Cecilia Jakobsson Bergstad, Amelie Gamble, Olle Hagman, Merritt Polk, Tommy Gärling, Dick Ettema, Margareta Friman & Lars E.

Transcription

1 Göteborg Psychological Reports University of Gothenburg SWEDEN Cecilia Jakobsson Bergstad, Amelie Gamble, Olle Hagman, Merritt Polk, Tommy Gärling, Dick Ettema, Margareta Friman & Lars E. Olsson Impacts of Routine Out-of-Home Activities on Subjective Well-Being Number 4 Volume

2 ISSN ISRN GU/PSYK/REP--2009/4--SE

3 No. 4:39, 1 Impacts of Routine Out-of-Home Activities on Subjective Well-Being Cecilia Jakobsson Bergstad, Amelie Gamble, Olle Hagman, Merritt Polk, Tommy Gärling, Dick Ettema, Margareta Friman, and Lars E. Olsson Jakobsson Bergstad, C., Gamble, A., Hagman, O., Polk, M., Gärling, T., Ettema, D., Friman, M., and Olsson, L. E. Impacts of routine out-of-home activities on subjective well-being. Göteborg Psychological Reports, 2009, 39, No. 4. Previous research has demonstrated impacts on subjective well-being (SWB) of performance of activities that are instrumental in achieving valued goals. While this research has primarily targeted activities that people themselves choose because they are important to them, the present study investigates the role of frequent out-of-home activities which people perform because of obligations, needs, or desires. A survey of a random sample of 1,330 Swedish residents assessed the relationships between affect associated with weekly performance of out-of-home routine activities (work or school; purchase of non-durables; other purchases; participating in sports, exercise or outdoor activities; participating in out-of-home hobby, religious, course or club activities; visiting relatives or friends; visiting restaurant, café or entertainment/culture events; chauffeuring children to school or day-care centre; participating in children s leisure activities), mood, affective SWB (AWB), and cognitive SWB (CWB). Multiple linear regression analyses showed that socio-demographic variables accounted for most variance in CWB (7%), next most in AWB (3%), and least in mood (2%). In agreement with previous research, CWB increased with income, employment, and cohabiting with a spouse, and had a U-formed relationship with age. Affect associated with the activities accounted for more variance than the socio-demographic variables in mood (30%), AWB (21%), and CWB (12%). Mood fully or partially mediated the effects on AWB of affect associated with the activities, mood and AWB fully mediated the effects on CWB of affect associated with the activities. A policy implication is that community-provided resources that facilitate performance of routine out-of-home activities would increase SWB. Key words: Subjective well-being; Routine out-of-home activities; Affect Subjective well-being (SWB) refers to people s judgments of the overall quality of their lives (Diener, 1984; Kahneman, 1999; Kahneman & Kreuger, 2006). SWB is assumed to be relatively stable across time and go beyond, but implicitly include, Dick Ettema is affiliated with Utrecht University, The Netherlands, Margareta Friman and Lars E. Olsson are affiliated with Karlstad University, Sweden.

4 No. 4:39, 2 satisfaction in domains such as family, work, and leisure (Schimmack, 2008). Yet, there is an increasing interest in understanding how SWB depends on context-specific factors including, for instance, various forms of consumption (Diener & Seligman, 2004; Diener, Lucas, Schimmack, & Helliwell, 2009). In the present paper we examine the impact on SWB of routine out-of-home activities related to consumption of public services. Diener, Emmons, Larsen, and Griffen (1985) posited that SWB consists of three components, intensity, frequency or duration of positive and negative affect, and a cognitive component of satisfaction with life as a whole (see also Arhaud-Day, Rode, Mooney, & Hear, 2005). The affective components may be assessed by immediate self-reports of specific emotions or moods (Stone, Shiffman, & DeVries, 1999). In such self-reports participants report their affective experience during an activity. An alternative is to obtain retrospective self-reports on rating scales such as the Positive and Negative Affect Scale (PANAS, Watson, Clark, & Tellegan, 1988) or the Swedish Core Affect Scale (SCAS, Västfjäll, Friman, Gärling, & Kleiner, 2002; Västfjäll & Gärling, 2007; Västfjäll, Gärling, & Kleiner, 2001;). The latter is derived from the affect circumplex (Russell, 1980, 2003; see also Diener & Emmons, 1984). Whereas retrospective self-reports refer to how people feel in general, the same scales may be used to measure current mood, that is how people feel at the moment. Cognitive SWB (hence abbreviated CWB) may be assessed by means of the 5-item Satisfaction With Life Scale (SWLS) (Diener et al., 1985; Pavot & Diener, 1993) or a single-item question (Eurobarometer, 2008; World Values Survey, 2005). At an individual level, both the affective and cognitive components of SWB are partly explained by stable, genetically influenced personality traits (Tkash & Lyubomirsky, 2006). It is estimated that about 50% of the variance is accounted for in this way (Lyubomirsky, King, & Diener, 2005). Specifically, extraversion and low emotional arousability seems to lead to higher SWB than introversion and high emotional arousability (Weiss, Bates, & Luciano, 2008). The influence of personality traits on SWB may partly be indirect, for instance, by facilitating social relationships and attainments of goals (DeNeve & Cooper, 1998), or being driven by affect (Davern, Cummins, & Stokes, 2007). Other research has demonstrated that SWB depends on life circumstances, explaining about 10% of the variance (Lyubomirsky et al., 2005). People with a higher income have higher SWB (e.g., Clark & Oswald, 1996; Ferrer-i-Carbonell, 2005). SWB does still not increase in proportion to increases in income over time. Aggregated levels of SWB have been rather stable over the past 50 years, substantiating the claim that SWB is only weakly related to economic growth (above a basic threshold) (Ingelhart, 1997). SWB usually has a U-shaped relationship to age (Diener & Suh, 1997), being at its lowest when people are around the age of 40, and then gradually increasing. Marriage tends to increase, divorce or death of spouse to decrease SWB (Diener, Suh, Lucas, 6 Smith, 1999). Education is positively correlated with SWB, although more pronounced in less wealthy countries. Unemployment reduces SWB, in particular if there is little social support (see Argyle, 1999). The results for gender are mixed, some studies showing that women have higher SWB than men, others that there are no differences, and still others that sex differences exist but vary across the life course (Tesch-Römer, Motel-Klingebiel, & Tomasik, 2008). Although regional differences have been observed (Schimmack, Schupp, & Wagner, 2008), no research seems to have explicitly investigated differences in SWB related to whether people live in urban, sub-urban, or rural areas.

5 No. 4:39, 3 An impact of activity performance on SWB has been demonstrated. For instance, Pychyl and Little (1998) proposed that SWB depends on progress towards life goals, and that activities organised in projects are instrumental for such progress. To test this proposition, affective SWB (hence abbreviated AWB) and CWB were measured, as well as personal meaning, social meaning, and stress related to the activities that were performed. It was found that personal and social meanings associated with the activities correlated positively with CWB, and that stress associated with the activities reduced AWB. Similar findings were reported by Oishi, Diener, Lucas, and Suh (1999) who investigated the extent to which daily satisfaction is explained by the types of activities that people perform. Gadermann and Zumbo (2007) found that daily hassles increase negative mood and have a negative effect on life satisfaction. Further support for a negative effect on SWB is provided by van Emmerik and Jawahar (2006) who found that work-related stress associated with time pressure negatively affected current mood both in the work and personal domain. MacLeod, Coates, and Hetherton (2008) showed that progress towards goals has a positive effect on SWB, and that if this progress is speeded up by improving planning skills, the positive effect is increased. Deci and Ryan (2008) and Waterman, Schwartz, and Conti (2008) concluded that performance of activities add to SWB because people make progress towards goals but also because of positive affect associated with their performance. Previous research of the influence of activity performance on SWB has focused on activities that people choose deliberately to achieve valued goals, often as part of projects (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). In the present study we investigate the role of mundane out-of-home activities that people perform routinely, many because of obligations, although needs or desires are also frequent motives. Examples include work or going to school, chauffeuring children to school or day-care center, shopping, and leisure activities. Since these routine activities constitute such a dominant part of people s lives, it seems likely that their performance would impact on SWB. Yet, in societies with a well-developed public service (such as Sweden where the present study was performed), there are probably small differences among people with respect to satisfaction with the outcome of the activities. The relationship between performance of the activities and SWB may therefore primarily depend on how positive their performance is experienced, perhaps for many of the activities (e.g. chauffeuring children) how much negative affect due to daily hassles and boredom one avoids, for other activities (e.g. playing with children) the degree of positive affect one experiences. It is hypothesized that affect associated with mundane out-of-home activities that people perform routinely will impact on their mood and therefore indirectly both on the affective (AWB) and cognitive components (CWB) of SWB. As illustrated in Figure 1, the effects on AWB of affect associated with the activities are hypothesized to be mediated by mood and the effects on CWB mediated both by mood and AWB. Affect Activities Mood AWB CWB Figure 1. Hypothesized direct (solid lines) and indirect (broken lines) effects on mood, affective subjective wellbeing (AWB), and cognitive subjective wellbeing (CWB) of affect associated with routine out-of-home activities.

6 No. 4:39, 4 A mail survey of a random sample of Swedish residents was conducted to assess socio-demographic variables, affect associated with nine out-of-home routine activities, mood, AWB, and CWB. In the survey questionnaire it was only feasible to obtain retrospective reports of affect associated with the activities (referring to the previous week). A short version of SCAS (Västfjäll & Gärling, 2007) was adapted to measure affect associated with the activities as well as mood and AWB. Although arguments have been raised that results from retrospective methods tend to show correlations with results from immediate methods (Bussmann, Ebner-Priemer, & Fahrenberg, 2009; Fahrenberg, Myrtek, Pawlik, & Perrez, 2007), studies by Kahneman et al. (2004) and Schwartz et al. (2008) demonstrate that ratings from memory of affect associated with recent activities may be highly correlated with the results of immediate methods. Both SWLS (Diener et al., 1985; Pavot & Diener, 1993) and the single-item question (Eurobarometer, 2008; World Values Survey, 2005) were employed to measure CWB. Multiple linear regression analysis is used to perform the mediation analyses, following the procedure outlined by Kenny, Kashy, and Bolger (1998). The mood mediation requires (i) that AWB is significantly influenced by affect associated with the activity, (ii) that mood is significantly influenced by affect associated with the activity, and (iii) that AWB is significantly influenced by mood when affect associated with the activity is controlled. Mediation is only partial if affect associated with the activity remains significant in the last analysis. By analogy, the mediation of the effect of affect associated with the activities on CWB requires (i) that CWB is significantly influenced by affect associated with the activity, (ii) that both mood and AWB, independently of each other, are significantly influenced by affect associated with the activity, and (iii) that CWB is significantly influenced by mood and, independently, by AWB when affect associated with the activity is controlled. In the regression analyses several socio-demographic variables were included. The purpose was to determine the relative strength of the effects on mood, AWB, and CWB of affect associated with the activities. Sociodemographic variables, many of which reflect the possession of material resources, may have more impact on CWB, which in part entails judgments of the availability of material resources, than on the affective components (mood and AWB), whereas affect associated with the activities may have more influence on mood and AWB. Because material resources may facilitate performance of routine activities, tests were also conducted of whether affect associated with the activities mediate some of the effects on mood, AWB, and CWB of the sociodemograhic variables. Method Sample and Procedure Between October and November in 2007, a survey questionnaire and a free-of-charge return envelope were mailed to a sample of 3,000 Swedish residents, 1,000 randomly selected from each of urban areas with populations larger than 200,000 residents, semi-rural areas with populations between 20,000 and 200,000 residents, and rural areas with populations less than 20,000 residents. The respondents were offered tickets to a national lottery in compensation for their participation. A combined thankyou and reminder card was sent after approximately one week, also giving the respondents an option to answer a web-based version of the questionnaire. Fifty-six questionnaires were answered in this way. A total of 1,330 (44.3 %) usable questionnaires were obtained, 543 (40.8 %) from the rural areas, 536 (40.3 %) from the semi-rural areas, and 196 (14.7 %) from the urban areas 11. In Table 1 sample 1 Response rates usually differ between urban, suburban, and rural areas.

7 No. 4:39, 5 descriptive are displayed separately for the three residential areas. Comparisons of these sample descriptives to population statistics (Statistics Sweden, 2009) show that the subsamples are representative for each of the three residential areas with respect to age, education, income, and employment. The number of women in the sample and the number of respondents from cohabiting households exceeded the numbers in the population. Questionnaire The questionnaire consisted of seven modules. On the first page the respondents were informed that the general aim was to investigate wellbeing. The questionnaire took between 15 and 20 minutes to answer. The following modules 2 were included in the questionnaire in this order: (1) Assessments of instrumental, affective, and symbolic motives for car use; (2) General Table 1. Sample descriptive for respondents living in rural, semi-rural, and urban areas. Rural areas Semi-rural areas Urban areas Population < 20,000 20, ,000 > 200,000 Sample size a Women (%) Mean age (M/SD) 47.7/ / /13.3 Highest education (%) University College High school Other % employment (M/SD) 66.0/ / /44.1 Full-time employment (%) Annual household gross income, in 000 SEK b (%) < > Missing Household type (%) Single households without children Single households with children Cohabiting households without children Cohabiting households with children Housing condition (%) Detached house Owned apartment Rented apartment Farm Other a For other than the income question internal missing values did not exceed 4.1 %. b SEK 100,000 was approximately equal to EUR 11,000 or USD 14,000 at the time of the survey. 2 Some of the modules were included with the aim of investigating the role of quality of daily travel for the relationship between performance of the activities and SWB. The results of this part of the questionnaire are reported in Jakobsson Bergstad et al. (2009a, 2009b).

8 No. 4:39, 6 questions about daily travel; (3) Assessments of frequency of performing out-of-home activities and choice of travel mode for performing the activities during the preceding week; (4) Affect ratings of the activities; (5) Assessments of mood and AWB; (6) Assessments of CWB, and; (7) Assessments of socio-demographic variables including sex, age, education, income, employment, household type, housing conditions, and car access and use. Measures Affect Ratings of Activities. Respondents indicated for each of nine out-of-home activities whether they had performed the activity during the previous week. The most frequently out-of-home activities reported in nationwide Swedish travel surveys (Jakobsson Bergstad et al., 2009a) were selected, including work or study; purchases of non-durables; other purchases; participating in sports, exercise or outdoor activities; participating in out-of-home hobbies, religious, course or club activities; visiting relatives and friends; visiting restaurants, cafés or entertainment/cultural events; chauffeuring children to school or day-care centre, and; participating in children s leisure activities. The short version of the Swedish Core Affect Scale (SCAS) (Västfjäll & Gärling, 2007) was used to retrospectively measure how strong positive or negative affect respondents felt while performing each of the activities they had performed at least once during the previous week. Ratings were made of valence and activation using two seven-point scales ranging from 0 to 6. The end-points of the single valence (unpleasantness pleasantness) scale were defined by the three adjectives sad, dissatisfied, depressed and satisfied, happy, joyful, respectively, and the end-points of the single activation (quietness excitement) scale sleepy, passive, dull and alert, awake, active, respectively. If an activity had been performed more than once, the respondents were asked to rate the most frequent affect associated with the activity. The correlation between the ratings of valence and activation was r =.77 across all activities, and between.69 and.91 for each activity separately. Averages were computed across the valence and activation ratings. Assessments of Mood and AWB. The same two valence and activation rating scales were employed to obtain measures first of how the respondents felt during last week (mood), then of how they feel on the whole (AWB). The correlations between the ratings of valence and activation were r =.80 for AWB and r =.75 for mood. An average was computed across the valence and activation ratings. Assessments of CWB. The assessment of CWB was made by the Satisfaction with Life Scale (SWLS) (Diener et al., 1985; Pavot & Diener, 1993). SWLS was computed as the average of the following 5 items rated on seven-point Likert scales ranging from 0 (do not agree) to 6 (completely agree): I am satisfied with my life; In most ways my life is close to my ideal; The conditions of my life are excellent; So far I have achieved the important things I want in life, and; If I could live my life over again, I would change almost nothing. In addition to the SWLS, happiness was rated on the same 7-point scale. The question was phrased: Taken all things together, how happy would you say you are? (World Values Survey, 2005). SWLS had a Cronbach s alpha of.90, with inter-item correlations between.54 and.79. The single happiness question was added to the SWLS. Cronbach s alpha then increased to.92. Results AWB, mood, and the affect ratings of activities were linearly transformed to a scale ranging from -3 to 3. Table 2 shows sample sizes, means and SDs for CWB, AWB, and mood, and for each activity the number of respondents who had performed the activity at least once previous week as well as means and SDs of the affect ratings of these activities. Product moment correlations between the variables are also shown.

9 No. 4:39, 7 As may be seen, Kolmogorov-Smirnovs s ds yielded significant deviations from normal distributions. These deviations are still minor (the measure ranges from 0 to 1) and, consistent with previous research (Diener et al., 1999), reflects that the distributions are negatively skewed. A first set of OLS multiple linear regression analyses were performed to determine the effects on mood, AWB, and CWB of the socio-demographic variables sex (man 1 vs. woman -1), age (old age [55 + years, 1] vs. young age [18 39 years, - 1], and middle age [40 54 years, 1] vs. young age [18 39 years, -1]), cohabiting (1 vs. single -1), children (1 vs. no children -1), university education (1 vs. lower -1), income (1-6), employment (%) and residential area (urban [1] vs. rural area [-1], and semi-rural [1] vs. rural area [-1]). Table 3 shows that the socio-demographic variables account for 2% of the variance in mood, 3% of the variance in AWB, and 7% of the variance in CWB. Income, employment and, with one exception, cohabitant have positive impacts on all three measures, age only on CWB. By entering the affect ratings of the activities 3 in a second step, explained variance in mood increases significantly by 30%, in AWB by 21%, and in CWB by 11%. In varying degrees all the affect ratings of activities except Visiting restaurant, café, or entertainment/ cultural events contributed to the increases. As indicated by Sobel tests of significance at p =.05, the affect ratings of the activities did not mediate the effects of the sociodemographic variables on mood, AWB, or CWB. In a second set of OLS multiple linear regression analyses with the same dependent variables, the affect ratings of the activities were entered in the first step. As Table 4 shows, these account for 30% of the variance in mood, 21% of the variance in AWB, and 12% of the variance in CWB. Work or school, purchases of non-durables, and visiting relatives and friends have positive effects on all three measures, participating in children s leisure activities only on mood, and participating in hobby, religious, course or club activities, and chauffeurining children to school or day-care center only on AWB. In the second step of the regression analysis on AWB, mood is significant and with one exception (visiting relatives and friends) mood reduced or eliminated (work or school) the effects on AWB of the affect ratings. Sobel tests of significance at p =.05 showed that mood mediated the effects on AWB of work or school (z = 7.34, p <.001), non-durable purchases (z = 6.18, p <.001), participating in sports, exercise or outdoor activities (z = 2.66, p =.008), and chauffeuring children to school or daycare center (z = 2.61, p =.009). Full mediation was indicated for work or school, partial mediation for the other activities. In the regression analysis on CWB, both mood and AWB were significant. Sobel tests showed that both mood and AWB mediated the effects of work or school (z = 4.59, z = 5.25, p <.001), non-durable purchases (z = 4.48, z = 5.08, p <.001), and visiting relatives or friends (z = 2.34, p =.019; z = 2.41, p =.016). Full mediation was indicated by the fact that entering mood and AWB rendered non-significant the effects on CWB of the activities. 3 Missing values for those who did not perform activities were replaced by the mean of the ratings by those who performed the activities because the mediation analyses would otherwise not have been valid. The replaced values were the most likely had the activity been performed, and imputing them did not influence the estimated regression coefficients.

10 Table 2. Sample sizes (n), means (M), standard deviations (SD) and product moment correlations between measures. n M SD d CWB AWB Mood Affect work or school Affect purchases non-durables Affect other purchases Affect sports, exercise or outdoor activities Affect hobby, religious, course or club activities Affect visiting relatives or friends Affect visiting restaurant, café or entertainment/culture events Affect chauffeuring children to school or day-care Affect participating in children s leisure activities CWB ranged from 0 to 6, with higher values indicating larger CWB. 2 AWB, mood, and affect ratings of the activities ranged from -3 to 3, with positive values indicating positive affect and negative values negative affect. 3 All Kolmogorov-Smirnov s ds yielded significant deviations at p <.05 from a normal distribution.

11 Table 3. OLS multiple linear regression analyses with the dependent variables mood, affective subjective wellbeing (AWB), and cognitive subjective wellbeing (CWB), and the independent variables sociodemographic variables entered in the first step and affect ratings of the activities entered in the second step. Mood AWB CWB Step 1 n β t p β t p β t p Sex (man 1 vs. woman -1) Age (36 54 year 1 vs years -1) Age (55 + year 1 vs years -1) Cohabitant (1 vs. single -1) marital status <.001 Children (1 vs. no children -1) University (1 vs. lower -1) education Income (1-6) <.001 Employment (%) < <.001 Urban (1 vs rural -1) residential area Semi-rural (1 vs rural -1) residential area Model R 2 adj =.02, F(10, 1319) = 4.17, p<.001 R 2 adj =.03, F(10, 1319) = 5.21, p<.001 R 2 adj =.07, F(10, 1319) = 11.83, p<.001 Step 2 n β t p β t p β t p Work or school < < <.001 Non-durables purchase < < <.001 Other purchases Participating in sports, exercise or outdoor activities Participating in hobby, religious, course or club activities Visiting relatives or friends < < Visiting restaurant, café or entertainment/culture event Chauffeuring children to school or day-care centre Participating in children s leisure activities Sex (man 1 vs. woman -1) Age (36 54 year 1 vs years -1) Age (55 + year 1 vs years -1) Cohabitant (1 vs. single -1) marital status <.001 Children (1 vs. no children -1) University (1 vs. lower -1) education Income (1-6) <.001 Employment (%) < <.001 Urban (1 vs rural -1) residential area Semi-rural (1 vs rural -1) residential area Increment Augmented model R 2 adj =.30, F(9, 1310) = 64.30, p<.001 R 2 adj =.32, F(19, 1310) = 33.60, p<.001 R 2 adj =.21, F(9, 1310) = 40.23, p<.001 R 2 adj =.23, F(19, 1310) = 22.54, p<.001 R 2 adj =.11, F(9, 1310) = 20.27, p<.001 R 2 adj =.18, F(19, 1310) = 16.65, p<.001

12 Table 4. OLS multiple linear regression analyses with the dependent variables mood, affective subjective wellbeing (AWB), and cognitive subjective wellbeing (CWB), and the independent variables affect ratings of the activities entered in the first step, weekly mood and affective SWB in the second step, and the sociodemographic variables in the third step. Mood AWB CWB Step 1 n β t p β t p β T p Work or school < < <.001 Non-durables purchase < < <.001 Other purchases Participating in sports, exercise or outdoor activities Participating in hobby, religious, course or club activities Visiting relatives or friends < < Visiting restaurant, café or entertainment/culture event Chauffeuring children to school or day-care centre Participating in children s leisure activities Model R 2 adj =.30, F(9, 1320) = 65.18, p<.001 R 2 adj =.21, F(9, 1320) = 39.87, R 2 adj =.12, F(9, 1320) = 21.12, p<.001 p<.001 Step 2 n β t p β T p Mood < <.001 AWB <.001 Work or school Non-durables purchase Other purchases Participating in sports, exercise or outdoor activities Participating in hobby, religious, course or club activities Visiting relatives or friends Visiting restaurant, café or entertainment/culture event Chauffeuring children to school or day-care centre Participating in children s leisure activities Increment R 2 adj =.27, F(1, 1319) = , p<.001 R 2 adj =.31, F(2, 1318) = , p<.001 Augmented model R 2 adj =.48, F(10, 1319) = , p<.001 R 2 adj =.43, F(11, 1318) = 91.08, p<.001

13 Table 5. OLS multiple linear regression analyses with the dependent variables mood, affective subjective wellbeing (AWB), and cognitive subjective wellbeing (CWB), and the independent variables the sociodemographic variables and the affect ratings of the activities. Mood AWB CWB n β t p β t p β t p Mood < <.001 AWB <.001 Sex (man 1 vs. woman -1) Age (36 54 year 1 vs years -1) Age (55 + year 1 vs years -1) Cohabitant (1 vs. single -1) marital status <.001 Children (1 vs. no children -1) University (1 vs. lower -1) education Income (1-6) Employment (%) < Urban (1 vs rural -1) residential area Semi-rural (1 vs rural -1) residential area Work or school < Non-durables purchase < Other purchases Participating in sports, exercise or outdoor activities Participating in hobby, religious, course or club activities Visiting relatives or friends < Visiting restaurant, café or entertainment/culture event Chauffeuring children to school or daycare centre Participating in children s leisure activities Model R 2 adj =.32, F(19, 1310) = 33.60, p<.001 R 2 adj =.47, F(20, 1309) = 60.90, p<.001 R 2 adj =.45, F(21,1308) = 53.19, p<.001

14 12 In additional regression analyses all the independent variables were entered simultaneously. As Table 5 shows, 32% of the variance in mood is accounted for by sex (women in a more positive mood than men), cohabiting, income, and employment, and affect associated with work/school, non-durable purchases, other purchases, hobby, religious, courses or club activities, and visiting relatives or friends. Furthermore, 47% of the variance in AWB is accounted for by mood, age (younger below 36 years more positive than older between 36 and 55 years), cohabiting, employment, and affect associated with chauffeuring children to school or day-care center. Finally, 45% of the variance in CWB is accounted for by mood, AWB, and age (younger below 36 years more satisfied than older between 36 and 55 years), cohabiting, income, and employment. Discussion In general agreement with the results of previous research (Lyubomirsky et al., 2005), it was shown that subjective wellbeing (SWB) increases with income, employment, and cohabiting as well as exhibits a U-formed relationship with age. The impacts of the socio-demographic variables were larger on the cognitive component of SWB (CWB) than on both mood and the affective component (AWB). At the same time, the affect associated with performance of routine out-of-home activities accounted for more of the variance in all three measures than did the sociodemographic variables, most in mood, next most in AWB, and least in CWB. The impacts of the socio-demographic variables on mood, AWB, and CWB were not mediated by the affect associated with the activities. The effects on CWB of the affect associated with the activities (work or school, non-durable purchases, and visiting relatives or friends) were fully mediated by mood and CWB, and the effects on AWB of the affect associated with the activities either fully (work or school) or partially (non-durable purchases, participating in sports, exercise or outdoor activities, and chauffeuring children to school or daycare center) mediated by mood. The conclusion is warranted that positive and negative affect associated with routine out-of-home activities contribute importantly to SWB, actually more than what socio-demographic variables were shown and are known to do. Mood and AWB fully mediated the effects on CWB of affect associated with the activities. Affect associated with performance of the nine activities, selected to be the most frequent out-of-home activities people perform, as reported in national travel diary surveys (Jakobsson Bergstad et al., 2009a), had in most cases positive effects on SWB. The causes of these positive effects are likely to be mixed, perhaps both that performing the activities are instrumental in achieving important goals (e.g., work or school; non-durable purchases; visiting relatives and friends), in which case these activities are likely to impact on CWB (although still partially mediated by AWB), or that performing the activities are enjoyable or stressful (other purchases; participating in sports, exercise or outdoor activities; participating in hobby, religious, course or club activities; chauffeuring children to school or daycare centre; participating in children s leisure activities), in which case they are likely to impact on mood and AWB. Some of the latter activities may have only transient effects (other purchases; participating in hobby, religious, course or club activities; participating in children s leisure activities), affecting mood, whereas other effects may be lingering (participating in hobby, religious, course or club activities; chauffeuring children to school or day care centre), affecting AWB (although still partially mediated by mood). These propositions are however not fully supported by the present results and

15 13 need to be investigated further. A more comprehensive account of the joys and hassles of people s daily lives is an important goal of such research (Diener et al., 2008) Some caveats need to be addressed. In the survey it was not feasible to employ direct measures of affect associated with activities. Retrospective judgments of experiences, as in the present survey, have been criticised for not being strongly correlated with direct judgments (see Bussmann et al., 2009; Fahrenberg et al., 2007). Still, Kahneman et al. (2004) and Schwartz et al. (2008) found sizeable correlations between retrospective and direct measures. Furthermore, despite the possible memory distortions that may influence retrospective judgments, such judgments still appears to be a good predictor of future behavior (Wirtz et al., 2003). Nevertheless, it is clearly desirable to employ complementary direct methods in future research. Another issue concerns the possibility of spuriously high correlations between the measures of mood and AWB since these measures only differed in their time referents, last week or in general. The latter time referent would likely elicit a cognitive judgment, perhaps distorted by memory; the former probably more closely reflecting current mood. As would then be expected, the present results showed that AWB is only partially influenced by mood, have a higher impact on CWB, is slightly more influenced by sociodemographic variables and slightly less influenced by affect associated with the activities. Despite the correlational nature of the present study, a causal direction between the measured variables has tacitly been assumed. However, it may be argued that because people are happy and satisfied they experience more positive (and less negative) affect in their daily lives (Lyubomirsky et al., 2005). In a similar vein, Schimmack (2008) reviewed research testing a reverse causal direction from global satisfaction to domain satisfaction. His conclusion, which may also apply to the present findings, is that a reverse causal direction exists but that it is weaker. In general, mood varies depending on internal as well external influences (Costa Galinha & Pais-Ribeiro, 2008). As we assume, one such external influence is affect associated with the performance of routine out-of-home activities that depends on the conditions under which the activities are performed (e.g. time pressure). A reverse direction is however also conceivable. An internal source (e.g. negative or positive thoughts) may cause a change in mood that in turn would moderate the affect associated with the performance of the activities. This interplay at an individual level has not been possible to investigate in the present study with its focus on interindividual rather than intra-individual differences. Therefore, intra-individual variability is still another future research topic. If performance of routine out-of-home activities has an impact on SWB larger than the impact of socio-demographic variables, policy should to focus on factors that influence affect associated with the activities. Such factors may be the availability of different resources. However, in the present study socio-demographic variables related to resources (e.g. income) did not seem to have any effect. Possibly, other factors more clearly related to daily joys and hassles would have larger effects. It may be speculated that various community-provided resources facilitating routine out-ofhome activities would be important to increase people s life satisfaction. One such factor is the quality of daily commuting (Ettema et al., 2009; Diener et al., 2008).

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