https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

Norms have never been absent from the study of international politics, but the sweeping “ideational turn” in the 1980s and 1990s brought them back as a central theoretical concern in the field. Much theorizing about norms has focused on how they create social structure, standards of appropriateness, and stability in international politics. Recent empirical research on norms, in contrast, has examined their role in creating political change, but change processes have been less well-theorized. We induce from this research a variety of theoretical arguments and testable hypotheses about the role of norms in political change. We argue that norms evolve in a three-stage “life cycle” of emergence, “norm cascades,” and internalization, and that each stage is governed by different motives, mechanisms, and behavioral logics. We also highlight the rational and strategic nature of many social construction processes and argue that theoretical progress will only be made by placing attention on the connections between norms and rationality rather than by opposing the two.

/pdf/international-norm-dynamics-and-political-change-37ig9r9dlq.pdf

International Norm Dynamics and Political Change

https://www.jstor.org/stable/info/1805621

Clio and the economics of QWERTY

Cobb’s concern was a long-worrisome circularity in the sociology of science based on the use of bright lines such as p< 0.05: “We teach it because it’s what we do; we do it because it’s what we teach.” This concern was brought to the attention of the ASA Board. The ASA Board was also stimulated by highly visible discussions over the last few years. For example, ScienceNews (Siegfried 2010) wrote: “It’s science’s dirtiest secret: The ‘scientific method’ of testing hypotheses by statistical analysis stands on a flimsy foundation.” A November 2013, article in Phys.org Science News Wire (2013) cited “numerous deep flaws” in null hypothesis significance testing. A ScienceNews article (Siegfried 2014) on February 7, 2014, said “statistical techniques for testing hypotheses...havemore flaws than Facebook’s privacy policies.” Aweek later, statistician and “Simply Statistics” blogger Jeff Leek responded. “The problem is not that people use P-values poorly,” Leek wrote, “it is that the vast majority of data analysis is not performed by people properly trained to perform data analysis” (Leek 2014). That same week, statistician and science writer Regina Nuzzo published an article in Nature entitled “Scientific Method: Statistical Errors” (Nuzzo 2014). That article is nowone of the most highly viewedNature articles, as reported by altmetric.com (http://www.altmetric.com/details/2115792#score). Of course, it was not simply a matter of responding to some articles in print. The statistical community has been deeply concerned about issues of reproducibility and replicability of scientific conclusions. Without getting into definitions and distinctions of these terms, we observe that much confusion and even doubt about the validity of science is arising. Such doubt can lead to radical choices, such as the one taken by the editors of Basic andApplied Social Psychology, who decided to ban p-values (null hypothesis significance testing) (Trafimow and Marks 2015). Misunderstanding or misuse of statistical inference is only one cause of the “reproducibility crisis” (Peng 2015), but to our community, it is an important one. When the ASA Board decided to take up the challenge of developing a policy statement on p-values and statistical significance, it did so recognizing this was not a lightly taken step. The ASA has not previously taken positions on specific matters of statistical practice. The closest the association has come to this is a statement on the use of value-added models (VAM) for educational assessment (Morganstein and Wasserstein 2014) and a statement on risk-limiting post-election audits (American Statistical Association 2010). However, these were truly policy-related statements. The VAM statement addressed a key educational policy issue, acknowledging the complexity of the issues involved, citing limitations of VAMs as effective performance models, and urging that they be developed and interpreted with the involvement of statisticians. The statement on election auditing was also in response to a major but specific policy issue (close elections in 2008), and said that statistically based election audits should become a routine part of election processes. By contrast, the Board envisioned that the ASA statement on p-values and statistical significance would shed light on an aspect of our field that is too often misunderstood and misused in the broader research community, and, in the process, provides the community a service. The intended audience would be researchers, practitioners, and science writers who are not primarily statisticians. Thus, this statementwould be quite different from anything previously attempted. The Board tasked Wasserstein with assembling a group of experts representing a wide variety of points of view. On behalf of the Board, he reached out to more than two dozen such people, all of whom said theywould be happy to be involved. Several expressed doubt about whether agreement could be reached, but those who did said, in effect, that if there was going to be a discussion, they wanted to be involved. Over the course of many months, group members discussed what format the statement should take, tried to more concretely visualize the audience for the statement, and began to find points of agreement. That turned out to be relatively easy to do, but it was just as easy to find points of intense disagreement. The time came for the group to sit down together to hash out these points, and so in October 2015, 20 members of the group met at the ASA Office in Alexandria, Virginia. The 2-day meeting was facilitated by Regina Nuzzo, and by the end of the meeting, a good set of points around which the statement could be built was developed. The next 3 months saw multiple drafts of the statement, reviewed by group members, by Board members (in a lengthy discussion at the November 2015 ASA Board meeting), and by members of the target audience. Finally, on January 29, 2016, the Executive Committee of the ASA approved the statement. The statement development process was lengthier and more controversial than anticipated. For example, there was considerable discussion about how best to address the issue of multiple potential comparisons (Gelman and Loken 2014). We debated at some length the issues behind the words “a p-value near 0.05 taken by itself offers only weak evidence against the null

https://www.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108

The ASA's Statement on p-Values: Context, Process, and Purpose

There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.

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A basic introduction to fixed‐effect and random‐effects models for meta‐analysis

In this revised second edition, Deirdre McCloskey demonstrates how economic discourse employs metaphor, authority, symmetry and other rhetorical means of persuasion. ""The Rhetoric of Economics"" shows economists to be human persuaders and poets of the marketplace, even in their most technical and mathematical moods. It is further enhanced by three new chapters and two new bibliographies.

The Rhetoric of Economics

"Statistical significance," a technique that dominates medicine, economics, psychology, and many other scientific fields, has been a huge mistake. The outcome is a case study in bad science - how it originates and how it grows. These sciences, from agronomy to zoology, the authors find, engage "testing" that doesn't test and "estimating" that doesn't estimate. Heedless of magnitude and of a genuine engagement with alternative hypotheses, they "testimate." "Null hypothesis significance testing" is in other words a scientific train-wreck, about which a small group of statisticians have been warning for a century.Ziliak and McCloskey's book shows field by field how the wreck happened, reports on the fatalities, and offers a quantitative way forward. The facts will startle the outside reader: how could a group of brilliant scientists wander so far away from scientific magnitudes? And it will inspirit the scientists who seek conscious interpretations of "oomph" rather than arbitrary columns of t-tests: how can the statistical sciences get back on track, and fulfill their quantitative promise?Ziliak and McCloskey measure the disaster in their home field of economics, and in psychology, epidemiology, and medical science. They touch as well on law, biology, psychiatry, pharmacology, sociology, political science, education, forensics, and other fields in the grip of "significance." The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots. Many statisticians have complained about it before, but have complained science-by-science.

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives

Statistical significance as used in economics has weak theoretical justification. In particular it merges statistical and substantive significance. The 182 papers using regression analysis in the American Economic Review in the 1980s were tested against 19 criteria for the accepted use of statistical significance. Most, some three-quarters of the papers, did poorly. Likewise, textbooks in econometrics do not distinguish statistical and economic significance. Statistical significance should not be the focus of empirical economics.

The Standard Error of Regressions

An economic history of Britain since 1700, in three volumes by 39 eminent historians and economists. It will appeal particularly to first and second year university students, but is also suitable for anyone interested in the history of the British economy.

The Economic History of Britain since 1700

The big economic story of our times is not the Great Recession. It is how China and India began to embrace neoliberal ideas of economics and attributed a sense of dignity and liberty to the bourgeoisie they had denied for so long. The result was an explosion in economic growth and proof that economic change depends less on foreign trade, investment, or material causes, and a whole lot more on ideas and what people believe. Or so says Deirdre N. McCloskey in "Bourgeois Dignity", a fiercely contrarian history that wages a similar argument about economics in the West. Here she turns her attention to seventeenth- and eighteenth-century Europe to reconsider the birth of the Industrial Revolution and the rise of capitalism. According to McCloskey, our modern world was not the product of new markets, but rather the result of shifting opinions about them. An utterly fascinating sequel to her critically acclaimed book "The Bourgeois Virtues", "Bourgeois Dignity" is a feast of intellectual riches from one of our most spirited and ambitious historians.

Donald N. McCloskey

Papers

The Rhetoric of Economics

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives

The Standard Error of Regressions

The Economic History of Britain since 1700

Bourgeois Dignity: Why Economics Can't Explain the Modern World