Handbook of Univariate and Multivariate Data Analysis with IBM SPSS (2ND)

258.18 SGD
会員価格
232.37
English

Product Description

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.

This accessible handbook explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. Updated with the SPSS statistical package for Windows, this second edition now covers more topics, including tests of assumptions and how to deal with missing data. It also offers three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation and expands coverage of factor analysis, path analysis, and structural equation modeling.

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