![]() they are measuring more than one construct/concept. This results in questionnaires that are multi-dimensional, i.e. ![]() This is because investigators often seek to find out as much information as possible whilst they have the attention of someone completing a questionnaire. In the context of questionnaires, it may be difficult to strike an acceptable balance between stability and the practicality. ![]() Biases can be subtle, for example the use of a Catell B intelligence quotient test in a population where English is not the first language may under measure IQ in those who do not communicate primarily in English. Biased measures can be very precise and repeatable, but in such situations they are precisely and repeatably under-measuring or over-measuring the true value, so they are unstable and thus invalid. If a measure has a large systematic error, for example weighing scales that always weight 1Kg too light, then it is biased or inaccurate. it is very noisy, it can not reliably discriminate differences that may be important. If a measure has a large random error, i.e. Stability is determined by random and systematic errors of the measure and the way the measure is applied in a study. Reliability in scientific investigation usually means the stability and repeatability of measures, or the ability of a test to produce the same results under the same conditions.įor a test to be reliable it must first be valid.
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