Exercises

Exercise 1

Let’s start with a simple exercise comparing life satisfaction measures of two groups – the unemployed people looking for work versus people in paid work. Calculate the means for the two groups across Europe. Remember to apply appropriate weights. You are going to use the variables ‘stflife - How satisfied with life as a whole’ and ‘mainact - Main activity last 7 days’. If you are using SPSS, calculate the t-statistic to determine whether the difference between the two groups is significant.

Solution - Nesstar

The table should now tell you that the people in paid work tend to be more satisfied than unemployed people. The mean life satisfaction for people in paid work is 6.7, and 5.1 for those unemployed looking for a job.

Solution - SPSS

We assume that you have downloaded the dataset and that you have SPSS available.

First you should weight the data: Select ‘Data’ on the menu, then ‘Weight cases’, ‘Weight cases by’, find the combined weight and click ‘OK’.

The second step is to perform the t-test. Select ‘Analyse’ on the menu, then ‘Compare Means’ - ‘Independent Samples T-Test’. Select ‘stflife - How satisfied with life as a whole’ as your ‘Test Variable’ and and ‘mainact - Main activity last 7 days’ as your ‘Grouping Variable’. Define the groups like this: Group 1 is the value 1 (paid work), and group 2 is the value 3 (unemployed and looking for a job).

The results should now tell you that the people in paid work tend to be more satisfied than unemployed people: Mean life satisfaction for people in paid work = 6.68, for those unemployed looking for a job = 5.08. t-score (assuming equal variances) = 11.38, p <0.001.

You could also copy this syntax and paste it into a syntax window in SPSS:

WEIGHT
BY COMWEIGHT.
 
T-TEST
GROUPS = MAINACT(1 3)
/MISSING = ANALYSIS
/VARIABLES = STFLIFE
/CRITERIA = CI(.95).

 

Next, let’s confirm the correlation between physical activity and life satisfaction (as noted in Dolan et al. 2006).1 Is it significant? Make sure you check the independent variable’s response codes to be sure – do high numbers mean high physical activity or do they mean low physical activity? Use the variables ‘stflife - How satisfied with life as a whole’ and ‘pactlot - My life involves a lot of physical activity’.

Solution - Nesstar

Make sure that the combined weight is still on.

The correlation should be 0.033, p<0.001.

By clicking the tab ‘Description’, you can see how the variables are coded. Larger values indicate more satisfaction and more activity. The positive correlation means that there is a tendency for activity to be accompanied by satisfaction.

Solution - SPSS

Make sure that the combined weight is still on.

Follow this path on the menu: Analyse – Correlate – Bivariate. Select the variables ‘stflife - How satisfied with life as a whole’ and ‘pactlot - My life involves a lot of physical activity’. Select the coefficient ‘Spearman’ or ‘Pearson’.

If you use Pearson, the results will be as follows: r = 0.033, p<0.001.

By looking at the value labels (the coding), you will see that larger values indicate more satisfaction and more activity. The positive correlation means that there is a tendency for activity to be accompanied by satisfaction.

You could also copy this syntax and paste it into a syntax window in SPSS:

CORRELATIONS
/VARIABLES=PACTLOT STFLIFE
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.

 

Do the results from the ESS corroborate the findings from Dolan et al.? If you like, check some of the other variables that we mentioned (e.g. marital status, income, social networks).

Exercise 2

Before moving on to Chapter 2, take a look at the list of questions in the survey and identify which you think could be included in subjective accounts of well-being. Be sure to choose questions which you think actually measure subjective well-being itself – i.e. how people feel about their experience of life, and not objective factors that might predict well-being (e.g. income). Don’t worry too much about any single question – the distinction between what someone feels and someone reports is often not clear cut. The framework that we will be presenting in the next chapter is just one way of making that cut.

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Footnotes