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The M&E is separated into two distinguished categories: evaluation and monitoring. An evaluation is a systematic and objective examination concerning the relevance, effectiveness, efficiency, impact and sustainabilities of activities in the light of specified objectives. [2]
In common usage, evaluation is a systematic determination and assessment of a subject's merit, worth and significance, using criteria governed by a set of standards.It can assist an organization, program, design, project or any other intervention or initiative to assess any aim, realizable concept/proposal, or any alternative, to help in decision-making; or to generate the degree of ...
KPI information boards. A performance indicator or key performance indicator (KPI) is a type of performance measurement. [1] KPIs evaluate the success of an organization or of a particular activity (such as projects, programs, products and other initiatives) in which it engages. [2]
A performance appraisal, also referred to as a performance review, performance evaluation, [1] (career) development discussion, [2] or employee appraisal, sometimes shortened to "PA", [a] is a periodic and systematic process whereby the job performance of an employee is documented and evaluated.
Psychological evaluation is a method to assess an individual's behavior, personality, cognitive abilities, and several other domains. [a] [3] A common reason for a psychological evaluation is to identify psychological factors that may be inhibiting a person's ability to think, behave, or regulate emotion functionally or constructively.
Example of a Business Process Model and Notation for a process with a normal flow. Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes in a business process model.
The Dunn index, introduced by Joseph C. Dunn in 1974, is a metric for evaluating clustering algorithms. [1] [2] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself.
The Davies–Bouldin index (DBI), introduced by David L. Davies and Donald W. Bouldin in 1979, is a metric for evaluating clustering algorithms. [1] This is an internal evaluation scheme, where the validation of how well the clustering has been done is made using quantities and features inherent to the dataset.