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Multi-level DEA for the construction of multi-dimensional indices.

ABSTRACT: Data Envelopment Analysis (DEA) is a non-parametric, mathematical programming method that is used to evaluate the performance of Decision Making Units. One variation of the method is focused on expanding the number of stages: inputs are transformed into intermediate measures and in turn those are transformed into outputs. DEA and its variations have been used to construct composite indicators. The purpose of the current paper is to propose a new variation of DEA that relies on a two-stage model for the construction of multi-dimensional indices. The proposed variation:•Uses a two-stage DEA model for the calculation of each sub-indicator that will be integrated into the final index•All the sub-indicators are integrated into the final index with the use of a Benefit-of-the-Doubt mathematical programming model.As it was mentioned, the proposed method can be used for the construction of multi-dimensional indicators and in the current paper is used to calculate the sustainability of the EU-28 countries.

SUBMITTER: Tsaples G

PROVIDER: S-EPMC7726711 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Multi-level DEA for the construction of multi-dimensional indices.

Tsaples Georgios G Papathanasiou Jason J

MethodsX 20201203

Data Envelopment Analysis (DEA) is a non-parametric, mathematical programming method that is used to evaluate the performance of Decision Making Units. One variation of the method is focused on expanding the number of stages: inputs are transformed into intermediate measures and in turn those are transformed into outputs. DEA and its variations have been used to construct composite indicators. The purpose of the current paper is to propose a new variation of DEA that relies on a two-stage model ...[more]

PMID: 33318961

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Project description:Housing is an important health determinant, in particular for asylum seekers and refugees (ASR) living in state-provided accommodation and struggling for residential autonomy. However, few validated objective measurement tools exist to measure housing quality in the sense of the deterioration of the housing environment. We aimed to construct and validate an instrument to enable resource-efficient monitoring of and health research on such housing quality. After considering existing theoretical frameworks on housing effects on health, we constructed an easily applicable tool measuring the degree of “Small-area Housing Environment Deterioration” (SHED), based on the “Broken Windows” - index. In a validation study, we tested SHED index's objectivity and reliability, measuring inter-/intra-rater reliability and internal consistency and discussed its strengths and limitations by means of cognitive testing. We ran a field-test as part of a population-based, cross-sectional refugee health survey in a random sample of 58 shared accommodation centers across 44 districts of the German federal state of Baden-Wuerttemberg, enabling us to test index applicability and convergence with ASR's satisfaction with their living place. The new SHED index provides a validated and field-tested measure of deterioration of small-area housing environment with substantial reliability. Serving both researchers and policy-makers, SHED offers an easily applicable index to support epidemiological analyses on housing as a contextual and social determinant of health as well as evidence-informed decision making in questions of housing policies. Highlights • Deterioration of housing may affect physical and mental health.• Validated, practicable measurement tools for deterioration do not currently exist.• We constructed an index to assess deterioration of small-area housing environment.• Validation study showed high inter/intra-rater reliability and internal consistency.• Cognitive interviews and field testing in refugee centers demonstrated applicability.

Dataset Information

Multi-level DEA for the construction of multi-dimensional indices.

Publications

Multi-level DEA for the construction of multi-dimensional indices.

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