Floor Effect Definition

A floor effect is when most of your subjects score near the bottom.

Floor effect definition.

1 a floor is the lowest acceptable limit as restricted by controlling parties usually involved in the management of corporations. This lower limit is known as the floor. In statistics and measurement theory an artificial lower limit on the value that a variable can attain causing the distribution of scores to be skewed. Statistics definitions the floor effect is what happens when there is an artificial lower limit below which data levels can t be measured.

For example the distribution of scores on an ability test will be skewed by a floor effect if the test is much too difficult for many of the respondents and many of them obtain zero scores. The term ceiling effect is a measurement limitation that occurs when the highest possible score or close to the highest score on a test or measurement instrument is reached thereby decreasing the likelihood that the testing instrument has accurately measured the intended domain. This is even more of a problem with multiple choice tests. This could be hiding a possible effect of the independent variable the variable being manipulated.

The specific application varies slightly in differentiating between two areas of use for this term. Ceiling effect is used to describe a situation that occurs in both pharmacological and statistical research. Floors can be established for a number of factors including. Floor effect basement effect.

Psychology definition of floor effect. Usually this is because of inherent weaknesses in the measuring devices or the measurement scoring system. There is very little variance because the floor of your test is too high. In statistics a floor effect also known as a basement effect arises when a data gathering instrument has a lower limit to the data values it can reliably specify.

In research a floor effect aka basement effect is when measurements of the dependent variable the variable exposed to the independent variable and then measured result in very low scores on the measurement scale. It essentially describes when the dependent variable has leveled. The ceiling effect is observed when 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 measurable. In layperson terms your questions are too hard for the group you are testing.