# Rolling hills

Another way to clearly visualise differences in components is in a ‘rolling hills’ graph, as shown below. In Figure 6 we present a given component (in this case ‘negative feelings’) and see how the order of countries differs for this component compared to its parent index – personal well-being index. The countries along the bottom are ordered in terms of the personal well-being index (with Denmark first, etc.). That way, it’s easy to spot, not only those countries with good scores on this component (e.g. Finland, Norway and Denmark), but also those countries that do particularly well or badly on this component relative to their overall position. For example, whilst Austria doesn’t do particularly badly (8th highest score), it is easy to see that it does very badly given its overall personal well-being score. In other words, Austrians suffer quite a high level of negative feelings given their overall high personal well-being.

## Exercise 9

Try and replicate the graph above with the same or another component, sub-component or even with the z-score for an individual question. If the indicator you have chosen is a subset of personal well-being, then order your countries as we have done, according to their mean personal well-being index. If, however, it is a subset of social well-being, then order them according to their mean social well-being indices.

Solution and SPSS syntax

Once you’ve calculated the means, transfer the scores and country names into Excel. Sort them by the overall scores, such that the country with highest overall index is top of the list (Denmark in both cases), and that with the lowest overall index is bottom (Ukraine). Then plot a graph with just the indicator, not the overall index.

WEIGHT BY DWEIGHT.

MEANS
TABLES= negative_affect personal_WBI BY CNTRY
/CELLS MEAN.

## Exercise 10: Income groups

Let’s do an analysis where the income group, rather than country, is the main independent variable. Take a single country. In the data set, there are two income variables. One retains the original 12 income bands in the ESS data set. The other compresses them into 4 bands so as to increase frequencies per band. We’ll use the latter of these, as there may not be enough data points across all 12 bands in a single country. Calculate mean values for a few components across the different income bands. If plotting these results, use the confidence intervals option to help draw conclusions (this is particularly important if you do use all 12 bands because it will help identify where there are few data points). What patterns do you find?

The income variable you want to use is HINCTNT2. To produce (in SPSS version 14.0) a line graph showing several variables, and select the ‘Line Chart’ function from the ‘Graphs’ menu, select ‘multiple’, with data in chart as ‘summaries of separate variables’. The option for confidence intervals (or error bars) is within the ‘Options’ menu

*Example, looking at self-esteem, autonomy, and trust & belonging, in Germany.

WEIGHT BY DWEIGHT.

COMPUTE filter_\$=(CNTRY='DE').
VARIABLE LABEL filter_\$ "CNTRY='DE' (FILTER)".
VALUE LABELS filter_\$ 0 'Not Selected' 1 'Selected'.
FORMAT filter_\$ (f1.0).
FILTER BY filter_\$.
EXECUTE.

MEANS
TABLES=selfesteem autonomy trust_belonging BY hinctnt2
/CELLS MEAN COUNT STDDEV.

GRAPH
/LINE(MULTIPLE)=MEAN(selfesteem) MEAN(autonomy) MEAN(trust_belonging) BY hinctnt2
/MISSING=LISTWISE
/INTERVAL CI( 95).

FILTER OFF.
USE ALL.
EXECUTE .
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