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"This book, written by three behavioral scientists for other behavioral scientists, addresses common issues in statistical analysis for the behavioral and educational sciences. Modern Statistical & Computing Methods for the Behavioral and Educational Sciences using R emphasizes the direct link between scientific research questions and data analysis. Purposeful attention is paid to the integration of design, statistical methodology, and computation to propose answers to specific research questions. Furthermore, practical suggestions for the analysis and presentation of results, in prose, tables and/or figures, are included. Optional sections for each chapter include methodological extensions for readers desiring additional technical details. Rather than focus on mathematical calculations like so many other introductory texts in the behavioral sciences, the authors focus on conceptual explanations and the use of statistical computing. Statistical computing is an integral part of statistical work, and to support student learning in this area, examples using the R computer program are provided throughout the book. Rather than relegate examples to the end of chapters, the authors interweave computer examples with the narrative of the book. Topical coverage includes an introduction to R, data exploration of one variable, data exploration of multivariate data - comparing two groups and many groups, permutation and randomization tests, the independent samples t-Test, the Bootstrap test, interval estimates and effect sizes, power, and dependent samples"--

Bootstrap (Statistics) --- Distribution (Probability theory) --- Psychology --- Random data (Statistics) --- R (Computer program language) --- Data processing

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Random effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predictive models. However, classical methods for model comparison are not well justified in such settings. This book presents state of the art methods for accommodating model uncertainty in random effects and latent variable models. It will appeal to students, applied data analysts, and experienced researchers. The chapters are based on the contributors’ research, with mathematical details minimized using applications-motivated descriptions. The first part of the book focuses on frequentist likelihood ratio and score tests for zero variance components. Contributors include Xihong Lin, Daowen Zhang and Ciprian Crainiceanu. The second part focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models. Contributors include David Dunson and collaborators Bo Cai and Saki Kinney. The final part focuses on structural equation models, with Peter Bentler and Jiajuan Liang presenting a frequentist approach, Sik-Yum Lee and Xin-Yuan Song presenting a Bayesian approach based on path sampling, and Joyee Ghosh and David Dunson proposing a method for default prior specification and efficient posterior computation. David Dunson is Professor in the Department of Statistical Science at Duke University. He is an international authority on Bayesian methods for correlated data, a fellow of the American Statistical Association, and winner of the David Byar and Mortimer Spiegelman Awards.

Statistics. --- Statistical Theory and Methods. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- Electronic books. -- local. --- Latent variables. --- Random data (Statistics). --- Latent variables --- Random data (Statistics) --- Mathematics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Data, Random (Statistics) --- Constructs, Hypothetical --- Hypothetical constructs --- Variables, Latent --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Statistics --- Stochastic processes --- Latent structure analysis --- Multivariate analysis --- Variables (Mathematics) --- Distribution (Probability theory. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Probability --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk

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Mathematical statistics --- Computer simulation --- Simulation par ordinateur --- 519.876 --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Theory of large systems --- Computer simulation. --- 519.876 Theory of large systems --- COMPUTER SIMULATION --- Simulation software --- STATISTICS --- Non - uniform distributions --- Random numbers --- Monograph --- Statistics. --- Numbers, Random. --- Random sampling numbers --- Random data (Statistics) --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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Monte Carlo method --- Numbers, Random --- Random number generators --- 519.245 --- 681.3*G3 --- Generators, Random number --- Electronic digital computers --- Random numbers --- Random sampling numbers --- Random data (Statistics) --- Sampling (Statistics) --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes --- 519.245 Stochastic approximation. Monte Carlo methods --- Stochastic approximation. Monte Carlo methods --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability theory

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Formal languages --- Langages formels --- Congresses. --- Congrès --- Mathematical Theory --- Algebra --- Mathematics --- Physical Sciences & Mathematics --- Numbers, Random --- Random numbers --- Random sampling numbers --- Computer science. --- Computers. --- Computer logic. --- Mathematical logic. --- Computer science --- Computer Science. --- Mathematical Logic and Formal Languages. --- Computation by Abstract Devices. --- Logics and Meanings of Programs. --- Discrete Mathematics in Computer Science. --- Symbolic and Algebraic Manipulation. --- Mathematics. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Computer science logic --- Logic, Symbolic and mathematical --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Informatics --- Science --- Random data (Statistics) --- Sampling (Statistics) --- Logic design. --- Computational complexity. --- Data processing. --- Complexity, Computational --- Design, Logic --- Design of logic systems --- Digital electronics --- Electronic circuit design --- Logic circuits --- Switching theory --- Computer science—Mathematics. --- Machine theory. --- Discrete mathematics. --- Formal Languages and Automata Theory. --- Theory of Computation. --- Computer Science Logic and Foundations of Programming. --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Abstract automata --- Abstract machines --- Automata --- Mathematical machine theory --- Algorithms --- Recursive functions --- Robotics