Non-statistical (qualitative) and statistical (quantitative) research are two fundamental approaches to conducting research, each with its own methods, purposes, and strengths.
QUALITATIVE (NON-STATISTICAL) RESEARCH aims to explore complex phenomena, understand meanings, and gain insights into people’s experiences, behaviors, and interactions. It focuses on providing a deep, contextual understanding of a specific issue or topic. Data is often obtained via interviews, focus groups, participant observations, and content analysis. Data analysis involves identifying patterns, themes, and narratives and is often interpretative and subjective, relying on the researcher’s ability to understand and articulate the meanings within the data.
QUANTITATIVE (STATISTICAL) RESEARCH aims to identify relationships or causal effects between concepts and/or phenomena. It seeks to produce results that can be generalized to larger populations. Data is often obtained via original data obtained through surveys or experiments and secondary data that has already been collected (such as information collected by the U.S. Census Bureau). Analysis involves using statistical methods to analyze numerical data. Techniques can range from basic descriptive statistics (ex: mean, median, mode) to complex inferential statistics (ex: linear regression analysis, ANOVA). Data analysis is typically more objective and replicable, with clear rules and procedures for conducting statistical tests.
While qualitative and quantitative research have distinct differences, they are often used together in mixed-methods research to provide a comprehensive understanding of a research problem. Qualitative research can provide context and depth to quantitative findings, while quantitative research can offer generalizability and precision to qualitative insights.