Quasi-experimental Study

A quasi-experimental study is a quantitative research design used to evaluate the causal effect of an intervention when random assignment of subjects to treatment and control groups is not feasible. It is crucial for fulfilling the 'Monitoring and Review' clause (6.6) of the ISO 31000:2018 risk management guidelines, which mandates the evaluation of risk treatment effectiveness. Unlike a randomized controlled trial (RCT), it uses existing groups or naturally occurring events. For instance, when assessing a new compliance policy, a company can use a difference-in-differences method to compare the change in violation rates between a department where the policy was implemented and one where it was not, thus isolating the policy's true impact.

Quasi-experimental studies transform risk control assessment from guesswork into measurable evidence. Key implementation steps include: 1) Define the risk treatment (e.g., a new cybersecurity training) and the key risk indicator (KRI) to measure its effect (e.g., phishing email click-rate). 2) Construct a counterfactual by selecting a comparable control group (e.g., another department not receiving the training) or using pre-intervention historical data. 3) Collect and analyze pre- and post-intervention data using statistical models to isolate the treatment's net effect. A global bank used this to evaluate an AML system upgrade, finding it led to a 25% reduction in false positive alerts in treated branches compared to control branches, justifying the investment.

Taiwanese enterprises face three main challenges: 1) Insufficient Data Infrastructure: Lack of long-term, consistent KRI data hinders effective analysis. The solution is to establish a centralized risk data warehouse and standardize data collection processes based on ISO 31000. 2) Lack of Methodological Expertise: Internal teams often lack the statistical skills for rigorous study design. Mitigation involves partnering with expert consultants and providing targeted training for risk and audit staff. 3) Difficulty Finding Control Groups: Smaller firms struggle to find comparable, untreated internal groups. The solution is to use methods like Interrupted Time Series Analysis, which uses the unit's own historical data as its baseline for comparison, making it more broadly applicable.

Winners Consulting specializes in quasi-experimental study for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact