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How to ensure reliability and validity in quantitative research

Quantitative research relies on the reliability and validity of its data. Reliability is all about consistency. In other words, however many times you measure something the results should always be the same. Validity is different. A test, for example, is valid if it can measure what it is designed to measure.


Validity and reliability are the watch words which underpin all research:

In the past the humanities were not really concerned about these constructs, but now all research demands rigour and reliability and validity in quantitative research as well as qualitative research is of paramount importance.

Your work should be reliable and valid so that other researchers can replicate your study and discover whether or not your data is reliable and valid.

Ways of establishing research is valid and reliable

Here is a list of the general methods that are used to establish the validity of a research study.

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Reliability means in part that measurements are consistent and in turn this means that if the exact same conditions prevail in another study, for example, then the same results should be obtained. When the results are reproducible, then this is a measure of reliability. This helps to demonstrate that the results of a study are trustworthy. A study can be replicated time after time to determine the reliability of the original research methods, for example.

Researchers, for this reason, have a duty to the scientific community to report their methodology, results and findings honestly.

Next let’s look at the idea of falsifiability: this basically means that if any hypothesis has credence it has to be possible to test whether or not is correct. All researchers should test their hypotheses and prove or disprove them before they disseminate the results of their study. If a theory cannot be tested others working in the same field will not be convinced of its worth or the scientific rigour of the study. Replication must be possible to test the study’s validity. If a study cannot be replicated, this might lead other researchers to believe that the results were fabricated, and this would invalidate the whole study.

Results and findings should also be generalisable. If the results are generalisable other researchers can interpret findings and apply them to broader contexts. This makes them both relevant and more meaningful.

Some ways of establishing validity and reliability are: -

  1. Statistical analysis involves the collection of data and exploring it in order to work out what trends and patterns there are.
  2. Data triangulation adds weight to your research study because a researcher can use evidence from various sources and comparing it. This strengthens the research you have undertaken because you can compare data from interviews, documents, observations photographs and public documents, for example.
  3. Theory triangulation is also valuable as it enables the researcher to use various theoretical approaches to support and interpret data.
  4. Feedback – this is important because not everyone will hold the same opinions or have exactly the same experiences in a given situation.

To begin with, the more sources of information you use, the richer will be your data. The different sources you use will provide you with more insight into your chosen study area.

It could be that one of your sources is not very reliable, so it is always necessary to check that several different sources confirm the same findings or results. When different sources confirm your data, then you know that it is valid and reliable.

When you employ data triangulation methods, you end up with more comprehensive data and when it is supported in various research studies and in different types of research, then it is easier to analyse your data set and draw conclusions regarding the outcomes of your research.

Perhaps one of the biggest benefits of triangulation is that you can more easily identify any inconsistencies in your data.

To measure construct validity, you can utilise three types of evidence: -

  • Homogeneity - The tool or instrument is only measuring one construct.
  • Convergence – can only be used when the instrument or tool is measuring concepts that are similar to those measured by other instruments. However, this might not be possible if there are no other similar instruments available.
  • Theory evidence – this can be utilised when, for example, a person’s behaviour is very similar to theoretical propositions that are measured by the instrument. An example might be that if an instrument is designed to measure levels of anxiety, it would be reasonable to expect that participants in a study who score highly on the anxiety scale would also show symptoms of anxiety in their everyday existences.

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