There are three major types of validity. These are described in table 1. The first category is content validity. This category looks at whether the instrument adequately covers all the content that it should with respect to the variable. In other words, does the instrument cover the entire domain related to the variable, or construct it was designed to measure? In an undergraduate nursing course with instruction about public health, an examination with content validity would cover all the content in the course with greater emphasis on the topics that had received greater coverage or more depth.
A subset of content validity is face validity , where experts are asked their opinion about whether an instrument measures the concept intended.
Construct validity refers to whether you can draw inferences about test scores related to the concept being studied. For example, if a person has a high score on a survey that measures anxiety, does this person truly have a high degree of anxiety? In another example, a test of knowledge of medications that requires dosage calculations may instead be testing maths knowledge. There are three types of evidence that can be used to demonstrate a research instrument has construct validity:. Convergence—this occurs when the instrument measures concepts similar to that of other instruments.
Although if there are no similar instruments available this will not be possible to do. Theory evidence—this is evident when behaviour is similar to theoretical propositions of the construct measured in the instrument. For example, when an instrument measures anxiety, one would expect to see that participants who score high on the instrument for anxiety also demonstrate symptoms of anxiety in their day-to-day lives.
The final measure of validity is criterion validity. A criterion is any other instrument that measures the same variable. Correlations can be conducted to determine the extent to which the different instruments measure the same variable.
Criterion validity is measured in three ways:. Convergent validity—shows that an instrument is highly correlated with instruments measuring similar variables. Divergent validity—shows that an instrument is poorly correlated to instruments that measure different variables.
In this case, for example, there should be a low correlation between an instrument that measures motivation and one that measures self-efficacy. Predictive validity—means that the instrument should have high correlations with future criterions. Reliability relates to the consistency of a measure. A participant completing an instrument meant to measure motivation should have approximately the same responses each time the test is completed.
Although it is not possible to give an exact calculation of reliability, an estimate of reliability can be achieved through different measures. The three attributes of reliability are outlined in table 2. How each attribute is tested for is described below. In split-half reliability, the results of a test, or instrument, are divided in half. Correlations are calculated comparing both halves. Strong correlations indicate high reliability, while weak correlations indicate the instrument may not be reliable.
The Kuder-Richardson test is a more complicated version of the split-half test. In this process the average of all possible split half combinations is determined and a correlation between 0—1 is generated.
This test is more accurate than the split-half test, but can only be completed on questions with two answers eg, yes or no, 0 or 1. In this test, the average of all correlations in every combination of split-halves is determined.
Instruments with questions that have more than two responses can be used in this test. An acceptable reliability score is one that is 0. Stability is tested using test—retest and parallel or alternate-form reliability testing. Test—retest reliability is assessed when an instrument is given to the same participants more than once under similar circumstances.
A statistical comparison is made between participant's test scores for each of the times they have completed it. This provides an indication of the reliability of the instrument.
Parallel-form reliability or alternate-form reliability is similar to test—retest reliability except that a different form of the original instrument is given to participants in subsequent tests. The domain, or concepts being tested are the same in both versions of the instrument but the wording of items is different. Generally speaking, a correlation coefficient of less than 0.
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Quantitative Research: Reliability and Validity. Reliability. Definition: Reliability is the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with .
In quantitative research, this is achieved through measurement of the validity and reliability.1 Validity is defined as the extent to which a concept is accurately measured in a quantitative study. For example, a survey designed to explore depression but which actually measures anxiety would not be considered valid.
Instrument, Validity, goodfilemq.cf version of this page. There are numerous statistical tests and measures to assess the validity of quantitative instruments, which generally involves pilot testing. Establishing validity and reliability in qualitative research can be less precise, though participant/member checks. 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. Validity is defined as the extent to which a concept is accurately measured in a quantitative study. For example, a survey designed to explore depression but which actually measures anxiety would not be considered valid. The second measure of quality in a quantitative study is reliability, or the accuracy of an goodfilemq.cf other words, the extent to which a research . PDF | On Jan 1, , Roberta Heale and others published Validity and reliability in quantitative research.