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Measuring Effectiveness of Anti-terrorism Programs Using Performance Logic Models

  • Robert G. Ross
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)

Abstract

For many government agencies, measuring effectiveness has proven to be extremely problematic. There are several reasons for this, but the manner in which government agencies have implemented the Government Performance and Results Act (GPRA) has not helped. Rather than create a rich suite of appropriate data to support decision-making at all levels of the organization, most agencies have been content to report a small number of high-level outcome measures whose relationship to more effective management is unclear. Despite clear requirements for measures of different types and at every level from specific program activities and products up to societal outcomes, existing GPRA guidance actually encourages agencies to misinterpret what is required. As a result, GPRA performance reporting is now little more than an exercise in satisfying checklist requirements. This paper presents a new approach for developing agency and program appropriate measurement and data schemas to support decision-making at every level of an organization. The approach presented integrates a number of existing management tools, such as Program Logic Models and Activity-Based Costing, and adds a new concept – Outcome-Oriented Activity Impact Measures. Several anti-terrorism functions are used to illustrate the concepts presented. A fundamental premise underlying this approach is that government agencies and programs impact societal outcomes only by performing activities for which there is an expectation of beneficial impact. The paper does not tell any agency what to measure. Rather, it provides a sound approach through which agency managers can identify measures of effectiveness and other data appropriate to their specific programs.

Keywords

Government Program Total Quality Management Brake System Coast Guard Maritime Transportation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Robert G. Ross
    • 1
  1. 1.Department of Homeland SecurityDeputy Director, Office of Comparative Studies, Science and Technology Directorate 

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