SPSS Statistics Essential Training (2019)

Course details

SPSS Statistics is a statistics and data analysis program for businesses, governments, research institutes, and academic organizations. In this course, Barton Poulson takes a practical, visual, and non-mathematical approach to SPSS Statistics, explaining how to use the popular program to analyze data in ways that are difficult or impossible in spreadsheets, but which don't require you to master programming languages like Python or R. From importing spreadsheets, to making data visualizations, to calculating descriptive statistics, Barton covers all the basics, with an emphasis on clarity, interpretation, communicability, and application. This course is ideal for first-time researchers and those who want to make the most of data in their professional and academic work.

Learning objectives
Making data visualizations
Creating regression variable plots
Importing data and recoding variables
Computing frequencies and correlations
Reliability analysis
k-means clustering
Decision tree classification
Analyzing data
Building predictive models
Exporting your work

Concepts

0. Introduction

01 - Welcome
02 - Using the exercise files

1. What Is SPSS

03 - SPSS in context
04 - Versions, releases, licenses, and interfaces

2. Getting Started

05 - Navigating SPSS
06 - Sample datasets
07 - Data types, measures, and roles
08 - Options and preferences
09 - Extending SPSS
10 - Saving and running syntax files

3. Data Visualization

11 - Visualizing data with Chart Builder
12 - Modifying Chart Builder visualizations
13 - Visualizing data with Graphboard templates
14 - Modifying Graphboard visualizations
15 - Using legacy dialogs - Boxplots for multiple variables
16 - Creating regression variable plots
17 - Comparing subgroups

4. Data Wrangling

18 - Importing data
19 - Variable labels
20 - Value labels
21 - Splitting files
22 - Selecting cases and subgroups

5. Recoding Data

23 - Recoding variables
24 - Reversing values with syntax
25 - Recoding by ranking cases
26 - Creating dummy variables
27 - Recoding with Visual Binning
28 - Recoding with Optimal Binning
29 - Preparing data for modeling
30 - Computing scores

6. Exploring Data

31 - Computing frequencies
32 - Computing descriptives
33 - Exploratory data analysis
34 - Computing correlations
35 - Computing contingency tables
36 - Factor analysis and principal component analysis
37 - Reliability analysis

7. Clustering and Classification

38 - Hierarchical clustering
39 - k-means clustering
40 - k-nearest neighbors classification
41 - Decision tree classification in SPSS
42 - Neural networks in SPSS - Multilayer perceptron classification
43 - Neural networks in SPSS - Radial basis function classification

8. Analyzing Data

44 - Comparing proportions
45 - Comparing one mean to a population - One-sample t-test
46 - Comparing paired means - Paired-samples t-test
47 - Comparing two means - Independent-samples t-test
48 - Comparing multiple means - One-way ANOVA
49 - Comparing means with two categorical variables - ANOVA

9. Building Predictive Models

50 - Computing a linear regression
51 - Variable selection
52 - Logistic regression
53 - Automatic linear modeling

10. Sharing Your Work

54 - Exporting charts and tables
55 - Web reports

Conclusion

56 - Next steps