Hostage videos in the War on Terror

About the book: The bodies of the dead or the soon to be dead litter the media landscape and they frame the representation of war, terror and conflict in the modern age. What are the meanings carried and conveyed by these war bodies on screen? The discussion of the war body on screen is best served by drawing upon multiple and diverging view points, differing academic backgrounds and methodological approaches. A multi-disciplinary approach is essential in order to capture and interpret the complexity of the war body on screen and its many manifestations. In this collection, contributors utilize textual analysis, psychoanalysis, post-colonialism, comparative analysis, narrative theory, discourse analysis, representation and identity as their theoretical footprints. Analysis of the impact of new media and information technologies on the construction and transmission of war bodies is also been addressed. The War Body on Screen has a highly original structure, with themed sections organized around ‘the body of the soldier’; ‘the body of the terrorist’; and ‘the body of the hostage’

Why we (usually) don't have to worry about multiple comparisons

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal Of Research On Educational Effectiveness on 04/03/2012, available online: https://doi.org/10.1080/19345747.2011.618213Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern