evaluatiemetrics

Evaluatiemetrics are quantitative and qualitative measures used to assess the performance, quality, or impact of a program, product, process, or system. They provide a standardized basis for judging outcomes, comparing alternatives, and guiding decision making. A well-chosen set of evaluatiemetrics reflects the objectives, scope, and stakeholders involved and is accompanied by clear targets and data sourcing plans.

Common categories include outcome metrics (what changed), process metrics (how a process performs), efficiency metrics (cost, time), quality metrics (defect rates, reliability), and user-related metrics (satisfaction, adoption). In machine learning and data science, typical evaluatiemetrics include accuracy, precision, recall, F1, ROC-AUC, RMSE, and MAE. In software engineering, metrics such as defect density and mean time to repair (MTTR) are used; in business contexts, return on investment (ROI) and payback period are common; in education, rubrics and pass rates are employed; in research, effect sizes and confidence intervals are considered.

Design and use of evaluatiemetrics involves aligning metrics with goals, ensuring data availability, defining measurement procedures, and establishing baselines and targets. It is important to consider the interpretability and robustness of metrics, avoid metric proliferation, and account for uncertainty. Validation of metrics for reliability and relevance, along with attention to biases and data quality, improves usefulness.

Limitations include the risk that metrics incentivize undesired behavior, fail to capture contextual factors, or reflect imperfect data. Best practices emphasize triangulation across multiple metrics, incorporating qualitative evidence, maintaining transparency, and involving diverse stakeholders in metric selection and interpretation.