There are primarily two types of validity:
Internal validity:It ensures the research design and its execution accurately reflect the cause-and-effect relationship being studied. External validity: It relates to how well the study's results can be generalized to other settings, populations, or circumstances.
Are you running a research project and want to ensure its validity? We are here to help you! In this blog post, we will shed more light on every aspect of this important criteria. Get ready to learn everything about test accuracy and its types. We will cover different cases and tell you how to determine whether your research is valid. This article is jam-packed with examples so you can fully understand how things work. Shall we get started?
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What Is Validity: Definition
Validity in research is an estimate that shows how precisely your measurement method works. In other words, it tells whether the study outcomes are accurate and can be applied to the real-world setting. Research accuracy is usually considered in quantitative studies.
For instance, research aimed at examining aggression in teens but which, in fact, measures low self-esteem will be invalid. Your research will only be accurate if the tool or method you are studying measures exactly what it is expected to measure.
Unlike reliability, here results shouldn’t necessarily be consistent in similar situations. However, you should pay attention to other important aspects. We will cover them in detail down below. Also read and find out our blog about validity vs reliability. You will get more facts for a better understanding.
Types of Validity
There are many various types of validity. They fall into two main categories:
Each of these categories are different depending on what they are designed to identify. Let’s begin with explaining the classical definitions of these groups. Expect to find great examples to get a complete picture about the different types of research accuracy.
Above we have mentioned that your research should have accurate methods of measurement and broad generalisability to be valid. And while the latter is related to the experimental studies (more on this later), the former is the main focus of a test validity.
In a nutshell, a test validity is the degree to which any test applied in research correctly measures the target object or phenomena. It is usually used in psychological or educational tests. It tells how much your supporting evidence and theory prove the interpretation of your test outcomes.
Below we will discuss the primary types you may encounter while measuring the accuracy of your test. Each of these types focuses on different aspects of research precision.
Construct Validity: Definition
Construct validity allows us to find out if an instrument used for measurement is actually what we're trying to measure. It's the most important factor in determining the general accuracy of a method. A construct is any feature or trait that researchers can’t examine. But it can be easily assessed through observation of other indicators connected with it.
Constructs may refer to the characteristics of people, such as intelligence, weight, or anxiety. They could also imply larger concepts that apply to social or business groups. For example, these can be race inequality or corporate sustainability.
Construct validity example
There aren’t any exact metrics that can help you measure aggression. However, you can rely on the related symptoms such as agitation and frequent irritability. To ensure construct accuracy, you should build a questionnaire that will help you assess the construct of aggression, but not the other constructs.
Content Validity: Definition
Now you may wonder what content validity is. Content validity determines the degree to which a test can represent all characteristics of a construct. In order to get an accurate outcome, the material used in assessment should consider all related aspects of the subject matter under the test. If certain aspects are not included in the measurements or when inapplicable elements are integrated, then the accuracy of such method is vulnerable.
Content validity example
You are designing a test in psychology to identify whether students understood how social cognition works. The test should cover every aspect of this construct. If any details are missing, then such results might not fully represent an overall understanding. Likewise, if you fail to include relevant details emphasized during your course, the test outcomes will also be invalid.
Face Validity: Definition
Face validity, also known as logical, is the extent to which a subjective measurement of content relevancy is accurate. Here, experts need to provide their opinion on whether a method assesses any phenomenon intended. This estimate is more personal and, thus, can be prone to prejudice. However, it’s a good measurement instrument if you are doing a preliminary assessment.
Face validity example
You are studying how post-traumatic stress disorder develops. You review a questionnaire where most questions are focused on the stages of shock after experiencing some traumatic event. On the face of it, this questionnaire seems to be valid.
Criterion Validity: Definition
A final measure of accuracy is criterion related validity. It shows how well your test represents or predicts a criterion. Here, you should understand what a criterion variable is. So let’s sort these things out. A criterion variable is something that is being predicted in your study. It’s otherwise called a response variable or a dependent variable. Criterion variables are usually considered valid.
To determine criterion accuracy, you need to compare your test outcomes with the criterion variable (the one that is believed to be true). If your results differ from this criterion, then your test is invalid.
Criterion validity example
You want to identify whether the hours students study affects a criterion variable – academic performance. If your test’s outcomes are similar to an already established criterion, then your test has a decent criterion validity.
There are three types of criterion accuracy:
We will cover the two fist types down below as they are rather widely used in research.
Predictive Validity: Definition
Predictive validity is an estimate that shows whether the test accurately predicts what it intends to predict. For example, you may want to know whether your prediction of any phenomena or human behavior is precise. Accordingly, if your assumptions are justified over time, this indicates that your measurement method has a high predictive accuracy.
Example of predictive validity
A good example of this estimate, will be any test showing academic performance at school. You predict how precisely this method will measure future performance.
Concurrent Validity: Definition
Concurrent validity, as its name suggests, shows how accurate the results are if the information about a predictor and criterion are obtained simultaneously. It can also mean the situation when one test is substituted with another test. This way, researchers can stay on budget.
Concurrent validity example
A great example of this estimate is a written English test that replaces an in-person examination with a teacher. Imagine that you want to assess academic success of thousands of students. One-to-one examinations might be too expensive. For this reason, you can conduct an affordable test which will measure performance in a similar manner.
Experimental validity determines whether an experiment design is built correctly. Without a properly constructed study design, you won’t be able to get valid research results. With this in mind, your research design should justify such factors to be valid:
- Have accurate results.
- Identify some relationship between variables.
- Be generalized to other situations.
Based on this, there are three main types of experimental validity:
- Internal validity When a cause-and-effect relationship is determined properly and not affected by other variables. If you can identify any causal connection between your treatment and subject’s reaction, then your experiment is internally accurate.
- External validity When research results can be applied to other similar populations. If you can employ your findings in other contexts, then your research has a high external accuracy.
- Statistical conclusion validity When your conclusion about causal relationship is correct. Any conclusion that you make should be solely based on data. Otherwise, it will be considered invalid.
If you need more information about this kind of validity, read the internal validity vs external validity article on our platform.
Validity: Key Takeaways
Identifying how thoroughly a student addressed different types of validity in their study is an important factor in any research critique. How well a scientist considers all factors determines whether research ‘makes sense’ and can be developed further. A high-quality study should offer evidence that proves the accuracy of chosen measurement methods. Make sure you consider each factor so you can conduct worthwhile research.
Frequently Asked Questions About Validity
1. What is a concurrent validity design?
A concurrent validity design is a study where two measurement tests are carried out simultaneously. One of these tests is already well established, while the other one is new. Once two tests are done, researchers compare the outcomes to see if a fresh approach works.
3. What is a good discriminant validity?
To make sure that your study has a good discriminant validity, you need to prove that concepts which shouldn’t be related don’t have any connection. There is no standard score for this estimate. However, an outcome around 0.75-0.85 implies there is a discriminant accuracy.
2. How do you determine predictive validity?
To determine predictive validity you should compare the performance or behavior during the test with the subsequent behavior for which this test was developed. If you find a strong correlation and results are as expected, then your test is accurate.
4. Why is validity important in research?
It’s important to have a high research validity because it allows us to identify what questions should be included in the questionnaire. Besides, it guides researchers in the right direction. In accurate research, a chosen method will measure what is intended to be measured.