This first edition of Statistics Using R: An Integrative Approach capitalizes on the versatility and power of the R software package to create a course of study that links good statistical and data science practice to the analysis of real data. It also benefits from the many years of the authors' experience teaching statistics to undergraduate students at a liberal arts university and to undergraduate and graduate students at a large research university from a variety of disciplines including education, psychology, health, and policy analysis. Because R, a free, open-source software, provides a command line approach to the analysis of data, and, in so doing, this textbook teaches readers the skills necessary to program statistical analyses"--
Statistics Using Stata uses a highly accessible and lively writing style to seamlessly integrate the learning of the latest version of Stata (17) with an introduction to applied statistics using real data in the behavioral, social, and health sciences. The text is comprehensive in its content coverage and is suitable at undergraduate and graduate levels. It requires knowledge of basic algebra, but no prior coding experience. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes a .do-file for each chapter, that was used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter.
Written in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a deep conceptual understanding of statistics and practical facility with the leading statistical software package. With one hundred worked examples, the textbook guides students through statistical practice using real data and avoids complicated mathematics. Numerous end-of-chapter exercises allow students to apply and test their understanding of chapter topics, with detailed answers available online. The third edition has been updated throughout and includes a new chapter on research design, new topics (including weighted mean, resampling with the bootstrap, the role of the syntax file in workflow management, and regression to the mean) and new examples and exercises. Student learning is supported by a rich suite of online resources, including answers to end-of-chapter exercises, real data sets, PowerPoint slides, and a test bank.
Higher education hails Asian American students as model minorities who face no educational barriers given their purported cultural values of hard work and political passivity. Described as “over-represented,” Asian Americans have been overlooked in discussions about diversity; however, racial hostility continues to affect Asian American students, and they have actively challenged their invisibility in minority student discussions. This study details the history of Asian American student activism at the University of Illinois Urbana-Champaign, as students rejected the university’s definition of minority student needs that relied on a model minority myth, measures of under-representation, and a Black-White racial model, concepts that made them an “unseen unheard minority.” This activism led to the creation on campus of one of the largest Asian American Studies programs and Asian American cultural centers in the Midwest. Their histories reveal the limitations of understanding minority student needs solely along measures of under-representation and the realities of race for Asian American college students.
Assessment and Culture challenges the classical approach to the assessment of minority populations by pointing out the deficiencies in this approach and offers instead a bio-cultural model of assessment. The principle objective of this book is to help mental health professionals to more accurately assess individuals from various ethnic, cultural and linguistic backgrounds. The culture-fair techniques and strategies of the book tap into a broad range of the abilities and aptitudes of the examinee. Assessment and Culture provides a cultural frame of reference which allows the examiner to take into account the individual's social and cultural factors in development, coping style and personal history. Individual chapters consider the practical aspects of assessing the intellectual, linguistic, academic, visual-motor, emotional and vocational functioning of culturally diverse children. An entire section of the book is devoted to writing the assessment report.
Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.
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