Module Description

Module 3 focuses on the practical design and implementation of a research study. Participants will learn how to select an appropriate research approach and design, define the study population, determine an appropriate sampling strategy, estimate sample size, and develop suitable data-collection instruments.

The second stage is highly practical and introduces the complete data-analysis workflow, from raw data and coding to data cleaning, statistical analysis, tables, and interpretation. Participants will gain practical familiarity with Excel, SPSS, SmartPLS, and NVivo, while developing an understanding of descriptive and inferential statistical techniques.

Learning Outcomes

By the end of this module, participants will be able to:

• Distinguish among major quantitative, qualitative, and mixed-method research designs.
• Select a research approach and design appropriate to a research problem.
• Differentiate between experimental and non-experimental approaches.
• Explain the applications of survey and case-study research.
• Define population, sample, and sampling frame.
• Distinguish between probability and non-probability sampling techniques.
• Develop appropriate inclusion and exclusion criteria.
• Understand the principles involved in estimating sample size.
• Select appropriate methods for primary and secondary data collection.
• Develop preliminary questionnaires and other research instruments.
• Understand the applications of interviews, observation, focus groups, and document analysis.
• Develop digital data-collection instruments using platforms such as Google Forms, KoboToolbox, and SurveyMonkey.
• Prepare data for analysis through coding, variable definition, data entry, and data cleaning.
• Identify and manage missing data.
• Prepare a research dataset for statistical analysis.
• Calculate and interpret basic descriptive statistics, including frequency, percentage, mean, median, and standard deviation.
• Develop and interpret cross-tabulations, tables, and graphs.
• Understand the principles of hypothesis testing.
• Select and interpret appropriate basic inferential statistical techniques, including t-tests, ANOVA, chi-square, correlation, and regression analysis.
• Gain practical familiarity with SPSS, SmartPLS, NVivo, and Excel for research-related tasks.
• Develop an initial research instrument, sampling plan, dataset, and data-analysis plan.