What Is Concurrent Validity?

This might sound technical, but it’s actually pretty straightforward: concurrent validity checks whether a new test gives results that match an already proven, well-established test when given at the same time.

Let’s break it down in a way that makes sense, with real-world examples and clear explanations.

🚀 1. What Is Concurrent Validity?

When researchers create a new test, they need to prove that it actually measures what it’s supposed to. That’s where concurrent validity comes in. This concept falls under criterion validity, which basically means testing how well a new assessment lines up with a gold-standard test that’s already trusted by experts.

Think of it like fact-checking. If you hear a wild rumor, you probably check it against a trusted source before believing it, right? Well, concurrent validity works the same way—new tests need to be compared with established ones to prove they’re accurate.

🧠 Real-Life Example: Testing a New IQ Assessment

Let’s say researchers develop a brand-new test to measure cognitive abilities—things like problem-solving, memory, and reasoning skills. Sounds cool, but before anyone starts using it, we need to know if it actually works.

So, they take a group of participants and give them two tests at the same time:

• ✅ The new cognitive test (the one being evaluated)
• ✅ The Wechsler Adult Intelligence Scale (WAIS)—one of the most trusted IQ tests out there

Now, here’s the magic:

• – If the scores on both tests are highly correlated (meaning people who score high on one also score high on the other), that suggests the new test is legit.
• – If the scores don’t align well, then we have a problem—the new test might not be measuring cognitive abilities as accurately as the WAIS.

📏 2. How Do Researchers Measure Concurrent Validity?

Measuring concurrent validity is more than just glancing at two sets of test scores and saying, “Looks close enough!”. Researchers follow a structured approach to make sure their results are scientifically sound.

Each step in the process is designed to ensure the new test is actually doing its job—and not just throwing out random numbers. Let’s break it down.

🛠 Step 1: Pick a Gold-Standard Test

Before anything else, researchers need a benchmark test—one that’s already widely accepted and known to be reliable. This is the test the new one will be compared against.

For example:

• – If someone is creating a new nonverbal reasoning test, they might compare it to the Raven Progressive Matrices (RPM), which is a go-to test for measuring abstract reasoning.
• – If it’s a new depression scale, they might compare it to the Beck Depression Inventory (BDI), a well-established measure for depression symptoms.

The idea? If the new test measures the same thing as the trusted one, their results should match up.

⏳ Step 2: Administer Both Tests Close Together

Timing is everything. Researchers give both tests to the same group of people within a short time frame—typically on the same day or within a week.

Why does this matter?

• – Avoids external influences: If too much time passes between tests, things like mood shifts, stress levels, or even sleep deprivation could skew results.
• – Reduces learning effects: If a test is taken too far apart, a person might have gained new knowledge or skills, which could change their performance.

So, by keeping the tests back-to-back or within a short window, researchers make sure they’re actually comparing the tests—not outside factors.

📊 Step 3: Calculate the Correlation

Now comes the real science: statistical analysis. Researchers don’t just compare scores visually—they calculate something called a correlation coefficient (r) to see how well the two tests align.

Here’s what those numbers mean:

• – r > 0.7 → Strong correlation → The new test is highly valid ✅
• – 0.4 < r < 0.7 → Moderate correlation → Some validity, but needs improvement 🤔
• – r < 0.4 → Weak correlation → The new test is not measuring the same thing ❌

Think of correlation as a compatibility test for assessments. If two tests measure the same thing, their scores should be in sync. If not, it’s a red flag that the new test might not be reliable.

🔍 Step 4: Interpret What the Correlation Means

A high correlation (above 0.7) means the new test is probably a solid alternative to the established test. But if the correlation is low, researchers have to rethink things.

Example:

• Let’s say a psychologist is testing a new reasoning ability questionnaire against the RPM (a widely trusted test). After analyzing the data, they find:
• ✅ A correlation of 0.85 → The new test is very similar to the RPM, meaning it has strong concurrent validity.
• ❌ A correlation of 0.3 → The new test doesn’t align well, so it might not actually be measuring reasoning ability properly.

When correlation is low, researchers don’t just scrap the test immediately—they refine it, adjust questions, or even rethink the way it measures the concept.

🔬 3. Real-Life Examples of Concurrent Validity

So, we know that concurrent validity is all about making sure a new test actually measures what it’s supposed to by comparing it to a trusted, existing test. But how does that play out in real life? Let’s check out some actual examples from psychology, education, and health research.

🧠 IQ Tests & Academic Performance

Ever wonder how we know an IQ test is actually measuring intelligence? Researchers don’t just hope for the best—they test its validity.

Let’s say a new IQ test comes out that promises to assess cognitive abilities in a fresh, modern way. Before it gets rolled out, researchers need to check if it aligns with established intelligence tests like the Wechsler Adult Intelligence Scale (WAIS) or the Stanford-Binet IQ test.

• 🔹 If students who score high on the new IQ test also score high on WAIS, that’s a strong correlation—which means high concurrent validity.
• 🔹 Researchers might also compare IQ test scores with academic performance (like GPA or standardized test scores). If students with higher IQ scores generally have better grades, that further supports the test’s validity.

If the new test fails to show strong correlation, it’s back to the drawing board—because there’s no point in using a test that doesn’t actually measure intelligence.

🏥 Depression Assessments

Mental health assessments are a huge area where concurrent validity matters. Imagine a psychologist is developing a new depression screening tool—they need to know if it works as well as existing tools before they can use it in clinical settings.

To test its validity, researchers give the new assessment to a group of people at the same time as a widely trusted depression scale like:

• ✔ Beck Depression Inventory (BDI)
• ✔ Patient Health Questionnaire-9 (PHQ-9)
• ✔ Clinician-led diagnostic interviews

If people who score high on the new depression test also score high on BDI or PHQ-9, that’s a strong indicator that the new test is valid.

On the flip side, if the correlation is weak (meaning people who score high on the new test don’t necessarily score high on the BDI), then something’s off—the new test might not be measuring depression accurately.

This process is crucial because mental health tools need to be reliable, accurate, and consistent before they can be used for diagnosing and treating real patients.

💡 Quality of Life & Health Studies

Health research relies heavily on surveys and self-report scales to measure things like pain levels, emotional well-being, and quality of life. But how do researchers make sure these surveys actually reflect a person’s real experiences?

Take, for example, a new cancer-related quality-of-life questionnaire. Before using it in hospitals or research studies, scientists need to test whether it’s actually capturing what it’s supposed to.

• 🔸 To do this, they compare the new questionnaire’s results to a well-established quality-of-life scale, like the FACT-G (Functional Assessment of Cancer Therapy-General).
• 🔸 In one study, researchers found a correlation of 0.76 between a new scale and the FACT-G. That’s strong concurrent validity, meaning the new test is likely a solid alternative to the existing one.

This is important because if a quality-of-life measure isn’t valid, doctors and researchers could make decisions based on inaccurate data—which could impact patient care and treatment outcomes.

🔄 4. Concurrent Validity vs. Other Types of Validity

So concurrent validity is super useful, but it’s not the only way researchers check if a test is doing its job. There are several other types of validity, each answering a different question about whether a test is measuring what it’s supposed to.

Let’s break it down in plain English so you can see how concurrent validity fits into the bigger picture.

🔄 Concurrent Validity vs. Predictive Validity

These two are often mixed up, but the difference is all about time.

• – Concurrent Validity = Does a new test match up with a trusted one right now?
• – Predictive Validity = Can a test predict future performance or outcomes?

Example:

• 🕒 Concurrent Validity: A new anxiety test is compared to the GAD-7 (a well-established anxiety measure). If scores are similar, it has strong concurrent validity.
• ⏳ Predictive Validity: The SAT is used to predict college GPA. If SAT scores actually correlate with how well students do in college, the SAT has good predictive validity.

Key takeaway:

Concurrent validity is all about the present moment, while predictive validity is about what happens later.

🏗 Concurrent Validity vs. Construct Validity

Construct validity is the big-picture question: Is this test actually measuring what we think it’s measuring?

• – Concurrent Validity = Does this test match an already proven one right now?
• – Construct Validity = Does this test even make sense as a measure of the concept?

Example:

• 🧩 Concurrent Validity: A new IQ test is compared to the Wechsler Adult Intelligence Scale (WAIS). If scores align, the new test has strong concurrent validity.
• 🛠 Construct Validity: A new personality test claims to measure extroversion. If its questions actually relate to social behavior and outgoing tendencies, it has good construct validity.

Key takeaway:

Concurrent validity is about matching up with another test, while construct validity is about whether the test actually measures the right concept in the first place.

📚 Concurrent Validity vs. Content Validity

Content validity is all about coverage—making sure a test includes everything it should to measure the concept properly.

• – Concurrent Validity = Does the test align with another trusted test?
• – Content Validity = Does the test fully cover the topic it’s supposed to measure?

Example:

• 📖 Concurrent Validity: A new math aptitude test is given alongside an existing one to see if the scores match.
• 📊 Content Validity: A math test is supposed to measure overall math skills, but if it only has algebra questions and skips geometry and calculus, it lacks content validity.

Key takeaway:

Concurrent validity is about how a test compares to another test, while content validity is about how well a test represents the full subject matter.

🛑 5. Limitations of Concurrent Validity

So yeah, concurrent validity is super useful, but let’s be real—it’s not foolproof. Just because a test correlates well with an established one doesn’t mean everything is perfect. There are a few things that can mess with the accuracy of concurrent validity, and if researchers aren’t careful, they might end up with flawed conclusions.

Let’s go over the main issues that can throw a wrench in the process.

❌ 1. The Benchmark Test Might Not Be As “Gold Standard” As You Think

Concurrent validity is only as good as the test you’re comparing against. If the established test (the “criterion”) isn’t actually that reliable, then using it as a benchmark isn’t very helpful.

Think about it like this:

• – Imagine a new anxiety questionnaire is being tested against an older anxiety scale.
• – But what if that old anxiety scale was created decades ago and doesn’t reflect modern research on mental health?
• – If the new test correlates well with it, does that really mean the new test is good? Or does it just mean both tests share the same outdated flaws?

Solution? Researchers need to double-check that their comparison test is actually solid. Otherwise, they might just be validating bad science.

⏳ 2. Timing Can Mess With Results

One of the biggest things about concurrent validity is that both tests need to be given at the same time (or really close together). But even then, external factors can still mess things up.

🚨 Example of why this matters:

Let’s say researchers are testing a new depression screening tool against the BDI (Beck Depression Inventory). They give both tests to a group of people in a morning session.

• – Some participants had coffee and are feeling alert.
• – Some barely slept the night before.
• – Some just got a stressful email before taking the test.

All of these outside factors could slightly affect how people answer the questions, which could affect the correlation between the tests.

Solution? Researchers should control for these variables as much as possible—consistent test environments, large sample sizes, and multiple testing conditions help reduce random noise.

🤷‍♂️ 3. Confusion with Convergent Validity

Here’s a mistake people make all the time: mixing up concurrent validity with convergent validity. While they both deal with correlations between tests, they’re not the same thing.

🆚 What’s the difference?

• – Concurrent Validity → Compares two tests that measure the exact same construct.
• – Example: A new IQ test vs. the WAIS IQ test. Both measure intelligence, so they should match up.
• – Convergent Validity → Compares tests that measure related but different constructs.
• – Example: A self-esteem test vs. a self-confidence test. They should be somewhat related, but they’re not measuring the exact same thing.

If researchers mistake convergent validity for concurrent validity, they might think their test is measuring one thing when it’s actually capturing something a little different.

Solution? Be clear about what’s being tested and make sure the comparison test is actually measuring the same construct, not just something kinda similar.

🔧 6. How to Improve Concurrent Validity

So, let’s say a new test flops when researchers check its concurrent validity—meaning it doesn’t match up well with a trusted, established test. Does that mean the test is completely useless? Nope. It just means it needs some fine-tuning before it can be taken seriously.

Here’s how researchers can fix the problem and improve concurrent validity without scrapping the entire test.

📌 Step 1: Use a Legit Comparison Test

First things first—double-check the benchmark test.

• – If researchers compare their new test to an outdated, unreliable, or flawed measure, the correlation is already set up to fail.
• – Just because a test has been around forever doesn’t mean it’s a perfect benchmark.

🚨 Example: Imagine a psychologist is developing a new social anxiety scale and compares it to an old, outdated test that doesn’t align with current diagnostic criteria. If the correlation is weak, it could be because the old test itself isn’t great—not necessarily because the new one is bad.

🔹 Fix it: Always choose a well-established, scientifically validated measure for comparison. The “gold standard” should actually be gold.

⏳ Step 2: Control for External Factors

A weak correlation might not be because the test itself is bad—it could be because external variables are messing things up.

🔹 Testing conditions matter. Imagine giving a new memory test in:

• – A quiet, well-lit lab (ideal conditions).
• – A loud, distracting coffee shop (absolute chaos).

If the same people take both the new test and the benchmark test under different conditions, their performance might not be comparable—and that can tank the concurrent validity results.

🔹 Fix it: Researchers should keep test conditions consistent:

• ✔ Same environment (quiet, distraction-free)
• ✔ Same time of day (since fatigue and alertness fluctuate)
• ✔ Same instructions and format (so participants aren’t confused)

Small tweaks in testing conditions can make a big difference in how well two tests correlate.

👥 Step 3: Increase Sample Size

A small test group can create random fluctuations that mess up correlation results.

🔹 Imagine testing only 10 people.

• – If even a couple of them have an off day, that could completely throw off the results.
• – With such a small group, it’s easy to get a misleadingly weak correlation just due to chance.

🔹 Fix it: Bigger samples = more stable results.

• ✔ Instead of testing 10 people, aim for 100+.
• ✔ Make sure the group is diverse (age, gender, background) to ensure results apply to a wide range of people.

More participants = a better, clearer estimate of how well the new test actually compares to the old one.

✍ Step 4: Refine the New Test

If the new test just isn’t matching up well with the established test, it might need some serious revisions.

🔹 Possible Issues:

• ❌ The questions might be unclear, confusing, or irrelevant.
• ❌ The scoring system might not reflect what it’s supposed to measure.
• ❌ The test format might be influencing results (e.g., a timed test vs. an untimed one).

🚨 Example: If a new depression test isn’t correlating well with the Beck Depression Inventory (BDI), researchers might look at:

• – Are the questions asking about the right symptoms?
• – Is the rating scale making sense? (e.g., 1-10 vs. multiple-choice)
• – Are certain items too broad or too specific?

🔹 Fix it: Researchers can adjust, reword, or restructure the test until it captures what it’s actually meant to measure.

🎯 7. Final Thoughts

Concurrent validity is a powerful tool in psychology and research, ensuring that new tests hold up against trusted, established measures. If a test shows strong concurrent validity, it’s a sign that it can be used confidently in real-world settings.

It’s used in:

• ✅ IQ tests to check intelligence measurement accuracy
• ✅ Depression and mental health screenings
• ✅ Education and aptitude assessments
• ✅ Medical and quality-of-life research

By understanding and applying concurrent validity, researchers can create better, more reliable tools for measuring everything from psychological traits to academic skills.

Got any questions? Let’s talk about it! 🔥