This groundbreaking work by leading social psychologists, who have all contributed in important ways to the psychology of group perception, focuses in particular on three interrelated issues: (1) whether groups are seen to be diverse or relatively homogeneous; (2) whether groups are seen as real and stable or only transitory and ephemeral; and (3) whether group membership derives from some essential quality of the members or rather is based on social constructions.

Contents:
V. Yzerbyt, C.M. Judd, O. Corneille, Perceived Variability, Entitativity, and Essentialism: Introduction and Overview. M.B. Brewer, Y. Hong, Q. Li, Dynamic Entitivity: Perceiving Groups as Actors. D.L. Hamilton, S.J. Sherman, J.S. Rodgers, Perceiving the Groupness of Groups: Entitativity, Homogeneity, Essentialism, and Stereotypes. N. Haslam, L. Rothschild, D. Ernst, Essentialism and Entitativity: Structures of Beliefs about the Ontology of Social Categories. M. Rothbart, B. Park, The Mental Representation of Social Categories: Category Boundaries, Entitativity, and Stereotype Change. V. Yzerbyt, C. Estrada, O. Corneille, E. Seron, S. Demoulin, Subjective Essentialism in Action: Self-anchoring and Social Control as Consequences of Fundamental Social Divides. J.E. Plaks, S.R. Levy, C.S. Dweck, S.J. Stroessner, In the Eye of the Beholder: Lay Theories and the Perception of Group Entitativity, Variability, and Essence. K.C. Klauer, K. Ehrenberg, I. Wegener, Components of Homogeneity: A Multiple-process Model of Social Categorization. C. McGarty, Forming Stereotypes of Entitative Groups. P.W. Linville, G.W. Fischer, From Basketball to Business: Expertise, Implicit Covariation, and Social Judgement. M. Rubin, M. Hewstone, R.J. Crisp, A. Voci, Z. Richards, Gender Out-group Homogeneity: The Roles of Differential Familiarity, Gender Differences, and Group Size. A. Guinote, Group Size, Outcome Dependency and Power: Effects on Perceived and Objective Group Variability. M. Brauer, C.M. Judd, M.S. Thompson, The Acquisition, Transmission, and Discussion of Social Stereotypes: Influences of Communication on Group Perceptions. Y. Kashima, Culture, Communication, and Entitativity: A Social Psychological Investigation of Social Reality. C.S. Ryan, D.R. Robinson, L.R.M. Hausmann, Group Socialization, Uncertainty Reduction, and the Development of New Members' Perceptions of Group Variability. R. Spears, D. Scheepers, J. Jetten, B. Doosje, N. Ellemers, T. Postmes, Entitativity, Group Distinctiveness and Social Identity: Getting and Using Social Structure. K.J. Reynolds, P.J. Oakes, S.A. Haslam, J.C. Turner, M.K. Ryan, Social Identity as the Basis of Group Entitativity: Elaborating the Case for the 'Science of Social Groups per se'. T. Wildschut, C.A. Insko, B. Pinter, The Perception of Outgroup Threat: Content and Activation of the Outgroup Schema. D. Abrams, J.M. Marques, G. Randsley de Moura, P. Hutchison, N.J. Bown, The Maintenance of Entitativity: A Subjective Group Dynamics Approach. E. Castano, On the Advantages of Reifying the Ingroup. M.A. Hogg, Uncertainty and Extremism: Identification with High Entitativity Groups under Conditions of Uncertainty. R.L. Moreland, J.G. McMinn, Entitativity and Social Integration: Managing Beliefs about the Reality of Groups.

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.