{"product_id":"factor-analysis-and-dimension-reduction-in-r-9781032246697","title":"Factor Analysis and Dimension Reduction in R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eFactor Analysis and Dimension Reduction in R\u003c\/em\u003e 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.\u003c\/p\u003e\u003cp\u003eThe 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, regulariz\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePART I: MULTIVARIATE ANALYSIS OF FACTORS AND COMPONENTS \u003cbr\u003eChapter 1: Factor Analysis: Purposes and Research Questions \u003cbr\u003eChapter 2: Dealing with the Assumptions and Limitations of Factor Analysis \u003cbr\u003eChapter 3: Fundamental Concepts and Functions in Factor Analysis \u003cbr\u003eChapter 4: Quick Start: Principal Axis Factoring (FA) in R \u003cbr\u003eChapter 5: Quick Start: Confirmatory Factor Analysis in R \u003cbr\u003eChapter 6. Quick Start: Principal Components Analysis (PCA) in R \u003cbr\u003eChapter 7: Oblique and Higher Order Factor Models \u003cbr\u003eChapter 8: Factor Analysis for Binary, Ordinal, and Mixed Data \u003cbr\u003eChapter 9: FA in Greater Detail \u003cbr\u003eChapter 10: PCA in Greater Detail\u003c\/p\u003e \u003cp\u003ePART II: ADDITIONAL TOOLS FOR DIMENSION REDUCTION \u003cbr\u003eChapter 11: Sixteen Additional Methods for Dimension Reduction (DimRed) \u003cbr\u003eChapter 12: Metrics for Comparing and Evaluating Dimension Reduction Models \u003cbr\u003eChapter 13: Recipes: An Alternative System for Dimension Reduction \u003cbr\u003eChapter14: Factor Analysis for Neural Models \u003cbr\u003eChapter 15: Factor Analysis for Time Series Data\u003c\/p\u003e \u003cp\u003eAPPENDICES \u003cbr\u003eI. Datasets used in this volume \u003cbr\u003e2. Introduction to R and RStudio\u003c\/p\u003e","brand":"Taylor \u0026 Francis","offers":[{"title":"Default Title","offer_id":51018921574743,"sku":"9781032246697","price":63.64,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781032246697.jpg?v=1750778670","url":"https:\/\/bookcurl.com\/products\/factor-analysis-and-dimension-reduction-in-r-9781032246697","provider":"Book Curl","version":"1.0","type":"link"}