Training Course in Data Analysis & Manipulation Using SPSS

Course Level: Foundation

Introduction

This course is tailored for professionals, researchers, and students seeking to leverage the IBM SPSS Statistics software for evidence-based decision-making.

Participants will gain hands-on experience in managing, analyzing, and visualizing data using SPSS’s intuitive graphical interface and powerful syntax capabilities.

Duration

5 days

What You’ll Learn

• Navigating and operating SPSS effectively
• Preparing and cleaning data for detailed analysis
• Summarizing data using descriptive statistics and visualization
• Performing inferential statistical analyses
• Building simple and multiple linear regression models to analyze relationships and predict outcomes
• Automating workflows using syntax
• Interpreting findings effectively

Who Should Attend

• Data analysts
• Researchers
• Data scientists
• Business analysts
• Social scientists
• Statisticians
• Students
• Anyone working with survey data or performing statistical analysis

Training Methodology

• Instructor-led presentations and live demonstrations
• Hands-on practical exercises per module
• Small group discussions and peer learning
• Real-world datasets for practice
• Supplementary materials and templates

Course Objectives

By the end of this training, participants will be able to:

• Master the SPSS interface, including Data View, Variable View, Output, and Syntax Editor, and manage data files with ease.
• Import, label, recode, validate, merge, and sort datasets to ensure accuracy and readiness for analysis.
• Create frequency tables, compute measures of central tendency and dispersion, and build informative charts
• Conduct hypothesis tests such as t-tests, ANOVA, chi-square, and correlation analyses to draw valid inferences from data.
• Develop linear, multiple, logistic, and other regression models, and explore multivariate techniques like factor and cluster analysis.
• Use SPSS syntax to automate repetitive tasks for reproducibility and efficiency.
• Export and format outputs, interpret statistical findings, and produce clear, professional reports of analysis.

Course Outline

Module I: Introduction to SPSS

• Overview of SPSS and its key applications
• Navigating the SPSS interface (Data View, Variable View, Syntax Editor)
• Importing and exporting data (Excel, CSV, text formats)
• Basic file management and SPSS workflow

Module II: Data Management & Manipulation

• Entering and organizing data in SPSS
• Defining, labeling, and managing variables
• Handling missing values and identifying outliers
• Merging, splitting, sorting files, and selecting cases
• Complex data reshaping and aggregation
• Using SPSS syntax for efficient data manipulation

Module III: Descriptive Statistics & Data Exploration

• Generating frequency tables and summary statistics
• Measures of central tendency and variability (mean, median, mode, SD, etc.)
• Cross-tabulations for categorical data examination
• Visual binning and exploring associations between variables

Module IV: Data Transformation & Computation

• Recoding variables and creating new computed fields
• Applying transformation commands via syntax
• Merging and splitting datasets
• Cleaning and preparing data through transformations

Module V: Data Visualization in SPSS

• Creating charts: histograms, bar charts, scatter plots, boxplots, etc.
• Customizing visuals for presentation clarity
• Best practices for visual data representation

Module VI: Inferential Statistics & Hypothesis Testing

• Conducting t-tests (one-sample, independent, paired)
• Analysis of variance (ANOVA) and post-hoc analysis
• Correlation measures: Pearson’s, Spearman’s, Kendall’s
• Running regression: simple and multiple linear regression
• Applying non-parametric tests such as chi-square, Mann-Whitney U, Kruskal–Wallis

Module VII: Advanced Modeling & Multivariate Analysis

• Logistic and ordinal regression techniques
• Performing factor analysis and principal component analysis (PCA)
• Cluster analysis: k-means and hierarchical clustering
• Conducting reliability tests (e.g., Cronbach’s Alpha)

Module VIII: SPSS Syntax, Automation & Reporting

• Writing and executing SPSS syntax for reproducibility
• Automating repetitive analyses with syntax and macros
• Exporting, customizing, and managing output for reporting
• Tailoring reports for different audiences (executives, technical reports, etc.)

Module IX: Survey & Longitudinal Data Analysis

• Handling and analyzing survey data (Likert scales, frequency, and cross-tabs)
• Time-series or longitudinal data techniques
• Advanced survey techniques: weighting, complex samples, and case studies

Module X: Final Project & Application

• Applying all tools to a comprehensive dataset
• Cleaning, analyzing, visualizing, and modeling data
• Presenting findings through polished outputs and dashboards