xx, 1078 p. ; 24 cm, Includes bibliographical references (p. 1032-1055) and index, Preface -- Author bios -- The basics of multivariate design -- An introduction to multivariate design -- Some fundamental research design concepts -- Data screening -- Data screening using IBM SPSS -- Univariate comparison of means -- Univariate comparison of means using IBM SPSS -- Multivariate analysis of variance -- Multivariate analysis of variance using IBM SPSS -- Predicting the value of a single variable -- Bivariate correlation and simple linear regression -- Bivariate correlation and simple linear regression using IBM SPSS -- Multiple regression : statistical methods -- Multiple regression : statistical methods using IBM SPSS -- Multiple regression : beyond statistical regression -- Multiple regression : beyond statistical regression using IBM SPSS -- Multilevel modeling -- Multilevel modeling using IBM SPSS -- Binary and multinomial logistic regression and roc characteristic analysis -- Binary and multinomial logistic regression and ROC analysis using IBM SPSS -- Discriminant function analysis -- Discriminant function analysis using IBM SPSS -- Principal components analysis and exploratory factor analysis -- Multidimensional scaling -- The structure of this chapter -- Cluster analysis using IBM SPSS -- Confirmatory factor analysis -- Confirmatory factor analysis using AMOS -- Path analysis : multiple regression -- Path analysis : structural modeling -- Model invariance : applying a model to different groups

ia/appliedmultivari0002edmeye.pdf

Lawrence S. Meyers, Glenn Gamst, A. J. Guarino

SAGE Publications, Incorporated

Corwin Press

2nd ed., Thousand Oaks, Calif, California, 2012

United States, United States of America

2nd ed, Los Angeles, ©2013

Volume 2, 2013

Second, 2012

Includes bibliographical references and index.

Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioral sciences are exposed to the complex multivariate statistical techniques such as correlation and multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling. This book is designed to provide full coverage of the wide range of multivariate topics in a conceptual, non-mathematical, approach. It is geared toward the needs, level of sophistication, and interest in multivariate methodology of students in applied programs in the social and behavioral sciencesathat need to focus on design and interpretation rather than the intricacies of specific computations

<p>This book provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter, using a conceptual, non-mathematical, approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in multivariate methodology that serves students in applied programs in the social and behavioral sciences. Readers are encouraged to focus on design and interpretation rather than the intricacies of specific computations.</p>

Covering a wide range of multivariate topics, this book provides students in the social and behavioural sciences with a conceptual, non-mathematical approach.

2023-06-28