{"title":"Education \u0026 Teaching","description":"\u003cp\u003eShop 650+ \u003cstrong\u003eeducation and teaching textbooks\u003c\/strong\u003e — from classroom management to educational psychology, multicultural education, and curriculum design. Built for pre-service teachers, in-service educators, and higher-ed faculty.\u003c\/p\u003e\u003ch2\u003eWhat's inside\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eFoundations of education and teaching methods\u003c\/li\u003e\n\u003cli\u003eEducational psychology and learning theory\u003c\/li\u003e\n\u003cli\u003eClassroom management and assessment\u003c\/li\u003e\n\u003cli\u003eMulticultural and inclusive education\u003c\/li\u003e\n\u003cli\u003eHigher education administration and student affairs\u003c\/li\u003e\n\u003cli\u003eAdult learning and professional development\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho it's for\u003c\/h2\u003e\u003cp\u003eEducation majors, teacher certification candidates, MEd and EdD students, instructional designers, school administrators, and professors.\u003c\/p\u003e\u003ch2\u003eWhy Book Shop Now\u003c\/h2\u003e\u003cp\u003eAuthentic US editions from Routledge, Sage, Pearson, Stylus, and Stenhouse — with fast shipping and 30-day returns.\u003c\/p\u003e","products":[{"product_id":"latent-variable-modeling-using-r-a-step-by-step-guide","title":"Latent Variable Modeling Using R A Step by Step Guide","description":"\u003cp\u003eThis step-by-step guide is written for \u003cb\u003eR\u003c\/b\u003e and latent variable model (LVM) novices. 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As a whole the book prepares readers to write about and interpret LVM results they obtain in \u003cb\u003eR\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eEach chapter features background information, boldfaced key　 terms defined in the glossary, detailed interpretations of \u003cb\u003eR\u003c\/b\u003e output, descriptions of how to write the analysis of results for publication, a summary, \u003cb\u003eR\u003c\/b\u003e based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own \u003cb\u003eR\u003c\/b\u003e syntax. Examples using data from published work across a variety of disciplines demonstrate how to use \u003cb\u003eR\u003c\/b\u003e syntax for analyzing and interpreting results. \u003cb\u003eR\u003c\/b\u003e functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R\u003cb\u003e　\u003c\/b\u003efunctions, packages, and datasets. The book and accompanying website at http:\/\/blogs.baylor.edu\/rlatentvariable\/ provides all of the data for the book's examples and exercises as well as \u003cb\u003eR\u003c\/b\u003e syntax so readers can replicate the analyses. The book reviews how to enter the data into \u003cb\u003eR\u003c\/b\u003e, specify the LVMs, and obtain and interpret the estimated parameter values.\u003c\/p\u003e \u003cp\u003eThe book opens with the fundamentals of using \u003cb\u003eR\u003c\/b\u003e including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. The book concludes with three Appendices: a review of common measures of model fit including their formulae and interpretation; syntax for other \u003cb\u003eR\u003c\/b\u003e latent variable models packages; and solutions for each chapter's exercises.\u003c\/p\u003e \u003cp\u003eIntended as a supplementary text for graduate and\/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, business, economics, and social and health sciences, this book also appeals to researchers in these fields. 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The book reviews research designs and how to assess the accuracy and reliability of data: how to determine whether data meet the assumptions of statistical tests; how to calculate and interpret effect sizes for intermediate statistics, including odds ratios for logistic and discriminant analyses; how to compute and interpret post-hoc power; and an overview of basic statistics for those who need a review. Unique chapters on multilevel linear modeling, multivariate analysis of variance (MANOVA), assessing reliability of data, and factor analysis are provided. 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It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting.\u003c\/p\u003e\n\u003cp\u003eIt presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. 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Chapters explore key theories and research, provide resources for developing personal and organizational leadership capacity, and guide leaders through a reflective process to help them link theory and practice. Hooper and Bernhardt's model for excellence and equity provides a pathway for educators to create and sustain learning communities in schools. 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