When you weigh a bag of chips on multiple scales, you would expect the same result, regardless of how many scales you use. Well, that’s what convergence validity does, it examines how well different measures of the same concept agree.

Let’s say you want to measure customer satisfaction with your new product, you could ask them “How satisfied are you with product A?” Then you check the convergent validity of this question by asking them another satisfaction question, such as “How likely are you to recommend our products to a friend?”

If you get similar responses, then you are sure your survey questions are truly measuring satisfaction. Let’s take a deep dive into convergence validity, its importance, and how you use it to improve your research.

Why Convergent Validity Matters

1. Prevents Invalid Measures

Invalid measures lead to misleading findings and inaccurate conclusions, which has several negative consequences including:

• Misleading conclusions: concluding invalid measures unavoidably leads to incorrect inferences about the relationships between variables, which in turn leads to the creation and implementation of inefficient interventions or policies.
• Wasted resources: If research uses invalid measures, you will get inaccurate and irrelevant data. This makes the study a waste of your time and money.
• Damage to public trust: When research studies produce misleading or inaccurate results, it damages public trust in the researcher and the field of study. It can also make it harder for you to get funding and support for your future research.

2. Strengthens the Trustworthiness of Research

Getting very similar results using different approaches confirms the accuracy of your research results and establishes a solid method for future research. As a result, other researchers can use your methods when performing similar research in the future.

Consequences of Lacking Convergent Validity With Real-World Examples

• Misdiagnosing patients- if a test for cancer is not sufficiently valid, then patients may be diagnosed with cancer when they do not have it, or they may not be diagnosed with cancer when they do.
• Placing Students in Wrong Classes– if a standardized test is not sufficiently valid, then students may be placed in advanced classes when they are not ready for them, or they may be placed in remedial classes when they do not need them.
• Poor product development decisions– if a customer satisfaction survey is not sufficiently valid, then companies may invest in products and services that customers don’t want or need.

Methods for Assessing Convergent Validity

• Inter-Rater Agreement and Reliability Measures

Inter-rater agreement and reliability measures allow you to assess convergent validity when the construct of interest is being rated by multiple people. Let’s say you want to measure teacher effectiveness you can have multiple raters (e.g., administrators, supervisors, and peers) rate each teacher’s performance.

If the ratings of the different raters are highly correlated with each other, then this provides evidence that the measure has good convergent validity. You can also use statistical techniques to measure the relationship between different rating scales using Cohen’s kappa, the Krippendorff alpha, or the intraclass correlation coefficient (ICC).

• Multiple Measurement Tools and Sources

Another way to assess convergent validity is to use multiple measurement tools and sources to measure the same construct. For example, you can use self-report and observational measures to measure anxiety triggers.

If the results of these two different types of measures correlate with each other, then this provides evidence that they are both measuring the same thing (i.e., anxiety triggers)

• Pilot Testing and Pre-validation Checks

Pilot tests and pre-validation checks help you to identify any potential problems with the measure, such as low convergent validity.

For example, you can pilot-test a new measure of anxiety on a small sample of participants. If the measure does not have good convergent validity in the pilot test, then you can revise the measure before using it in a larger study.

Convergent vs. Discriminant Validity

Convergent and discriminant validity are two sides of the same coin-construct validity. Construct validity is the degree to which a measure measures what it is intended to measure.

Convergent validity assesses the degree to which two or more measures of the same construct correlate. Discriminant validity assesses the degree to which two or more measures of different constructs do not correlate with each other.

In simpler terms, convergent validity focuses on similarities, while discriminant validity focuses on differences. So, a good measure should have high convergent validity and low discriminant validity.

Here is an example:

• Construct: Anxiety
• Measure 1: Self-report questionnaire on anxiety symptoms
• Measure 2: Physiological measure of anxiety (e.g., heart rate or blood pressure)

If the two measures of anxiety correlate highly with each other, then this provides evidence that they are both measuring the same thing (i.e., anxiety). This demonstrates convergent validity.

But if the measures of anxiety do not correlate with each other, then it shows discriminant validity.

Convergent vs. Criterion Validity

Convergent validity assesses the degree to which two or more measures of the same construct correlate with each other. Criterion validity assesses the degree to which a measure correlates with an external criterion.

An external criterion is a measure of a different construct that is known to be related to the construct of interest. For example, if you are developing a new measure of anxiety, you could use a measure of depression as an external criterion.

If the new anxiety measure closely matches the depression measure, then this supports the validity of the new anxiety measure because depression is associated with anxiety.

However, it is important to note that criterion validity is not always possible or appropriate to assess. For example, there is no single gold standard measure of anxiety, so you still have to rely on convergent validity to assess the validity of your research.

Convergent vs. Construct Validity

Construct validity is the extent to which a measure measures what it is supposed to measure. Convergent validity, on the other hand, is a type of construct validity that looks at the relationships between different measures of the same construct.

Here is an example:

• Construct: Intelligence
• Measure 1: IQ test
• Measure 2: Scholastic achievement test

If the IQ test and the scholastic achievement test correlate highly with each other, then this provides evidence that they are both measuring the same thing (i.e., intelligence). This demonstrates convergent validity.

However, convergent validity is not a guarantee of construct validity. For example, two measures might correlate highly with each other, but they might both be measuring something other than the construct of interest.

Advantages of Establishing Convergent Validity

• Boosts confidence in the validity of a measure: When a measure has high convergent validity, you can be sure that it’s measuring what it’s supposed to measure. This makes it easier to trust research studies that use this measure.
• Improved interpretation of research findings: having high convergent validity also makes you more confident about your results.
• Increased generalizability of research findings: good convergent validity means your methods are reliable, which allows them to be applied to similar research.

Limitations of Convergent Validity

• Time and resource-intensive: Establishing convergent requires developing and collecting data from multiple measures of the same construct, which requires time and resources.
• Subjectivity: convergent validity isn’t always. For example, researchers need to decide what constitutes a high correlation between two measures.
• Limited by the quality of existing measures: If there is no good convergent validity for existing construct measures, then it might be hard to establish the validity of a new construct measure.

Applications of Convergent Validity

• Research: Convergent validity allows researchers to assess the validity and reliability of new measures. This builds a clear roadmap of valid methods for researchers to use in future studies.
• Clinical practice: Convergent validity helps diagnose and assess mental health and other conditions that don’t necessarily have physical symptoms.
• Education: Convergent validity can be used to assess the validity of measures that are used to assess student achievement and other educational outcomes.
• Business: You can use convergent validity to evaluate the effectiveness of marketing campaigns, product development initiatives, and other business interventions. For example, a company might use multiple measures of customer satisfaction (e.g., surveys, focus groups, and customer complaints) to evaluate the effectiveness of a new marketing campaign.

Examples of Convergent and Discriminant Validity

Examples of Convergent validity

• Example 1- Establishing New Measure for Anxiety

Let’s say you designed a new questionnaire to measure anxiety in adults, you would establish the convergent validity of this measure by using an already established measure, e.g. high blood pressure.

If you see a strong correlation between the two measures- using blood pressure and the questionnaire; it shows there’s convergent validity in your new measure.

• Example 2: Measuring Intelligence

Let’s say you want to use an alternative IQ test to measure student intelligence to improve teaching methods. You would need a well-established method such as a standardized IQ to make sure the new measure measures what it should.

Examples of Discriminant validity

• Example 1- Measuring Acute Pain vs. Depression

If you’re looking at acute pain symptoms and comparing them to the symptoms of someone who’s depressed, and your results do not match up. The fact that they don’t match up shows that they’re two different things.

• Example 2: Intelligence vs. Creativity

Intelligence and creativity are not the same thing but people often use them to measure one another. Getting non-correlating results would prove the discriminant validity relationship between them.

Frequently Asked Questions About Convergent Validity

• Why Is Discriminant Validity Important?

Discriminant validity is a way to make sure that when we’re studying or measuring different things. Think of it as not expecting the same taste from apples and oranges, even though they are both fruits

• Why Are Convergent and Discriminant Validity Often Evaluated Together

Convergent and discriminant validity are often evaluated together because they provide complementary information about the validity of a measure. When a measure has high convergent and low discriminant validity, then you are sure the measure is measuring what it should.

• What Is a Good Convergent Validity Score

There is no “right” answer to this question because the convergent validity score depends on the construct being measured and on the other measures used for comparison.

However, a general rule of thumb is having a convergent validity correlation of .50 or higher.

• Can High Internal Consistency and High Test-Retest Reliability Lead to Low Convergent Validity

Yes, it is possible for a measure to have high internal consistency and high test-retest reliability, but low convergent validity. This typically happens when the measure is measuring something other than the construct of interest.

• What Is the Relationship Between Internal Consistency, Test-Retest Reliability, and Convergent Validity

Internal consistency is the correlation between the items on the measure, while test-retest reliability is the consistency of a measure over time. Convergent validity is the correlation of a measure with other measures in the same construct.

As a result, a measure can have high internal consistency and high test-retest reliability without having good convergent validity. A measure can also have good convergent validity without having high internal consistency or high test-retest reliability.

• Which Are the Best Statistics to Determine Convergent and Discriminant Construct Validity

The best statistics to determine convergent and discriminant construct validity depend on the type of data that is being collected and the specific research questions that are being asked.

The most common statistics used are the Pearson correlation coefficient, Spearman’s rank correlation coefficient, Point-biserial correlation coefficient, and Phi coefficient.

• How Can I Carry Out Convergent or Discriminant Validity for a Questionnaire

Start by collecting data from multiple measures of the same construct (for convergent validity) or multiple measures of different constructs (for discriminant validity).

Once you have collected the data, you can use the most suitable statistical methods listed above to calculate the correlation between the measures.

If the correlation between the measures is high, then this provides evidence of convergent or discriminant validity, depending on which type of validity you are assessing.

Conclusion

Convergent validity assesses the degree to which two or more measures of the same construct correlate with each other. This ensures that the results of the research are accurate and prevents misleading conclusions.

You can also use other methods like discrimination and criterion validity to complement the accuracy of your convergent validity results. We hope this guide helps you improve the quality of your research!