Statistical Analysis using (SPSS)

Course Description

Statistical Package for Social Scientists (SPSS) is a statistical software suite developed by IBM
for data management, advanced analytics, multivariate analysis, business intelligence, and
criminal investigation. It was produced by SPSS Inc. before being acquired by IBM in 2009 and
renamed IBM SPSS Statistics.

Course Objectives:

• Understand the fundamental statistical concept and their applications in data analysis
• Conduct descriptive and inferential statistical analysis using IBM SPSS Statistics
• Manage and organize quantitative data effectively for analysis
• Apply appropriate statistical tests
• Link theoretical knowledge of statistics to practical, real world data problems
• Generate insights from data to support evidence-based decision problems

Course Outline:

Module 1. Review of basic statistical concepts 

• Classification of Data: Nominal, Ordinal, Interval, Ratio
• Descriptive Statistics: Mean, Median, Mode, Standard Deviation, Variance
• Probability Distributions: Normal, Binomial, Poisson
• Hypothesis Testing: Null and Alternative Hypotheses, p-values, Significance Level
• Confidence Intervals

Module 2. General Introduction to the SPSS User Interface 

• Overview of SPSS Windows-Data View, Variable View, Output Viewer
• Toolbars & Menu-File, Edit, View, Data, Transform, Analyze, Graphs
• Setting Preferences and Options
• Navigation of Help files and Documentation

Module 3. Definition and coding of variables in SPSS

• Variable Types-Numeric, String, Date
• Variable Labels and Value Labels
• Setting Measurement Levels: Scale, Ordinal, Nominal
• Missing Values Definitions and How to Handle Them

Module 4. Data entry, import, cleaning and validation

• Manual Entry of Data into SPSS
• Importing Data from Excel, CSV, and Other Formats
• Cleaning Data-Detection of Duplicates, Outliers, and Mistakes
• Validation Sharing

Module 5. Exploratory Data Analysis

• Frequency Distribution and Histograms
• Central Tendency and Variability
• Cross-Tabulation Techniques for Categorical Data
• Data Presentation Bar Charts, Pie Charts, Scatterplots

Module 6. Test hypotheses about individual variables

• One Sample T-Test
• Chi Squares Goodness of Fit Test
• Binomial Test
• Kolmogorov-Smirnov Tests of Normality

Module 7. Test the relationship between categorical variables

• Chi-Square Test of Independence
• Fisher Exact Test

Module 8. Test on the difference between two group means

• Independent Sample T-Test
• Paired Sample T-Test
• T-Test Assumptions and Their Checks

Module 9. Test on differences between more than two group means

• One-Way ANOVA
• Post hoc tests-Tukey, Bonferroni, Scheffé
• ANOVA Assumptions and Their Checks

Module 10. Test the relationship between scale variables

• Pearson Product-Moment Correlation Coefficient
• Spearman Rank Correlation
• Partial Correlation
• Scatter Plots and Correlation Matrices

Module 11. Predict a scale variable: Regression

• Simple Regression
• Multiple Linear Regression
• The Assumptions of the Analysis
• The Coefficients of Regression with R-Squared.

Module 12. Overview of multivariate procedure

• Factor Analysis
• Principle Components Analysis (PCA)
• Multivariate Analysis of Variance (MANOVA)
• Basics of Logistic Regression
• Multivariate Techniques

Deliverables

• Learners should define and code variables in SPSS while ensuring suitable naming, labeling, and treatment of any missing values.
• Learners will import and clean and validate a dataset into SPSS, identify and correct any missing values and outliers.
• Learners will conduct exploratory data analysis (EDA) using descriptive statistics, visualization, and key insight summaries.
• Learners will carry out hypothesis testing-t-tests, ANOVA, and Chi-square test and submit a report with a structured interpretation of results.
• Learners will use regression and multivariate analysis techniques, interpret outputs, and present findings in the final report of the project. 
• Certificate of Completion.