As we anticipate the readers of this journal are aware, there has been a growing number of conversations in recent years regarding equity and justice. These discussions have spread throughout many fields and sectors, including among those of us in the public policy analysis and management social sciences. Our own organization (Mathematica) joined with eight others in the policy research field to form the Evidence and Equity Collaborative.1 Staff within our organizations connect to share strategies that promote diversity, equity, and inclusion within our institutions; collaborate together on partnerships and opportunities that could expand the diversity of those exploring policy research careers; and discuss equitable methods and approaches for conducting policy research. By sharing and learning alongside one another, we hope to build a foundation for analysis ready to advance equitable systems, policies, and programs across the nation. Throughout our experience, we have learned that there are a variety of perspectives on what it means to promote equity in research and evaluation methodologies, and that individuals hold different beliefs about how equity can or should be incorporated into this work.This column breaks from our traditional format of having two authors share a back-and-forth exchange on a specific policy area. However, we uphold the spirit of the Point/Counterpoint column by sharing different authors' perspectives on a key topic of interest to many in the JPAM audience. We have enlisted contributors from member organizations in the Evidence and Equity Collaborative to share their views and experiences using equitable approaches to research and evaluation methods. Nitya Venkateswaran of RTI begins with an essay on how researchers can conceptualize the principles of diversity, equity, and inclusion in their work and provides concrete examples of how those principles can be embedded. Next, Vanessa Hoffman and Glynnis Melnicove of the American Institutes for Research share an essay describing their experience using a participatory research approach in a Ugandan community to involve those who would traditionally be considered as research subjects into the research process itself. Then, Marjorie Dorim & eacute;-Williams of MDRC provides her reflections on how quantitative research methods, including disaggregating data, have the opportunity to more accurately reflect the experiences of those from historically underrepresented communities; however, she warns that unless equitable practices are used in these methods, such as critical quantitative inquiry, they can result in further harm to the communities. Then, John Hotchkiss, Divya Vohra, and So O'Neil of Mathematica discuss how agent-based modeling can be designed to honor equity by simulating real world interactions that recognize the complex roles and identities of individuals, which can be used to answer what if questions in places where experiments may be infeasible or unethical. To conclude the column, we provide a brief response to these four essays to summarize themes and share considerations for the further advancement of our field.

Journal of Policy Analysis and ManagementVolume 39, Issue 3 p. 835-835 Point/Counterpoint IMPROVING THE EFFECTIVENESS OF PLACE-BASED POLICIES TO ADDRESS POVERTY AND JOBLESSNESS Paul Decker, Corresponding Author Editor PDecker@mathematica-mpr.com Correspondence Paul Decker Email: PDecker@mathematica-mpr.comSearch for more papers by this author Paul Decker, Corresponding Author Editor PDecker@mathematica-mpr.com Correspondence Paul Decker Email: PDecker@mathematica-mpr.comSearch for more papers by this author First published: 22 May 2020 https://doi.org/10.1002/pam.22223Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume39, Issue3Summer 2020Pages 835-835 RelatedInformation

Journal of Policy Analysis and ManagementVolume 35, Issue 4 p. 881-883 Research Article Editors' Overview of Special Section on Big Data and Public Policy Julia Lane, Julia LaneSearch for more papers by this authorPaul T. Decker, Paul T. DeckerSearch for more papers by this author Julia Lane, Julia LaneSearch for more papers by this authorPaul T. Decker, Paul T. DeckerSearch for more papers by this author First published: 20 July 2016 https://doi.org/10.1002/pam.21936Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume35, Issue4Fall 2016Pages 881-883 RelatedInformation

Recent years have seen an increase in the amount of statistics describing different phenomena based on "Big Data." This term includes data characterized not only by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, and the way in which they are collected and disseminated is fundamental. This change constitutes a paradigm shift for survey research. There is great potential in Big Data, but there are some fundamental challenges that have to be resolved before its full potential can be realized. This report provides examples of different types of Big Data and their potential for survey research; it also describes the Big Data process, discusses its main challenges, and considers solutions and research needs.

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.