Introduction

Overview

As research serves as a crucial foundation for shaping effective educational practice and policy, the ability to analyze data rigorously has never been more important. This training course is designed to improve participants' theoretical grounding and practical competence in both qualitative and quantitative data analysis in social sciences. With a strong emphasis on methodological integrity, the program offers a structured pathway from data collection to interpretation and reporting.

Participants will explore the epistemological and methodological assumptions that underpin both qualitative and quantitative paradigms, enabling them to make informed decisions about their research design. They will engage with a variety of qualitative data collection techniques, including interviews, focus groups, observations, questionnaires, document analysis, and audiovisual materials, while learning how to integrate these methods into ethically sound and contextually sensitive research procedures.

Building on this foundation, the workshop guides learners in constructing analytical frameworks, developing interview protocols, and collecting rich, narrative data through rigorously designed instruments. Emphasis is placed on the coding process, both inductive and deductive, as well as the synthesis of codes into coherent, meaningful themes. Learners will also explore methods to enhance the trustworthiness of their findings through techniques such as triangulation, member checking, and reflexivity.

Participants will gain hands-on experience with NVivo, learning to organize, code, and analyze complex qualitative datasets using its full range of analytical tools, from framework matrices to data visualizations and thematic mapping. They will also define participant cases and conduct cross-case analyses to deepen interpretive insight.

The course further extends to the quantitative domain, introducing participants to the philosophical and statistical foundations of numerical analysis with SPSS in social science research. Learners will design and manage variables, classify and recode data, and construct clear codebooks to ensure transparency and replicability. Instruction in data cleaning, descriptive statistics, inferential techniques, and correlation and regression analyses equips participants with the skills to test hypotheses and draw meaningful conclusions from sample data.

Whether analyzing interviews or surveys, this dynamic and interactive course empowers early-career and emerging researchers to approach data analysis with clarity, precision, and confidence. This helps them to bridge theory and practice in the pursuit of meaningful, impactful research.