A researcher must test the collected data before making any conclusion. Every research design needs to be concerned with reliability and validity to measure the quality of the research.
Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.
Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.
Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.
Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid.
If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid.
Example: Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.
Example: Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.
Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.
Example: If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.
One of the key features of randomised designs is that they have significantly high internal and external validity.
Internal validity is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the variables.
Example: age, level, height, and grade.
External validity is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.
Also, read about Inductive vs Deductive reasoning in this article.