Qass 2012 Top Up Package Volumes 167-171

We offer these texts bundled together at a discount for your students.Harry J. Khamis, The Association Graph and the Multigraph for Loglinear Models Volume 167This practical guide teaches nonstatisticians how to analyze and interpret loglinear models using the multigraph The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.Pamela Paxton, John R. Hipp, Sandra Marquart-Pyatt, Nonrecursive Models: Endogeneity, Reciprocal Relationships, and Feedback Loops Volume 168Nonrecursive Models is a clear and concise introduction to the estimation and assessment of nonrecursive simultaneous equation models. This unique monograph gives practical advice on the specification and identification of simultaneous equation models, how to assess the quality of the estimates, and how to correctly interpret results.Wijbrandt H. van Schuur, Ordinal Item Response Theory: Mokken Scale Analysis Volume 169Measurement in the social sciences often refers to standardized answers to close-ended questions, in which answers are analyzed as if they were measurements on an interval scale. This volume presents a measurement model that maintains the ordinal aspects of the data in order to establish how well the model fits and how it measures subjects and items. It relaxes the most stringent assumptions from parametric item response theory, while maintaining its advantages over classical measurement methods, such as reliability and factor analysis. This volume is less technical than other books on the topic and is ideal for introductory courses in social science measurement.Richard F. Haase, Multivariate General Linear Models Volume 170This book provides an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). Beginning with an overview of the univariate general linear model, this volume defines the key steps in analyzing linear model data and introduces multivariate linear model analysis as a generalization of the univariate model. Richard F. Haase focuses on multivariate measures of association for four common multivariate test statistics, presents a flexible method for testing hypotheses on models, and emphasizes the multivariate procedures attributable to Wilks, Pillai, Hotelling, and Roy. The volume concludes with a discussion of canonical correlation analysis that is shown to subsume all the multivariate procedures discussed in previous chapters. The analyses are illustrated throughout the text with three running examples drawing from several disciples, including personnel psychology, anthropology, environmental epidemiology, and neuropsychology.Valentim R. Alferes, Methods of Randomization in Experimental Design Volume 171 This book presents the main procedures of random assignment and local control in between-subjects experimental designs and the counterbalancing schemes in within-subjects or cross-over experimental designs. Alferes uses a pedagogical strategy that allows the reader to implement all randomization methods by relying on the materials given in the appendices and using common features included in most word processor software.A companion website at www.sagepub.com/alferes provides downloadable IBM SPSS and R versions of SCRAED, a package that performs simple and complex random assignment in experimental design, including the 18 randomization methods presented in Chapters 2 and 3. See more