HOW THE DATA ARE HOMOGENIZED
This study collects the result of investigations that used acceptable measures of happiness. These acceptable measures are not quite identical. This chapter explains how the divergent data were classified into equivalent categories. It further considers three techniques for transforming responses to dissimilar questions into comparable scores.
Grouping comparable findings
This database presents the data by kind of happiness assessed. This breaks the data collection into four main parts: one big part on ‘overall happiness’, a smaller one on ‘hedonic level’ and two minor ones referring to ‘contentment’ and ‘mixed indicators’. Within these parts the collection is further differentiated in tables of near-identical indicators. This results in tables by ‘question-type’. Most of the tables with identical items concern overall happiness. Among these, three groups of questions some can be discerned which ask essentially the same thing, but that differ only in the rating of response. Though not ‘identical’, the items in these clusters are ‘equivalent’. As such they qualify for conversion to a common scale. The possibilities for converting average scores on divergent indicators of happiness are however limited.
Transformation of scores on slightly differing measures of happiness
Scores on indicators of different happiness variants can not be converted to the same standard. They measure essentially different things that do not necessarily coincide.
- Scores on different indicators of the same happiness variant can be converted in principle. However, in practice it is quite difficult to estimate the method effects involved. If sufficient data are available, we can inspect whether there is a linear relationship between responses yielded by different indicators in the same populations. Such data are only available for some single questions on overall happiness. We found a reliable relation in the nation scores on the two pairs of items: 1) 10-step life-satisfaction by 4-step satisfaction with way-of-life, and 2) 11-step life-satisfaction by 11-step best-worst possible life. In these cases missing values on one variable can be reliably estimated by linear regression on the basis of observed scores on the other; interpolation is less risky than extrapolation. In three pairs we found no reliable relation however: 1) happiness-in-life by satisfaction-with-life, 2) happiness-in-life by best-worst possible life, and 3) happiness-in-life by delighted-terrible life. In these latter cases we deem transformation inadvisable.
- Conversion is better possible when indicators (questions) are substantially equivalent, and differ only in number and labeling of response categories. In that case standardization by expert-weighting is justified. The expert-transformation applied here successfully passed a test for congruent validity. If differences between equivalent items concern only the length of a graphic or numerical rating scale, simple linear transformation (stretching) is most appropriate.
- Only the latter two standardization methods (expert-weighting and stretching) are applied in this data collection. In the tables transformed scores are mentioned for equivalent items. Transformed means are presented next to the original means.
- New methods for transformation have been developed in the context of this project, but are not yet applied to this collection of distributional findings on happiness in nations.
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