Share Description
In this course, you will :
- takes a hands-on, visual, and non-mathematical approach to SPSS Statistics, demonstrating how to use the popular programme to analyse data in ways that are difficult or impossible in spreadsheets but do not necessitate mastery of programming languages like Python or R.
- Covers everything from importing spreadsheets to creating data visualisations to calculating descriptive statistics, with an emphasis on clarity, interpretation, communicability, and application.
- This course is ideal for first-time researchers and those who want to use data effectively in their professional and academic work.
Syllabus :
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
- Extending SPSS
- Saving and running syntax files
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
7. Clustering and Classification
- Hierarchical clustering
- k-means clustering
- k-nearest neighbors classification
- Decision tree classification in SPSS
- Neural networks in SPSS: Multilayer perceptron classification
- Neural networks in SPSS: Radial basis function classification
8. Analyzing Data
- Comparing proportions
- Comparing one mean to a population: One-sample t-test
- Comparing paired means: Paired-samples t-test
- Comparing two means: Independent-samples t-test
- Comparing multiple means: One-way ANOVA
- Comparing means with two categorical variables: ANOVA
9. Building Predictive Models
- Computing a linear regression
- Variable selection
- Logistic regression
- Automatic linear modeling
10. Sharing Your Work
- Exporting charts and tables
- Web reports
