As a student working on academic research, it’s important to understand different types of validity that can help evaluate the quality and accuracy of your findings. One key concept is concurrent validity, a criterion validity subtype that provides insights into how well your research measures against other established assessments.
What is Concurrent Validity?
Concurrent validity refers to the degree to which a new assessment or measurement tool correlates with an existing, well-established measure of the same underlying construct. In other words, it examines how well your new test, scale, or instrument agrees with a “gold standard” already known to be valid.
The basic idea is that the two should produce highly correlated results if your new measure truly assesses the same thing as the existing benchmark. Strong concurrent validity suggests your new tool is an accurate and trustworthy way to evaluate the characteristic or behavior.
Concurrent Validity Example
Suppose you develop a new test to measure introversion called the “Introversion Scale” (IS). To assess its concurrent validity, you compare the results of the IS with those of a well-established measure, such as the “Myers-Briggs Type Indicator” (MBTI). You administer both tests to 50 participants and calculate the correlation between their IS and MBTI scores on the introversion dimension.
If the correlation is high (e.g., r = 0.85), the IS measures the same construct as the MBTI’s introversion dimension, providing evidence of concurrent validity.
Concurrent vs. Predictive Validity
It’s important to distinguish concurrent validity from predictive validity, another important type of validity.
Predictive validity refers to how well a measure can forecast or predict some future outcome. For example, a university entrance exam would have predictive validity if its scores accurately predicted students’ future academic performance.
Concurrent validity, on the other hand, is about how well a new measure agrees with an existing “gold standard” assessment in the present moment. The focus is on establishing that the new tool accurately captures the same underlying construct as the benchmark.
Example: Concurrent vs. predictive validity
You develop a new programming aptitude test called the “Coding Aptitude Test” (CAT) and want to validate it using concurrent and predictive validity.
For concurrent validity, you administer the CAT to 100 current software developers and correlate their scores with their current job performance ratings. A high correlation would indicate that the CAT is a valid measure of programming aptitude for current programmers.
For predictive validity, you administer the CAT to 100 college students before they take a programming course. After the course, you correlate their CAT scores with their final grades. A high correlation would suggest that the CAT is a valid predictor of future success in programming courses.
Limitations of Concurrent Validity
While concurrent validity can provide valuable information about a new test or measure, it has some important limitations:
- Criterion Dependence: Concurrent validity depends on the quality and appropriateness of the criterion measure used for comparison. The concurrent validity evidence will be misleading if the criterion is flawed or does not truly assess the same construct.
- Temporal Concerns: Concurrent validity only speaks to how the new measure performs at a single point in time. It does not address whether the new test will remain valid over time or in different contexts.
- Lack of Predictive Power: Demonstrating concurrent validity does not necessarily mean the new measure can predict future outcomes or performance, as predictive validity does.
Researchers often examine concurrent and predictive validity when thoroughly evaluating a new psychological or educational assessment tool to address these limitations. This provides a more comprehensive understanding of the measure’s validity and utility.
Overall, concurrent validity is an important concept that helps establish the credibility of a new test or measure by demonstrating its strong relationship with an already validated assessment of the same construct.