The solution of most MCDM-problems involves measuring the characteristics of
a research object, converting the estimations into a confidence distribution
specified on a set of qualitative gradations and aggregating the estimations
in accordance with the structure of the criteria system. The quality of the
problems solution as a whole directly depends on the quality of measuring
the characteristics of a research object. Data for obtaining estimations of
the characteristics are often inaccurate, incomplete, approximate. Modern
researches either fragmentarily touch on the questions of measurement
quality, or focus on other questions. Our goal is to choose such parameters
for converting the value of the quantitative characteristic of a research
object into a confidence distribution, which provide the best measurement
quality. Based on the observation channel (OC) concept proposed by G. Klir,
we refined the measurement quality criteria, determined the composition of
the OC parameters, developed an algorithm for calculating the measurement
quality criteria and choosing the best OC for the most common MCDM-problems.
As calculations have shown, in the most common MCDM-problems, the best is
OC, which is built on the basis of a bell-shaped membership function and has
a scale of seven blocks. The obtained result will allow researchers to
justify the choice of OC parameters from the view-point of the maximum
quality of measuring the quantitative characteristics of a research object
in MCDM-problems and uncertainty conditions.