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A Primer on the Data Cleaning Pipeline, "The statistical & methodological questions around data integration, or merging multiple data sources, have grown. Specifically, the science of the 'data cleaning pipeline' contains 4 stages that allow an analyst to perform downstream tasks, predictive analyses, or statistical analyses on 'cleaned data.' This article provides a review of this emerging field, introducing terminology and commonly used methods." academic.oup.com/jssam/article

OUP AcademicA Primer on the Data Cleaning PipelineAbstract. The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are o

"This webinar will cover the evidence base and brief recommendations from the National Academies, outline the content and guidance described in the Federal Evidence Agenda on LGBTQI+ Equity, along with addressing OMB’s Recommendations on Best Practices for SOGI [Sexual Orientation / Gender Identity] Data on Statistical Surveys. These panelists will share the roadmap federal agencies are embarking on with their SOGI Data Action Plans." portal.aapor.org/integratedEve

My term as Editor-in-Chief of the Journal of Survey Statistics and Methodology (@JSurvStatMeth) has about one year left, and the search for the next editorial team is underway!

I'll be at #AAPOR and at the Joint Statistical Meetings and happy to chat with anyone who is interested in chatting about the role, the journal, and more.

The formal call for editors is here:
academic.oup.com/jssam/pages/c

Oxford AcademicJSSAM Call for Editor ApplicationsThe American Association for Public Opinion Research (AAPOR), the American Statistical Association (ASA), and publisher Oxford University Press invite applicati

It's International Love Data Week!
JSSAM loves #data that follow #AAPOR minimal disclosure standards, including

* Defn of the pop'n & sample selection procedures
* AAPOR response rates
* Dates the survey was conducted.
* Exact wording of all qns
* Model equations, including the numerical values of the parameter estimates, the respective standard errors, p-values and/or confidence or prediction intervals, and goodness-of-fit statistics for the model.