An experiment is a study that attempts to establish a cause-and-effect relationship between an independent and dependent variable.
In experimental design, the researcher first forms a hypothesis. They then test this hypothesis by manipulating an independent variable while controlling for potential confounds that could influence results. Changes in the dependent variable are recorded, and data are analyzed to determine if the results support the hypothesis.
Nonexperimental research does not involve the manipulation of an independent variable. Nonexperimental studies therefore cannot establish a cause-and-effect relationship. Nonexperimental studies include correlational designs and observational research.
Continue reading: What is an experiment?
Test validity refers to whether a test or measure actually measures the thing it’s supposed to. Construct validity is considered the overarching concern of test validity; other types of validity provide evidence of construct validity and thus the overall test validity of a measure.
Experimental validity concerns whether a true cause-and-effect relationship exists in an experimental design (internal validity) and how well findings generalize to the real world (external validity and ecological validity).
Verifying that an experiment has both test and experimental validity is imperative to ensuring meaningful and generalizable results.
Continue reading: What is the difference between test validity and experimental validity?
Psychology and other social sciences often involve the study of constructs—phenomena that cannot be directly measured—such as happiness or stress.
Because we cannot directly measure a construct, we must instead operationalize it, or define how we will approximate it using observable variables. These variables could include behaviors, survey responses, or physiological measures.
Validity is the extent to which a test or instrument actually captures the construct it’s been designed to measure. Researchers must demonstrate that their operationalization properly captures a construct by providing evidence of multiple types of validity, such as face validity, content validity, criterion validity, convergent validity, and discriminant validity.
When you find evidence of different types of validity for an instrument, you’re proving its construct validity—you can be fairly confident it’s measuring the thing it’s supposed to.
In short, validity helps researchers ensure that they’re measuring what they intended to, which is especially important when studying constructs that cannot be directly measured and instead must be operationally defined.
Continue reading: Why is validity so important in psychology research?
In short, yes! The terms discriminant validity and divergent validity are often used synonymously to refer to whether a test yields different results than other tests that measure unrelated concepts. However, “discriminant validity” is the more commonly used and accepted term.
Continue reading: Are discriminant and divergent validity the same thing?
Convergent validity and concurrent validity both indicate how well a test score and another variable compare to one another.
Convergent validity indicates how well one measure corresponds to other measures of the same or similar constructs. These measures do not have to be obtained at the same time.
Concurrent validity instead assesses how well a measure aligns with a benchmark or “gold-standard,” which can be a ground truth or another validated measure. Both measurements should be taken at the same time.
Continue reading: What is the difference between concurrent validity and convergent validity?
Convergent validity and discriminant validity (or divergent validity) are both forms of construct validity. They are both used to determine whether a test is measuring the thing it’s supposed to.
However, each form of validity tells you something slightly different about a test:
- Convergent validity indicates whether the results of a test correspond to other measures of a similar construct. In theory, there should be a high correlation between two tests that measure the same thing.
- Discriminant validity instead measures whether a test is similar to measures of a different construct. There should be a low correlation between two tests that measure different things.
If a test is measuring what it is supposed to, it should correspond to other tests that measure the same thing while differing from tests that measure other things. To assess these two qualities, you must determine both convergent and discriminant validity.
Continue reading: Why are convergent and discriminant validity often evaluated together?
Construct validity evaluates how well a test reflects the concept it’s designed to measure.
Criterion validity captures how well a test correlates with another “gold standard” measure or outcome of the same construct.
Although both construct validity and criterion validity reflect the validity of a measure, they are not the same. Construct validity is generally considered the overarching concern of measurement validity; criterion validity can therefore be considered a form of evidence for construct validity.
Continue reading: What is the difference between construct and criterion validity?
Construct validity assesses how well a test reflects the phenomenon it’s supposed to measure. Construct validity cannot be directly measured; instead, you must gather evidence in favor of it.
This evidence comes in the form of other types of validity, including face validity, content validity, criterion validity, convergent validity, and divergent validity. The stronger the evidence across these measures, the more confident you can be that you are measuring what you intended to.
Continue reading: How do you measure construct validity?
Concurrent validity and predictive validity are both types of criterion validity. Both assess how well one test corresponds to another, theoretically related, test or outcome. However, the key difference is when each test is conducted:
- Concurrent validity compares one measure to a second, well-established measure that acts as a gold-standard. Both measures should be obtained at the same time, or concurrently.
- Predictive validity instead captures how well a measure corresponds to a measure taken later in time (i.e., how well one measure predicts a future measure).
Continue reading: What is the difference between concurrent and predictive validity?
A construct is a phenomenon that cannot be directly measured, such as intelligence, anxiety, or happiness. Researchers must instead approximate constructs using related, measurable variables.
The process of defining how a construct will be measured is called operationalization. Constructs are common in psychology and other social sciences.
To evaluate how well a construct measures what it’s supposed to, researchers determine construct validity. Face validity, content validity, criterion validity, convergent validity, and discriminant validity all provide evidence of construct validity.
Continue reading: What is a construct?
