4. PROPUESTA DE INNOVACIÓN
4.1 Presentación de la propuesta
5.6.4.1 Objectives
The objectives of these chapters were to:
Explore potential latent and pathway effects from childhood socioeconomic position to quality of life in early old age.
Investigate the relationships by welfare regime.
2 Note that individuals who reported they were currently looking after the home or family were included in the overall analysis if their own (or their partner’s) previous occupational information was available.
5.6.4.2 Overall results
First, the overall Pearson’s correlation coefficients between the measures of
socioeconomic position were examined (converted to standardised socioeconomic ranks as described above). This was done to check whether any correlation coefficients were considered too high, which could indicate collinearity issues. The statistical models calculated in this chapter were at risk of multicollinearity, as they contained multiple measures of socioeconomic position. Therefore, this issue is explored in further detail.
Multicollinearity is “an interdependency condition that can exist quite apart from the nature, or even the existence, of dependence between X and y” (Farrar & Glauber, 1967:
p93). In other words, multicollinearity is a problem arising from statistical models which contain independent variables that are highly correlated. Although independent variables in a model are often weakly correlated, problems may arise if the correlation between variables exceeds around 0.8 (Farrar & Glauber, 1967). If severe, multicollinearity can result in estimates that have increased variance, which leads to the greater likelihood of accepting the null hypothesis (Rockwell, 1975).
In this study, multicollinearity could be present in models containing multiple measures of socioeconomic position. Thus, it was important to investigate whether this was going to be an issue in the statistical models. Several practices are recommended to assess the degree of multicollinearity. As well as examining the correlations between the
explanatory variables, the variance inflation factor (VIF) can be calculated. The VIF provides a practical measure of the effects of multicollinearity on the variance of the specific regression coefficients (O’Brien, 2007). A VIF of around 10 is often used as a criterion indicating that multicollinearity may be an issue. However, this is arbitrary and O’Brien (2007) has argued that VIFs of even 40 or over do not discount the results of regression analyses and does not imply that independent variables should be removed from the model or be combined into a single index.
Examination of the correlation coefficients between the standardised socioeconomic ranks relating to each measure of socioeconomic position demonstrated that none of the correlations were above 0.5, the strongest being found between the skill level of the main occupation and education level (full results are discussed in chapter 7). The Stata
command ‘collin’ provides estimates for the VIF. Examining all of the measures of socioeconomic position together, demonstrated that the VIFs were below 10, the recommended value that may indicate multicollinearity requires further investigation (Table 5.3). Therefore, multicollinearity was not considered to be an issue in the statistical models.
Table 5.3: Multicollinearity assessment for the measures of socioeconomic position (using their standardised socioeconomic ranks)
Variable VIF
Childhood
Number of books 1.43
Rooms per capita 1.16
Amenities in household 1.31
Occupation of main breadwinner (skill level) 1.19 Adulthood
Education level 1.45
Main occupation (skill level) 1.37
Current income 1.22
Current wealth 1.17
Mean VIF 1.29
VIF=variance inflation factor
Next, models were calculated to see whether associations between the measures of childhood socioeconomic position and quality of life remained after including adulthood socioeconomic position variables. This was achieved by assessing the association between each measure of childhood socioeconomic position and quality of life, and comparing these models with further models that added adulthood measures of socioeconomic position in a stepwise fashion. If the association between childhood socioeconomic position and quality of life is attenuated and no longer statistically associated after including adulthood socioeconomic position, it is suggestive of a pathway effect.
Whereas, if the association remains statistically significant it suggests there may be a latent effect. However, the two processes can operate together (as discussed in chapter 1). Next, all measures of socioeconomic position from across the life course were included in a model to examine which measures of socioeconomic position were most strongly associated with quality of life.
Following this, a path analysis approach was used to test a base model of the hypothesised relationships between the socioeconomic variables, as well as their
influence of quality of life. The first step in a path analysis involves drawing a path diagram, which is a visual representation of the hypothesised relationships between the observed variables (Figure 5.5). The path diagram shows that the measures of childhood socioeconomic position were hypothesised to influence quality of life in early old age indirectly via the respondent’s education level, main occupation, current income, and wealth. The education level, main occupation, current income, and wealth variables were hypothesised to have direct effects on quality of life. In addition, the model allows the occupation of the main breadwinner in childhood to have a direct effect on the
respondent’s main occupation. The childhood measures of socioeconomic position were also allowed to influence current wealth, because wealth may have been inherited inter-generationally. In addition, the childhood socioeconomic variables were allowed to correlate with one another. The respondent’s education level was considered to
influence their main occupation, which then influenced their current income and current wealth. Income was also allowed to influence wealth and vice versa. This theoretically driven model facilitated investigation of the pathway from childhood socioeconomic position and quality of life. The addition of direct effects from the childhood
socioeconomic position variables to current quality of life then allowed the examination of latent (direct) effects from childhood. The model fit can also be compared to the base model in order to see which model fitted the data best. To calculate the direct, indirect, and total effects from childhood socioeconomic position to quality of life, the model which included any statistically significant direct paths from childhood socioeconomic position to quality of life was used, removing direct effects from childhood which were not statistically significant. All path models were controlled for age and country (fixed) effects.
5.6.4.3 The influence of the welfare regime
To investigate differences in pathway and latent effects by welfare regime, the above analyses were repeated separately for each welfare regime.
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Grey boxes indicate measures of childhood socioeconomic position; model also allows the measures of childhood socioeconomic position to be correlated but these are not shown in the diagram
Education level Main occupation
Current wealth Current income
Current quality of life
Number of books
Rooms per capita
Amenities
Breadwinner occupation
Figure 5.5: Path diagram showing the hypothesised relationships between the observed variables