Buch, Englisch, 384 Seiten, Format (B × H): 241 mm x 161 mm, Gewicht: 680 g
Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Buch, Englisch, 384 Seiten, Format (B × H): 241 mm x 161 mm, Gewicht: 680 g
Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
ISBN: 978-1-138-46454-4
Verlag: Taylor & Francis Ltd
Drawing on the authors varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models.
After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.
Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional exercises, encouraging readers to explore new ground in social science research.
Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.
Zielgruppe
Undergraduate
Autoren/Hrsg.
Weitere Infos & Material
Preface. Setting the Scene.Cluster Analysis.Multidimensional Scaling.Correspondence Analysis.Principal Components Analysis.Regression Analysis.Factor Analysis.Factor Analysis for Binary Data. Factor Analysis for Ordered Categorical Variables.Latent Class Analysis for Binary Data. Confirmatory Factor Analysis and Structural Equation Models.Multilevel Modeling. References. Index.
