IDEAS home Printed from https://ideas.repec.org/a/sae/evarev/v16y1992i2p131-150.html
   My bibliography  Save this article

Establishing Benchmarks for Outcome Indicators

Author

Listed:
  • Gary T. Henry

    (Georgia State University)

  • Matthew J. McTaggart

    (University of Akron)

  • James H. McMillan

    (Virginia Commonwealth University)

Abstract

Indicators of program performance are used as evaluative measures in a variety of fields. A particularly vexing problem for evaluation is the development of empirically based performance expectations. Should program sponsors be satisfied with average performance? How should evaluators account for client differences? This article presents a statistical technique for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit, in this case a school district, a unique comparison group is selected. The performance of the district is compared to the performance of its benchmark group. Then the credibility, predictability, and equity of the method are tested. The approach meets or exceeds these test criteria and appears to be a viable, albeit controversial, approach for developing comparative performance standards.

Suggested Citation

  • Gary T. Henry & Matthew J. McTaggart & James H. McMillan, 1992. "Establishing Benchmarks for Outcome Indicators," Evaluation Review, , vol. 16(2), pages 131-150, April.
    • Handle: RePEc:sae:evarev:v:16:y:1992:i:2:p:131-150
    DOI: 10.1177/0193841X9201600202
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0193841X9201600202
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0193841X9201600202?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cook, Thomas J. & Vansant, Jerry & Stewart, Leslie & Adrian, Jamie, 1995. "Performance measurement: Lessons learned for development management," World Development, Elsevier, vol. 23(8), pages 1303-1315, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    2. Colosimo Bianca Maria & Moya Ester Gutierrez & Moroni Giovanni & Petrò Stefano, 2008. "Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM," Stochastics and Quality Control, De Gruyter, vol. 23(1), pages 109-121, January.
    3. Sürücü, Lütfi & YIKILMAZ, İbrahim & MASLAKÇI, Ahmet, 2022. "Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations," OSF Preprints fgd4e, Center for Open Science.
    4. Hatem Jemmali & Mohamed Salah Matoussi, 2012. "A Multidimensional Analysis of Water Poverty at A Local Scale- Application of Improved Water Poverty Index for Tunisia," Working Papers 730, Economic Research Forum, revised 2012.
    5. Pattravadee Ploykitikoon & Charles M. Weber, 2019. "Knowledge Pathways and Performance: An Empirical Study of the National Laboratories in a Technology Latecomer Country," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 1-37, May.
    6. Gweneth Leigh & Milica Muminovic & Rachel Davey, 2023. "Enjoyed by Jack but Endured by Jill: An Exploratory Case Study Examining Differences in Adolescent Design Preferences and Perceived Impacts of a Secondary Schoolyard," IJERPH, MDPI, vol. 20(5), pages 1-14, February.
    7. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    8. Jérome SARACCO & Marie CHAVENT & Vanessa KUENTZ, 2010. "Clustering of categorical variables around latent variables," Cahiers du GREThA (2007-2019) 2010-02, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    9. Véronique Cariou & Stéphane Verdun & Emmanuelle Diaz & El Qannari & Evelyne Vigneau, 2009. "Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 3(3), pages 227-241, December.
    10. Huseyin Aytug & Siong Hook Law & Nirvikar Singh, 2018. "What Can We Learn from Global and Regional Rankings of Countries?," Millennial Asia, , vol. 9(2), pages 119-139, August.
    11. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    12. Bauer, Jan O. & Drabant, Bernhard, 2021. "Principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    13. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    14. Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    15. Zeynep Reva & Oğuz Polat, 2023. "Road Rage as a Type of Violation of Well-Being in Traffic: The Case of Turkey," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    16. Diego Bernardo Avanzini, 2009. "Designing Composite Entrepreneurship Indicators: An Application Using Consensus PCA," WIDER Working Paper Series RP2009-41, World Institute for Development Economic Research (UNU-WIDER).
    17. Gary T. Henry & James H. McMillan, 1993. "Performance Data," Evaluation Review, , vol. 17(6), pages 643-652, December.
    18. Brint, Andrew & Genovese, Andrea & Piccolo, Carmela & Taboada-Perez, Gerardo J., 2021. "Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer," International Journal of Production Economics, Elsevier, vol. 233(C).
    19. Peter Sinka & Peter J. Zeitsch, 2022. "Hedge Effectiveness of the Credit Default Swap Indices: a Spectral Decomposition and Network Topology Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1375-1412, December.
    20. Smirnova, Maria M. & Rebiazina, Vera A. & Frösén, Johanna, 2018. "Customer orientation as a multidimensional construct: Evidence from the Russian markets," Journal of Business Research, Elsevier, vol. 86(C), pages 457-467.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:evarev:v:16:y:1992:i:2:p:131-150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.