PROJECTQuantitative Data Analysis

Project Navigation
  • Project Home
  • Inference
  • Impact Analysis
  • Bias
  • Experiments
  • Paired Testing
  • Quasi-experimental Methods
  • Difference-in-Difference and Panel Methods
  • Instrumental Variables
  • Propensity Score Matching
  • Regression Discontinuity
  • Regression Techniques
  • Generalized Linear Model
  • Linear Regression
  • Logit and Probit Regression
  • Segregation Measures
  • Inequality Measures
  • Decomposition Methods
  • Descriptive Data Analysis
  • Microsimulation
  • The Dynamic Simulation of Income Model DYNASIM
  • The Health Insurance Policy Simulation Model HIPSM
  • The Model of Income in the Near Term (MINT)
  • The Tax Policy Center Microsimulation Model
  • The Transfer Income Model TRIM
  • Performance Measurement and Management

  • Performance measurement is a tool to help government agencies and nonprofits know whether their programs and services are leading to desired results. Through the identification of indicators, program managers can learn how efficiently and effectively they are allocating resources and to what end. Although performance measurement and performance management are hardly new to the public and nonprofit sectors, they are used more today than ever because of demands for greater accountability and growing expectations that organizations need to “do more with less.”

    Performance measurement can help organizations in a number of ways:

    • identify the conditions under which a program is doing well or poorly and thus stimulate remedial actions
    • raise questions regarding a service that can help staff develop and carry out improvement strategies
    • provide clues to problems and sometimes to what can be done to improve future outcomes
    • help assess the success of remedial actions

    A typical cycle of performance management begins with a clear mission statement and identification of target audience or customer base. Next, organizations must identify what to measure; for most, this will include various inputs (resources, staff), outputs (what an organization produces), and a range of outcomes or results (changes in knowledge, attitudes, behaviors, or conditions). After figuring out what to measure, organizations need to identify data sources and tools to capture information and then develop systems for analyzing and reporting data to various stakeholders. This cycle represents a continuous feedback loop within an organization and, where used appropriately, can help organizations regularly monitor performance of their programs and inform program practice. Performance management occurs when organizations move from measurement to analysis and use of information to inform practice.

    Performance data can come from various sources. The most typical include program or agency records, surveys (including interviews and focus groups), and trained observer ratings. Each source is accompanied by its own advantages and disadvantages and its own trade-offs related to cost, completeness of information, ease of administration, and timeliness to name a few.

    How does performance measurement relate to other evaluation techniques?

    Performance measurement is an important part of the evaluation continuum, but it differs from program evaluation in a number of important ways. First, performance measurement systems do not tell why the measured values occurred. Program evaluations are needed for this purpose. Performance measures can, however, provide more timely information to help program managers ask better questions and make mid-course corrections when needed. Program evaluation also typically happens ad hoc and considerably less frequently, and these studies can be costly. Data from a robust performance measurement system can form the basis for a program evaluation. However, not all programs are at a sufficient stage in development to warrant a program evaluation. Finally, performance measurement systems are typically designed with knowledge of current literature about what actions are likely to contribute to what outcomes or results and, in this sense, assume at least a basic level of attribution.


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    Research Methods Data analysis Quantitative data analysis Research methods and data analytics