A Step By Step Guide To Exploratory Factor Analysis With R And Rstudio
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A Step By Step Guide To Exploratory Factor Analysis With R And Rstudio
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Author : Marley W. Watkins
language : en
Publisher: Routledge
Release Date : 2020-12-30
A Step By Step Guide To Exploratory Factor Analysis With R And Rstudio written by Marley W. Watkins and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-30 with Psychology categories.
This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open source software R. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of R and RStudio code, and recommends evidence-based best practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face while applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.
A Step By Step Guide To Exploratory Factor Analysis With Spss
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Author : Marley W. Watkins
language : en
Publisher: Routledge
Release Date : 2021-06-21
A Step By Step Guide To Exploratory Factor Analysis With Spss written by Marley W. Watkins and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-21 with Computers categories.
This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using SPSS. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots and code from SPSS and recommends evidence-based best-practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon to help facilitate grasp of the key issues users will face while applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.
Assessing Psychometric Fitness Of Intelligence Tests
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Author : Gary L. Canivez
language : en
Publisher: Rowman & Littlefield
Release Date : 2025-03-25
Assessing Psychometric Fitness Of Intelligence Tests written by Gary L. Canivez and has been published by Rowman & Littlefield this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-25 with Psychology categories.
Assessing Psychometric Fitness of Intelligence Tests: Toward Evidence-Based Interpretation Practices addresses issues and concerns regarding appropriate ethical and scientific underpinnings for the appropriate interpretation of intelligence tests. Ethical test interpretation requires test users to consider the empirical evidence for individual and all test score comparisons and to make appropriate clinical decisions accordingly. This requires test users to have competencies in advanced psychometric principles. The chapters in this edited volume present a variety of topics, including the intersection of ethical principles, test standards, and psychometric properties that guide evidence-based interpretation; surveys of empirical evidence in the literature for qualifying major intelligence test interpretations, and psychological measurement topics that impact psychometric understanding of what current intelligence tests can and cannot do. This critical discussion has implications for basic undergraduate and graduate instruction, as well as supervision in clinical and research applications.
Factor Analysis And Dimension Reduction In R
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Author : G. David Garson
language : en
Publisher: Taylor & Francis
Release Date : 2022-12-16
Factor Analysis And Dimension Reduction In R written by G. David Garson and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-16 with Psychology categories.
Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book’s coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.
Multivariate Statistical Methods
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Author : Bryan F. J. Manly
language : en
Publisher: CRC Press
Release Date : 2024-10-04
Multivariate Statistical Methods written by Bryan F. J. Manly and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-04 with Mathematics categories.
Multivariate Statistical Methods: A Primer offers an introduction to multivariate statistical methods in a rigorous yet intuitive way, without an excess of mathematical details. In this fifth edition, all chapters have been revised and updated, with clearer and more direct language than in previous editions, and with more up-to-date examples, exercises, and references, in areas as diverse as biology, environmental sciences, economics, social medicine, and politics. Features • A concise and accessible conceptual approach that requires minimal mathematical background. • Suitable for a wide range of applied statisticians and professionals from the natural and social sciences. • Presents all the key topics for a multivariate statistics course. • The R code in the appendices has been updated, and there is a new appendix introducing programming basics for R. • The data from examples and exercises are available on a companion website. This book continues to be a great starting point for readers looking to become proficient in multivariate statistical methods, but who might not be deeply versed in the language of mathematics. In this edition, we provide readers with conceptual introductions to methods, practical suggestions, new references, and a more extensive collection of R functions and code that will help them to deepen their toolkit of multivariate statistical methods.
Scientific Data Analysis With R
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Author : Azizur Rahman
language : en
Publisher: CRC Press
Release Date : 2024-11-28
Scientific Data Analysis With R written by Azizur Rahman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Mathematics categories.
In an era marked by exponential growth in data generation and an unprecedented convergence of technology and healthcare, the intersection of biostatistics and data science has become a pivotal domain. This book is the ideal companion in navigating the convergence of statistical methodologies and data science techniques with diverse applications implemented in the open-source environment of R. It is designed to be a comprehensive guide, marrying the principles of biostatistics with the practical implementation of statistics and data science in R, thereby empowering learners, researchers, and practitioners with the tools necessary to extract meaningful knowledge from biological, health, and medical datasets. This book is intended for students, researchers, and professionals eager to harness the combined power of biostatistics, data science, and the R programming language while gathering vital statistical knowledge needed for cutting-edge scientists in all fields. It is useful for those seeking to understand the basics of data science and statistical analysis, or looking to enhance their skills in handling any simple or complex data including biological, health, medical, and industry data. Key Features: Presents contemporary concepts of data science and biostatistics with real-life data analysis examples Promotes the evolution of fundamental and advanced methods applying to real-life problem-solving cases Explores computational statistical data science techniques from initial conception to recent developments of biostatistics Provides all R codes and real-world datasets to practice and competently apply into reader’s own domains Written in an exclusive state-of-the-art deductive approach without any theoretical hitches to support all contemporary readers
Survey Development
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Author : Tony Chiu Ming Lam
language : en
Publisher: Taylor & Francis
Release Date : 2023-05-26
Survey Development written by Tony Chiu Ming Lam and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-26 with Social Science categories.
Survey Development: A Theory-Driven Mixed-Method Approach provides both an overview of standard methods and tools for developing and validating surveys and a conceptual basis for survey development that advocates establishing and testing of hypotheses pertaining to presumptions and score-interpretation and use inferences and mixing quantitative and qualitative methods. The book has 14 chapters which are divided into four parts. Part A includes six chapters that deal with theory and methodology. Part B has five chapters and it gets into the process of constructing the survey using both quantitative and qualitative methods. Part C comprises two chapters devoted to assessing the quality or psychometric properties (reliability and validity) of survey responses. Finally, the one chapter in Part D is an attempt to present a synopsis of what was covered in the previous chapters in regard to developing a survey with the TDMM framework for developing survey and conducting survey research. This provides a full process for survey development intended to yield results that can support valid interpretation and use of scores. Including detailed online resources, this book is suitable for graduate students who use or are responsible for interpretation of survey research and survey data as well as survey methodologists and practitioners who use surveys in their field.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
R For Marketing Research And Analytics
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Author : Chris Chapman
language : en
Publisher: Springer
Release Date : 2019-03-28
R For Marketing Research And Analytics written by Chris Chapman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-28 with Computers categories.
The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. The 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.
Discovering Statistics Using R
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Author : Andy Field
language : en
Publisher: SAGE
Release Date : 2012-03-07
Discovering Statistics Using R written by Andy Field and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-07 with Reference categories.
Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field′s books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you′re doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book′s accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.