SPSS introductory course designed for beginners in statistical data analysis, focusing on a structured, hands-on learning approach. The course assumes no prior experience in SPSS or statistics and will cover the basic concepts and tools needed to analyze data effectively.

Course Title:

Introduction to SPSS for Statistical Data Analysis

Duration:

4 Weeks (12 Hours Total)

• 2 sessions per week
• 1.5 hours per session

Course Objectives:

By the end of this course, participants will:

• Understand the basic concepts of statistics and data analysis.
• Be able to navigate the SPSS interface and perform common data analysis tasks.
• Learn how to import, manage, and clean data.
• Perform basic descriptive and inferential statistical analyses.
• Create visual representations of data in SPSS.
• Interpret and report statistical outputs effectively.

Week 1: Introduction and SPSS Interface

Session 1: Getting Started with SPSS

• Overview of SPSS and its applications in data analysis.
• Installing SPSS and navigating the user interface.
  • Data View vs. Variable View.
  • Menus, toolbars, and functions.
• Basic statistics review (mean, median, mode, range, variance, standard deviation).
• Hands-on: Entering data manually in SPSS.
  • Define variables, data types, and labels.

Session 2: Importing and Managing Data

• Data file types: Excel, CSV, and text files.
• Importing external data into SPSS.
• Data formatting and transformation.
  • Recoding variables, and handling missing data.
• Hands-on: Importing a dataset and organizing variables.

Week 2: Descriptive Statistics and Data Exploration

Session 3: Descriptive Statistics

• Understanding frequency distributions, central tendency, and dispersion.
• Using SPSS to generate descriptive statistics.
  • Frequencies, Descriptives, Explore commands.
• Hands-on: Analyzing a dataset using descriptive statistics.

Session 4: Data Visualization

• Introduction to charts and graphs in SPSS.
  • Histograms, bar charts, pie charts, and boxplots.
• Hands-on: Creating visualizations to summarize data.
• Interpretation of results: how to report descriptive statistics.

Week 3: Inferential Statistics

Session 5: Hypothesis Testing and t-Tests

• Introduction to inferential statistics.
  • Concept of population vs. sample, null vs. alternative hypothesis.
• Performing t-tests in SPSS.
  • One-sample, independent-sample, paired-sample t-tests.
• Hands-on: Conducting a t-test and interpreting results.

Session 6: ANOVA and Correlation

• Introduction to ANOVA (Analysis of Variance).
  • One-way ANOVA in SPSS.
• Introduction to correlation.
  • Pearson’s correlation coefficient.
• Hands-on: Conducting an ANOVA and correlation analysis.

Week 4: Advanced Topics and Reporting

Session 7: Regression Analysis

• Introduction to linear regression.
  • Simple linear regression in SPSS.
  • Interpreting regression outputs (R-squared, coefficients).
• Hands-on: Performing a linear regression analysis.

Session 8: Reporting and Presenting Results

• Exporting results from SPSS (tables, graphs).
• Best practices for reporting statistical findings.
  • Writing clear interpretations of SPSS outputs.
  • Presenting visual data in reports.
• Final Project: Analyze a dataset and present a report.

Learning Materials:

• Textbooks:
  • “SPSS for Beginners” by Vijay Gupta.
  • “SPSS Survival Manual” by Julie Pallant.
• Datasets: Pre-loaded datasets for hands-on practice.
• Online Resources: Links to SPSS tutorials and guides.

Assessment:

• Homework Assignments: Weekly data analysis tasks using SPSS.
• Final Project: A report analyzing a dataset, including interpretation of findings and visualizations.

Certification:

Participants who complete the course and the final project will receive a certificate of completion.