There are four key levels of management that play irreplaceable roles decisive the mathematical and statistical procedures in data management. At the nominal level of measurement, numbers and letters are employed to divide data. Take for instance the classification gender based data where ‘M’ represents men and ‘F’ represents women. Ordinal level of measurement on the other hand focuses of ordered affiliations existing within the listed items. For instance in the order of merit, the highest score take the highest rank. Interval level categorizes and remits the measurements as well as specify the relevant scale used to set the differences between a certain set of data. Ratio level of measurement divides data in a scale system such that the divisions amid the points have equal distance separating them (Rodchua, 2009).
The concept of validity for design entails development of elite conclusions regarding a certain survey that is arrived at through appraisal of the available data. Organizational are structured in a specific design such that the organizational structure brings together different variables in the quest to achieve the organizational goal.it is the concept of validity for design that is used by the top level of management to develop the organizational policies, rules and regulations (Rodchua,2009).
The concept of validity for measurement on the other hand focuses on the application of different levels of measurement to evaluate the achievements of an organization from every unit of this organization. Right from employee evaluation, cost benefit amylases and auditing of how the assets in an organization are utilized to achieve the organizational goals lies under the concept of validity of measurement. It is the management criterion that ensures all assets and liabilities in the history of an entity are sustained at a breakeven point to ensure that an organization gains from all its operations (Rodchua, 2009).
Rodchua, S. (2009). Comparative analysis of quality costs and organization sizes in the manufacturing environment. Quality Management Journal, 16(2), 34–43..