Open AccessUsing Signal Processing Diagnostics to Improve Public Sector Evaluations
Author(s) -
Matthews Mark
Publication year - 2016
Publication title -
asia and the pacific policy studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.529
H-Index - 14
ISSN - 2050-2680
DOI - 10.1002/app5.110
Subject(s) - context (archaeology) , public sector , corporate governance , quality (philosophy) , intervention (counseling) , test (biology) , public economics , economics , business , actuarial science , risk analysis (engineering) , psychology , management , economy , paleontology , philosophy , epistemology , psychiatry , biology
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 B ayesian 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 N ational A udit O ffice review of evaluation quality are used to illustrate how a B ayesian diagnostic framework for use in public sector evaluations could be developed.