What is the difference between reliability and validity?

Knowing that your research produces accurate, quality data is a must when it comes to applying or presenting your results. Here is an overview of what constitutes research that is reliable and valid, and how to make sure your experiments fit into both categories.

Reliability indicates the extent to which the results of an experiment can be replicated when it is performed multiple times. Effective research and experiments should produce similar results over time when performed by other people, as long as instructions and conditions (methodology) are followed correctly or in the same manner each time. 

Experiments that generate unusually large differences in results should be questioned. Just because an experiment can be reproduced correctly doesn’t make the results or outcomes reliable. 

Validity indicates the extent to which your research usefully and accurately measures what you are trying to measure and how that stacks up with other established concepts.  Validity can be harder to determine than reliability, but a high level of reliability assists in proving that your research is valid.  

Reliability and validity provide slightly different indications about the overall quality of your research and whether the data you obtain can accurately be used in service of your reason for doing the experiment. It is possible for an experiment to be reliable but not valid, or valid but not reliable, but the most accurate experiments produce strong, consistent results in both categories. 

Experiments that cannot be replicated with similar results and those that do not adequately address the question they were designed to solve produce results that should not be used for further research or as a basis for important decisions and/or policies. Since reliability and validity are different, quality research should reflect both of these principles.

Several types of assessments can be used to determine whether the information you gather is reliable and valid. Here are some of the most common tests used to assess the reliability and validity of research.

Test-retest reliability, internal reliability, and inter-rater reliability are among the most common types of reliability assessment. Each option can be used to help you determine the overall accuracy and consistency of your research. External reliability, parallel forms reliability, or other assessment options may also be appropriate, depending on the nature of the research you are conducting.

Each of these reliability types looks at a different factor that may affect the outcome of your research. This means that it is generally a good idea to use a combination of tests to create the most complete picture of the reliability of your data. 

Internal reliability

Many complex experiments include two or more components that are intended to measure the same type of data. This technique, which is known as internal reliability, assesses whether elements that are supposed to produce the same results do so consistently. Strong internal reliability increases the overall confidence that your data is accurate, consistent, and replicable. 

External reliability

External reliability indicates how consistent a type of measure is over a period of time or with different types of survey conditions, such as different individuals, and how successfully this can be generalized. Reliability can be determined if standard operating procedures (SOPs) are used to manage the way research is conducted so it can be reproduced. 

Test-retest reliability

Test-retest reliability measures how well tests produce similar results over time. This type of assessment provides insights into whether your experiment yields stable, consistent data when it is repeated multiple times externally, including at a later date. 

Inter-rater reliability

Well-designed tests should produce similar results regardless of who is performing them. Tests that result in vastly different data when different types of individuals are used in an experiment, for example, can indicate that there is too much variation in the equipment or tools the researchers are using. Other possibilities are that the instructions are not clear enough to be followed exactly how the experiment's designers intended them to be.

Likewise, there are several types of validity assessments that can be combined to better understand how well your data represents what you want it to represent. 

Some types of validity that may be used include: 

• Internal

• External 

• Convergent 

• Concurrent 

• Construct 

• Content 

• Face 

• Criterion 

• Predictive 

Ensuring reliability and validity is a crucial step in knowing that the information your research provides is accurate, consistent, and valuable. Making sure that methods are applied consistently and taking steps to conduct research in conditions that are as similar as possible can help ensure your research is reliable.

In addition, choosing appropriate sampling, measurement tools, and methods can help make sure your research is valid. Being vigilant about ensuring reliability and validity in your research from the beginning can help you get the most out of the time, money, and other resources that are used to conduct it. 

It is possible for your research to fit into one category but not the other. Your data may show consistent results for something other than the question you are actually studying, or inconsistent results that do address your question but indicate that another issue is skewing your data.

Information you gain from research that is only reliable or only valid can form a helpful starting point in continuing to develop your research, but it should not be used as your final results.