Creating composite indicators with DEA and robustness analysis: The case of the technology achievement index.

Composite indicators are regularly used for benchmarking countries’ performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of sub-indicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries’ composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.Indexes; Indicators; Robustness; Technology;

Creating Composite Indicators with DEA and Robustness Analysis: the case of the Technology Achievement Index

Composite indicators are regularly used for benchmarking countries’ performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of sub-indicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries’ composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.factor is more important in explaining the observed progress.composite indicators, aggregation, weighting, Internal Market

Comparative Analysis of Alcohol Control Policies in 30 Countries

Using an index that gauges the strength of national alcohol policies, a clear inverse relationship was found between policy strength and alcohol consumption

University rankings:What do they really show?

University rankings as developed by the media are used by many stakeholders in higher education: students looking for university places; academics looking for university jobs; university managers who need to maintain standing in the competitive arena of student recruitment; and governments who want to know that public funds spent on universities are delivering a world class higher education system. Media rankings deliberately draw attention to the performance of each university relative to all others, and as such they are undeniably simple to use and interpret. But one danger is that they are potentially open to manipulation and gaming because many of the measures underlying the rankings are under the control of the institutions themselves. This paper examines media rankings (constructed from an amalgamation of variables representing performance across numerous dimensions) to reveal the problems with using a composite index to reflect overall performance. It ends with a proposal for an alternative methodology which leads to groupings rather than point estimates

Improving benchmarking by using an explicit framework for the development of composite indicators: an example using pediatric quality of care

<p>Abstract</p> <p>Background</p> <p>The measurement of healthcare provider performance is becoming more widespread. Physicians have been guarded about performance measurement, in part because the methodology for comparative measurement of care quality is underdeveloped. Comprehensive quality improvement will require comprehensive measurement, implying the aggregation of multiple quality metrics into composite indicators.</p> <p>Objective</p> <p>To present a conceptual framework to develop comprehensive, robust, and transparent composite indicators of pediatric care quality, and to highlight aspects specific to quality measurement in children.</p> <p>Methods</p> <p>We reviewed the scientific literature on composite indicator development, health systems, and quality measurement in the pediatric healthcare setting. Frameworks were selected for explicitness and applicability to a hospital-based measurement system.</p> <p>Results</p> <p>We synthesized various frameworks into a comprehensive model for the development of composite indicators of quality of care. Among its key premises, the model proposes identifying structural, process, and outcome metrics for each of the Institute of Medicine's six domains of quality (safety, effectiveness, efficiency, patient-centeredness, timeliness, and equity) and presents a step-by-step framework for embedding the quality of care measurement model into composite indicator development.</p> <p>Conclusions</p> <p>The framework presented offers researchers an explicit path to composite indicator development. Without a scientifically robust and comprehensive approach to measurement of the quality of healthcare, performance measurement will ultimately fail to achieve its quality improvement goals.</p