Researchers Wright and MacRae used large-scale meta analysis to identify bias and variability in the forecasts from such scales. They found that converting purchase intentions to linear probability scales or proportions resulted in unbiased forecasts.
The same was true of 11-point probability scales, but these had lower dispersion of forecast errors. This result gives strong support to Principle 11.4 of the Forecasting Principles, as scale-point adjustments were not required to get unbiased forecasts. It also supports Principle 8.4, as the use of the longer 11-point scale reduced forecast error, and Principle 8.7 as there was much greater variability in forecast errors for studies with small samples. The meta-analysis was restricted to existing products and services, and did not investigate accuracy for new products.
Source: Principles of Forecasting