With roughly one-year delay: see attached article.

McBride (2005) suggests the following descriptive scale for values of the concordance correlation coefficient (for continuous variables):

Value of ρc Strength of agreement

< 0.90 Poor

0.90 - 0.95 Moderate

0.95 - 0.99 Substantial

>0.99 Almost perfect

However, this assessment or scaling is arbitrary, it is not justified. Comparing the two questionnaires in psychology should not have the same criteria such as method comparison study when comparing two methods for the determination of glucose in clinical biochemistry.  In both cases is CCC the appropriate tool of measurement.

All such guidelines are completely arbitrary. Regarding kappa, see wikipedia https://en.wikipedia.org/wiki/Cohen's_kappa

"Nonetheless, magnitude guidelines have appeared in the literature. Perhaps the first was Landis and Koch, who characterized values < 0 as indicating no agreement and 0–0.20 as slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1 as almost perfect agreement. This set of guidelines is however by no means universally accepted; Landis and Koch supplied no evidence to support it, basing it instead on personal opinion. It has been noted that these guidelines may be more harmful than helpful. Fleiss's equally arbitrary guidelines characterize kappas over 0.75 as excellent, 0.40 to 0.75 as fair to good, and below 0.40 as poor."

With roughly one-year delay: see attached article.

McBride (2005) suggests the following descriptive scale for values of the concordance correlation coefficient (for continuous variables):

Value of ρc Strength of agreement

< 0.90 Poor

0.90 - 0.95 Moderate

0.95 - 0.99 Substantial

>0.99 Almost perfect

However, this assessment or scaling is arbitrary, it is not justified. Comparing the two questionnaires in psychology should not have the same criteria such as method comparison study when comparing two methods for the determination of glucose in clinical biochemistry.  In both cases is CCC the appropriate tool of measurement.

I agree with Rudolf. For example, in quality of life measurement  I would bet that comparisons of very few psychometric or preference-based instruments would have correlations in the vicinity of 0.9. I would think one would be doing well to have correlations at 0.5 or better.