Finding patterns that either corroborate or disconfirm particular hypotheses and answer the evaluation questions
Evaluator control and ability to manipulate the setting, which improves the internal validity, the statistical conclusions validity, and the construct validity of the research designs
Use of sample sizes with sufficient statistical power to detect expected outcomes
Holistic approach: looking for an overall interpretation for the evaluation results
In-depth, detailed data collection
Emphasis on measurement procedures that lend themselves to numerical representations of variables
Measuring instruments that are constructed with a view to making them reliable and valid
Understanding the subjective lived experiences for program stakeholders
Inductive approach to data gathering, interpretation, and reporting
Using natural language throughout the evaluation process
