[Comparative quality measurements part 3: funnel plots]

Comparative quality measurements between organisations or institutions are common. Quality measures need to be standardised and risk adjusted. Random error must also be taken adequately into account. Rankings without consideration of the precision lead to flawed interpretations and enhances "gaming". Application of confidence intervals is one possibility to take chance variation into account. Funnel plots are modified control charts based on Statistical Process Control (SPC) theory. The quality measures are plotted against their sample size. Warning and control limits that are 2 or 3 standard deviations from the center line are added. With increasing group size the precision increases and so the control limits are forming a funnel. Data points within the control limits are considered to show common cause variation; data points outside special cause variation without the focus of spurious rankings. Funnel plots offer data based information about how to evaluate institutional performance within quality management contexts.