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What Are We Weighting For?
When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does.
Journal Article
Beyond cybersecurity : protecting your digital business
\"The book's content is based on McKinsey's proprietary research, client experience and interviews with over 200 executives, regulators and security experts. This effort has four main findings: Pervasive digitization, open and inter-connected technology environments and sophisticated attackers make cyber-attacks a critical social and business issue that could materially slow the pace of technology and bushiness innovation with as much as $3 trillion in aggregate impact. There's a high degree of consensus among enterprise technology executives about the nature of a step-change capability improvement required to make their institutions cyber-resilient. While institutions need to start acting to improve their capabilities now, there should be enhanced collaboration across the cyber-security ecosystem to get alignment on a broad range of policy issues. Progress toward cyber-resiliency can only be achieved with active engagement from the most senior business and public leaders\"--Publisher's description.
Book
The MID4 dataset, 2002–2010
Understanding the causes of interstate conflict continues to be a primary goal of the field of international relations. To that end, scholars continue to rely on large datasets of conflict in the international system. This paper introduces the latest iteration in the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 4 data. In this paper we first outline the updated data-collection process for the MID4 data. Second, we present some minor changes and clarifications to the coding rules for the MID4 datasets, as well as pointing out how the MID coding procedures affect several notable \"close call\" cases. Third, we introduce updates to the existing MID datasets for the years 2002–2010 and provide descriptive statistics that allow comparisons of the newer MID data to prior versions. We also offer some best practices and point out several ways in which the new MID data can contribute to research in international conflict.
Journal Article
Passwords : philology, security, authentication
Today we regard cryptology, the technical science of ciphers and codes, and philology, the humanistic study of human languages, as separate domains of activity. But the contiguity of these two domains is a historical fact with an institutional history. From the earliest documented techniques for the statistical analysis of text to the computational philology of early twenty-first-century digital humanities, what Brian Lennon calls \"crypto-philology\" has flourished alongside, and sometimes directly served, imperial nationalism and war. Lennon argues that while computing's humanistic applications are as historically important as its mathematical and technical origins, they are no less marked by the priorities of institutions devoted to signals intelligence. The convergence of philology with cryptology, Lennon suggests, is embodied in the password, an artifact of the linguistic history of computing that each of us uses every day to secure access to personal data and other resources. The password is a site where philology and cryptology, and their contiguous histories, meet in everyday life, as the natural-language dictionary becomes an instrument of the hacker's exploit.-- Provided by publisher
Book
Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database
Objective This article documents wide‐ranging revisions to the Standardized World Income Inequality Database (SWIID), which seeks to maximize the comparability of income inequality estimates for the broadest possible coverage of countries and years. Methods Two k‐fold cross‐validations, by observation and by country, are used to evaluate the SWIID's success in predicting the Luxembourg Income Study (LIS), recognized in the field as setting the standard for comparability. Results The cross‐validations indicate that the new SWIID's estimates and their uncertainty are even more accurate than previous versions, extending its advantage in comparability over alternate income inequality data sets. Conclusion Given its superior coverage and comparability, the SWIID remains the optimum source of data for broadly cross‐national research on income inequality.
Journal Article
OAuth 2 in action
This book teaches you practical use and deployment of OAuth 2 from the perspectives of a client, an authorization server, and a resource server. You'll begin with an overview of OAuth and its components and interactions. Next, you'll get hands-on and build an OAuth client, an authorization server, and a protected resource. Then you'll dig into tokens, dynamic client registration, and more advanced topics
Book
On information quality
We define the concept of information quality 'InfoQ' as the potential of a data set to achieve a specific (scientific or practical) goal by 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. We survey statistical methods for increasing InfoQ at the study design and post-data-collection stages, and we consider them relatively to what we define as InfoQ. We propose eight dimensions that help to assess InfoQ: data resolution, data structure, data integration, temporal relevance, generalizability, chronology of data and goal, construct operationalization and communication. We demonstrate the concept of InfoQ, its components (what it is) and assessment (how it is achieved) through three case-studies in on-line auctions research. We suggest that formalizing the concept of InfoQ can help to increase the value of statistical analysis, and data mining both methodologically and practically, thus contributing to a general theory of applied statistics.
Journal Article
Crowdsourcing Consumer Research
Data collection in consumer research has progressively moved away from traditional samples (e.g., university undergraduates) and toward Internet samples. In the last complete volume of the Journal of Consumer Research (June 2015–April 2016), 43% of behavioral studies were conducted on the crowdsourcing website Amazon Mechanical Turk (MTurk). The option to crowdsource empirical investigations has great efficiency benefits for both individual researchers and the field, but it also poses new challenges and questions for how research should be designed, conducted, analyzed, and evaluated. We assess the evidence on the reliability of crowdsourced populations and the conditions under which crowdsourcing is a valid strategy for data collection. Based on this evidence, we propose specific guidelines for researchers to conduct high-quality research via crowdsourcing. We hope this tutorial will strengthen the community’s scrutiny on data collection practices and move the field toward better and more valid crowdsourcing of consumer research.
Journal Article