Using signal processing diagnostics to improve public sector evaluations

Mark Matthews

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    Abstract

    False positive test results that overstate intervention impacts can distort and constrain the capability to learn and adapt in governance, and are therefore best avoided. This article considers the benefits of using the Bayesian techniques used in signal processing and machine learning to identify cases of these false positive test results in public sector evaluations. These approaches are increasingly used in medical diagnosis—a context in which (like public policy) avoiding false positive and false negative test results in the evidence base is very important. The findings from a UK National Audit Office review of evaluation quality are used to illustrate how a Bayesian diagnostic framework for use in public sector evaluations could be developed.
    Original languageEnglish
    Pages (from-to)320-335
    Number of pages15
    Journal Asia & The Pacific Policy Studies
    Volume3
    Issue number2
    DOIs
    Publication statusPublished - May 2016

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    public sector
    diagnostic
    audit office
    evaluation
    public policy
    governance
    learning
    evidence
    Public sector
    Evaluation
    Diagnostics
    Governance
    Public policy
    Audit
    Quality evaluation
    Machine learning

    Cite this

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    Using signal processing diagnostics to improve public sector evaluations. / Matthews, Mark.

    In: Asia & The Pacific Policy Studies, Vol. 3, No. 2, 05.2016, p. 320-335.

    Research output: Contribution to journalArticleResearchpeer-review

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