Understanding Reliability

Reliability refers to the consistency or stability of a measure. If a method yields consistent results, it is considered as reliable.
It’s concerned with the accuracy of measurements - if you were to repeat the measurement, would you get the same result?
Also, an important consideration is whether the method consistently measures what it is intended to measure.

Types of Reliability

Test-retest reliability: It measures the degree to which test scores are consistent from one test administration to another. This requires repeating the same test on the same subjects at two different point of times.
Split-half reliability: This involves splitting the test into two halves and correlating the scores. The test must be a single dimension since it assumes item homogeneity.
Inter-rater reliability: This refers to the degree of agreement among raters or judges. If everyone who is measuring the thing, even though they’re all using the same scale, isn’t getting the same result, then you have a low inter-rater reliability.

It can be improved by clear instructions so that there is no ambiguity in how to perform the test.
Standardised procedures can be used to ensure conditions are same for all participants.
Pilot studies can help in identifying potential issues and refinements needed in the measures.
In the case of low inter-rater reliability, providing more extensive training can improve the agreement among raters.

A test may be reliable without being valid. That is, it consistently measures an incorrect concept. Hence, reliability is a necessary but not sufficient condition for validity.
It’s also important to acknowledge that human behaviour can be inconsistent which can affect reliability. Factors like mood, health etc. at the time of testing may affect the performance and hence the reliability.
Reliability coefficients (e.g. Cronbach Alpha) give an indication of the reliability of a test. However, any measure has measurement error.

Without reliable measures, research findings may be due to measurement error rather than the phenomenon under study.
Unreliable measures could make it harder to identify the real associations or differences.
Reliability is essential to establishing the overall validity of a method.