Concurrent validity is a type of criterion validity that shows whether a new test is accurate. Researchers check this by correlating the new test with an existing, validated measure of the same construct, given to the same people at the same time.
Key Takeaways
- Definition: Concurrent validity is a type of criterion validity that measures how well a test correlates with a previously validated measure of the same construct, given at the same time.
- Purpose: It checks a test’s accuracy against an established benchmark, confirming the test measures what it claims to measure right now.
- Applications: It is used widely, from wellbeing and IQ measures to educational assessment tests.
What Is Concurrent Validity?
Concurrent validity is a type of criterion validity that measures the degree to which a new test or measurement tool correlates with a well-established measure (criterion) of the same construct, when both are administered at the same time.
In other words, it assesses how well the new test can predict or agree with the results of the existing, validated measure in the present moment.
If a new depression assessment correlates highly with scores on the Beck Depression Inventory, a validated measure for depression, this supports the new assessment’s concurrent validity. Both are given at the same time.
This is because the new assessment successfully distinguishes between individuals with varying levels of depression, as measured by an established criterion.
How to measure concurrent validity
In general, any correlation between two related measures taken at the same point in time can be used to demonstrate concurrent validity.
To measure concurrent validity, follow these steps:
Identify a well-established, validated measure (criterion) that assesses the same construct as the new measure you want to validate.
This criterion measure should have demonstrated reliability and validity, serving as a benchmark for comparison.
This matters because reliability sets a ceiling on validity. An unreliable criterion measure caps how high any correlation with it can go, however accurate the new test actually is (Spearman, 1904).
For example, if you’re validating a new test for depression, you might use the Beck Depression Inventory as your criterion measure.
Administer both the new measure and the established criterion measure to a sample of participants at the same time or within a short time frame.
Ensure an adequate sample size to obtain a reliable estimate of the correlation coefficient. A larger sample size will generally lead to a more precise and stable estimate of the relationship.
Keep the two measures independent. Don’t let scores on one influence the other, and avoid criterion contamination.
Differences in administration conditions can introduce unwanted variability and reduce the observed concurrent validity.
For instance, if teachers are aware of students’ scores on an aptitude test, this knowledge might unintentionally bias their ratings of those students’ academic performance.
Collect the data from both measures for each participant.
Calculate the correlation coefficient between the scores on the new measure and the scores on the established criterion measure.
Common correlation coefficients include Pearson’s r for continuous data and Spearman’s rho for ordinal data.
Interpret the correlation coefficient:
- A strong positive correlation (e.g., r > 0.7) indicates high concurrent validity, meaning the new measure is assessing the same construct as the established criterion.
- A moderate positive correlation (e.g., 0.4 < r < 0.7) suggests acceptable concurrent validity.
- A weak or no correlation (e.g., r < 0.4) indicates poor concurrent validity, suggesting the new measure may not be assessing the same construct as the established criterion.
Remember that concurrent validity is just one type of validity evidence. Comprehensively evaluating a new measure also means gathering other evidence, such as content validity and predictive validity.
Examples of concurrent validity
Depression Questionnaires
Depression is a common mental health issue that affects many people, so it is important to assess its severity accurately.
One way of doing this is through the use of depression questionnaires. Concurrent validity here means comparing scores on the questionnaire with scores from other, related measures. Examples include clinician-rated scales or DSM-based interviews.
This lets researchers check whether higher scores on the questionnaire go with higher scores on the other measures, and vice versa. That pattern is the evidence for the questionnaire’s concurrent validity (Bowers, 2004).
IQ Tests
IQ tests are widely used to measure intelligence, but they must first demonstrate concurrent validity before they can be relied upon as accurate.
Numerous circumstances affect the accuracy of IQ tests over time. For example, researchers have established that the average IQ score of test takers increases by three points every decade.
Since IQ is designed to be a fundamentally curved scale, using concurrent validity as a way of confirming new means is essential to its usability. There are many ways that researchers can ensure the concurrent validity of IQ tests.
For example, researchers can measure whether students’ scores on an IQ test are positively correlated with their grades in school.
Otherwise, they may test if the calculated IQ scores of their assessment correlate with the calculated IQ scores of others (Hays et al., 2002).
Quality of Life Research
One example of concurrent validity involves the Satisfaction with Life Domains Scale for Cancer (SLDS-C), a self-report measure of quality of life. Researchers verified it against another cancer-specific quality of life measure, the Functional Assessment of Cancer Therapy Scale-General (FACT-G). The two scales correlated at 0.76.
These two scales both measure functional well-being, emotional well-being, and physical well-being.
Because these subscales correlate strongly, researchers can reasonably assume that people with higher life satisfaction also report more positive affect. They also tend to have higher levels of health status (Baker et al., 2007).
What are the limitations of concurrent validity?
- Criterion Quality: Concurrent validity depends heavily on the quality and relevance of the chosen criterion; a weak or irrelevant criterion compromises the test’s validity.
- Snapshot Limitation: It offers a snapshot of the test-criterion relationship at one point in time, potentially overlooking how some variables change.
- Overlap With Convergent Validity: The term is sometimes used interchangeably with convergent validity, which can cause confusion between the two concepts.
FAQs
Is concurrent validity internal or external?
Concurrent validity is a type of criterion validity, not external validity. The two terms sound alike but differ. Criterion validity asks whether a test’s scores relate to an established external standard. This is not about generalisability.
External validity instead asks whether a study’s findings generalise beyond the people, setting and time it was conducted in. Internal consistency is different again: it looks at whether items within the same test correlate with each other.
For instance, checking whether items in a depression assessment give consistent answers is about internal consistency, not concurrent validity.
Is concurrent and convergent validity the same?
Concurrent validity refers to how closely a test or measurement tool correlates with another, previously validated measure of the same construct. Both measures are taken at the same time.
In other words, it assesses how well a new test compares to an existing, well-established test.
Convergent validity is a subtype of construct validity. It refers to the degree to which two measures of constructs that theoretically should be related are, in fact, related.
Convergent validity is assessed by comparing the new test to other measures that are theoretically related to the construct being measured.
How can concurrency validity be improved?
The degree of concurrent validity in a study can be improved by implementing a number of best practices. Preparation matters most.
Firstly, researchers can ensure that the data used for concurrent review is complete and up to date.
Secondly, researchers can use design techniques that reduce the chance of conflicting updates from different sources.
Thirdly, researchers can consider implementing automated conflict resolution protocols if appropriate.
Fourthly, they can create standard operating procedures that are consistently followed and regularly reviewed to minimize errors in concurrent reviews.
Finally, documenting the results of each concurrent review can help identify areas where improvements could be made or conflicts avoided in future reviews.
By adhering to these principles, organizations can ensure that their concurrent reviews are conducted accurately and reliably (Lin & Yao, 2014).
What is the relationship between concurrent validity and construct validity?
Concurrent validity is one aspect of the broader concept of construct validity.
It serves as one piece of evidence supporting construct validity, especially when combined with other forms of evidence like content validity, predictive validity, convergent validity, and discriminant validity.
Construct validity seeks to confirm that a test genuinely measures the intended theoretical construct.
References
American Psychological Association. (n.d.). Concurrent validity. In APA dictionary of psychology.
Baker, F., Denniston, M., Hann, D., Gesme, D., Reding, D. J., Flynn, T., & Kennedy, J. S. (2007). Factor structure and concurrent validity of the Satisfaction with Life Domains Scale for Cancer (SLDS-C). Journal of Psychosocial Oncology, 25(2), 1–17.
Bowers, A. (2004). Concurrent Validity Study of the Clinical Assessment of Depression with the Beck Depression Inventory. University of Kentucky.
Gregory, R. J. (2000). Psychological testing, history, principles, and applications (4th ed.). Allyn & Bacon.
Hays, J. R., Reas, D. L., & Shaw, J. B. (2002). Concurrent validity of the Wechsler abbreviated scale of intelligence and the Kaufman brief intelligence test among psychiatric inpatients. Psychological reports, 90(2), 355-359.
Lin, W. & Yao, G. Concurrent Validity. In Michalos, A. C. (Ed.). (2014). Encyclopedia of quality of life and well-being research (pp. 311-1). Springer Netherlands.
Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72–101. https://doi.org/10.2307/1412159
