The practice of working with data to extract meaningful information. This can include description, statistical model fitting, qualitative coding, and visualization to gain insights from that data. This includes practices such as descriptive statistics (e.g. finding means, medians, and counts of different groups), inferential statistics (e.g. finding correlations between variables to know that if as one value, such as height, increases, another value, such as weight, either increases, decreases or has no relation), qualitative description (e.g. coding interviews for themes), and using data visualization to detect patterns in data. Sometimes data collection and data cleaning are included in a more expansive definition of data analysis.
An example of quantitative data analysis would be using numerical data from an Electronic Health Record to determine if underlying conditions such as diabetes or asthma have an effect on COVID-19 outcomes.
An example of qualitative data analysis would be analyzing data from interviews with patients on their perception of a healthcare system to gain insight into how that system might better serve their needs.
There are a myriad of different tools for doing data analysis. Dedoose or AtlasTI are two popular tools for performing qualitative data analysis. SPSS, SAS, and Stata are tools designed for performing statistical analyses. R and Python are programming languages with a broad range of uses, including data analysis. Excel is another tool that is very popular for data analysis.
This article provides a concise description of qualitative, quantitative, and mixed methods research methods:
Rutberg, Shannon, and Christina D. Bouikidis. "Focusing on the Fundamentals: A Simplistic Differentiation Between Qualitative and Quantitative Research." Nephrology Nursing Journal, vol. 45, no. 2, Mar.-Apr. 2018, pp. 209+. Gale Academic OneFile, https://link.gale.com/apps/doc/A537590662/AONE