Ceiling effect (statistics)

The ceiling effect is the level at which an independent variable no longer has an effect on a dependent variable, or the level above which variance in an independent variable is no longer measured or estimated. An example of the first meaning, a ceiling effect in treatment, is pain relief by some kinds of analgesic drugs, which have no further effect on pain above a particular dosage level (see also: ceiling effect in pharmacology). An example of the second meaning, a ceiling effect in data-gathering, is a survey that groups all respondents into income categories, not distinguishing incomes of respondents above the highest level asked about in the survey instrument.

A ceiling effect in data-gathering, when variance in an dependent variable is not measured or estimated above a certain level, is a commonly encountered practical issue in gathering data in many scientific disciplines. Such a ceiling effect is often the result of constraints on data-gathering instruments. When a ceiling effect occurs in data-gathering, there is a bunching of scores at the upper level reported by an instrument.

A population survey about lifestyle variables influencing health outcomes might include a question about smoking habits. To guard against the possibility that a respondent who is a heavy smoker might decline to give an accurate response about smoking, the highest level of smoking asked about in the survey instrument might be "two packs a day or more." This results in a ceiling effect in that persons who smoke three packs or more a day are not distinguished from persons who smoke exactly two packs. A population survey about income similarly might have a highest response level of "$100,000 per year or more," rather than including higher income ranges, as respondents might decline to answer at all if the survey questions identify their income too specifically. This too results in a ceiling effect, not distinguishing persons who have an income of $500,000 per year or higher from those whose income is exactly $100,000 per year.

The range of data that can be gathered by a particular instrument may be constrained by inherent limits in the instrument's design. Often design of a particular instrument involves tradeoffs between ceiling effects and floor effects. When many subjects have scores on a variable at the upper limit of what an instrument reports, data analysis is difficult because some actual variation in the data is not reflected in the scores obtained from that instrument.

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