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Investigating the Dimensions of Youth Wellbeing: An Exploratory Structural Equation Modelling Approach Applied to Palestine

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This paper illustrates the “Sen-Nussbaum-type” capability approach to the measurement of youth wellbeing using the newly developed Exploratory Structural Equation Modelling (ESEM). It offers insights into how the capability to achieve wellbeing can be measured in a conflict-affected and resource-constrained setting. The methodology is applied to nationally representative data taken from the Palestinian Family Survey. The population of interest is youth aged 15 to 29. Three capability dimensions are identified: health awareness, knowledge and living conditions. Results show an interrelation between capability dimensions. It is especially important to note the effect of knowledge capabilities on both health awareness and living conditions indicators. Results also confirm the importance of some (exogenous) factors such as the education of the household head in the conversion of capabilities into achievements. Capabilities are shown to be highest in the West Bank for both knowledge and living conditions compared to the Gaza Strip.

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Correspondence to Maame Esi Woode.

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This work has been carried out thanks to the support of the A*MIDEX project (no. ANR-11-IDEX-0001-02) funded by the “Investissements d’Avenir” French Government program, managed by the French National Research Agency (ANR).

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Table S1

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Appendix 1

Fig. 1
figure 1

ESEM in MIMIC model

Table 6 Item correlation
Table 7 Factor loadings with correlations

Appendix 2: Generalised ESEM

Following Asparouhov and Muthén (2009), assume there exists p dependent underlying unobserved variables, Y*, one for each categorical variable, Y and q independent variables X with m latent variables η. In addition, assume that there exists a set of parameters, τ, for each categorical variable such that Y = k if τ k  < Y* < τ k + 1. Then the generalised ESEM (GESEM) model is specified using the following two equations,

$$ \boldsymbol{Y}=\boldsymbol{\nu} +\boldsymbol{\varLambda} \boldsymbol{\eta} +\mathbf{K}\mathbf{X}+\varepsilon $$
$$ \boldsymbol{\eta} =\alpha +\mathbf{B}\boldsymbol{\eta } +\varGamma \mathbf{X}+\zeta $$

Equation (1) represents the measurement part – also referred to as the Qualitative Response Model (QRM). The QRM specifies how the latent variables are related to the observed responses. ν is the vector of intercepts. Equation (2) represents the latent variable model or the structural simultaneous equation model (SEM), with Γ and B being the respective coefficient matrices and α a vector if intercepts. The latent variables, η, are made up of both explanatory factors and item factors (confirmatory). The respective error terms of the SEM and QRM vectors (ε and ζ) are assumed to be (i) with zero expectations, (ii) uncorrelated with each other (ζ uncorrelated with ε), but (iii) correlated within each. Formally,

$$ \boldsymbol{E}\left({\boldsymbol{\varepsilon}}_{\boldsymbol{i}}\right)=0,\ \boldsymbol{E}\left({\boldsymbol{\zeta}}_{\boldsymbol{i}}\right)=0;\ \boldsymbol{V}\left({\boldsymbol{\varepsilon}}_{\boldsymbol{i}}\right)=\boldsymbol{E}\left({\boldsymbol{\varepsilon}}_{\boldsymbol{i}},\ {\overset{\prime }{\boldsymbol{\varepsilon}}}_{\boldsymbol{i}}\right)=\boldsymbol{\varPhi};\ \boldsymbol{V}\left({\boldsymbol{\zeta}}_{\boldsymbol{i}}\right)=\boldsymbol{E}\left({\boldsymbol{\zeta}}_{\boldsymbol{i}},\ \overset{^{\prime }}{{\boldsymbol{\zeta}}_{\boldsymbol{i}}}\right)=\boldsymbol{\varPsi} $$

where Ф and Ψ are the covariance matrices for the residuals in the QRM and the SEM equations, respectively; Ψ is assumed to be diagonal and Λ non-singular. Please see Figure S1 for a graphical representation of the GESEM model.

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Abu-Zaineh, M., Woode, M.E. Investigating the Dimensions of Youth Wellbeing: An Exploratory Structural Equation Modelling Approach Applied to Palestine. Child Ind Res 11, 57–78 (2018).

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