Data management and analysis in SPSS

Module 1: Introduction to SPSS

1. Overview of SPSS:
   • Introduction to SPSS software.
   • Installation and setup.
2. Navigating the SPSS Interface:
   • Understanding the SPSS data editor and output viewer.
   • Basics of syntax and menus.

Module 2: Data Import and Preparation

1. Importing Data into SPSS:
   • Loading data from different sources (Excel, CSV, databases).
   • Handling missing data and data types.
2. Data Cleaning in SPSS:
   • Identifying and handling outliers.
   • Recoding variables and creating new variables.

Module 3: Descriptive Statistics in SPSS

1. Descriptive Statistics:
   • Calculating measures of central tendency and dispersion.
   • Generating frequency distributions and summary tables.
2. Graphical Representation:
   • Creating charts and graphs in SPSS.
   • Customizing visualizations.

Module 4: Inferential Statistics in SPSS

1. Hypothesis Testing:
   • Conducting t-tests, chi-square tests, and ANOVA.
   • Interpreting results.
2. Regression Analysis in SPSS:
   • Performing simple and multiple regression.
   • Assessing regression assumptions.

Module 5: Advanced Data Management

1. Data Transformation:
   • Creating computed variables.
   • Reshaping datasets.
2. Combining Datasets:
   • Merging datasets in SPSS.
   • Concatenating datasets.

Module 6: Factor Analysis and Reliability Testing

1. Factor Analysis:
   • Understanding factor analysis concepts.
   • Conducting factor analysis in SPSS.
2. Reliability Testing:
   • Assessing internal consistency using SPSS.
   • Calculating Cronbach’s alpha.

Module 7: Multivariate Analysis in SPSS

1. Multivariate Analysis of Variance (MANOVA):
   • Conducting MANOVA in SPSS.
   • Interpreting results.
2. Cluster Analysis in SPSS:
   • Hierarchical and k-means clustering.
   • Visualizing clusters.

Module 8: Real-world Applications and Case Studies

1. Case Studies:
   • Applying data management and analysis techniques to real-world scenarios.
   • Analyzing and interpreting results.
2. Capstone Project:
   • Undertaking a comprehensive data analysis project using SPSS.
   • Presenting findings and insights.

Additional Considerations:

• Hands-On Exercises and Labs:
  • Practical application of SPSS concepts with real datasets.
  • Group exercises and coding labs.
• Interactive Sessions:
  • Q&A sessions and discussions.
  • Peer-to-peer learning activities.
• Resources and Further Learning:
  • Providing additional resources for self-paced learning.
  • Sharing relevant books, online courses, and forums.