Construct vs. content validity in research

Achieving valid results is one of the toughest parts of research. The quality of your results depends on the research instrument you use. If it isn’t up to par, you can get consistently poor results without realizing it. This is why you should validate your measurement instrument before starting your research.

Construct validity and content validity are two effective approaches for validating your research instrument. Both measure your research instrument’s ability to do its job in slightly different ways.

Let’s take a closer look at construct versus content validity in research.

A “construct” is a theoretical concept that’s not directly measurable.

Construct (also called concept) validity measures how well your test, approach, or instrument measures the concept it’s intended to measure.

To verify construct validity, you can compare your measuring method to others that measure similar qualities. For example, to demonstrate the construct validity of a customer satisfaction survey, you can compare it to surveys suggested by top marketing experts in your industry.

Let’s say you are designing a survey to measure how happy your existing customers are with a new feature. You might come up with questions like:

• Did the new feature help you achieve your goals?

• What are the new feature’s downsides?

• Would you want to change something about the new feature?

• How happy are you with our product?

• Did the product help you achieve your goals?

The first three questions in the survey measure the customer’s satisfaction with the new feature, while the last two focus on the product as a whole. Those last two questions are important, but they don’t measure what the survey is intended to measure. Accordingly, this survey has low construct validity.

Product survey questions you need to ask

Differentiating the construct from related constructs is essential. You can’t get clear results if you mix several constructs in one test, even if they are closely related.

Types of construct validity

There are two types of construct validity:

• Convergent validity: how measures of the same or similar constructs correspond to each other

• Divergent (or discriminant) validity: how your test is unrelated or negatively related to tests of distinct constructs

These two types of construct validity evaluate how your test compares to related or unrelated constructs.

For example, if your customers rate your product’s new feature highly, it’s likely they will rate the overall product highly too. Meanwhile, customers who rate your product highly won’t necessarily do the same for your customer support.

In a test with a high construct validity:

• Results have a strong positive correlation with the results of tests that measure the same concept.

• Results don’t correlate with the results of tests that measure a different concept.

Ideally, you should compare your test to several others to determine its construct validity.

Content validity evaluates how well the test covers the construct it intends to measure.

For example, imagine you need to measure a salesperson’s knowledge of a certain product. The test should cover all aspects of the product, including its parts, how it’s used, and what it does. Otherwise, you can’t fully assess how well the salesperson understands it.

High content validity means the test covers the topic extensively. Low validity shows the test is missing important measurement elements.

The following are examples of content validity applications:

• A test that evaluates a person’s knowledge in different areas (like physics, biology, or psychology)

• A test that assesses the customer’s success with a product or service

• A test that measures employee engagement

• Standardized tests (like the SAT, GRE, or bar exam)

To measure content validity, you need to assess each question on the test and ask experts whether it targets at least one aspect of the construct.

You will need to compare the test to its goals. Going through each question and determining its value systematically can help ensure you don’t overlook any important parts.

Expert opinion is an integral part of measuring content validity. You can also gain insight by comparing your test to other assessments with the same purpose. For example, if you are creating a test that assesses knowledge of the English language, you can compare it to the Test of English as a Foreign Language (TOEFL) exam.

Construct and content validity are integral parts of evaluating your measurement instrument. By measuring construct and content validity separately, you can gain insight into your instrument’s quality. However, you won’t get the full picture without measuring them together.

The main difference between construct and content validity is that the former measures the assessment tool’s overall value, and the latter evaluates its quality.

For example, imagine you are designing a survey that evaluates your customers’ satisfaction with the support team.

To achieve high construct validity, you need to ensure that all survey questions are closely related to the subject. In other words, the questions need to measure your customers’ satisfaction with the support team—not their satisfaction with your product or business as a whole.

For high content validity, the questions in the survey should cover all aspects of customer satisfaction with your support team. These aspects might include the team’s response time, clarity, politeness, and efficiency.

Construct and content validity can help you evaluate how well you are assessing your customers, products, and services. Asking the right questions in the right way is key to achieving customer satisfaction and taking your business to the next level.

Construct validity and content validity both measure the quality and efficiency of your assessment method.

Construct validity measures how accurately an instrument can assess something. Meanwhile, construct reliability measures how consistent this method is in its evaluation.

Construct validity evaluates how well an instrument can measure something, while predictive validity assesses its ability to predict a future outcome.