Title: On Information Quality (InfoQ) of Official and Establishment Statistics
- Speakers: Ron S. Kenett, KPA Ltd., Raanana, Israel and University of Turin, Turin, Italy
- Chair: Yulei He
- Date and Time: Monday, November 3rd, 12:30 - 1:30 p.m.
- Location: Bureau of Labor Statistics Conference Center
- Sponsor: Methodology Section
- POC e-mail: ron@kpa-group.com
- WebEx event address for attendees: https://dol.webex.com/dol/j.php?MTID=m0e6043056bf607dff2a501ab6d30a0ce
- For audio: Call-in toll-free number (Verizon):
Attendee access code: 1-866-747-9048 (US)
Call-in number (Verizon): 1-517-233-2139 (US)
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- Materials: Slides
Abstract
In a recent paper in the Journal of the Royal Statistical Society (Series A), Kenett and Shmueli define the concept of Information Quality (InfoQ) as the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method. InfoQ is different from data quality and analysis quality, but is dependent on these components and on the relationship between them. Eight dimensions are used to deconstruct InfoQ and thereby provide an approach for assessing it: Data Resolution, Data Structure, Data Integration, Temporal Relevance, Generalizability, Chronology of Data and Goal, Operationalization and Communication. The talk will demonstrate the concept of InfoQ, its components (what it is) and assessment (how it is achieved) through several case studies including a survey conducted by an industrial association in the North of Italy and student test reports produced by school systems in the US.
The talk will compare and contrast the InfoQ dimensions with those typically used by statistical agencies to assess the quality of their products. As noted by Paul Biemer and Lars Lyberg in a 2014 JSM invited session, InfoQ provides a general framework applicable to data analysis in a broader sense than product quality and is a step away from the one-size-fits-all frameworks., The talk will also discuss how the InfoQ approach can be used to optimize integrated data analysis combining official data with administrative and organizational data, an important area with significant research opportunities.
