Applied multivariate research
Subtitle: design and interpretation
By: Lawrence S. Meyers, Glenn Gamst, A. J. Guarino
Contributors: Gamst, Glenn; Guarino, A. J.
Edition: 2nd ed.
Publisher: SAGE Publications
Published: 2012
Place of publication: Thousand Oaks, Calif
Language: eng
Number of pages: 1078
ISBN-13: 9781412988117
Subjects: Statistical methods; Multivariate analysis; Social sciences
Notes: Includes bibliographical references and index.
Table of contents: title: Preface; title: Author bios; title: The basics of multivariate design; title: An introduction to multivariate design; title: Some fundamental research design concepts; title: Data screening; title: Data screening using IBM SPSS; title: Univariate comparison of means; title: Univariate comparison of means using IBM SPSS; title: Multivariate analysis of variance; title: Multivariate analysis of variance using IBM SPSS; title: Predicting the value of a single variable; title: Bivariate correlation and simple linear regression; title: Bivariate correlation and simple linear regression using IBM SPSS; title: Multiple regression : statistical methods; title: Multiple regression : statistical methods using IBM SPSS; title: Multiple regression : beyond statistical regression; title: Multiple regression : beyond statistical regression using IBM SPSS; title: Multilevel modeling; title: Multilevel modeling using IBM SPSS; title: Binary and multinomial logistic regression and roc characteristic analysis; title: Binary and multinomial logistic regression and ROC analysis using IBM SPSS; title: Discriminant function analysis; title: Discriminant function analysis using IBM SPSS; title: Principal components analysis and exploratory factor analysis; title: Multidimensional scaling; title: The structure of this chapter; title: Cluster analysis using IBM SPSS; title: Confirmatory factor analysis; title: Confirmatory factor analysis using AMOS; title: Path analysis : multiple regression; title: Path analysis : structural modeling; title: Model invariance : applying a model to different groups.