Added: Adell Elmer - Date: 18.11.2021 03:24 - Views: 12139 - Clicks: 745
Try out PMC Labs and tell us what you think. Learn More. The sample of 5, is drawn randomly from an on-line pool of respondents, a group well placed to have and value on-line friendships. We find three key. First, the of real-life friends is positively correlated with subjective well-being SWB even after controlling for income, demographic variables and personality differences. Second, the size of online networks is largely uncorrelated with subjective well-being.
Third, we find that real-life friends are much more important for people who are single, divorced, separated or widowed than they are for people who are married or living with a partner. Findings from large international surveys the European Social Surveys — are used to confirm the importance of real-life social networks to SWB; they also indicate a ificantly smaller value of social networks to married or partnered couples.
There are constant changes in the types of activities that people engage in, and in the technologies they use to establish and enjoy their social connections. Some commentators and researchers argued that there were new types of social connection, possibly more effective in nature, that were growing and possibly offsetting the effects of declines elsewhere.
The internet could thereby be seen as providing ways of enhancing or replacing face-to-face friends through the availability of on-line social networks. How can the effects of these differing trends be compared? To judge the importance and value of differing forms of friendship requires a common basis for valuation. The broadening availability of data for subjective well-being offers one possible solution to this valuation problem. Only very recently has there been a survey that provided comparable measures of networks of face-to-face and on-line friends, set in the context of a well-being survey of sufficient size and scope to permit comparable assessments of the two types of friends.
Friends and family are a long-established support for subjective well-being. Friends matter to happiness both for being potential sources of social support and for the pleasures from time spent together, whether at work, at play, or in activities for the benefit of others. Data from the Gallup World Poll suggest that having someone to call on in times of trouble is associated with a life evaluation that is higher, on a 0 to 10 scale, by almost half a point in . There is also a dose-response relationship, so that having more friends is better than having fewer.
Evidence from the Canadian General Social Survey shows that, compared to respondents having no close friends, to have 3 to 5 close friends is associated with life satisfaction 0. Also notable is that happiness depends not just on the of close friends, but also the frequency with which they are seen .
The same survey also asks about the of close relatives, and the frequency with which they are seen. An interesting difference appears between friends and family. The of close family matters more than the of close friends, about twice as much up to 15 inwith no gain thereafter, while frequency of seeing family contributes only half as much as the frequency of seeing close friends .
A similar result is found in US and other Canadian data analyzed by where it is shown that the frequency of seeing friends adds twice as much to subjective well-being as does the frequency of seeing family. The US and Canadian surveys in  also reveal a strong relation between subjective well-being and the frequency of seeing friends, with those seeing friends most frequently having subjective well-being higher by 0.
All of these are based on fully specified models with many other control variables, although there is no doubt likely to be some remaining element of mutual causality between subjective well-being and the frequency of seeing friends. For example, those who are at the bottom end of the subjective well-being scale, and especially those who are clinically depressed, often reduce the extent to which they reach out to friends.
Indeed social withdrawal is a key element in the Beck Depression Inventory BDI as supported in subsequent factor-analytic work by . Thus some of the strong positive linkages between friends and happiness may reflect causal influences running in both directions. This is likely to apply for both real-life and on-line friends, and hence should not affect our comparisons in this paper between these two types of friends.
There are few studies of the linkages between on-line friendships and subjective well-being. One study  found a positive relation between subjective well-being and of Facebook friends among a sample of college-age subjects. Another study of college-age respondents in the United States, while not directly investigating the links between Facebook usage and subjective well-being, did find evidence that Facebook usage was correlated with proxy measures of social capital, but only for those with relatively low levels of satisfaction with campus life . An earlier study of social capital and internet usage in a sample of US adolescents  found no ificant relation between subjective well-being and time spent on-line.
Those who spent more time messaging with close real-life friends were happier. Conversely, the relation between on-line time and subjective well-being was negative for those in contact with strangers or purely on-line friends. A recent study of Egyptian students found no ificant relation between life satisfaction and intensity of Facebook usage .
Although there are many studies showing the effects of marital status on subjective well-being, we have not found attempts to see if the happiness effects of either real-life or on-line friends differ by marital status. Using two different surveys, we look for, and find, a large interaction effect in the happiness effects of marital status and real-life friends, but no ificant differences for the effects of on-line friends. We think that our are the first to compare the happiness effects of real-life and on-line friends.
Hence there are no directly comparable prior studies. Based on a meta-analysis  of fifty years of studies showing ificantly more effective cooperation in conflict resolutions using face-to-face rather than written communications, we might conjecture that a similar difference might exist to differentiate the happiness effects of real-life and on-line friends. The primary dataset for the paper is the Happiness Monitor survey sponsored by Coca-Cola and conducted in Canada between January 20 and 31, by Leger Marketing, using their online panel LegerWeb.
The sample includes 5, Canadian residents, aged 16 and over, drawn from all ten Canadian provinces. The survey focuses on subjective well-being, and has questions that cover self-evaluation of life and other questions that can be used to construct alternative measures of well-being. A section called Canadiana has occasionally light-hearted questions such as what is the happiest job in Canada, with a list that includes Zamboni driver and lumberjack. From our perspective, the most interesting questions other than the ones on well-being are those on the size of social networks, separately for real-life friends and on-line friends.
This presents an opportunity for us to examine potential differences between these two types of networks, specifically in their contributions to subjective well-being. We use regression analysis to relate measures of subjective well-being to the sizes of social networks, as well as income and demographic controls. We will also use control variables to pick up differences in personalities; such variables include self-reported stress, time spent exercising and contributions to charitable causes.
The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time? We plot the distribution of sample responses in the first panel of Figure 1. The sample mean is 6. It is possible to construct two other measures of well-being from the survey.
The second panel of Figure 1 shows the distribution. Very happy, somewhat happy, somewhat unhappy, very unhappy. We will use these two measures of well-being for robustness tests. Very low, Low, Medium, High, Very high. We now move on to the two questions on social networks. The first question concerns real-life friends. The distribution of the network size is shown in the upper panel of Figure 2. The distribution is shown on the lower panel of Figure 2. The two network questions have different s of steps, and both have some steps with sparse responses see Figure 2.
This way, we turn the two network sizes into a comparable scale of four steps. Table 1 presents summary statistics of other variables. The average age is The income information is based on categorical responses of income intervals. We estimate the midpoint of each interval under the assumption that income follows a log-normal distribution. We then as respondents in each interval the corresponding midpoint estimate.
We use a dummy variable to indicate such a status in the regression analysis. The average time spent on moderate to high intensity exercising is 1. We use the cumulative file for rounds 1—4,that has 34 participating countries. The ESS does not have information relating to online social networks. Figure 4 plots the distributions. Table 2 presents summary statistics of other variables. By covering many different countries, adopting a different way of measuring interactions with friends, and by having additional measures of subjective well-being, the ESS increases the power and generality of our findings about the happiness effects of real-life friends.
The right-hand side includes an intercepta vector of control variables inas well asthe size of real-life network of friends, andthe size of online network. The control vector includes age, gender and marital status, education, income and unemployment status. To help remove possible effects of unmeasured personality differences, we also include the time spent on exercise, whether the respondent volunteers or contributes to charitable causes and self-reported daily stress levels.
These variables are likely to be influenced by individual personality differences, as are the size of networks of friends. If our key for the effects of friends hold whether or not our equations include these other variables, they we are more confident in concluding that our are not being driven by personality differences. The measure of life ladder is ordinal; but as commonly found in the literature, little is lost if we treat it as cardinal. For example,  reported that the choice between probit regressions, which treats dependent variables as ordinal, and Ordinary Least Squares OLSwhich treats dependent variables as cardinal, makes virtually no difference to the estimated relationships between happiness and important explanatory variables.
In this paper, we will present from the method of OLS; Ordered Probit estimations yield qualitatively similar findings. In terms of quantitative evaluations, our discussions will be based largely on the concept of compensating differentials: we will compare the estimated effects of social networks to the estimated effects of income. The ratios of coefficients are almost invariant to the choice of regression methods, as switching between OLS and Ordered Probit affects estimated coefficients almost proportionally see  for an example.
The variables of interest on the right-hand side are the sizes of social networks in real life and on-line. In both cases, the size information is based on categorical choices from a set of intervals the distributions are shown in Figure 2. We enter the size information into the regressions in two different ways. The first approach uses a set of dummy variables to indicate the intervals. This avoids making assumptions regarding the functional form of the relationships between network sizes and subjective well-being. The second approach imposes an assumption that the relationship is log-linear.
To implement the log-linear approach, we turn the intervals into continuous values by asing the midpoint of an interval to observations in that interval. Table 3 shows the regression output. In all columns, the dependent variable is the 0—10 point life ladder. In the first column, we enter the network sizes as a set of categorical variables. In the second column, the network sizes are in logged continuous values.
The first two columns show the happiness effects of real-life and on-line friends without the inclusion of other variables. In columns 3 and 4 we add a full set of control variables to be described below, and in columns 5 and 6 we further test the robustness of our findings by adding a measure of psychological stress.Friends needs some 46 Columbia
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Comparing the Happiness Effects of Real and On-Line Friends