For further details and specific examples, see the Resources Links on the right side of this page. Validity is seen by many as being the primary issue that should be examined. The following Slideshare presentation, General Issues in Research Design, discusses validity in further depth, along with other issues that should be addressed in research studies.
Reliability and Validity — The following resources defines and discusses reliability and validity and discusses threats to each. Validity and Reliability Issues in Educational Research — This journal article discusses the importance of validity and reliability in educational and social research. Generalizability and Transferability — The resource below provides basic defines as well as additional links on the right side that further explore generalizability and transferability in research.
Falsifiability — This link leads to a brief discussion of falsifiability in research. Replication — This resource provides a brief discussion of the importance of replicability of research results and contains links that further examine the topic.
This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. Key Issues in Quantitative Research The purpose of this module is to examine the key issues related to quantitative research that must be addressed to ensure a quality research study that is valid, reliable, generalizable and reproducible.
Define validity, reliability, falsifiability, generalizability, and reproducibility as they relate to quantitative research. Explain the importance of each in a quantitative study. Following is a description of these issues: Validity The term validity refers to the strength of the conclusions that are drawn from the results.
There are several types of validity that are commonly examined and they are as follows: 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 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. 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. 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. External validity is the process of examining the results and questioning whether there are any other possible causal relationships. Control groups and randomization will lessen external validity problems but no method can be completely successful. This is why the statistical proofs of a hypothesis called significant , not absolute truth. Any scientific research design only puts forward a possible cause for the studied effect.
There is always the chance that another unknown factor contributed to the results and findings. This extraneous causal relationship may become more apparent, as techniques are refined and honed.
If you have constructed your experiment to contain validity and reliability then the scientific community is more likely to accept your findings. Eliminating other potential causal relationships, by using controls and duplicate samples, is the best way to ensure that your results stand up to rigorous questioning. Check out our quiz-page with tests about:. Martyn Shuttleworth Oct 20, Retrieved Sep 11, from Explorable.
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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 the same subjects. In short, it is the repeatability of your measurement.
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 .
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 se66rthaae-1fboc6.ga other words, the extent to which a research instrument. Like reliability and validity as used in quantitative research are providing springboard to examine what these two terms mean in the qualitative research paradigm, triangulation as used in quantitative research to test the reliability and validity can also illuminate some ways to test or maximize the validity and reliability of a qualitative study.