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VALIDITY OF MEASUREMENT S. P M V Subbarao Professor Mechanical Engineering Department. Justification for Selection of Concepts to Hardware ?????. How To Measure Any Other Property?.
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VALIDITY OF MEASUREMENT S P M V Subbarao Professor Mechanical Engineering Department Justification for Selection of Concepts to Hardware ?????
How To Measure Any Other Property? • It is essential to invent a scientific principle, which connects the property to be measured and physical displacement/length/ Just a Real Number. • One needs to identify/ develop hardware which can work as per the scientific principle. • What is the guarantee that the hardware exactly works as per the principle? • How to develop high degree of confidence in a measurement?
Validity of Measurement • When we decide to study a variable, we need to devise some way to measure it. • Some variables are easy to measure and others are very difficult. • We try to develop the best measures we can whenever we are doing research. • A good measuring instrument or test is one that is reliable and valid. • Let us look at test validity first.
Test Validity • Test Validity refers to the degree to which a measuring strategy (instrument, machine, or test) measures what is to be measured. • This sounds obvious; right? • A valid measure is the one that accurately measures the variable being studied. • There are four/five ways to establish that your measure is valid: • Content validity • Construct validity • Predictive validity • Concurrent validity • Convergent validity and/or Discriminant validity.
Content Validity • Content validity is established if your measuring instrument samples from the areas of skill or knowledge that compose the variable. • This assumes that you have a good detailed description of the domain, something that's not always true. • More the number of valid theories/skills, more will be the number of measurement strategies. • Consider measurement of temperature: • Most popular valid theory for construction is ?
Coefficient of Thermal Expansion • It is important to realise that: • The CTE is often not the same in all axes (that is, not ‘isotropic’). • The CTE is rarely linear. • The variation in CTE with temperature is only a fairly smooth function if the material is undergoing no phase transitions.
Construct validity is the approximate truth of the conclusion that the measurement accurately reflects truth. • The degree of translation of property to be measured into the measure. • Construct validity is based on designing a measure that logically follows from a theory or hypothesis. • Predictive validity, assesses the measurement's ability to predict something it should theoretically be able to predict. • Concurrent validity, assesses the measurement's ability to distinguish between groups that it should theoretically be able to distinguish between. • Refers to the ability of any measure to separate subjects who possess the attribute being studied from those who do not. • Convergent validity assesses the degree to which the measurement is similar to (converges on) other measurements that it theoretically should be similar to. • It is used when a valid measure exists for your variable but you want to design another measure that is perhaps easier to use or faster to take. • Discriminant validity, examines the degree to which the measurement is not similar to (diverges from) other measurement that it theoretically should be not be similar to.
Reliability • Reliability is the consistency with which our measure measures. • If you cannot get the same answer twice with your measure it is not reliable. • A measuring strategy can be reliable and not valid, but if the instrument is not reliable it is also not valid. • Measurement is never exact. • At some point our measures always break down and errors creep into our data. • This is when the concept of Error of Measurement becomes important. • In order to be able to use any measure we need to know its error of measurement.
The First Law of Measurements A good measuring strategy is reliable and, because it is reliable, it has a small amount of error in its observations.