Description

Introduction of IBM SPSS

IBM SPSS (Statistical Package for the Social Sciences) is a powerful statistical software suite widely used for data analysis, data management, and data visualization. This course is designed to help participants master SPSS to perform statistical analysis, data management, and data visualization. Whether you are new to SPSS or looking to sharpen your skills, this course provides a comprehensive learning experience covering the essential features and advanced techniques of SPSS.

 Prerequisites

• Basic Understanding of Statistics: Participants should have a fundamental knowledge of statistical concepts, including mean, median, standard deviation, and correlation.
• Basic Computer Skills: Familiarity with using software applications and navigating through user interfaces is recommended.
• No Prior SPSS Experience Required: This course is suitable for beginners as well as those with some prior exposure to SPSS.

Table of Contents

1 Introduction to SPSS

1.1 Overview of SPSS Software
1.2 Installation and Setup
1.3 Navigating the SPSS Interface
1.4 Understanding Data Types and Structures
1.5 Importing and Exporting Data

2 Data Management

2.1 Data Entry and Editing
2.2 Handling Missing Data
2.3 Data Transformation and Recoding
2.4 Sorting and Filtering Data
2.5 Merging and Splitting Datasets

3 Descriptive Statistics

3.1 Generating Descriptive Statistics (Mean, Median, Mode)
3.2 Frequency Distributions
3.3 Cross-tabulation and Pivot Tables
3.4 Visualizing Data with Charts and Graphs

4 Inferential Statistics

4.1 Hypothesis Testing
4.2 T-tests (One-Sample, Independent, Paired)
4.3 ANOVA (Analysis of Variance)
4.4 Chi-Square Tests
4.5 Correlation and Regression Analysis

5 Advanced Data Analysis

5.1 Factor Analysis
5.2 Cluster Analysis
5.3 Discriminant Analysis
5.4 Logistic Regression
5.5 Time Series Analysis

6 Reporting and Presentation

6.1 Customizing Output
6.2 Exporting Results to Different Formats (PDF, Word, Excel)
6.3 Creating and Formatting Reports
6.4 Sharing SPSS Outputs with Others

7 Automation and Scripting

7.1 Introduction to SPSS Syntax
7.2 Automating Tasks with Syntax Scripts
7.3 Custom Macros and Functions
7.4 Using SPSS with R and Python for Advanced Analysis

8 Case Studies and Real-World Applications

8.1 Case Study 1 Market Research Analysis
8.2 Case Study 2 Healthcare Data Analysis
8.3 Case Study 3 Financial Data Analysis (Ref: Apache Pig with Data Analysis Program)
8.4 Real-World Applications in Various Industries

9 Tips Tricks and Best Practices

9.1 Optimizing SPSS Performance
9.2 Common Pitfalls and How to Avoid Them
9.3 Best Practices for Data Management and Analysis

10 Final Project

10.1 Real-World Data Analysis Project
10.2 Preparing and Presenting the Final Report
10.3 Q&A and Wrap-Up

Conclusion:
This comprehensive SPSS training equips participants with skills in data management, statistical analysis, automation, and reporting, culminating in a real-world project to apply their knowledge effectively.

Reference