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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.
IBM SPSS Statistics Software (“SPSS”) is a powerful tool that is capable of conducting just about any type of data analysis used in the social sciences, the natural sciences, or in the business world. Mathematics is the language of science, and data analysis is the dialect of research. The present book is designed to make data analysis more comprehensible and less toxic.
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.
For this book to be effective when you conduct data analysis with SPSS, you should have certain limited knowledge of statistics and have access to a computer that has the necessary resources to run SPSS.
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 27
A new chapter on a priori power analysis helps researchers determine the sample size needed for their research before starting data collection