Thousand Oaks, CA: Sage; 1999. White MP, Dolan P. Accounting for the richness of daily activities. Psychological Science. 2009; 20:1000–1008. [PubMed: 19549079]
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Author Manuscript Author Manuscript Author Manuscript Figure 1.
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Relationships between positive affect and resources and time use between and within activities, converted from log hours to raw hours.
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Table 1
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Affect, resources, and time use between and within activities. Effects reflect the number of hours difference in time use for each 1-point change in the predictor (range = 0–6) and are reported with their 95% confidence interval. Predictor None
Positive affect
Negative affect
Resources
Raw hours Intercept
4.66 [3.54 – 5.77]
Between-activity effect Within-activity effect Between-activity variance Between-person variance
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Person*activity variance AIC
4.19 [2.30 – 9.89]
4.74 [3.51 – 5.81]
4.75 [3.69 – 5.81]
4.64 [3.49 – 5.79]
−0.06 [−2.93 – 2.81]
5.96 [−0.66 – 12.59]
0.28 [−2.12 – 2.68]
0.09 [−0.13 – 0.31]
0.30 [−0.01 – 0.62]
0.23 [0.007 – 0.45]
4.46 [2.42 – 10.84]
3.62 [1.96 – 8.82]
4.46 [2.41 – 10.85]
4.59 [3.37 – 6.64]
4.50 [3.28 – 6.55]
4.59 [3.36 – 6.64]
4.39 [3.21 – 6.38]
11.54 [10.65 – 12.55]
11.56 [10.67 – 12.58]
11.52 [10.63 – 12.53]
11.54 [10.65 – 12.56]
6842.5
6849.9
6843.3
6846.4
0.03 [−1.00 – 1.06]
0.67 [−1.92 – 3.26]
0.35 [−0.50 – 1.19]
0.16 [0.06 – 0.27]
0.08 [−0.07 – 0.23]
0.19 [0.08 – 0.30]
Log hours Between-activity effect Within-activity effect Between-activity variance
0.52 [0.28 – 1.24]
0.56 [0.30 – 1.37]
0.54 [0.29 – 1.33]
0.53 [0.28 – 1.31]
Between-person variance
1.07 [0.79 – 1.55]
1.01 [0.74 – 1.47]
1.08 [0.79 – 1.57]
1.05 [0.76 – 1.52]
Person*activity variance
2.67 [2.47 – 2.91]
2.67 [2.46 – 2.90]
2.67 [2.47 – 2.91]
2.65 [2.45 – 2.88]
5004.3
5007.2
5011.0
4999.0
AIC
AIC = Aikake’s information criterion. Lower values represent a more informative model. Maximum likelihood estimation was used to derive AIC.
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