• The Communist Manifesto: A Graphic Novel

    On the 170th anniversary of the publication of Karl Marx's and Friedrich Engels' The Communist Manifesto, British graphic novelist Martin Rowson has produced an illustrated adaptation. Apart from a few pages of prose, the whole work is presented in the style of a graphic novel.


    The preface describes how the middle-aged Rowson became smitten by Marx and Engels' exciting prose when he was only 16. Aside from expressing his great admiration for Marx's writing, as well as his own critical stance, he furnishes the reader with some historical backdrop to the completion of The Manifesto. Marx had been commissioned to write it by a socialist group in the summer of 1847, but, under pressure, succeeded in producing it at the beginning of 1848. Significantly, that was before the outbreak of revolutionary movements in Europe later on in 1848. Rowson goes on to explain that the initial publication failed to attract the attention of many people. Only after the events of the Paris Commune in 1871 did the pamphlet receive a wide audience and a publication renewal.

    The illustrations create an atmospheric accompaniment to the Marx figures whose speaking balloons relay the text of The Manifesto. The graphics pair nicely with the text with dense images that impart the feeling of the clashes of historical forces (classes) or with the dramatic rendering of the first lines of The Manifesto in which a spectre appears, so Hamlet-like in two dark and foreboding images to haunt the reader's mind. There is plenty of theatricality too: images of Marx interacting from a stage with a hostile audience (Rowson's added flourishes added to enhance the exposition in a stimulating theatrical way).

    As a literary work, the illustrations do justice to the marvelously compressed, yet sweeping, literary quality of Marx's verbal imagery and present readers. Though I had read The Manifesto years ago, I found the adaptation to be both a refresher and newly insightful.


    The Communist Manifesto: A Graphic Novel [Martin Rowson/SelfMadeHero]

  • Statistics Done Wrong: The Woefully Complete Guide

    For 15 years, I've been a faculty member in the Ed.D. program at Nova Southeastern University where the majority of my doctoral students employ quantitative methods in their education research. Despite the fact that students take a mandatory methods class and get templates providing a rough statistical framework, they experience great confusion when it comes to designing their methodology and analyzing their data.

    It's not just the students: despite my own background in mathematics (I teach linear and abstract algebra), I sometimes find myself uncertain about advising my students about their data analysis and also in conflict with some colleagues about what counts as being statistically valid. Typically, I turn to statistical textbooks and other colleagues for advice.

    An article in the April 16, 2015 edition of Scientific American boldly claimed that research psychologists are wringing their hands over the inadequacy of the statistical tools they have been using. It seems that the use of p values as gold standard tests for significance has gone into disrepute as a consequence of over-reliance and inadequacy in determining the quality of the results. This is where Alex Reinhart comes in.

    Reinhart is a physicist turned statistician who has set out to write a book whose aim is to improve the quality of statistical education and understanding that researchers need to have. Statistics Done Wrong is not a textbook. It is a highly informed discussion of the frequent inadequacy of published statistical results and confronts the sacred cow: the p value. Here is what he has to say on page 2.

    Since the 1980s, researchers have described numerous statistical fallacies and misconceptions in the popular peer-reviewed scientific literature and have found that many scientific papers — perhaps more than half — fall prey to these errors. Inadequate statistical power renders many studies incapable of finding what they're looking for, multiple comparisons and misinterpreted p values cause numerous false positives, flexible data analysis makes it easy to find a correlation where none exists, and inappropriate model choices bias important results. Most errors go undetected by peer reviewers and editors, who often have no specific statistical training, because few journals employ statisticians to review submissions and few papers give sufficient statistical detail to be accurately evaluated.

    Astonishing to my eyes was his conclusion that

    The methodological complexity of modern research means that scientists without extensive statistical training may not be able to understand most published research in their fields.

    Reinhart advises users of statistics to replace point estimates (p values) with confidence intervals (estimates of uncertainty). He discusses statistical power, (a way of determining the degree of confidence associated with statistical tests using the null hypothesis). He discusses and illustrates with clear and uncomplicated examples such things as the effects of sample size and reasonable estimates of bias (suggestive of the Bayesian approach).

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