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Bayes Factors
, 1995
"... In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null ..."
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Cited by 981 (70 self)
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In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is onehalf. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this paper we review and discuss the uses of Bayes factors in the context of five scientific applications in genetics, sports, ecology, sociology and psychology.
Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman & Donald B. Rubin, and Robert M. Hauser, and a Rejoinder)
 SOCIOLOGICAL METHODOLOGY 1995, EDITED BY PETER V. MARSDEN, CAMBRIDGE,; MASS.: BLACKWELLS.
, 1995
"... It is argued that Pvalues and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a singl ..."
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Cited by 253 (19 self)
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It is argued that Pvalues and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a single model, they ignore model uncertainty and so underestimate the uncertainty about quantities of interest. The Bayesian approach to hypothesis testing, model selection and accounting for model uncertainty is presented. Implementing this is straightforward using the simple and accurate BIC approximation, and can be done using the output from standard software. Specific results are presented for most of the types of model commonly used in sociology. It is shown that this approach overcomes the difficulties with P values and standard model selection procedures based on them. It also allows easy comparison of nonnested models, and permits the quantification of the evidence for a null hypothesis...
Bayes factors and model uncertainty
 DEPARTMENT OF STATISTICS, UNIVERSITY OFWASHINGTON
, 1993
"... In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null ..."
Abstract

Cited by 89 (6 self)
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In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is onehalf. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of Pvalues, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this paper we review and discuss the uses of Bayes factors in the context of five scientific applications. The points we emphasize are: from Jeffreys's Bayesian point of view, the purpose of hypothesis testing is to evaluate the evidence in favor of a scientific theory; Bayes factors offer a way of evaluating evidence in favor ofa null hypothesis; Bayes factors provide a way of incorporating external information into the evaluation of evidence about a hypothesis; Bayes factors are very general, and do not require alternative models to be nested; several techniques are available for computing Bayes factors, including asymptotic approximations which are easy to compute using the output from standard packages that maximize likelihoods; in "nonstandard " statistical models that do not satisfy common regularity conditions, it can be technically simpler to calculate Bayes factors than to derive nonBayesian significance
Intergenerational occupational mobility in britain and the us since 1850
 American Economic Review
, 2012
"... The U.S. tolerates more inequality than Europe and believes its economic mobility is greater than Europe’s, though they had roughly equal rates of intergenerational occupational mobility in the late twentieth century. We extend this comparison into the nineteenth century using 23,000 nationallyrepr ..."
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Cited by 4 (1 self)
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The U.S. tolerates more inequality than Europe and believes its economic mobility is greater than Europe’s, though they had roughly equal rates of intergenerational occupational mobility in the late twentieth century. We extend this comparison into the nineteenth century using 23,000 nationallyrepresentative British and U.S. fathers and sons. The U.S. was more mobile than Britain through 1900, so in the experience of those who created the U.S. welfare state in the 1930s, the U.S. had indeed been “exceptional. ” The U.S. mobility lead over Britain was erased by the 1950s, as U.S. mobility fell from its nineteenth century levels. [W]e have really everything in common with America nowadays, except, of course, language. Oscar Wilde, The Canterville Ghost (1887). The economies of Britain and the U.S. have had much in common over the two centuries since the American Revolution: their legal traditions and property rights systems, sources of labor, capital, and technology, political ties and alliances in two world wars, and – Wilde’s quip notwithstanding – language and culture are the most obvious. One significant respect in which
Bayes Factors and BIC: Comment on Weakliem
, 1998
"... Weakliem agrees that Bayes factors are useful for model selection and hypothesis testing. He reminds us that the simple and convenient BIC approximation corresponds most closely to one particular prior on the parameter space, the unit information prior, and points out that researchers may have diffe ..."
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Cited by 3 (0 self)
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Weakliem agrees that Bayes factors are useful for model selection and hypothesis testing. He reminds us that the simple and convenient BIC approximation corresponds most closely to one particular prior on the parameter space, the unit information prior, and points out that researchers may have different prior information or opinions. Clearly a prior that represents the available information should be used, although the unit information prior often seems reasonable in the absence of strong prior information. It seems that, among the Bayes factors likely to be used in practice, BIC is conservative in the sense of tending to provide less evidence for additional parameters or "effects". Thus if a Bayes factor based on additional prior information favors an effect, but BIC does not, the prior information is playing a crucial role and this should be made clear when the research is reported. BIC may well have a role as a baseline reference analysis to be provided in routine reporting of research results, perhaps along with Bayes factors based on other priors. In Weakliem's 2 x 2 table examples, BIC and Bayes factors based on Weakliem's preferred priors lead to similar substantive conclusions, but both differ from those based on P values. When there is additional prior information, the technology now exists to express it as
Centre for Applied
, 2003
"... Which background factors matter more in intergenerational educational attainment: Social class, cultural capital or cognitive ability? A random effects approach ..."
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Which background factors matter more in intergenerational educational attainment: Social class, cultural capital or cognitive ability? A random effects approach
Event History Modeling of World Fertility Survey Data
, 1993
"... Event history analysis seems ideally suited for the analysis of World Fertility Survey (WFS) data, which consists of full birth histories and related information. However, it has not been much used for this purpose, and most analyses of WFS data have consisted of tabulations of standard fertility ra ..."
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Event history analysis seems ideally suited for the analysis of World Fertility Survey (WFS) data, which consists of full birth histories and related information. However, it has not been much used for this purpose, and most analyses of WFS data have consisted of tabulations of standard fertility rates, and regressions with children ever born as the dependent variable, both of which have disadvantages. We suggest that this is because event history analysis has practical drawbacks for WFS data, even though, in principle, it provides a superior analytic framework. These are the many partial dates, the computational burden of discretetime event history analysis, the need to take account of five clocks at once (age, period, cohort, time since last event, and parity), and the difficulty of interpreting the coefficients. We propose a modeling strategy for the event history analysis of WFS data which aims to overcome these problems, and we apply it to the previously unanalyzed WFS data from...
Effects of SelfRated Disability and Subjective Health on Job
"... This paper represents the views of the author and does not necessarily reflect the opinions of Statistics Canada. Data in many forms Statistics Canada disseminates data in a variety of forms. In addition to publications, both standard and special tabulations are offered. Data are available on the In ..."
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This paper represents the views of the author and does not necessarily reflect the opinions of Statistics Canada. Data in many forms Statistics Canada disseminates data in a variety of forms. In addition to publications, both standard and special tabulations are offered. Data are available on the Internet, compact disc, diskette, computer printouts, microfiche and microfilm, and magnetic tape. Maps and other geographic reference materials are available for some types of data. Direct online access to aggregated information is possible through CANSIM, Statistics Canada’s machinereadable database and retrieval system. How to obtain more information Inquiries about this product and related statistics or services should be directed to: Client Services, Income
Unexamined Assumptions and Neglected Questions in Social Mobility Research
"... Research on social mobility, not just in Britain, has come to mean the increasingly sophisticated measurement of social fluidity, i.e. the continuing inequalities of relative mobility chances. This narrow focus has not only been at the expense of contributing to wider sociological debates, but has a ..."
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Research on social mobility, not just in Britain, has come to mean the increasingly sophisticated measurement of social fluidity, i.e. the continuing inequalities of relative mobility chances. This narrow focus has not only been at the expense of contributing to wider sociological debates, but has also led to the neglect of the individual and structural impact of actual mobility trends as well as their implications for general sociological theory. This could be remedied by closer attention to absolute rates of upward and downward mobility and their effects upon the homogeneity of social strata. It is argued that research should be directed to the cultural context of mobility as well as the economic and political. The Maturing of Mobility Studies More than any other topic in sociology the literature on intergenerational occupational mobility, over the past half century, has demonstrated the growing professionalism of sociological research. Unsubstantiated generalisation and critically unexamined data will no longer do. The methodological rigour and mathematical sophistication of