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White Hip Hoppers Language and Identity in Post-Modern America

White Hip Hoppers Language and Identity in Post-Modern America

This book examines language and identity among White American middle and upper-middle class youth who affiliate with Hip Hop culture. Hip Hop youth engage in practices that range from the consumption of rap music and fashion to practices like MC-ing (writing and performing raps or rhymes) DJ-ing (mixing records to produce a beat for the MC) graffiti tagging and break-dancing. Cutler explores the way in which these young people stylize their speech using linguistic resources drawn from African American English and Hip Hop slang terms. She also looks at the way they construct their identities in discussions with their friends and how they talk about and use language to construct themselves as authentic within Hip Hop. Cutler considers the possibility that young people experimenting with AAVE-styled speech may improve the status of AAVE in the broader society. She also addresses the need for educators to be aware of the linguistic patterns found in AAVE and Hip Hop language and ways to build on Hip Hop skills like rhyming and rapping in order to motivate students and promote literacy. | White Hip Hoppers Language and Identity in Post-Modern America

GBP 46.99
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Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Throughout the physical and social sciences researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years the number and variety of distributions to choose from-all with their own formulas tables diagrams and general properties-continue to create problems. For a specific application which of the dozens of distributions should one use? What if none of them fit well?Fitting Statistical Distributions helps answer those questions. Focusing on techniques used successfully across many fields the authors present all of the relevant results related to the Generalized Lambda Distribution (GLD) the Generalized Bootstrap (GB) and Monte Carlo simulation (MC). They provide the tables algorithms and computer programs needed for fitting continuous probability distributions to data in a wide variety of circumstances-covering bivariate as well as univariate distributions and including situations where moments do not exist. Regardless of your specific field-physical science social science or statistics practitioner or theorist-Fitting Statistical Distributions is required reading. It includes wide-ranging applications illustrating the methods in practice and offers proofs of key results for those involved in theoretical development. Without it you may be using obsolete methods wasting time and risking incorrect results. | Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

GBP 59.99
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Bayesian Statistics for the Social Sciences Second Edition

Bayesian Statistics for the Social Sciences Second Edition

The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty Bayesian variable selection and sparsity and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo Bayesian linear regression and generalized linear models model evaluation and comparison multilevel modeling models for continuous and categorical latent variables missing data and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www. guilford. com/kaplan-materials) provides data sets and code for the book's examples. New to This Edition *Utilizes the R interface to Stan-faster and more stable than previously available Bayesian software-for most of the applications discussed. *Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. *Chapters on Bayesian variable selection and sparsity model uncertainty and model averaging and Bayesian workflow for statistical modeling. | Bayesian Statistics for the Social Sciences Second Edition

GBP 57.99
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