What Is a Ceiling Effect? | Definition & Examples

A ceiling effect occurs when too large a percentage of participants achieve the highest score on a test. In other words, when the scores of the test participants are all clustered near the best possible score, or the ‘ceiling’, the measurement loses value. This phenomenon is problematic because it defeats the purpose of the test, which is to accurately measure something.

A ceiling effect can be observed in surveys, standardised tests, or other measurements used in quantitative research. 

What is a ceiling effect?

A ceiling effect is a measurement problem that places a limitation to the maximum level an individual can achieve on a test. As a result, there is a discrepancy between a person’s test score and their ‘true’ score, or reality.

Depending on the scientific area, the term signifies one of the following:

• A ceiling effect in medicine and pharmacology refers to the phenomenon in which a drug reaches a maximum effect, so that increasing the dosage does not increase its effectiveness. For example, researchers sometimes observe that there is a threshold above which a painkiller has no additional effect. Even if they increase the dosage, there is no added benefit regarding pain relief. In this context, the ceiling effect occurs due to human biology.

• A ceiling effect associated with statistics in social sciences refers to the phenomenon in which the majority of the data are close to the upper limit or highest possible score of a test. This means that (almost) all of the test participants achieved the highest (or very near to the highest) score.

What causes a ceiling effect?

In the context of statistics, a ceiling effect can occur in survey data because of the limited ability of survey instruments to accurately measure participants’ true responses, as well as distinguish them from others’ responses. This can be due to:

• Efforts to limit response bias. In an attempt to prevent biases like social desirability bias, researchers might create ceiling effects due to the way they phrase the possible responses.  For example, when asking respondents about their alcohol consumption, the highest possible option might be ‘2 drinks per day or more’. This makes it easier for heavy drinkers to fill in the question without feeling too exposed. However, researchers then lose the ability to differentiate between those who consume 3, 4, 6, or more drinks per day.
• Instrument design constraints. Due to poor design, a questionnaire might not be able to measure a variable above a certain limit. For example, when a college exam is too easy, everyone will get more or less the same high score. The ceiling effect creates an artificially low threshold, since anyone is able to pass the exam. As a result, the exam fails to measure what it’s supposed to measure (aptitude) beyond a certain (low) level.

Why is the ceiling effect a problem?

Because of the ceiling effect, tests, surveys and other measures fail to capture the true range of values or responses, resulting in little variance in the data.

Ceiling effects cause a number of problems in data analysis including the inability to:

• Determine the central tendency of the data, or the true average in a dataset.
• Compare the means between two groups, e.g., between a treatment and a control group.
• Get an accurate measure of variability, such as standard deviation.
• Form conclusions about the effect of the independent variable on any dependent variables.
• Rank individuals according to their score.

Overall, a ceiling effect hinders the accurate interpretation of data and can render results meaningless.

Ceiling effect examples

Ceiling effects can be observed in surveys that include response categories that do not fully capture the range of possible answers above a certain point.

A ceiling effect can create a low threshold, making it easy for participants to reach the highest possible score on a test.

How to avoid ceiling effects?

Ceiling effects can impact the quality of your data collection. It’s really important to take the necessary steps to prevent this phenomenon. There are a few strategies you can use to avoid ceiling effects in your research:

• Use previously-validated instruments, such as pre-existing questionnaires measuring the concept you are interested in. In this way, you can ensure that the questionnaire will allow you to capture a wide range of responses.
• If no such instrument exists, run a pilot survey or experiment to check for ceiling effects. Running a small-scale trial of your survey will give you the opportunity to adjust your questions in case you do notice a ceiling effect.
• When your survey includes sensitive or personal topics, like questions about income or drug use, provide anonymity, and don’t set artificial limits on responses. Instead, you could let participants fill in the higher value themselves.

Frequently asked questions

What is the difference between ceiling and floor effect?

The terms ceiling effect and floor effect are opposites but they refer to the same phenomenon: the clustering of individual survey responses around a certain value. More specifically, ceiling effects occur when a considerable percentage of participants score the best or maximum possible score, while floor effects occur when the opposite happens, i.e.,  a considerable percentage of participants obtain the worst or minimum available score. This can be observed, for example, when a test is too easy (ceiling effect) or too difficult (floor effect). As a result, researchers can’t use the test to rank participants at either end of the scale.

What is a ceiling effect in pharmacology

In pharmacology a ceiling effect is the point at which an independent variable (the variable being manipulated) is no longer affecting the dependent variable (the variable being measured). This can be seen with analgesic or pain-relieving medication. Even if researchers increase the dosage, there is a certain point beyond which the effectiveness of the medication will no longer increase.

Why is the ceiling effect a problem?

The ceiling effect is a problem in statistical analysis and data interpretation because it restricts the range of values that a variable can take. Due to this, there is a difference between the reported values and the ‘real’ values which means that the survey, test, or other measure used fails to collect accurate data.