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Books on the topic 'Indicator data'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

(Europe), NEC Electronics. FIP fluorescent indicator panel data book. Düsseldorf: NEC Electronics, 1993.

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2

Cobb, Clifford. The genuine progress indicator: Summary of data and methodology. [San Francisco, CA]: Redefining Progress, 1995.

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3

Cunningham, James K. Risk indicator monitoring system (RIMS): Initial measures and findings : data report. [Santa Ana, CA]: County of Orange Health Care Agency, Office of Policy and Planning Coordination, 1992.

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4

Ontario. Ministry of Northern Development and Mines. Ontario Geological Survey. Kimberlite heavy mineral indicator data release / by T.F. Morris ... [et al.]. Sudbury, ON: The Minstry, 1995.

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5

Will-Wolf, Susan. Analyzing lichen indicator data in the Forest Inventory and Analysis Program. Portland, OR: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2010.

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6

Loveday, Barry. Managing crime: Police use of crime data as an indicator of effectiveness. Portsmouth: University of Portsmouth,Institute of Criminal Justice Studies, 2000.

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7

Hopkins, Shaun M. Social indicator and demographic data report for the district of Sudbury and Manitoulin. Sudbury, Ont: Children's Services Coordinating and Advisory Group for the Districts of Sudbury and Manitoulin, 1985.

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8

Zegarac, George. Secondary school reform in Ontario and the role of research, evaluation and indicator data. [Toronto, Ont: Ontario Ministry of Education, 2007.

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9

Johannes, Roseboom, and International Service for National Agricultural Research., eds. ISNAR agricultural research indicator series: A global data base on national agricultural research systems. Cambridge [England]: Cambridge University Press, 1989.

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10

(SSRAG), Social Service Research and Advisory Group. Demographic data and selected social indicator profile for Manitoulin and Sudbury Districts, 1993: Part 1. Sudbury, Ont: Social Service Research and Advisory Group, 1993.

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11

O'Neill, Katherine P. Soils as an indicator of forest health: A guide to the collection , analysis, and interpretation of soil indicator data in the Forest Inventory and Analysis Program. St. Paul, MN: USDA Forest Service, North Central Research Station, 2005.

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12

Tume ya Kudhibiti Ukimwi Tanzania. Youth and HIV/AIDS: Data from the 2007-08 Tanzania HIV/AIDS and malaria indicator survey (THMIS). Dar es Salaam: Tanzania Commission for AIDS, 2008.

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13

Morace, Jennifer L. Fecal-indicator bacteria in the Yakima River Basin, Washington: An examination of 1999 and 2000 synoptic-sampling data and their relation to historical data. Portland, Or: U.S. Department of the Interior, U.S. Geological Survey, 2002.

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14

Morace, Jennifer L. Fecal-indicator bacteria in the Yakima River Basin, Washington: An examination of 1999 and 2000 synoptic-sampling data and their relation to historical data. Portland, Or: U.S. Department of the Interior, U.S. Geological Survey, 2002.

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15

Riley, John A. A comparison of multivariate statistical analysis and the use of an indicator ion for the interpretation of water quality data. Moscow, ID: University of Idaho, 1990.

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16

Gokhberg, L. M. Indicators of Innovation: Data Book. Moscow: Higher School of Economics, 2004.

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17

Meyer, Susan. Understanding economic data. New York: Rosen Pub., 2012.

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18

Bidani, Benu. Decomposing social indicators using distributional data. Washington, D.C: World Bank, Policy Research Dept., Poverty and Human Resources Division, 1995.

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19

Sheram, Katherine. The development data book. 2nd ed. Washington, D.C: World Bank, 1988.

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20

World Bank. The little data book. Washington, D. C: World Bank, 2009.

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21

World Bank. The little green data book. Washington, D.C: International Bank for Reconstruction and Development/The World Bank, 2009.

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22

Kim, Insun. Priority data needs: Sources of national, state, and local-level data and data collection systems. [Hyattsville, Md: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics, 1997.

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23

Kim, Insun. Priority data needs: Sources of national, state, and local-level data and data collection systems. [Hyattsville, Md: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics, 1997.

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24

Bank, World. The development data book: A guide to social and economic statistics : with a comprehensive data table. 3rd ed. Washington, D.C: World Bank, 1995.

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25

Sheram, Katherine. The development data book: A guide to social and economic statistics with a comprehensive data table. 2nd ed. Washington, D.C., U.S.A: World Bank, 1988.

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26

World Bank. The little data book on Africa. Washington, D.C: World Bank, 2008.

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27

Inc, NetLibrary. The little green data book 2005. Washington, D.C: World Bank, 2005.

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28

World Bank. The little data book on Africa. Washington, D.C: World Bank, 2009.

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29

Mishchenko, Aleksandr, and Elena Miheeva. Methods of assessment of efficiency of management of production and financial activity of the enterprise. ru: INFRA-M Academic Publishing LLC., 2019. http://dx.doi.org/10.12737/monography_5d1ae60d82d6d9.87533425.

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The proposed book describes the static and dynamic models of optimization of production and financial activities of the enterprise in the conditions of deterministic source data, and taking into account the uncertainty and risk. In the latter case, when choosing a management decision, not only the amount of expected profit, but also various types of risks, as well as such an indicator as the stability of the selected option of production and economic activity to changes in the market environment, are taken into account.
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30

(Zambia), Participatory Assessment Group, ed. Baseline data survey feeder roads, report: Key performance indicators and socio-economic indicators. [Lusaka]: Participatory Assessment Group, 1997.

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31

Ysseldyke, James E. Possible sources of data for grade 8 indicators. Minneapolis, MN: National Center on Educational Outcomes, the College of Education, University of Minnesota, 1995.

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32

Devina, Silvia. Data tren sektor kesehatan berdasarkan Susenas 1995-2006 =: Health sector trends data based on Susenas 1995-2006. [Jakarta]: SMERU Research Institute, 2009.

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33

How teachers can turn data into action. Alexandria, Va: ASCD, 2014.

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34

Population Research Centre (Institute for Social and Economic Change). Assessing the quality of district data for improved planning and monitoring of development programmes. New Delhi: United Nations Population Fund, 2011.

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35

Kibuuka, P. K. Training data and indicators for development: A discussion document. Halfway House, South Africa: Development Bank of Southern Africa, 1995.

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36

Lindgren, Göran. World data in figures. Uppsala: Dept. of Peace and Conflict Research, Uppsala University, 1990.

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37

Johnson, Leonard. Aging management indicators. [Washington, D.C: Administration on Aging, Office of Human Development Services, Dept. of Health and Human Services, 1985.

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38

Stanton, Cynthia. DHS maternal mortality indicators: An assessment of data quality and implications for data use. Calverton: Macro International, 1997.

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39

SDG indicator 10.7.2: Data Booklet. UN, 2020. http://dx.doi.org/10.18356/d54dcb0f-en.

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40

Gugerty, Mary Kay, and Dean Karlan. Collecting High-Quality Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199366088.003.0007.

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Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.
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41

(Editor), Philip G. Pardey, and Johannes Roseboom (Editor), eds. ISNAR Agricultural Research Indicator Series: A Global Data Base on National Agricultural Research Systems (ISNAR Agricultural Research Indicator). Cambridge University Press, 2004.

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42

Center for Substance Abuse Treatment (U.S.), ed. Multiple indicator analysis: Using secondary data to analyze illicit drug use. Rockville, Md. (5600 Fishers Lane, Rockville 20857): U.S. Dept. of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Substance Abuse Treatment, 2001.

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43

Heather, Breckenridge, and Social Service Research and Advisory Group., eds. Demographic data and selected social indicator profile for Manitoulin and Sudbury districts. Sudbury, Ont: Social Service Research and Advisory Group, 1989.

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44

Tammy, Gran, and Social Service Research and Advisory Group., eds. Demographic data and selected social indicator profile for Manitoulin and Sudbury districts part 1. Sudbury, Ont: Social Service Research and Advisory Group, 1993.

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45

Gandrud, Christopher. The Reproducibility of Governance Indicators. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817062.003.0009.

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Reproducibility is a core tenant of scientific enquiry, including the study of governance and government outputs. Having full access to the data and procedures that researchers used to study a phenomenon is vital for being able to understand and trust their findings. This chapter sets out best practices for the reproducibility of governance indicators. For a governance indicator project to be ‘really reproducible’, the full data as well as data gathering and analysis procedures should be easily and persistently accessible. Indicator development should be fully documented, especially via a version control system. The chapter surveys the status quo level of reproducibility among prominent governance indicator projects. While most had some reproduction material available, none were really reproducible. The chapter concludes with recommendations, including calling for a shared governance indicator hosting service that focuses on encouraging reproducibility.
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46

Dinkmeier, Carolyn A. Baccalaureate faculty members' attitudes toward computer use as an indicator of computer integration in nursing education curricula. 1992.

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47

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 contracting body risk indicators. The findings indicate that the best measurement instruments focus on the transaction level (micro level) while allowing for consistent aggregations for time series and cross-country comparisons. Such actionable indicators capture behaviour as directly as possible rather than remaining at the country level. They also retain the relational or transactional aspects of governance, revealing a much more dynamic picture than widely used population and expert surveys.
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48

Feigenblatt, Hazel. Governance Indicators and the Broken Feedback Loop. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817062.003.0010.

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This chapter presents an overview of the role of communications in governance indicators and discusses challenges to understanding whether, how, and why their intended audiences use or fail to use rankings, indices, and related data. These include long-standing challenges associated with ensuring that information meets the needs of different target audiences, engaging with traditional media, and using rankings to present indicators. As new technologies have changed information flows and dynamics, new challenges have emerged, including echo chambers and data graveyards. The chapter shows a broken feedback loop between governance indicator creators and their intended users that can be traced to the understanding of communications as an accessory activity, without integrating user research and frank self-assessments into the indicator creation cycle. More research should be conducted about the extent to which the current offer of indicators is meeting users’ needs and the extent to which underlying theories of change remain valid.
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49

An Overview of "political conflict in South Africa data trends, 1984-1988": Indicator SA issue focus sequel. Durban, South Africa: Indicator Project South Africa, 1989.

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50

Warren, Mark Craig. The development of a computerized system and report program for the Myers-Briggs Type Indicator. 1994.

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