Abstract
The subjective measures of well-being are mainly constructed on ordinal indicators. Therefore, the choice of the correct method to synthesize the information collected becomes fundamental. This is particularly true for studies involving large populations of respondents, as in the case of official statistical. In this study, we deal with the analysis of subjective well-being data from a European harmonized official statistical survey: the European Union Survey on Income and Living Conditions (Eu-SILC). We take into account the ordinal nature of items. In particular, we propose a two-step synthesis process: first, we address the synthesis of the indicators used to measure the emotional state, and then move on to the synthesis of three dimensions of the subjective well-being, namely life satisfaction, meaning of life and emotional status. Finally, we propose an analysis of the differences in well-being assessment among different sub-populations of respondents. Our analysis intends to safeguard the multidimensionality of the phenomenon, applying the Partially Ordered Set methodology to the micro-data analysis.
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Notes
- 1.
e.g. the attitude scales (Likert, Thurstone), social distance scale (Bogardus), semantic differential scales (Osgood).
- 2.
Developed by a group of scholars at the New England Medical Center in Boston coordinated by J. H. Ware (1993).
- 3.
It should be made clear that the choice of the measurement model does not depend on a free choice of the researcher, but exclusively on the nature of the latent variable measured (Alaimo and Maggino 2020).
- 4.
The choice of Pearson’s correlation coefficient reveals the questionable assumption that variables are quantitative.
- 5.
The two items are both revealed on a scale from 0 to 10. Recoding defined 3 modalities: low if the level is less than six, median if the level is between 6 and 8 and high for levels 9 and 10.
- 6.
Q: How much of the time, during the past four weeks have you been/felt: Very nervous; Down (in the dump); Calm and peaceful; Downhearted and depressed; Happy.
- 7.
A: All of the time; Most of the time; Some of the time; A little of the time; None of the time.
- 8.
Mental status is calculated as the average score of the answers to the five questions on emotional states, reporting the value on a scale from 0 to 100.
- 9.
There is a large literature on the treatment and synthesis of multidimensional systems of ordinal data using non-aggregative methods, allowing the construction of synthetic measures without the aggregation of the scores of basic indicators. Within this approach, poset has become a reference over the years, as demonstrated by many works in different fields of research (for instance, see: Annoni and Bruggemann 2009; Fattore et al. 2015; Carlsen and Bruggemann 2017; Arcagni et al. 2019). However, poset can also be suitable for quantitative data (see: Fattore 2018; Alaimo 2020; Alaimo et al. 2020a, b, c), allowing the overcoming of some limitations of the aggregative methods.
- 10.
EC Regulation n.1177/2003.
- 11.
Full-time employees (EFT), Part-time employees (EPT), Full-time self-employed (SEFT), Part-time self-employed (SEPT), Unemployed (UNE), Students (STU), Retired (RET), Unfit to work (UNF), Fulfilling domestic care (HOU), Other inactive (INA).
- 12.
We only measure linear relationship. It is therefore perfectly possible that while there is strong non-linear relationship between the variables, r is close to 0.
- 13.
For an example of the distribution of homogeneous profiles within the total ones, see: Conigliaro (2018).
- 14.
Other thresholds could also be added to better characterize and distinguish profiles. However, in this case we have not identified another suitable point of discrimination at conceptual level.
- 15.
For a complete definition of the identification function and its computation, please see: Fattore et al. 2012.
- 16.
All the operations were carried out using the R package PARSEC (Arcagni and Fattore 2018).
- 17.
MPI is a partially non-compensatory composite indicator based on a non-linear function which, starting from the arithmetic mean of the normalized indicators (indicators are standardized by means of a variant of z-scores, which transforms the indicators into distributions with mean 100 and standard deviation 10) introduces a penalty for the units with unbalanced values of the indicators. We chose this method because various analyses have shown that it is more robust than others are (for instance, see: Mazziotta and Pareto 2015; Alaimo 2020). For more information on the MPI, please see: Mazziotta and Pareto 2016 and 2017.
- 18.
In the normalization, it is necessary to define the polarity of the basic indicators, i.e. the sign of the relation between the indicator itself and the phenomenon to be measured. Therefore, the type of composite we want to construct defines polarity. In our case, the average rank has positive polarity, while the identification and the severity functions negative.
- 19.
Recoding method: (0–5) = 1 – “Low”; (6–7) = 2 – “Medium”; (8–10) = 3 – “High”.
- 20.
We consider three categories of labour status (unemployed, part-time employees and full-time employees), three categories of gender (female, male and total population) and their interactions.
- 21.
For a definition of the two functions and their computational procedures, please see: Arcagni and Fattore 2018.
- 22.
The relative wealth is the average graph distance from the maximum threshold element, over the sampled linear extensions divided by the maximum wealth.
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Alaimo, L.S., Conigliaro, P. (2021). Assessing Subjective Well-being in Wide Populations. A Posetic Approach to Micro-data Analysis. In: Bruggemann, R., Carlsen, L., Beycan, T., Suter, C., Maggino, F. (eds) Measuring and Understanding Complex Phenomena. Springer, Cham. https://doi.org/10.1007/978-3-030-59683-5_16
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