Chapter 1, Installing and Configuring SPSS, covers the initial installation of SPSS and the configuration of the system for use on the user’s machine.
Chapter 2, Accessing and Organizing Data, covers the process of opening various types of data files (Excel, CSV, and SPSS) in SPSS and performing some simple tasks, such as labeling data elements. It demonstrates how to save new versions of the data that incorporate the changes so that they are available for subsequent use.
Chapter 3, Statistics for Individual Data Elements, is about the tools in SPSS that are available for obtaining descriptive statistics for each field in a data file.
Chapter 4, Dealing with Missing Data and Outliers, focuses on assessing data quality with respect to missing information and extreme values. It also deals with the techniques that can be used to address these problems.
Chapter 5, Visually Exploring the Data, discusses topics such as histograms, bar charts, box and whisker plots, and scatter plots.
Chapter 6, Sampling, Subsetting and Weighting, describes the options available in SPSS for taking samples from a dataset, creating subgroups with the data, and assigning weights to individual rows.
Chapter 7, Creating New Data Elements, discusses when it is useful to define new data elements to support analysis objectives and the process involved in building these elements in SPSS.
Chapter 8, Adding and Matching Files, describes the process of combining multiple data files to create a single file for use in an analysis. Both appending multiple files and merging files to add information are addressed.
Chapter 9, Aggregating and Restructuring Data, is about two topics--changing the unit of analysis via aggregation, and restructuring the data from wide to long or long to wide to facilitate analysis.
Chapter 10, Crosstabulation Patterns for Categorical Data, covers descriptive and inferential analysis of categorical data in two-way and multi-way contingency tables.
Chapter 11, Comparing Means and ANOVA, is about descriptive and inferential analysis involving the mean of a variable across groups.
Chapter 12, Correlations, discusses descriptive and inferential analysis of associations involving numeric variables via the use of the Pearson correlation coefficient and some analogs.
Chapter 13, Linear Regression, covers using linear regression to develop predictions of numeric target variables.
Chapter 14, Principal Components and Factor Analysis, is about the use of principal components analysis and factor analysis to understand patterns among the variables.
Chapter 15, Clustering, covers methods to find groups in the data through analyzing the data rows.
Chapter 16, Discriminant Analysis, discusses using discriminant analysis to develop classifications involving categorical target variables.