Data Management, Analysis and Visualization Using SPSS

Course overview

A comprehensive, hands-on training course designed to equip participants with practical skills in data preparation, data management, statistical analysis, interpretation and visualization using IBM SPSS Statistics. Participants learn how to transform raw datasets into meaningful statistical insights for research, business, monitoring and evaluation, social sciences, health, development programmes and evidence-based decision-making.

Master data management, statistical analysis and visualization using SPSS through practical, hands-on training covering data cleaning, descriptive statistics, hypothesis testing, correlation, regression, ANOVA, chi-square, reliability analysis, charts and statistical reporting.

Course objectives

• Understand the SPSS environment and navigate the major tools and features used for statistical data analysis.
• Create and interpret cross-tabulations for analysing relationships between categorical variables.
• Apply inferential statistical techniques to test research questions and hypotheses.
• Apply SPSS in research, monitoring and evaluation, programme analysis, and organizational reporting.
• Apply inferential statistical techniques to test research questions and hypotheses.
• Interpret SPSS statistical outputs and distinguish statistically significant from non-significant findings.
• Conduct correlation analysis to examine relationships between variables.
• Develop effective charts and visualizations such as bar charts, histograms, pie charts, boxplots and scatterplots.
• Conduct regression analysis for examining relationships and developing predictive models.

Who should attend

Course content

• Module 1: Introduction to SPSS and Statistical Data Analysis Introduction to SPSS Statistics Applications of SPSS in research, business, M&E and social sciences Understanding quantitative data Types and levels of measurement Variables, cases and observati
• Module 2: Data Entry, Import and Export Creating a new dataset Defining variables Variable names and labels Value labels Measurement levels Importing data from Excel Importing CSV and text files Exporting SPSS results Saving and managing datasets
• Module 3: Data Cleaning and Quality Management Identifying data-entry errors Detecting duplicates Identifying missing values Handling invalid and out-of-range values Data validation Identifying inconsistent responses Managing missing data Checking data qu
• Module 4: Data Transformation and Management Compute Variable Recode variables Creating categorical variables Selecting and filtering cases Sorting cases Split File Aggregating data Merging and restructuring datasets
• Module 5: Descriptive Statistics and Exploratory Data Analysis Frequencies and percentages Mean, median and mode Range, variance and standard deviation Percentiles and quartiles Distribution analysis Exploring relationships and patterns Interpreting descr
• Module 6: Data Visualization Using SPSS Principles of effective data visualization Bar charts Pie charts Histograms Boxplots Line charts Scatterplots Chart customization and interpretation Exporting visualizations for reports
• Module 7: Cross-Tabulation and Survey Data Analysis Creating cross-tabulations Row and column percentages Analysing categorical variables Likert-scale data Multiple-response questions Survey data analysis Interpreting cross-tabulation results
• Module 8: Statistical Inference and Hypothesis Testing Research questions and hypotheses Null and alternative hypotheses p-values Confidence intervals Significance levels Type I and Type II errors Statistical versus practical significance Selecting approp
• Module 9: T-Tests and Group Comparisons One-sample t-test Independent-samples t-test Paired-samples t-test Comparing group means Testing assumptions Confidence intervals Effect sizes Interpretation and reporting
• Module 10: ANOVA and Non-Parametric Tests One-way ANOVA Multiple-group comparisons Post-hoc tests Effect sizes Mann-Whitney U Wilcoxon signed-rank Kruskal-Wallis Friedman test Selecting appropriate te
• Module 11: Correlation and Regression Analysis Pearson correlation Spearman correlation Understanding relationships between variables Simple linear regression Multiple linear regression Regression coefficients R and R² Model significance Prediction and i
• Module 12: Advanced Statistical Analysis Logistic regression General Linear Models ANCOVA MANOVA Multivariate analysis concepts Model assumptions Selecting appropriate advanced techniques Interpreting complex SPSS outputs
• Module 13: Reliability and Factor Analysis Reliability analysis Cronbach's Alpha Item-total statistics Questionnaire scale evaluation Exploratory factor analysis KMO and Bartlett's tests Eigenvalues and scree plots Factor extraction and rotation Interpret
• Module 14: SPSS Output, Interpretation and Statistical Reporting Reading SPSS output Interpreting statistical tables Interpreting p-values and confidence intervals Reporting descriptive and inferential statistics Creating professional statistical tables P
• Module 15: Practical SPSS Capstone Project Participants complete an end-to-end real-world data analysis project: Raw Data → Data Import → Cleaning → Transformation → Descriptive Analysis → Visualization → Hypothesis Testing → Advanced Anal

General notes

• The training combines theory with extensive hands-on exercises, using realistic datasets from areas such as research, business, monitoring and evaluation, health, social sciences and development programmes. Participants will work with SPSS to perform data cleaning, transformation, descriptive analysis, cross-tabulation, hypothesis testing, t-tests, ANOVA, correlation, regression, reliability analysis and selected advanced statistical techniques.

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Schedule & fees