The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software
The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.
In her bestselling guide, 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 and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the 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 sixth edition is fully revised and updated to accommodate changes to IBM SPSS procedures, screens and output. It covers new SPSS tools for generating graphs and non-parametric statistics, importing data, and calculating dates. 5 star Amazon review:
"This is the book I wish I had whilst studying SPSS and experimental design on my MSc in social research methods. It is the clearest guide to SPSS that I have come across and it is very practical and easy to use. It has allowed me to revise statistical methods in a matter of days and I have gained a better understanding of these techniques than I had through using other much lengthier texts."
Preface Data files and website
Part One Getting started 1 Designing a study 2 Preparing a codebook 3 Getting to know IBM SPSS
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 correleation 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