Training Course on Research Design, Data Management and Statistical Analysis using SPSS-Rwanda

About the Course

In the socio-economic and business context, conducting research, data management, and data analysis are imperative for informed decision making. IBM SPSS Statistics is a powerful statistical software platform. It delivers a robust set of features that lets your organization extract actionable insights from its data. The software is more popular in social sciences. Sound knowledge about the methodology of conducting research and the use of SPSS is very beneficial for to researchers. Upon completion of workshop, participants will develop competence in quantitative techniques in research design, data collection, and management, statistical data analysis, interpretation and reporting of results.

Target Participants

Ideal participants for this course include evaluators, researchers, students and all persons who are interested in enhancing their data analysis skills.

What you will learn

By the end of the course the learner should be able to:

• Understand quantitative research approaches
• 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.

Course duration

5 days

Course Outline

Module 1: Introduction

Introduction to Research

• Introduction to research
• Different types of research
• Formulation of research problem statement
• Formulation of research hypothesis 

Research Design

• Quantitative Research Approaches
• Qualitative Research Approaches 

Sampling

• Sampling Techniques
• Sample size determination 

Data Collection tools in Research

• Survey Questionnaire design
• FGD guide design
• KII guide design 

Developing Research Protocol

• What is a research protocol?
• Basic concepts of a research protocol
• Structure of a research protocol 

Statistical Concepts and Introduction to SPSS

• Statistical Concepts
• Types of data
• Data Structures and Types of Variables
• Overview of SPSS
• Working with the SPSS software (file management, editing functions, viewing options, etc)
• Output Management
• Basics programming of SPSS 

Module 2: Data Entry/Management

• Entering categorical and continuous data
• Defining and labeling variables
• Validation and Sorting variables
• Transforming, recording and computing variables
• Restructuring data
• Replacing missing values
• Merging files and restructuring
• Splitting files, Selecting cases  and weighing cases
• Syntax and output

Module 3:

Statistical Inference and Descriptive Statistics

• Tests of Association
• Tests of Difference
• Hypothesis testing

Measures of Variability and Central Tendency  

• Describing quantitative data
• Describing qualitative data 

Graphics in Data Analysis

• Graphing quantitative data
• Graphing qualitative data
• Advanced graphics options 

Correlation  

• Correlation of bivariate data
• Subgroup Correlations
• Scatterplots of Data by Subgroups
• Overlay Scatterplots 

Module 4: Test statistics and tests of associations

Comparing Means

• One Sample t-tests
• Paired Sample t-tests
• Independent Samples t-tests
• Comparing Means Using One-Way ANOVA 
• MANOVA: multivariate analysis of variance
• Repeated Measures ANOVA 

Tests of associations

• Goodness of Fit Chi Square All Categories Equal
• Goodness of Fit Chi Square Categories Unequal
• Chi Square for Contingency Tables
• Pearson Correlation
• Spearman Correlation

Nonparametric Statistics 

• Mann-Whitney Test
• Wilcoxon’s Matched Pairs Signed-Ranks Test
• Kruskal-Wallis One-Way ANOVA
• Friedman’s Rank Test for k Related Samples

Comparing Means Using Factorial ANOVA 

• Factorial ANOVA Using GLM Univariate
• Simple Effects 

Module 5:

Comparing Means Using Repeated Measures ANOVA 

• Using GLM Repeated Measures to Calculate Repeated Measures ANOVAs
• Multiple Comparisons

Other topics

• Multiple Regression Analysis
• Logistic regression (Binary & Multinomial)
• Cluster Analysis
• Factor analysis