The purpose of this guide is to provide both basic understanding of statistical concepts (know- why) as well as practical tools to analyse quantitative data in SPSS (know-how). We wanted to keep the guide completely free of formulas (i.e. brain-freezing mathematical equations). In doing so, we have tried to explain everything at the most elementary level and only include aspects that are important in actual research. As such, this guide is pragmatic and research-oriented. Hopefully, you will find it useful.
This guide consists of two parts. The first part (Chapters 1-5) concerns various aspects concerning data management and descriptive statistics. Next, we discuss issues related to statistical significance (Chapter 6). The following part deals with some basic types of statistical analysis, such as t-tests, ANOVA, chi-square, correlation analysis, and factor analysis (Chapters 7-10). Then we discuss theoretical and practical dimensions of regression analysis (Chapters 11- 12) before continuing into how to actually conduct regression analysis, including interaction analysis (Chapters 13-17).
There are two data materials used in the guide. The first is “SPSS_Data1” which is mostly based on a hypothetical data set available through SPSS. The second is “SPSS_Data2”, which is primarily based on several waves of data collection related to the Word Values Survey.