Applying quality control charts to the analysis of single-subject data sequences

Area of Science:

• Industrial Engineering
• Cognitive Psychology
• Statistical Process Control

Background:

• Quality control techniques can assess individual performance quality.
• Stable performance baselines allow for deviation evaluation using control charts.
• Assessing cognitive performance changes requires sensitive and specific evaluation methods.

Purpose of the Study:

• To test the effectiveness of quality control charts for evaluating cognitive performance.
• To determine the sensitivity and specificity of various control chart techniques.
• To explore applications in workforce readiness-to-perform screening.

Main Methods:

• Utilized databases from 10 participants across 174 trials and 23 cognitive measures.
• Applied Shewhart, cumulative-sum (CUSUM), and exponentially weighted moving average (EWMA) control charts.
• Analyzed sensitivity and specificity of each control chart method.

Main Results:

• The optimal control chart technique varied based on the specific cognitive measure and performance change.
• Sensitivity and specificity reached up to 100% for the most effective techniques.
• Demonstrated the utility of control charts for individual performance monitoring.

Conclusions:

• Quality control charts are valuable tools for evaluating individual cognitive performance over time.
• These methods can be applied to industrial worker readiness-to-perform screening.
• Enhancing workforce health and safety through performance monitoring is a key application.