How is the Hierarchical Model used?

The first mainframe database management systems were essentially the birth place of the Hierarchical model.

The hierarchical relationships between varying data made it easier to seek and find specific information.

Ads

Though the model is idea for viewing relationships concerning data many applications no longer use the model. Still some are finding that the Hierarchical model is idea for data analysis.

Perhaps the most well known use of the Hierarchical model is the Family Tree, but people began realizing that the model could not only display the relationships between people but also those between mathematics, organizations, departments and their employees and employee skills, the possibilities are endless.

Simply put this type of model displays hierarchies in data starting from one “parent” and branching into other data according to relation to the previous data.

Commonly this structure is used with organizational structures to define the relationship between different data sets.

Normally this contains employees, students, skills, and so forth. Yet we are beginning to see the model used in more professional and meta-data oriented environments such as large organizations, scientific studies, and even financial projects.

Though the Hierarchical model is rarely used some of its few uses include file systems and XML documents.

The tree like structure is idea for relating repeated data, and though it is not currently applied often the model can be applied to many situations.

Issues Related to Hierarchical Models

The Hierarchical model can present some issues while focusing on data analysis. There is the issue of independence of observations, when data is related it tends to share some type of background information linking it together, therefore the data is not entirely independent.

However, most diagnostic methods have need of independence of observations as a key hypothesis for the analysis.

This belief is corrupted in the incident of hierarchical data; such as when ordinary minimum square regressions turn out typical miscalculations that are too small.

Ads

Subsequently, this usually results in a greater likelihood of rejection of an unacceptable assumption than if:

(1) a suitable statistical analysis was performed, or 
(2) the data contained within honestly self governs observation.

Next Page: Hierarchical Model Structures