This training module introduces SPSS (Statistical Package for the Social Sciences), a powerful statistical software suite widely used for data analysis. This training is designed to help participants understand key SPSS functionalities and its applications in data analytics, enabling them to manage, analyze, and interpret data efficiently.
Who can Attend?
- Students
- Corporate and Govt Employees
- Data analysts
- Researchers
- Social scientists
- Business analysts
- Anyone interested in learning SPSS for data analysis
1: Introduction to SPSS and Data Analytics
- Understand the basics of SPSS, its interface, and its role in data analytics.
- What is SPSS?
- Definition and Overview
- History and Development of SPSS
- Key Applications of SPSS in Data Analytics
- SPSS Interface Overview:
- SPSS Windows: Data View, Variable View, and Output View
- Menus, Toolbars, and Navigation
- File Operations (open, save, export)
- Types of Data in SPSS:
- Categorical Data
- Continuous Data
- Missing Data and Handling Missing Values
2: Data Management in SPSS
- Learn how to import, export, and manipulate data in SPSS.
- Loading Data into SPSS:
- Importing Data from Excel, CSV, and Other File Formats
- Creating a New Dataset
- Variable Management:
- Creating and Editing Variables
- Setting Variable Properties (Type, Label, Value Labels)
- Defining Measurement Levels (Nominal, Ordinal, Scale)
- Data Cleaning and Transformation:
- Handling Missing Data
- Recoding Variables
- Creating New Variables and Computing Variables
- Sorting, Filtering, and Selecting Data
3: Data Analysis in SPSS
- Learn how to perform various statistical analyses using SPSS.
- Descriptive Statistics:
- Frequency Tables
- Measures of Central Tendency (Mean, Median, Mode)
- Measures of Dispersion (Standard Deviation, Range, Variance)
- Visualizing Data:
- Creating Graphs and Charts (Bar, Histogram, Pie, Scatter Plot)
- Customizing Graphs
- Interpreting Graphical Representations
- Basic Inferential Statistics:
- T-Tests (One-Sample, Independent, Paired Samples)
- Chi-Square Test
- Correlation Analysis (Pearson, Spearman)
- ANOVA (Analysis of Variance)
- Regression Analysis:
- Linear Regression
- Multiple Regression
- Interpreting Coefficients and Model Outputs
4: Advanced Analytics in SPSS
- Gain insights into advanced statistical methods and their applications in SPSS.
- Factor Analysis:
- Understanding Factor Analysis
- Performing Factor Analysis in SPSS
- Interpreting Factor Loadings
- Cluster Analysis:
- Introduction to Cluster Analysis
- K-Means Clustering
- Hierarchical Clustering
- Reliability Analysis:
- Cronbach’s Alpha and Internal Consistency
- Interpreting Reliability Coefficients
- Time Series Analysis:
- Introduction to Time Series
- Decomposition of Time Series Data
- Forecasting with SPSS
5: Reporting and Output Interpretation
- Learn how to interpret and present SPSS results effectively.
- Interpreting SPSS Output:
- Understanding Output Tables (Descriptive Stats, Tests, Regression)
- Significance Levels and P-Values
- Confidence Intervals
- Creating Reports in SPSS:
- Exporting Results to Word, Excel, and PDF
- Formatting Output for Presentation
- Creating Custom Tables
- Best Practices for Data Presentation:
- Clear Visuals and Charts
- Communicating Results in a Storytelling Manner
6: Hands-on Practice
- Apply SPSS skills in a real-world scenario.
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