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

In today’s data-driven socio-economic and business environment, the ability to design research, manage data effectively, and perform accurate statistical analysis is critical for informed decision-making. With the growing availability of datasets and research methodologies, professionals must be equipped with the right tools and skills to conduct systematic and credible research.

This course provides a comprehensive and practical foundation in research design, data management, and statistical analysis using SPSS. It is designed to equip participants with the competencies needed to plan and execute research studies, manage datasets efficiently, and perform statistical analyses with confidence.

Participants will learn how to develop robust research designs, including defining research problems, formulating hypotheses, and selecting appropriate methodologies. The course also covers data collection techniques, data cleaning, coding, and management, ensuring high-quality datasets ready for analysis.

Using SPSS (Statistical Package for the Social Sciences), participants will gain hands-on experience in performing descriptive and inferential statistical analysis, including hypothesis testing, correlation, and regression analysis. The training emphasizes interpretation of statistical outputs and effective reporting, enabling participants to translate data into actionable insights.

Through practical exercises, case studies, and real-world datasets, participants will build the skills needed to conduct end-to-end research from design to analysis and reporting.

By the end of the course, participants will be able to design research studies, manage and analyze data using SPSS, and present findings in a clear and impactful manner.

Duration

10 Days

Who Should Attend

• Researchers and research assistants
• Monitoring and evaluation (M&E/MEAL) professionals
• Public health and social science practitioners
• Policy analysts and program evaluators
• Academics and graduate students
• Data analysts and statisticians
• Professionals involved in research and data-driven decision-making

Organizational Impact

• Enhance research integrity and credibility through standardized best practices.

• Improve efficiency and accuracy in data analysis, reducing time and costs.

• Foster a data-literate culture for informed, evidence-based decisions.

Personal Impact

• Gain in-demand skills for a career in research and data analysis.

• Advance into senior research, PI, or data stewardship roles.

• Lead and manage complex research projects with confidence.

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

• Understand the principles and methodologies of research design.
• Formulate research questions and hypotheses.
• Design and implement surveys and experiments.
• Collect, manage, and clean data using SPSS.
• Perform descriptive and inferential statistical analyses using SPSS.
• Interpret and report the results of statistical analyses.
• Apply appropriate statistical techniques to different types of data.
• Enhance their decision-making and problem-solving skills using statistical evidence.
• Create visual representations of data and statistical results.
• Develop a comprehensive understanding of SPSS functionalities and tools.

Module 1: Introduction to research

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

Module 2: Overview of Evaluation

• Evaluation Objectives
• Evaluation Criteria
• Evaluation Questions

Module 3: Research Design

• Quantitative Research Approaches
• Qualitative Research Approaches

Module 4: Sampling

• Sampling Techniques
  • Probability
  • Non-probability
• Sample size determination

Module 5: Data Collection Methods in Research

• Quantitative data collection methods
• Qualitative data collection
• Creating an evaluation framework

Module 6: Data Collection tools in Research

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

Module 7: Developing Research Protocol

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

Module 8: Mobile Data Collection and Processing (ODK)

• Introduction to mobile data gathering
• Design of survey forms using ODK build and XLSForm
• Use ODK collect to gather data
• Use ODK aggregate to upload data to the server
• Work with spatial data (GPS coordinates)

Module 9: Introduction to SPSS statistical software

• SPSS interface and features
• Key terminologies used in SPSS
• Views: Variable, Data views, Syntax editor
• Data file preparation
• Data entry into SPSS
• Data manipulation: merge files, spit files, sorting files, missing values

Module 10: Basic Statistics using SPSS

• Descriptive statistics for numeric variables
• Frequency tables
• Distribution and relationship of variables
• Cross tabulations of categorical variables
• Stub and Banner Tables

Module 11: Graphics using SPSS

• Introduction to graphs in SPSS
• Graph commands in SPSS
• Different types of Graphs in SPSS

Module 12: Statistical Tests using SPSS

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

Module 13: Statistical Associations in SPSS

• Chi-Square test
• Pearson’s Correlation
• Spearman’s Rank-Order Correlation

Module 14: Predictive Models using SPSS

• Linear Regression
• Multiple Regression
• Logistic Regression
• Ordinal Regression

Module 15: Longitudinal Analysis using SPSS

• Features of Longitudinal Data
• Exploring Longitudinal data
• Longitudinal analysis for continuous outcomes

Module 16: Qualitative Data Analysis using NVivo

• Introduction to NVivo
• NVivo workspace
• Uploading qualitative data into NVivo
• Coding and making nodes
• Use of queries
• Project visualization

Module 17: Survey Report writing and Dissemination

• Survey report format
• Survey report content
• Survey findings dissemination
• Use of survey findings for decision making