Quantitative data focuses on numbers and numeric data, including raw numbers, percentages, percentiles, and averages (mean, median, mode). Quantitative data tends to be objective in nature and numbers support research outcomes. Analysis of quantitative data involves statistical techniques and the type of data collected guides the analysis process.
Qualitative data includes text, words, ideas, and observed behaviors. In general, those working with qualitative data attempt to describe and interpret human behavior based primarily on the words and actions of selected individuals. Qualitative data tends to be subjective in nature and uses information derived from experiences to support research findings. The analysis of qualitative data can come in many forms including highlighting key words, extracting themes or behaviors, and elaborating on concepts.
Big Data is a term used to refer to datasets that are too large or complex for traditional computational methods. As with smaller-scale data, big data analysis includes ingesting, storing, cleaning, analyzing, and visualizing results. However, it is often performed by high performance computing, also know as cluster computing.
When thinking about Big Data, keep in mind the four V's:
Icons via the Noun Project
Data that is encountered on a daily basis comes from all stages of the Data Lifecycle in the form of documents and spreadsheets, laboratory notes, questionnaires, videos, source code, metadata, databases & database content, models, algorithms, weather and news facts and figures.
The Data Lifecycle provides a high level overview of management and preservation for the use and reuse of data. It also provides the junctures at which data is likely to be encountered. Knowing where in the lifecycle data occurs, can tell about the data, its security needs, and places where it is at risk for alterations, manipulation, and bias:
Data literacy can be defined as the component of information literacy that enables individuals to access, interpret, critically assess, manage, handle and ethically use data.
Data Literacy includes:
When encountering or working with data, understanding the origins, assumptions, and methods involved in its creation will help frame whether or not it is usable and trustworthy. Some good questions to ask before making any decisions with data are:
Fontichiaro, K., & J.A. Oehrli (2016), Why Data Literacy Matters, Knowledge Quest, 44 5
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