# 2 Legit 2 Quit

MC Hammer. Now those were interesting pants! Heard of the slang expression “Seems legit”? Well “legit” (short for legitimate) was popularized my MC Hammer’s song 2 Legit 2 Quit. I had blocked the memories of that video for many years. Painful – and no I never owned a pair of Hammer pants!

Whenever you sarcastically say “seems legit” you are suggesting that you question the validity of the finding. We have been talking about agreement lately and we have covered precision (see Repeat After Me), accuracy (see Men in Tights), and reliability (see Mr Reliable). Today let’s cover validity.

So, we have talked about how reliable a measure is under different circumstances and this helps us gauge its usefulness. However, do we know if what we are measuring is what we think it is. In other words, is it valid? Now reliability places an upper limit on validity – the higher the reliability, the higher the maximum possible validity. So random error will affect validity by reducing reliability whereas systematic error can directly affect validity – if there is a systematic shift of the new measurement from the reference or construct. When assessing validity we are interested in the proportion of the observed variance that reflects variance in the construct that the method was intended to measure.

***Too much stats alert*** Take a break and listen to Ice, Ice, Baby from the same era as MC Hammer and when you come back we will finish up with validity. Pants seem similar – agree? 🙂

OK, we’re back. The most challenging aspect of assessing validity is the terminology. There are several different types of validity dependent of the type of reference standard you decide to use (details to follow in later posts):

1- Content:  the extent to which the measurement method assesses all the important content.

2- Construct: when measuring a hypothetical construct that may not be readily
observed.

3- Convergent: new measurement is correlated with other measurements of the same construct.

4- Discriminant: new measurement is not correlated with unrelated constructs.

So why do we assess validity? because we want to know the nature of what is being measured and the relationship of that measure to its scientific aim or purpose.

I’ll leave you with another “seem legit” picture that my kids would appreciate…

See you in the blogosphere,

Pascal Tyrrell