Quantitative Data Analysis vs Qualitative Data Analysis
Data Analysis is divided into two distinct approaches: quantitative and qualitative. While both contribute significantly to understanding information, they do so in fundamentally different ways. Key differences between Quantitative and Qualitative Data Analysis are below:
Feature | Quantitative Data Analysis | Qualitative Data Analysis |
---|---|---|
Focus | Numbers and statistics | Words, images, non-numerical data |
Data types | Numbers, measurements, percentages, frequencies | Interviews, observations, documents, videos, images |
Methods | Surveys, experiments, observational studies, statistical tests, regression analysis | Thematic analysis, discourse analysis, grounded theory, narrative analysis |
Strengths | Objectivity, precision, generalizability | Rich understanding of experiences, captures emotions and meanings, reveals context |
Weaknesses | Limited in capturing subjective aspects, overlooks context | Difficulty generalizing, subjective interpretation, time-consuming analysis |
When to use | Measuring, comparing, testing hypotheses, generalizing | Understanding “why” and “how”, exploring experiences, capturing subjective meanings |
To delve more into the depth of differences between them, refer to link.
What is Quantitative Data Analysis?
Quantitative data analysis is like using a magnifying glass to understand numbers better. Quantitative data analysis helps look closely at these numbers to see if there are any interesting patterns or trends hiding in them. In this article, let’s discuss Quantitative Data Analysis in depth.
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