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Books on the topic 'Indicator aggregation'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Kaufmann, Daniel. Aggregating governance indicators. World Bank, Development Research Group, Macroeconomics and Growth, 1999.

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2

Fund, International Monetary. Aggregation of economic indicators across countries: Exchange rate versus PPP based GDP weights. International Monetary Fund, 1992.

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3

1957-, Banaian King, and Roberts Bryan W, eds. The design and use of political economy indicators: Challenges of definition, aggregation, and application. Palgrave Macmillan, 2008.

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4

Kraay, Aart, Pablo Zoido-Lobaton, and Daniel Kaufmann. Aggregating Governance Indicators. The World Bank, 1999. http://dx.doi.org/10.1596/1813-9450-2195.

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5

Fazekas, Mihály, Luciana Cingolani, and Bence Tóth. Innovations in Objectively Measuring Corruption in Public Procurement. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817062.003.0007.

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While there is continued interest in measuring governance, disagreement on how best to do so has only grown over time. To provide pointers at innovative and rigorous indicator building, this chapter documents innovations in measuring a particularly challenging governance dimension: corruption in public procurement. In hopes of inspiring future research, the chapter critically reviews objective corruption proxies using administrative data on government purchases falling in four broad categories: tendering risk indicators, political connections indicators, supplier risk indicators, and contracti

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6

Auvinen, Ari-Pekka, Gregor Levin, Anders Glimskär, Bo Normander, Gudmundur A. Gudmundsson, and Odd Stabbetorp. Aggregation of indicators for biological diversity in the Nordic countries. Nordic Council of Ministers, 2006. http://dx.doi.org/10.6027/tn2006-554.

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7

Aggregation of Indicators for Biological Diversity in the Nordic (Termanord). Nordic Council of Ministers, 2006.

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8

Schulze-Gattas, Marianne, and Anne-Marie Gulde. Aggregation of Economic Indicators Across Countries: Exchange Rate Versus PPP Based GDP Weights. International Monetary Fund, 1992.

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9

Schulze-Gattas, Marianne, and Anne-Marie Gulde. Aggregation of Economic Indicators Across Countries: Exchange Rate Versus Ppp Based Gdp Weights. International Monetary Fund, 1992.

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10

Roberts, B., and K. Banaian. Design and Use of Political Economy Indicators: Challenges of Definition, Aggregation, and Application. Palgrave Macmillan, 2015.

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11

Roberts, B., and K. Banaian. Design and Use of Political Economy Indicators: Challenges of Definition, Aggregation, and Application. Palgrave Macmillan Limited, 2008.

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12

Schulze-Gattas, Marianne, and Anne-Marie Gulde. Aggregation of Economic Indicators Across Countries: Exchange Rate Versus Ppp Based Gdp Weights. International Monetary Fund, 1992.

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13

Goertz, Gary. Concepts, Theories, and Numbers: A Checklist for Constructing, Evaluating, and Using Concepts or Quantitative Measures. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0005.

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This article presents guidance on how to think about the concepts. It proposes to explore some issues in the assessment of concepts and quantitative measures. It presents the basic problem in its general outlines. It then offers a very short example, typically using published research. The structuring and aggregating concepts and measures indicates that one must first consider the theory embodied in the concept. Then one should survey plausible aggregation and structural relationships that could be applied in a quantitative measure. It is noted that one needs to ask about the existence or not

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14

Bianconi, Ginestra. Communities. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0008.

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Multilayer networks have a mesoscale structure organized in multilayer communities, spanning different layers and often revealing important functional properties of the network. In this chapter the major techniques proposed for detecting and characterizing the multilayer communities are described, including: generalized modularity, consensus clustering, multilayer infomaps, multilink communities, tensorial decomposition, Normalized Mutual Information, theta indicators. The main benefits and limitations of these approaches are discussed and revealed by analysing the results obtained on real dat

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15

Maestas, Cherie. Expert Surveys as a Measurement Tool. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.13.

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Polling political elites, academics, or other types of knowledgeable experts has provided scholars with a trove of information about political institutions, processes, and local context. Expert surveys are an especially useful method for measuring concepts that would be difficult or impossible to measure through alternative strategies and permits scholars to create indicators that are comparable across diverse contextual settings. This chapter provides an overview of the key questions researchers face when using experts as a tool of measurement. It highlights considerations regarding survey de

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16

Balik, Martin. Perioperative cardiac care of the high-risk non-cardiac patient. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0076.

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Non-cardiac surgery conveys a cardiac risk related to the status of the patient’s cardiovascular system. Cardiac-related risk of surgery can be assessed by integrating the risk and urgency of the procedure with cardiovascular risk factors, which include age, ischaemic heart disease, heart failure, stroke, diabetes mellitus, chronic obstructive pulmonary disease, and renal dysfunction. An individual assessment can include simple multivariate scoring systems, developed with the aim of evaluating cardiac risk prior to non-cardiac surgery. Patient assessment can be extended for indicated additiona

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17

Balik, Martin. Perioperative cardiac care of the high-risk non-cardiac patient. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199687039.003.0076_update_001.

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Non-cardiac surgery conveys a cardiac risk related to the status of the patient’s cardiovascular system. Cardiac-related risk of surgery can be assessed by integrating the risk and urgency of the procedure with cardiovascular risk factors, which include age, ischaemic heart disease, heart failure, stroke, diabetes mellitus, chronic obstructive pulmonary disease, and renal dysfunction. An individual assessment can include simple multivariate scoring systems, developed with the aim of evaluating cardiac risk prior to non-cardiac surgery. Patient assessment can be extended for indicated additiona

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18

Balik, Martin. Perioperative cardiac care of the high-risk non-cardiac patient. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199687039.003.0076_update_002.

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Abstract:

Non-cardiac surgery conveys a cardiac risk related to the status of the patient’s cardiovascular system. Cardiac-related risk of surgery can be assessed by integrating the risk and urgency of the procedure with cardiovascular risk factors, which include age, ischaemic heart disease, heart failure, stroke, diabetes mellitus, chronic obstructive pulmonary disease, and renal dysfunction. An individual assessment can include simple multivariate scoring systems, developed with the aim of evaluating cardiac risk prior to non-cardiac surgery. Patient assessment can be extended for indicated additiona

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19

Umbach, Gaby. Measuring (Global) Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793342.003.0003.

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The chapter questions how we can measure global governance. It critically examines existing approaches to the measurement of global governance. It pays particular attention to key conceptual and methodological concerns of the overall endeavour to quantify and/or qualify global governance. The chapter focuses on the measurement of global governance as a multidimensional paradigm of international political and institutional practice that, being not measurable per se, requires complex aggregations of indicators and statistical data to serve as proxies to capture its broad conceptual character. As

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