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Data Analysis Data Analysis includes all of the steps necessary to prepare a dataset for analysis and perform numeric, qualitative, or spatial analysis to produce new results or to calculate quantitative metrics describing the data. These steps can include inspecting, scrubbing, filtering, querying, transforming and modeling data.
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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
The word qualitative implies an emphasis on the qualities of entities and on processes and meanings that are not experimentally examined or measured [if measured at all] in terms of quantity, amount, intensity, or frequency. Qualitative researchers stress the socially constructed nature of reality, the intimate relationship between the researcher and what is studied, and the situational constraints that shape inquiry. Such researchers emphasize the value-laden nature of inquiry. They seek answers to questions that stress how social experience is created and given meaning. In contrast, quantitative studies emphasize the measurement and analysis of causal relationships between variables, not processes. Qualitative forms of inquiry are considered by many social and behavioral scientists to be as much a perspective on how to approach investigating a research problem as it is a method.
Research that systematically combines qualitative and quantitative inquiry. MMR “relies on qualitative and quantitative viewpoints, data collection, analysis, and inference techniques combined according to the logic of MMR to address one’s research question(s); and is cognizant, appreciative, and inclusive of local and broader sociopolitical realities, resources, and needs”
