![]() ![]() We want to make sure that two different researchers who measure the same person for depression get the same depression score. We would want the scale to be a reliable measure of depressive symptoms. Think about it: A basic tenet of science is replication, so without reliability, how can we be sure a study wasn’t replicated solely due to measurement error?įor example, say we were testing a new antidepressant drug on symptoms of depression, with the outcome assessed via a series of questions that measure depression. ![]() ![]() This makes reliability very important for both social sciences and physical sciences. Well, researchers would have a very hard time testing hypotheses and comparing data across groups or studies if each time we measured the same variable on the same individual we got different answers. So, why do we care? Why make such a big deal about reliability? That instrument could be a scale, test, diagnostic tool as reliability applies to a wide range of devices and situations. Not only do you want your measurements to be accurate (i.e., valid), you want to get the same answer every time you use an instrument to measure a variable. Think of reliability as consistency or repeatability in measurements. Whenever a measurement has a potential for error, a key criterion for the soundness of that measurement is reliability. ![]() Some variables are straightforward to measure without error – blood pressure, number of arrests, whether someone knew a word in a second language.īut many – perhaps most – are not. ![]()
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