Data Management and Statistical Data Analysis using SPSS Course

Introduction

The ability to manage and analyze data accurately is a critical skill across academic, governmental, and business sectors. The Data Management and Statistical Data Analysis using SPSS course is designed to provide participants with practical knowledge and skills to work with quantitative data using IBM’s Statistical Package for the Social Sciences (SPSS) a powerful, user-friendly tool for data analysis.

The course introduces participants to the core features of SPSS, including data entry, cleaning, transformation, and management. Learners will become familiar with the SPSS interface and learn how to organize datasets, define variables, handle missing data, and prepare data for analysis. Emphasis is placed on good data management practices to ensure data quality and analytical accuracy.

Participants will explore a variety of statistical techniques using SPSS, ranging from basic descriptive statistics to more advanced inferential methods such as t-tests, ANOVA, correlation, regression, and non-parametric tests. These methods will be applied through real-world datasets to develop the ability to select appropriate techniques, interpret outputs, and draw meaningful conclusions.

By the end of the course, learners will be equipped to conduct independent statistical analyses and produce professional outputs for research, reports, and presentations. This course is ideal for students, researchers, analysts, and professionals who need to understand, manage, and analyze data effectively using SPSS in academic, policy, or business environments. 

Related course – click the course title to view the course outline

·       Research Design, Mobile Data Collection and Data Analysis using SPSS and Nvivo Course

Course Objective

By the end of this course, participants will be able to

·       Efficiently perform data analysis tasks using IBM SPSS, from data import to result interpretation.

·       Manage and manipulate datasets using SPSS tools, including sorting, filtering, merging, and transforming data.

·       Define, recode, and transform variables; create dummy variables; apply case selection and weighting; and split files for subgroup analysis.

·       Create and customize various types of charts and graphs, including bar charts, line charts, scatterplots, and boxplots to support data visualization.

·       Conduct basic statistical procedures such as Frequencies, Descriptives, Explore, Means, and Crosstabs for univariate and bivariate analysis.

·       Assess data distribution and assumptions by testing for normality and identifying outliers within data series.

·       Apply variable transformations to improve data analysis accuracy and meet statistical assumptions.

·       Perform key one-sample analyses including the one-sample t-test, binomial test, and chi-square goodness-of-fit test.

·       Conduct tests of association such as Pearson and Spearman correlations, partial correlation, chi-square test of independence, and loglinear analysis for categorical data.

Duration

5 days

Who should attend?

This course is designed for individuals who work with data and are seeking to enhance their skills in statistical analysis using IBM SPSS. It is ideal for:

• Researchers in the social sciences, education, public health, business, and other fields that rely on quantitative data.
• Monitoring and evaluation officers, program managers, and data analysts involved in survey analysis, impact assessments, and evidence-based reporting.
• Public sector employees and NGO staff responsible for data collection, management, and interpretation to support policy and program development.
• Academics and faculty who wish to integrate SPSS into their teaching or research methodologies.
• Professionals and decision-makers who need to interpret statistical results for informed decision-making.

Course content

Module 1: Foundations of Statistical Analysis

• Understanding the research process and its key steps
• Differences between populations and samples
• Experimental vs. non-experimental research designs
• Types of variables: independent, dependent, and control variables

Module 2: Introduction to SPSS Statistical Software

• Overview of SPSS and its applications
• Navigating the SPSS interface: Data View, Variable View, and Syntax Editor
• Key terminologies and file types in SPSS
• Preparing and entering data into SPSS
• Managing and organizing data files (merge, split, sort)
• Identifying and coding missing values

Module 3: Data Cleaning and Preparation

• Detecting and handling missing or duplicate data
• Recoding variables (e.g., reverse coding, creating categories)
• Creating dummy variables and computed fields
• Selecting, splitting, and weighting cases for analysis

Module 4: Descriptive Statistics and Data Summarization

• Generating descriptive statistics for numerical and categorical variables
• Producing frequency tables and summary reports
• Exploring data distributions, central tendency, and dispersion
• Cross-tabulations and use of stub and banner tables

Module 5: Data Visualization in SPSS

• Introduction to graphing tools and the Chart Builder
• Creating bar charts, histograms, pie charts, line graphs, and boxplots
• Customizing visualizations: titles, labels, axes, legends
• Visualizing relationships: scatterplots and grouped charts

Module 6: Hypothesis Testing and Mean Comparisons

• Assessing normality (Shapiro-Wilk, Kolmogorov-Smirnov tests)
• One-Sample T-Test, Independent Samples T-Test, Paired Samples T-Test
• One-Way ANOVA and post-hoc comparisons

Module 7: Tests of Association and Correlation

• Chi-Square test of independence
• Pearson’s and Spearman’s correlation coefficients
• Bivariate plots and testing relationships among scale variables
• Partial correlation analysis

Module 8: Predictive Modeling and Regression Analysis

• Simple linear regression and interpretation
• Multiple regression analysis and diagnostics
• Binary logistic regression for binary outcomes
• Ordinal regression for ordered categorical variables

Module 9: Nonparametric Statistical Tests

• When and why to use nonparametric tests
• Running Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, and Friedman tests
• Using the Nonparametric Tests dialog and interpreting output

Module 10: Reliability and Scale Validation

• Conducting reliability analysis using Cronbach’s Alpha
• Item-total statistics and interpreting alpha values
• Exploratory Factor Analysis (EFA): Principal Components and Varimax Rotation
• Interpreting factor loadings and eigenvalues

Module 11: Longitudinal Data Analysis in SPSS

• Characteristics of longitudinal and panel data
• Organizing and exploring repeated measures data
• Performing repeated measures ANOVA
• Interpreting within- and between-subject effects

Module 12: Time Series Analysis and Forecasting

• Introduction to time series concepts and forecasting fundamentals
• Smoothing techniques (moving averages, exponential smoothing)
• Regression models for time series
• ARIMA modeling and diagnostic checking
• Intervention analysis for policy or event impact

Module 13: Decision Tree Modeling in SPSS

• Introduction to decision trees and classification methods
• Applications of SPSS Decision Trees for prediction and segmentation
• Overview of CHAID, CRT, and QUEST algorithms
• Interpreting tree diagrams and rules for decision-making

Module 14: Reporting and Communicating Results

• Exporting tables and graphs from SPSS to Word/Excel
• Writing clear interpretations and summaries of statistical output
• Best practices for presenting findings in academic and professional contexts
• Avoiding common reporting pitfalls and ensuring data transparency

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.