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The concept of saturation was first defined in the context of grounded theory as theoretical saturation. In qualitative research the word saturation is extensively used almost interchangeably with data saturation, thematic saturation, theoretical saturation and conceptual saturation. Saturation can be simply defined as data satisfaction.
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power .
A pulse oximeter probe applied to a person's finger. A pulse oximeter is a medical device that indirectly monitors the oxygen saturation of a patient's blood (as opposed to measuring oxygen saturation directly through a blood sample) and changes in blood volume in the skin, producing a photoplethysmogram that may be further processed into other measurements. [4]
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.
Coding reliability [4] [2] approaches have the longest history and are often little different from qualitative content analysis. As the name suggests they prioritise the measurement of coding reliability through the use of structured and fixed code books, the use of multiple coders who work independently to apply the code book to the data, the measurement of inter-rater reliability or inter ...
The saturation intent is designed to present eye-catching business graphics by preserving the saturation (colorfulness). It is most useful in charts and diagrams, where there is a discrete palette of colors that the designer wants saturated to make them intense, but where specific hue is less important.
Data sanitization involves the secure and permanent erasure of sensitive data from datasets and media to guarantee that no residual data can be recovered even through extensive forensic analysis. [1] Data sanitization has a wide range of applications but is mainly used for clearing out end-of-life electronic devices or for the sharing and use ...
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc.