In research, validity is defined as the degree to which a measure accurately reflects the construct it is intended to measure. In order for a measure to be considered valid, it must first be reliable.
When conducting research, validity is important because it helps to ensure that the conclusions drawn from the data are accurate and correctly represent what was actually measured. Without validity, research findings can be misleading and lead to wrong decisions being made.
There are several different types of validity, which are all important to consider when designing research:
- Internal validity: This refers to how well a study measures what it is supposed to measure. It’s important to ensure that the constructs you are interested in are actually being measured by your study and that any changes you observe in these constructs are actually due to the independent variable you manipulated and not some other confounding factor.
- External validity: This refers to how well the results of a study can be generalized to other people and settings. If a study has good external validity, then its results can be applied to other people and contexts.
- Construct validity: This refers to how well a measure captures the concept it is supposed to represent. For example, if you are interested in measuring happiness, you need to ensure that your measure of happiness actually captures what happiness is and that it is not picking up other related constructs such as satisfaction or well-being.
- Convergent validity: This refers to how well a measure correlates with other measures of the same construct. For example, if you have two different measures of happiness, you would expect them to be highly correlated with each other because they are both measuring the same construct.
- Discriminant validity: This refers to how well a measure does not correlate with measures of other constructs that it is not supposed to be related to. For example, you would expect a measure of happiness to have a low correlation with a measure of anxiety because these are two different constructs.