2nd Edition

Handbook of Univariate and Multivariate Data Analysis with IBM SPSS

By Robert Ho Copyright 2014
    586 Pages 850 B/W Illustrations
    by CRC Press

    586 Pages 850 B/W Illustrations
    by Chapman & Hall

    Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.

    New to the Second Edition

    • Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation

    • New section on how to deal with missing data

    • Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity

    • Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples

    • Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling

    Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book’s CRC Press web page.

    Inferential Statistics and Test Selection. Introduction to SPSS. Multiple Response. t Test for Independent Groups. Paired-Samples t Test. One-Way Analysis of Variance, with Post Hoc Comparisons. Factorial Analysis of Variance. General Linear Model (GLM) Multivariate Analysis. General Linear Model: Repeated Measures Analysis. Correlation. Linear Regression. Factor Analysis. Reliability. Multiple Regression. Multiple Discriminant Analysis. Logistic Regression. Canonical Correlation Analysis. Structural Equation Modeling. Nonparametric Tests. Appendix. Bibliography. Index.


    Robert Ho