Meaning
Statistical adjustment aligns multiple data paths to a single starting point or scale to allow for a direct comparison of their shapes. Through trajectory normalisation, a researcher can see if different projects are following the same growth curve and accounts for different start times. Standardised paths reveal the rate and direction of travel.
It is a primary step in benchmarking performance against a peer group.
Baseline Alignment
Setting a common origin point ensures that every path is measured against the same standard of progress. When trajectory normalisation is applied, the relative speed of each entity becomes apparent. This view highlights the outliers that are either succeeding or failing at an unusual rate.
Scale Adjustment
Scaling the data points prevents a large operation from overshadowing the trends in a smaller one. The use of trajectory normalisation reveals the underlying health of the process regardless of its size. These insights help in the identification of best practices.
Final Validity
Accuracy of the final comparison depends on the quality of the data used in the initial adjustment. Because trajectory normalisation relies on a consistent baseline, any error in the start date or value will distort the results. Clean data is the foundation of this analysis.
