Links

Melhorar controle interno pode reduzir o valor pago para auditoria (pesquisa)

Capitalismo investe em ingenuidade humana, não em ouro

Índia pune quem distribui/publica mapas com informação topográfica falsa ou errada

Popularidade do presidente e desempenho da economia no Brasil (gráfico) (ou o fim do efeito Teflon)

KPMG, Ilha de Man e planejamento tributário no Canadá

A forma como o PCAOB trabalha pode mudar

Os erros de Freakonomics

Freakonomics: What Went Wrong?

Examination of a very popular popular-statistics series reveals avoidable errors

Andrew Gelman, Kaiser Fung

The nonfiction publishing phenomenon known as Freakonomics has passed its sixth anniversary. The original book, which used ideas from statistics and economics to explore real-world problems, was an instant bestseller. By 2011, it had sold more than four million copies worldwide, and it has sprouted a franchise, which includes a bestselling sequel, SuperFreakonomics; an occasional column in the New York Times Magazine; a popular blog; and a documentary film. The word “freakonomics” has come to stand for a light-hearted and contrarian, yet rigorous and quantitative, way of looking at the world.

The faces of Freakonomics are Steven D. Levitt, an award-winning professor of economics at the University of Chicago, and Stephen J. Dubner, a widely published New York–based journalist. Levitt is celebrated for using data and statistics to solve an array of problems not typically associated with economics. Dubner has perfected the formula for conveying the excitement of Levitt’s research—and of the growing body of work by his collaborators and followers. On the heels of Freakonomics, the pop-economics or pop-statistics genre has attracted a surge of interest, with more authors adopting an anecdotal, narrative style.

As the authors of statistics-themed books for general audiences, we can attest that Levitt and Dubner’s success is not easily attained. And as teachers of statistics, we recognize the challenge of creating interest in the subject without resorting to clichéd examples such as baseball averages, movie grosses and political polls. The other side of this challenge, though, is presenting ideas in interesting ways without oversimplifying them or misleading readers. We and others have noted a discouraging tendency in the Freakonomics body of work to present speculative or even erroneous claims with an air of certainty. Considering such problems yields useful lessons for those who wish to popularize statistical ideas.

[...]

Fonte: Freakonomics: What went wrong? American Scientist. (Andrew Gelman and Kaiser Fung)

Links

Metade dos estudos biomédicos não podem ser reproduzidos

As consequências de uma tatuagem no mercado de trabalho

Tudo é mensurável, inclusive sua ingenuidade

Contágio temporal e edições limitadas

Melhores países para a mulher (gráfico)

Do Instagram

Adaptado livremente via aqui

Rir é o melhor remédio

Mercado das empresas de contabilidade em 2015

Uma pesquisa do IFAC com mais de seis mil respondentes, que possuem mais de 800 mil clientes de pequeno e médio tamanho (SME) em mais de cem países, mostrou aspectos interessantes sobre a contabilidade mundial. Destaco a figura abaixo, que apresenta os fatores ambientais. O principal risco, na percepção dos respondentes, é o regulatório.

A pesquisa pode ser acessada aqui

Audit Study

Ao ler o texto “Factors Determining Callbacks to Job Applications by the Unemployed: An Audit Study” tive o interesse voltado para o método de pesquisa “audit study”. Mas não e nada de mais. É um tipo de pesquisa que consiste em mandar currículos falsos para trabalhos verdadeiros. E medir a taxa de interesse dos empregadores.

Pode ser usado para medir discriminação racial, de gênero, de profissão etc. Por exemplo: o pesquisador manda currículos iguais, mas em metade deles com o nome de homem e o restante de mulher. Se a taxa de marcação de entrevista for maior para os currículos do homem, pode-se dizer existe discriminação de gênero:

An audit study consists in sending fake resumes to actual job postings and measuring the incidence of callback rates. The main estimates consists in differences in callback rates based on randomly assigned differences in resume characteristics, such as age, job characteristics, or employment dates. It is therefore paramount that the fake resumes and the variation in the informational content be constructed as realistic as possible.