Course Outline:
Introduction to SPSS:
Overview of SPSS and its applications
Installing and setting up SPSS
Navigating the SPSS interface (data view, variable view)
Data Entry and Management:
Importing data from various sources (Excel, CSV, databases)
Entering data manually
Defining variables and variable types
Data cleaning and preprocessing
Descriptive Statistics:
Measures of central tendency (mean, median, mode)
Measures of variability (range, variance, standard deviation)
Frequency distributions and cross-tabulations
Using the Descriptive Statistics menu
Data Manipulation:
Sorting and filtering data
Computing new variables
Recoding variables
Handling missing values
Visualization:
Creating charts and graphs (bar charts, histograms, pie charts, scatter plots)
Customizing graphs (titles, labels, legends, colors)
Using the Chart Builder
Exporting and printing graphs
Inferential Statistics:
Hypothesis testing (t-tests, chi-square tests)
Analysis of variance (ANOVA)
Correlation and regression analysis
Non-parametric tests
Advanced Statistical Techniques:
Multivariate analysis (MANOVA, factor analysis)
Cluster analysis
Discriminant analysis
Logistic regression
Reliability and Validity:
Testing for reliability (Cronbach’s alpha)
Exploratory and confirmatory factor analysis
Construct validity
Survey Analysis:
Designing and analyzing surveys
Weighting cases
Cross-tabulation and chi-square analysis
Analyzing survey data using complex samples
Syntax and Automation:
Introduction to SPSS Syntax
Writing and running syntax commands
Automating tasks with syntax
Debugging and error handling in syntax
Data Transformation:
Merging datasets
Splitting files
Aggregating data
Restructuring data (transposing)
Reporting:
Creating tables and reports
Using the Output Viewer
Exporting output to other formats (Word, Excel, PDF)
Customizing and formatting output
Advanced Data Management:
Using the Data Editor
Working with large datasets
Advanced data selection techniques
Creating and using SPSS macros
Practical Applications and Case Studies:
Real-world examples and case studies
Practical exercises and projects
Best practices and tips for efficient analysis
Skills Gained:
Proficiency in using SPSS for data analysis and management
Ability to perform descriptive and inferential statistical analyses
Skills in data visualization and reporting
Competence in advanced statistical techniques and multivariate analysis
Knowledge of SPSS syntax for automation and advanced data manipulation
Target Audience:
Social scientists
Market researchers
Health researchers
Business analysts
Students and professionals in data-driven fields