Ceiling effect is used to describe a situation that occurs in both pharmacological and statistical research. In pharmacology a ceiling effect is the point at which an independent variable (which is the variable being manipulated) is no longer affecting the dependent variable (which is the variable being measured). It essentially describes when the dependent variable has leveled out and is no longer responding to the independent variable. This can be seen with pain relieving medication. You can increase the dosage to increase the pain relief up to a certain point but then it will no longer increase in effectiveness because the maximum level at which it works has been reached.
In statistics a ceiling effect can be seen when a variable is no longer measured or estimated at a certain point. In a census there are categories for things such as age and income that have a selective number of choices. For the top ranges (examples would be income of $100,000 or higher, 65 years of age or older) there is an open ended component to the selection that prevents measurement past the cutoff point. There is not difference between someone that makes $100,000 and $1 million dollars as there is no difference between a 65 and an 80 year old.