Final answer:
Institutional data can be categorized for accountability based on legal compliance, research, operational processes, and hierarchy of authority. These high-level categories align with a defined reporting structure and the levels of data measurement impacting data governance and management in organizations.
Step-by-step explanation:
Institutional or organizational data is frequently categorized to identify different levels of accountability and responsibility within an organization's reporting structure. For the purpose of assigning accountability and responsibility, high-level categories of organizational data could be structured around various areas such as legal compliance, research, operational processes, and hierarchy of authority. It is essential for a professional organization to have a clearly defined reporting structure which relays factual information and answers who, what, when, where, why, and how questions.
The way a set of data is measured, known as the level of measurement, is critical because correct statistical procedures depend on a researcher being familiar with these levels. The four levels of measurement are nominal scale level, ordinal scale level, interval scale level, and ratio scale level. For instance, when categorizing high school soccer players by their athletic ability, an ordinal scale of measurement is applied with categories such as superior, average, and above average. This reflects a hierarchy where data is ordered but the intervals between categories are not necessarily equal.
In the realm of bureaucracy, the hierarchy of authority outlines the chain of command, where each individual or office is accountable to those above. This spans from ground-level employees who report to their managers, all the way up to CEOs who must answer to the board members, just as it is in a large organization like Walmart.