When you’re working with MindSonar, it can be important to have a basic understanding of statistics. Why? First of all, it’s good to know as much as possible about a system you’re working with. For instance, if you want to construct a real solid benchmark profile, it is useful to understand what correlation is. Or when you see a standard deviation in a team profile, it’s good to understand precisely what that means.
But also, answering questions about things like ‘validity’ or ‘Cronbachs alpha’ can be important if you want to be seen as a MindSonar expert. It’s good to know about these kinds of statistics, for instance, when you are discussing a large project with a potential client – or with the experts they bring in. And it’s not all that difficult, really. Especially since I scoured YouTube and found you some well designed instructive movies.
Reliability and Validity
The two statistical terms you will come across most often are ‘Reliability’ and Validity’. For some reason those two are usually presented as a duo. Maybe because these are the two most basic concepts to evaluate a test with. Reliability asks the question: “Are the test scores consistent?” The principle is, that if you are measuring the same thing repeatedly, you should get the same result each time. Validity asks a different question: “Does the test measure what it is supposed to measure?” With validity the basic idea is that you cannot measure temperature with a yardstick, you need a thermometer.
Reliability and validity can vary independently. All combinations are possible: reliable but not valid, valid but not reliable, neither reliable nor valid and finally the holy grail of test psychology: both reliable and valid. This graphic illustrates these four options:
In this first video, Donna Gregory gives you a very quick and easy overview of reliability and validity. The basics are really that simple!
In the next video Andrew Conway, Senior Lecturer at the University of South Carolina, explains reliability and validity in some more depth. He also describes how reliability and validity may be measured in different ways.