validity and reliability in research methods

Most scientific experiments are inextricably linked in terms of validity and reliability.

Read: What is Participant Bias? How to Detect & Avoid Itįor example, before I can conclude that all 12-inch rulers are one foot, I must repeat the experiment several times and obtain very similar results, indicating that 12-inch rulers are indeed one foot.

Looking at most experiments (especially physical measurements), the standard value that establishes the accuracy of a measurement is the outcome of repeating the test to obtain a consistent result.

Here’s where things get tricky: to establish the validity of a test, the results must be consistent. What is Validity?Ī measurement or test is valid when it correlates with the expected result. So, if you’re getting similar results, reliability provides an answer to the question of how similar your results are. Reliability allows you to assess the degree of consistency in your results. So, even if there is a minor difference in the outcomes, as long as it is within the error margin, your results are reliable. But of course, reliability doesn’t mean your outcome will be the same, it just means it will be in the same range.įor example, if you scored 95% on a test the first time and the next you score, 96%, your results are reliable. When a measurement is consistent it’s reliable. Read: Internal Validity in Research: Definition, Threats, Examples What is Reliability? In this article, we’ll look at how to assess data reliability and validity, as well as how to apply it. If one of the measurement parameters, such as your scale, is distorted, the results will be consistent but invalid.ĭata must be consistent and accurate to be used to draw useful conclusions. So, while reliability and validity are intertwined, they are not synonymous. This means that if the standard weight for a cup of rice is 5 grams, and you measure a cup of rice, it should be 5 grams. The validity, on the other hand, refers to the measurement’s accuracy. For example, if you measure a cup of rice three times, and you get the same result each time, that result is reliable.

When it comes to data analysis, reliability refers to how easily replicable an outcome is. However, in research and testing, reliability and validity are not the same things. In everyday life, we probably use reliability to describe how something is valid.

Validity and reliability in research methods