Reliability and validity are two important characteristics of any measurement procedure. Show Reliability has been defined as ‘the extent to which results are consistent over time… and if the results of a study can be reproduced under a similar methodology, then the research instrument is considered to be reliable.’ (Joppe 2000). This means a test is considered reliable if the same results are produced repeatedly, if it were to be carried out again. The more consistent the results produced, the higher the reliability of the measurement procedure. Reliability is addressed in a variety of ways. These include:
Despite having methods to ensure reliability, there are issues which arise which can affect the reliability of the test and results. The researchers are human, and that means the experiment is open to human judgement and error. However, this can be solved by carefully reporting methodology in the study and, if using qualitative methods, double coding. These solutions make reliability much easier to assess. Validity, on the other hand, determines whether the research truly measures what it intended to measure, (Joppe, 2000). This means it looks at the extent to which a test measures what it claims to measure, and therefore, answers the research question or hypothesis. How valid a test is, depends on the purpose of the research. Validity is addressed in a variety of ways, and include:
Despite having methods in place to ensure validity, there are threats. There are two main threats: experimenter bias and demand characteristics. Again, the researcher is human and this means the study will always be open to human error. The experimenter may influence the outcome of the research because of his/hers expectations regarding the results. This however, can be solved through the use of single blind or double blind studies, in which the researcher has no idea what the predicted outcome is. Again, the participants are human, and this can lead to the problem of demand characteristics, in which participants behave in a different way. Participants normally modify their behaviour in response to the fact they are participating in a study and are aware they are being measured. They strive to be a good participant. Although it is essentially impossible to prevent participants from modifying their behaviour, there are methods in place which can reduce this effect. Solutions include: using observations or concealing the measurement procedure. Despite being very different, both reliability and validity are important in research. As the saying goes ‘a valid test is always reliable but a reliable test is not necessarily valid’, but it is important to ensure that both reliability and validity are demonstrated. This entry was posted in Uncategorized. Bookmark the permalink. Statistics Definitions > Reliability and Validity Contents:
Overview of Reliability and ValidityOutside of statistical research, reliability and validity are used interchangeably. For research and testing, there are subtle differences. Reliability implies consistency: if you take the ACT five times, you should get roughly the same results every time. A test is valid if it measures what it’s supposed to. Tests that are valid are also reliable. The ACT is valid (and reliable) because it measures what a student learned in high school. However, tests that are reliable aren’t always valid. For example, let’s say your thermometer was a degree off. It would be reliable (giving you the same results each time) but not valid (because the thermometer wasn’t recording the correct temperature). What is Reliability?Reliability is a measure of the stability or consistency of test scores. You can also think of it as the ability for a test or research findings to be repeatable. For example, a medical thermometer is a reliable tool that would measure the correct temperature each time it is used. In the same way, a reliable math test will accurately measure mathematical knowledge for every student who takes it and reliable research findings can be replicated over and over. Of course, it’s not quite as simple as saying you think a test is reliable. There are many statistical tools you can use to measure reliability. For example:
Internal vs. External ReliabilityInternal reliability, or internal consistency, is a measure of how well your test is actually measuring what you want it to measure. External reliability means that your test or measure can be generalized beyond what you’re using it for. For example, a claim that individual tutoring improves test scores should apply to more than one subject (e.g. to English as well as math). A test for depression should be able to detect depression in different age groups, for people in different socio-economic statuses, or introverts. One specific type is parallel forms reliability, where two equivalent tests are given to students a short time apart. If the forms are parallel, then the tests produce the same observed results. The Reliability CoefficientA reliability coefficient is a measure of how well a test measures achievement. It is the proportion of variance in observed scores (i.e. scores on the test) attributable to true scores (the theoretical “real” score that a person would get if a perfect test existed). The term “reliability coefficient” actually refers to several different coefficients: Several methods exist for calculating the coefficient include test-retest, parallel forms and alternate-form:
The range of the reliability coefficient is from 0 to 1. Rule of thumb for preferred levels of the coefficient:
What is Validity?Click on the link to visit the individual pages with examples for each type:
ReferencesEveritt, B. S.;
Skrondal, A. (2010), The Cambridge Dictionary of Statistics, Cambridge University Press. ---------------------------------------------------------------------------
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