When does a study of clustered data arise?
Clustered data arise when the data from the whole study can be classified into a number of different groups, referred to as clusters. Each cluster contains multiple observations, giving the data a “nested” or “hierarchical” structure, with individual observations nested within the cluster.
What happens if clustered data is not taken into account?
If the clustering is not taken into account for this type of data, then the variability is likely to be underestimated and the results of the analysis invalidated. Throughout this document, we will refer to two classes of clustered data, which for convenience we will call case 1 data and case 2 data.
How are clustering algorithms used in machine learning?
Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have
How are observations within a cluster related to each other?
Thus, observations within a cluster are correlated, whereas observations from separate clusters are regarded as independent. Since observations within a cluster do not contribute completely independent information, the “effective” sample size is less than the total number of observations from all clusters.
Which is a feature of a clustered data structure?
Each cluster contains multiple observations, giving the data a “nested” or “hierarchical” structure, with individual observations nested within the cluster. The key feature of clustered data is that observations within a cluster are “more alike” than observations from different clusters.
What is the effective sample size of a cluster?
Since observations within a cluster do not contribute completely independent information, the “effective” sample size is less than the total number of observations from all clusters. Our focus is on testing hypotheses by comparing observations from two groups, such as a treated group versus a control group.