There are several important principles. First, a test can be considered reliable, but not valid. Consider the SAT, used as a predictor of success in college. It is a reliable test high scores relate to high GPA , though only a moderately valid indicator of success due to the lack of structured environment — class attendance, parent-regulated study, and sleeping habits — each holistically related to success.
Second, validity is more important than reliability. Using the above example, college admissions may consider the SAT a reliable test, but not necessarily a valid measure of other quantities colleges seek, such as leadership capability, altruism, and civic involvement.
Finally, the most useful instrument is both valid and reliable. Proponents of the SAT argue that it is both.
Thus far, we have discussed Instrumentation as related to mostly quantitative measurement. Some qualitative researchers reject the concept of validity due to the constructivist viewpoint that reality is unique to the individual, and cannot be generalized.
These researchers argue for a different standard for judging research quality. This resource was created by Dr. Patrick Biddix Ph. Research Rundowns. Instrument, Validity, Reliability. Example usability problems include: Students are asked to rate a lesson immediately after class, but there are only a few minutes before the next class begins problem with administration. Students are asked to keep self-checklists of their after school activities, but the directions are complicated and the item descriptions confusing problem with interpretation.
For now, we can identify five usability considerations: How long will it take to administer? Are the directions clear? How easy is it to score? Reliability and validity seem to be synonymous, but they do not mean the same thing. They are actually different things, different terms when they are explained in a technical manner.
These terms are often used on scholastic outputs such as thesis studies, term papers, research papers, and the likes. So to avoid confusion, here are the differences of the two.
Reliability is when your measurement is consistent. It means if you are using a certain kind of instrument for a test and the results on the subjects you are testing is the same for the first and second try, then it is considered reliable. There are two ways in estimating whether a certain thing is reliable or not. The first way is the test or retest and the other is the internal consistency. The test and retest is quite easy. You simply test an idea twice, test 1 and test 2.
It must be measured twice in different times, then compare the similarities of the results of the two tests. Then, if the results of the two tests are the same, it means that certain measurement is reliable.
The next way in estimating reliability is internal consistency. This can be done by questioning. Make different sets of question that can measure the same factor. Let this be answered by different people or different groups.
If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable. Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.
High reliability is one indicator that a measurement is valid. However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation. Validity is harder to assess than reliability, but it is even more important.
To obtain useful results, the methods you use to collect your data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.
Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types. The validity of a measurement can be estimated based on three main types of evidence.
Each type can be evaluated through expert judgement or statistical methods. To assess the validity of a cause-and-effect relationship, you also need to consider internal validity the design of the experiment and external validity the generalizability of the results. Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing.
See editing example. The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data. Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know.
They should be thoroughly researched and based on existing knowledge. For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded. To produce valid generalizable results, clearly define the population you are researching e. Ensure that you have enough participants and that they are representative of the population.
Reliability should be considered throughout the data collection process. Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved. For example, if you are conducting interviews or observations, clearly define how specific behaviours or responses will be counted, and make sure questions are phrased the same way each time.
When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results. For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions. Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.
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