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.
And even if different people answered these different questions, but still came out with the right thought, then it must be reliable. This is the definition of reliability. Now to differentiate it with validity, it is best to define validity as well. In this way, the confusion between the two terms may be fixed. With this it will be easier to distinguish the two from each other. If reliability is more on consistency, validity is more on how strong the outcomes of the hypothesis are.
This means the validity too is strong. Validity is categorized into four types, the conclusion, internal validity, construct validity, and external validity. The conclusion validity is focused more on the relationship between the outcome and the program. Complex systems, human behavior and biological organisms are subject to far more random error and variation.
While any experimental design must attempt to eliminate confounding variables and natural variations, there will always be some disparities in these disciplines.
Reliability and validity are often confused; the terms describe two inter-related but completely different concepts. This difference is best described with an example:. A researcher devises a new test that measures IQ more quickly than the standard IQ test:. Reliability is an essential component of validity but, on its own, is not a sufficient measure of validity.
A test can be reliable but not valid, whereas a test cannot be valid yet unreliable. A test that is extremely unreliable is essentially not valid either.
A bathroom scale that measures your weight one day as kg and the next day as 2 kg is not unreliable, it merely is not measuring what it is meant to. There are several methods to assess the reliability of instruments.
In the social sciences and psychology, testing internal reliability is essentially a matter of comparing the instrument with itself. How could you determine whether each item on an inventory is contributing to the final score equally? One technique is the split-half method which cuts the test into two pieces and compares those pieces with each other. The test can be split in a few ways: Split-half methods can only be done on tests measuring one construct — for example an extroversion subscale on a personality test.
The internal consistency test compares two different versions of the same instrument, to ensure that there is a correlation and that they ultimately measure the same thing. For example, imagine that an examining board wants to test that its new mathematics exam is reliable, and selects a group of test students.
For each section of the exam, such as calculus, geometry, algebra and trigonometry, they actually ask two questions, designed to measure the aptitude of the student in that particular area. If there is a high internal consistency, i. The test - retest method involves two separate administrations of the same instrument, while internal consistency measures two different versions at the same time.
Researchers may use internal consistency to develop two equivalent tests to later administer to the same group. A statistical formula called Cronbach's Alpha tests the reliability and compares various pairs of questions. Luckily, modern computer programs take care of the details saving researchers from doing the calculations themselves. There are two common ways to establish external reliability: The Test-Retest Method is the simplest method for testing external reliability, and involves testing the same subjects once and then again at a later date, then measuring the correlation between those results.
One difficulty with this method lies with the time between the tests. This method assumes that nothing has changed in the meantime. If the tests are administered too close together, then participants can easily remember the material and score higher on the second round.
But if administered too far apart, other variables can enter the picture: To prevent learning or recency effects, researchers may administer a second test that is different but equivalent to the first. Anyone who has watched American Idol or a cooking competition will understand the principle of inter-rating reliability.
An example is clinical psychology role play examinations, where students are rated on their performance in a mock session. Another example is a grading of a portfolio of photographic work or essays for a competition. Processes that rely on expert rating of performance or skill are subject to their own kind of error, however. Inter-rater reliability is a measure of the agreement of concordance between two or more raters in their respective appraisals, i.
The principle is simple: If, however, the judges have wildly different assessments of that performance, their assessments show low reliability. Importantly, reliability is a characteristic of the ratings, and not the performance being rated. In psychometry, for example, the constructs being measured first need to be isolated before they can be measured. For this reason, extensive research programs always involve comprehensive pre-testing, ensuring that the instruments used are both consistent and valid.
Those in the physical sciences also perform instrumental pre-tests, ensuring that their measuring equipment is calibrated against established standards. Check out our quiz-page with tests about:.
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Reliability is a necessary ingredient for determining the overall validity of a scientific experiment and enhancing the strength of the results. Debate between social and pure scientists, concerning reliability, is robust and ongoing.
Relationship between reliability and validity. If data are valid, they must be reliable. If people receive very different scores on a test every time they take it, the test is not likely to predict anything. However, if a test is reliable, that does not mean that it is valid.
The use of reliability and validity are common in quantitative research and now it is reconsidered in the qualitative research paradigm. Since reliability and validity are rooted in positivist perspective then they should be redefined for their use in a naturalistic approach. Like reliability and validity as used in quantitative research are providing . Issues of research reliability and validity need to be addressed in methodology chapter in a concise manner. Reliability refers to the extent to which.
When we look at reliability and validity in this way, we see that, rather than being distinct, they actually form a continuum. On one end is the situation where the concepts and methods of measurement are the same (reliability) and on the other is the situation where concepts and methods of measurement are different (very discriminant validity). Test-retest reliability is a measure of reliability obtained by administering the same test twice over a period of time to a group of individuals. The scores from Time 1 and Time 2 can then be correlated in order to evaluate the test for stability over time.