Abstract
This chapter presents a confirmatory type of analysis using structural equation modeling (SEM) in order to examine research question Iv): Is there statistical evidence that Financial Needs and Expectations (operationalized by socio-professional category, health status and frequency of social participation) play a mediating role between economic resources and self-assessed economic strain (difficulties in making ends meet)? Among various approaches that exist in moving from theory to a structural equation model, the one proposed here has been referred to as ‘model generating’: if the initially estimated theoretical model fails to fit the data well, we will consult modification indices with the objective of finding a model that is rooted in theory and that fits the data well.
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Notes
- 1.
As previously explicated, it was not possible to include income into the regression models because this variable constitutes the basis on which the Objective Measure is built.
- 2.
The general assumption that Background Characteristics are not relevant in models that integrate the variable wealth was tested for the model shown in Fig. 20.2. The standardized beta coefficients of the model controlling for age, sex, marital status and education only varied by maximum 0.03 from the original model, confirming the choice of the original, more parsimonious model.
- 3.
With listwise delition, the entire record of a given respondent is excluded from analysis if any single value is missing.
- 4.
The standardized solution rescales all variables – observed and latent – to have a variance of 1.0.
- 5.
The RMSEA is a goodness-of-fit indicator that penalizes the model for unnecessary added complexity by measuring how much error there is for each degree of freedom. It is recommended that the RMSEA be less than 0.05 for a good fit and less than 0.10 for an acceptable fit.
- 6.
The SRMR reports how close the model comes to reproducing each correlation, on average.
- 7.
The CFI compares the estimated model with a null model, indicating how much better the estimated model fits the data.
- 8.
It took 15 instead of the previous 6 iterations.
- 9.
Stata indicates this situation with the report ‚not concave’.
- 10.
The number of iteration decreased to 9.
- 11.
Since this particular effect is not the object of our research, we refrain from discussing in detail the direct and indirect effects of economic resources on social participation, which is, however, important for social policy interventions. (See Baeriswyl, Marie, 2016).
- 12.
The Stata command for this option is method(ml) vce(robust). It uses the Huber-White sandwich estimator for the variance-covariance matrix (Acock 2013, p.15).
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Henke, J. (2020). A Structural Equation Model for Self-Assessed Economic Vulnerability. In: Revisiting Economic Vulnerability in Old Age. Life Course Research and Social Policies, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-36323-9_22
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