Chapter 6 Glossary



sensitive, sensitivity: a measure's ability to detect differences among participants on a given variable. (p. 197)


ceiling effect: The effects of the treatment or combination of treatments is underestimated because the dependent measure places too low a ceiling on what the highest response can be. (p. 204)


floor effect: the effect of a treatment or combination of treatments is underestimated because the dependent measure is not sensitive to values below a certain level. In other words, the "floor" for low scores is too high.  (p. 204)


nominal scale numbers: numbers that substitute for names. Different numbers represent different types, kinds, categories, or qualities, but larger numbers do not represent more of a quality than smaller numbers. (p. 207)


ordinal scale numbers: numbers that can be meaningfully ordered from lowest to highest. With ordinal numbers, we know that the higher scoring participant has more of a quality than the lower scoring participant, but we don't know how much more of the quality the higher scoring participant has. (p. 208)


interval scale data: data for which equal numerical intervals represent equal psychological intervals. That is, the difference between scoring a "2" and a "1" and the difference between scoring a "7" and a "6" is the same not only in terms of scores (both are a difference of 1), but also in terms of the actual amount of the psychological characteristic being measured. Interval scale measures allow us to compare participants in terms of how much of a quality they have. (p. 209)


ratio scale numbers: numbers having all the qualities of interval scale numbers, but that also result from a measure that has an absolute zero (zero on the measure means the complete absence of the quality). As the name implies, ratio scale numbers allow you to make ratio statements about the quality that you are measuring (Steve is two times as friendly as Tom). (p. 211)


face validity: the extent to which a measure looks valid to the ordinary person. Face validity has nothing to do with scientific validity. However, for practical or political reasons, you may decide to consider face validity when comparing measures. (p.220)