Books by "G. David Garson"
4 books found
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.
Self-Management in Yugoslavia and the Developing World
by Ukandi G Damachi, Hans D Seibel
1982 · Springer
Americans have long prided themselves on living in a country that serves as a beacon of democracy to the world, but from the time of the founding they have also engaged in debates over what the criteria for democracy are as they seek to validate their faith in the United States as a democratic regime. In this book John Gunnell shows how the academic discipline of political science has contributed in a major way to this ongoing dialogue, thereby playing a significant role in political education and the formulation of popular conceptions of American democracy. Using the distinctive “internalist” approach he has developed for writing intellectual history, Gunnell traces the dynamics of conceptual change and continuity as American political science evolved from a focus in the nineteenth century on the idea of the state, through the emergence of a pluralist theory of democracy in the 1920s and its transfiguration into liberalism in the mid-1930s, up to the rearticulation of pluralist theory in the 1950s and its resurgence, yet again, in the 1990s. Along the way he explores how political scientists have grappled with a fundamental question about popular sovereignty: Does democracy require a people and a national democratic community, or can the requisites of democracy be achieved through fortuitous social configurations coupled with the design of certain institutional mechanisms?
Graphical displays that researchers can employ as an integral part of the data analysis process are frequently more revealing than traditional, numerical summary statistics. Providing strategies for examining data more effectively, this volume focuses on: univariate methods such as histograms, smoothed histograms, univariate scatterplots, quantile plots, box plots, dot plots. It describes bivariate methods such as scatterplot construction guidelines, jittering for overplotted points, marginal boxplots, scatterplot slicing, the Loess procedure for nonparametric scatterplot smoothing, and banking to 45 degrees for enhanced visual perception.