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IBM SPSS Statistics 27 Step by Step 17th Edition 2021 Hardbound at Meripustak

IBM SPSS Statistics 27 Step by Step 17th Edition 2021 Hardbound by Darren George, Paul Mallery , Routledge

Books from same Author: Darren George, Paul Mallery

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  • General Information  
    Author(s)Darren George, Paul Mallery
    PublisherRoutledge
    Edition17th Edition
    ISBN9781032070902
    Pages404
    BindingHardbound
    LanguageEnglish
    Publish YearDecember 2021

    Description

    Routledge IBM SPSS Statistics 27 Step by Step 17th Edition 2021 Hardbound by Darren George, Paul Mallery

    IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference, seventeenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Output for each procedure is explained and illustrated, and every output term is defined. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. This book covers the basics of statistical analysis and addresses more advanced topics such as multidimensional scaling, factor analysis, discriminant analysis, measures of internal consistency, MANOVA (between- and within-subjects), cluster analysis, Log-linear models, logistic regression, and a chapter describing residuals. The end sections include a description of data files used in exercises, an exhaustive glossary, suggestions for further reading, and a comprehensive index.IBM SPSS Statistics 27 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved an invaluable aid to thousands of researchers and students.New to this edition:Screenshots, explanations, and step-by-step boxes have been fully updated to reflect SPSS 27A new chapter on a priori power analysis helps researchers determine the sample size needed for their research before starting data collection.



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