SPSS Statistics Essential Training
- Offered byLinkedin Learning
SPSS Statistics Essential Training at Linkedin Learning Overview
Duration | 6 hours |
Total fee | ₹899 |
Mode of learning | Online |
Credential | Certificate |
SPSS Statistics Essential Training at Linkedin Learning Highlights
- Earn a certificate of completion
SPSS Statistics Essential Training at Linkedin Learning Course details
- 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
SPSS Statistics Essential Training at Linkedin Learning Curriculum
Introduction
Welcome
Using the exercise files
1. What Is SPSS?
SPSS in context
Versions, releases, licenses, and interfaces
2. Getting Started
Navigating SPSS
Sample datasets
Data types, measures, and roles
Options and preferences
3. Data Visualization
Visualizing data with Chart Builder
Modifying Chart Builder visualizations
Visualizing data with Graphboard templates
Modifying Graphboard visualizations
Using legacy dialogs: Boxplots for multiple variables
Creating regression variable plots
Comparing subgroups
4. Data Wrangling
Importing data
Variable labels
Value labels
Splitting files
Selecting cases and subgroups
5. Recoding Data
Recoding variables
Reversing values with syntax
Recoding by ranking cases
Creating dummy variables
Recoding with Visual Binning
Recoding with Optimal Binning
Preparing data for modeling
Computing scores
6. Exploring Data
Computing frequencies
Computing descriptives
Exploratory data analysis
Computing correlations
Computing contingency tables
Factor analysis and principal component analysis
Reliability analysis
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