Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Machine Learning for Social and Behavioral Research
Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big 5 Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects-models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.
Insights In: Theoretical and Philosophical Psychology
We are now entering the third decade of the 21st Century, and, especially in the last years, the achievements made by scientists have been exceptional, leading to major advancements in the fast-growing field of Psychology. Frontiers has organized a series of Research Topics to highlight the latest advancements in science in order to be at the forefront of science in different fields of research. This editorial initiative of particular relevance, led by Prof. Anna Borghi and Dr. Chiara Fini, Specialty Chief and Assistant Chief Editors of the section, Theoretical and Philosophical Psychology, is focused on new insights, novel developments, current challenges, latest discoveries, recent advance...
Machine Learning for Social and Behavioral Research
Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.
Longitudinal Structural Equation Modeling
Beloved for its engaging, conversational style, this valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation...
Bayesian Statistics for the Social Sciences
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 ...
Handbook of Structural Equation Modeling
"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--
Structural Equation Modeling of Multiple Rater Data
The use of multiple raters can improve the validity of conclusions made on self- (and other) reports of emotions, attitudes, goals, and self-perceptions of personality. Yet analyzing these ratings requires special psychometric models that take into account the specific nature of these data. From leading authorities, this book offers the first comprehensive introduction to structural equation modeling (SEM) of multiple rater data. Rather than taking a one-size-fits-all approach, the book shows how the choice of a model should be guided by measurement design and purpose. Practical recommendations are provided for selecting suitable measurement designs, raters, and psychometric models. Models for different combinations of rater types and for cross-sectional as well as longitudinal research designs are described step by step, with a strong emphasis on the substantive meaning of the latent variables in the models. User-friendly features include equation boxes, application boxes, and a companion website with Mplus and lavaan code for the book’s examples.
Longitudinal Multivariate Psychology
This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes. The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.
Computerized Adaptive Testing
Used worldwide in assessment and professional certification contexts, computerized adaptive testing (CAT) offers a powerful means to measure individual differences or make classifications. This authoritative work from CAT pioneer David J. Weiss and Alper Şahin provides a complete how-to guide for planning and implementing an effective CAT to create a test unique to each person in real time. The book reviews the history of CAT and the basics of item response theory used in CAT. It walks the reader through developing an item bank, pretesting and linking items, selecting required CAT options, and using simulations to design a CAT. Available software for CAT delivery is described, including links to free and commercial options. Engaging multidisciplinary examples illustrate applications of CAT for measuring ability, achievement, proficiency, personality, attitudes, perceptions, patients’ reports of their symptoms, and academic or clinical progress.
Analysis of an Intelligence Dataset
In this issue, psychometrics researchers were invited to make reanalyses or extensions of a previously published dataset from a recent paper by Myszkowski and Storme (2018). The dataset analyzed consisted of responses to a multiple-choice logical reasoning nonverbal test, comprising the last series of Raven’s (1941) Standard Progressive Matrices. Although the original paper already proposed several modeling strategies, this issue presents new or improved procedures to study the psychometrics properties of tests of this type.
