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 instrument. In other words, the extent to which a research instrument consistently has the same results if it is used in the same situation on repeated occasions.
A simple example of validity and reliability is an alarm clock that rings at 7: It is very reliable it consistently rings the same time each day , but is not valid it is not ringing at the desired time. It's important to consider validity and reliability of the data collection tools instruments when either conducting or critiquing research.
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. Some variables are more stable constant than others; that is, some change significantly, whilst others are reasonably constant. Therefore, the score measured e. The true score is the actual score that would reliably reflect the measurement e.
The error reflects conditions that result in the score that we are measuring not reflecting the true score , but a variation on the actual score e. This error component within a measurement procedure will vary from one measurement to the next, increasing and decreasing the score for the variable. It is assumed that this happens randomly, with the error averaging zero over time; that is, the increases or decreases in error over a number of measurements even themselves out so that we end up with the true score e.
Provided that the error component within a measurement procedure is relatively small , the scores that are attained over a number of measurements will be relatively consistent ; that is, there will be small differences in the scores between measurements.
As such, we can say that the measurement procedure is reliable. Take the following example:. Intelligence using IQ True score: Actual level of intelligence Error: Caused by factors including current mood, level of fatigue, general health, luck in guessing answers to questions you don't know Impact of error on scores: Would expect measurements of IQ to be a few points up and down of your actual IQ, not to points, for example i.
By comparison, where the error component within a measurement procedure is relatively large , the scores that are obtained over a number of measurements will be relatively inconsistent ; that is, there will be large differences in the scores between measurements.
By following a few basic principles, any experimental design will stand up to rigorous questioning and skepticism. The idea behind reliability is that any significant results must be more than a one-off finding and be inherently repeatable. Other researchers must be able to perform exactly the same experiment , under the same conditions and generate the same results. This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis.
Without this replication of statistically significant results , the experiment and research have not fulfilled all of the requirements of testability. This prerequisite is essential to a hypothesis establishing itself as an accepted scientific truth. For example, if you are performing a time critical experiment, you will be using some type of stopwatch.
Generally, it is reasonable to assume that the instruments are reliable and will keep true and accurate time. However, diligent scientists take measurements many times, to minimize the chances of malfunction and maintain validity and reliability.
At the other extreme, any experiment that uses human judgment is always going to come under question. Human judgment can vary wildly between observers , and the same individual may rate things differently depending upon time of day and current mood.
This means that such experiments are more difficult to repeat and are inherently less reliable. 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. Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method.
For example, there must have been randomization of the sample groups and appropriate care and diligence shown in the allocation of controls. Internal validity dictates how an experimental design is structured and encompasses all of the steps of the scientific research method.
Validity and reliability in social science research items can first be given as a test and, subsequently, on the second occasion, the odd items as the alternative form.
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 .
Reliability in research Reliability, like validity, is a way of assessing the quality of the measurement procedure used to collect data in a dissertation. In order for the results from a study to be considered valid, the measurement procedure must first be reliable. PDF | On Jan 1, , Roberta Heale and others published Validity and reliability in quantitative research For full functionality of ResearchGate it is necessary to .
4 Reliability & Validity-7 Internal Consistency: Homogeneity Is a measure of how well related, but different, items all measure the same thing. Is applied to groups of items thought to measure different aspects of the same concept. A single item taps only one aspect of a concept. If several different items are used to gain information. Internal validity dictates how an experimental design is structured and encompasses all of the steps of the scientific research method. Even if your results are great, sloppy and inconsistent design will compromise your integrity in the eyes of the scientific community. Internal validity and reliability are at the core of any experimental design.