Contrasts In Command

The bloody two-day battle was fought on the doorstep of the Confederate capital. It was the first major combat in the Eastern Theater since Bull Run/Manassas almost a year earlier, left more than 11,000 casualties in its wake, and cost the primary Southern field army its commander. The possession of Richmond hung in its balance. Yet, almost nothing has been written about Seven Pines/Fair Oaks. Victor Vignola’s Contrasts in Command: The Battle of Fair Oaks, May 31–June 1, 1862, which focuses primarily on the Fair Oaks portion of the battle, is a major contribution to the historiography of the war. Maj. Gen. George B. McClellan marched his Army of the Potomac up the Virginia Peninsula during the early spring of 1862 and placed his inexperienced IV Corps at the tip of the spear south of the flood-prone Chickahominy River. McClellan’s opponent Joe Johnston took the opportunity to strike and crafted an overly complex attack plan for his Virginia army to crush the exposed corps. A series of bungled marches, piecemeal attacks, and a lack of assertive leadership doomed the Southern plan. One of the wounded late in the day on May 31 was Johnston, whose injury led to the appointment of Robert E. Lee to take his place—a decision that changed the course of the entire Civil War. Vignola’s use of primary and archival sources, many of which have never been used, helped craft a wholly original tactical and leadership study that directly challenges conventional accounts. His stunning reassessment has led to renewed interest in Fair Oaks and the acquisition of a significant parcel of land by the American Battlefield Trust. Contrasts in Command: The Battle of Fair Oaks, May 31–June 1, 1862, will be hailed as one of the most important tactical studies ever published.

A collection of 11 of Richard Kayne's recent articles in theoretical syntax, with an emphasis on comparative syntax, which uses syntactic differences among languages to probe the properties of the human language faculty.

A hands-on guide to using R to carry out key statistical practices in educational and behavioral sciences research Computing has become an essential part of the day-to-day practice of statistical work, broadening the types of questions that can now be addressed by research scientists applying newly derived data analytic techniques. Comparing Groups: Randomization and Bootstrap Methods Using R emphasizes the direct link between scientific research questions and data analysis. Rather than relying on mathematical calculations, this book focus on conceptual explanations and the use of statistical computing in an effort to guide readers through the integration of design, statistical methodology, and computation to answer specific research questions regarding group differences. Utilizing the widely-used, freely accessible R software, the authors introduce a modern approach to promote methods that provide a more complete understanding of statistical concepts. Following an introduction to R, each chapter is driven by a research question, and empirical data analysis is used to provide answers to that question. These examples are data-driven inquiries that promote interaction between statistical methods and ideas and computer application. Computer code and output are interwoven in the book to illustrate exactly how each analysis is carried out and how output is interpreted. Additional topical coverage includes: Data exploration of one variable and multivariate data Comparing two groups and many groups Permutation tests, randomization tests, and the independent samples t-Test Bootstrap tests and bootstrap intervals Interval estimates and effect sizes Throughout the book, the authors incorporate data from real-world research studies as well aschapter problems that provide a platform to perform data analyses. A related Web site features a complete collection of the book's datasets along with the accompanying codebooks and the R script files and commands, allowing readers to reproduce the presented output and plots. Comparing Groups: Randomization and Bootstrap Methods Using R is an excellent book for upper-undergraduate and graduate level courses on statistical methods, particularlyin the educational and behavioral sciences. The book also serves as a valuable resource for researchers who need a practical guide to modern data analytic and computational methods.

Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally. Features new to this edition include: NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3) A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises) Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.

Intermediate Statistics: A Conceptual Course is a student-friendly text for advanced undergraduate and graduate courses. It begins with an introductory chapter that reviews descriptive and inferential statistics in plain language, avoiding extensive emphasis on complex formulas. The remainder of the text covers 13 different statistical topics ranging from descriptive statistics to advanced multiple regression analysis and path analysis. Each chapter contains a description of the logic of each set of statistical tests or procedures and then introduces students to a series of data sets using SPSS, with screen captures and detailed step-by-step instructions. Students acquire an appreciation of the logic of descriptive and inferential statistics, and an understanding of which techniques are best suited to which kinds of data or research questions.

Information structure, or the way the information in a sentence is 'divided' into categories such as topic, focus, comment, background, and old versus new information, is one of the most widely debated topics in linguistics. This volume incorporates exciting work on the relationship between syntax and information structure. The contributors are united in rejecting accounts that assume designated syntactic positions associated with specific information-structural interpretations, and aim instead to derive information-structural conditions on word order and other phenomena from the way syntax and syntax-external systems interact. Beyond this shared aim, the authors of the various chapters advocate a number of approaches, based on different types of data (syntactic, semantic, phonological/phonetic) from a range of languages. The book is aimed at specialists in syntax and/or information structure, as well as students and linguists in related fields keen to familiarise themselves with current issues in this fascinating area of research.