Overview

Statistical data analysis using SPSS is a critical skill for evidence-based decision making in social science, business, and development research. This course provides practical, hands-on training in IBM SPSS Statistics, a leading statistical software for data management, quantitative analysis, and reporting. Participants gain strong competencies in research design, data cleaning, descriptive and inferential statistics, regression analysis, and results interpretation, enabling them to generate accurate insights and produce high-quality analytical reports for research and policy use.

Target Audience

The course is useful for professionals who use data as part of their work and who need to make decisions from data analysis. This course does not assume previous knowledge and competency in using SPSS software.

Learning Outcomes / Objectives

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

• Easily collect high quality data using mobile devices such as tablets and phones.
• Clean their data for use in subsequent statistical analysis.
• Identify and fix errors in datasets.
• Analyze and better understand their data, and solve complex business and research problems through a user-friendly interface.
• More quickly understand large and complex data sets with advanced statistical procedures that help ensure high accuracy and quality decision-making.
• Gain high level skills on statistical results interpretation and report writing.

Duration

5 days

Modules / Course Content

Module 1: Introduction Introduction to Statistical Data Analysis

• Introduction to statistical concepts
• Descriptive and inferential statistics
• The research/survey process
• Research designing

Introduction to SPSS statistical software

• Installing the software (key consideration and procedures)
• SPSS interface and features
• SPSS terminologies
• SPSS views
• Data entry into SPSS
• Data manipulation: merge files, spit files, sorting files, missing values

Basic Statistics using SPSS

• Introduction to descriptive and inferential statistics
• Descriptive statistics – Measures of centres, distribution, dispersion
• Frequency distribution tables

Module 2: Data/Output Management and Graphics Data Management

• Defining and labeling variables
• Cleaning data
• Sorting data
• Transforming, coding and computing variables
• Restructuring data
• Dealing with missing values
• Merging files
• Splitting files
• Selecting cases
• Weighing cases
• Key syntax in SPSS
• Output management in SPSS

Graphics using SPSS

• Introduction to graphs in SPSS
• Graph commands in SPSS
• Types of SPSS graphs (Bar graph; Scatter plot; Line chart; Histogram; Box plot; Pie chart; Q-Q plot; P-P plot)

Module 3: Inferential Statistics (Statistical Tests) using SPSS Test of differences in means

• One Sample T Test
• Independent Samples T Test
• Paired Samples T Test
• One-Way ANOVA

Test of associations

• Chi-Square test
• Pearson's Correlation
• Spearman's Rank-Order Correlation
• Bivariate Plots and Correlations for Scale Variables

Module 4: Regression Analysis and Non-Parametric Tests in SPSS Regression Models using SPSS

• Linear regression (simple and multiple regression)
• Binary logistic regression
• Multinomial logistic regression
• Ordinal regression
• 2-stage least square regression

Nonparametric Tests

• Application of non-parametric tests
• Options available in Nonparametric Tests procedure dialog box and tabs
• Interpretation of nonparametric tests results

Module 5: Longitudinal and Time-Series Data Analysis Longitudinal Analysis using SPSS

• Introduction to panel data
• Benefits of panel data
• Problem with panel data
• Features of Longitudinal Data
• Exploring Longitudinal data
• Regression models with panel data (random effects; fixed effects; between-within models)

Time Series and Forecasting using SPSS

• The basics of forecasting
• Smoothing time series data
• Regression with time series data
• ARIMA models
• Intervention analysis

Training Methodology

The course will employ a hands-on, practical approach to ensure participants develop both conceptual understanding and technical proficiency. Each module will integrate interactive lectures, guided software demonstrations, and individual or group exercises based on real-world illustrations. Participants will receive continuous feedback and personalized coaching to reinforce learning. By the end of the training, they will have completed a mini project that demonstrates their ability to apply the acquired skills in a practical context.

More Details

Upon successful completion of this course, participants will be issued a certificate.