SPSS Survival Manual

The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.
In her bestselling manual, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic through to advanced statistical techniques. She outlines each technique clearly, providing step by step procedures for performing your analysis, a detailed guide to interpreting data output and examples of how to present your results in a report.
For both beginners and experienced users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential text. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing.
This seventh edition is fully revised and updated to accommodate changes to IBM SPSS Statistics procedures, screens and output.
A website with support materials for students and lecturers is available at https://routledgetextbooks.com/textbooks/9781760875534/

JULIE PALLANT has spent many years helping students overcome 'statistics phobia' in her roles as Director of Research and Graduate Studies in the Department of Rural Health at the University of Melbourne, and Senior Lecturer in Applied Statistics at Swinburne University. She has worked as a counselling psychologist, and has taught psychology, statistics and research methods at a number of universities. She now works as a research and statistical consultant internationally.

Preface Data files and website Introduction and overview Part One Getting started 1 Designing a study 2 Preparing a codebook 3 Getting to know IBM SPSS Statistics Part Two Preparing the data file 4 Creating a data file and entering data 5 Screening and cleaning the data Part Three Preliminary analyses 6 Descriptive statistics 7 Using graphs to describe and explore the data 8 Manipulating the data 9 Checking the reliability of a scale 10 Choosing the right statistic Part Four Statistical techniques to explore relationships among variables 11 Correlation 12 Partial correlation 13 Multiple regression 14 Logistic regression 15 Factor analysis Part Five Statistical techniques to compare groups 16 Non-parametric statistics 17 T-tests 18 One-way analysis of variance 19 Two-way between-groups ANOVA 20 Mixed between-within subjects analysis of variance 21 Multivariate analysis of variance 22 Analysis of covariance Appendix: Details of data files Recommended reading References Index