Validity refers to whether in an experiment a tool actually measures what it claims to and whether the results could actually be generalised to the wider world. There are two main types of validity ; internal and external.
Internal validity relates to whether changes made to a dependent variable actually change and influence the independent variable – in other words whether the tool measures what it is meant to. If there is high internal validity, you could expect the dependent variable to manipulate the independent variable, whereas if there was low internal validity it would be suspected that confounding variables were playing a part in effecting the independent variable.
There are various ways to assess to internal validity:
Face validity indicates whether a measure tests what it is meant to. Face validity can be low if for example in a questionnaire there is leading questions that influence a participant to answer a specific way.
Concurrent validity suggests whether a new test produces results that are similar to an existing test in the same field. If the test produces similar results to the existing valid tool than it is presumed to be valid.
Predictive validity indicates whether a new measure can predict future consequences.
Internal validity can be improved in a few simple ways. In order to ensure an investigating is measuring what it is meant to, investigators can use single and double-blind techniques. A single-blind study is where the participant does not know the condition they are in, and double-blind is where neither the participant nor the experimenter knows what the groups represent. This method ensures there is little demand characteristics, such as trying to behave a certain way because they think that is what is expected of them, and also decreases experimenter effects, as they can not even accidently have a bias to a certain group. Another process that is effective when a repeated measure has been used is counterbalancing and random allocation, as they remove order effects, such as boredom, fatigue.
External validity on the other hand, refers to things that happen outside of the investigation and can effect whether findings are representative and can be generalised.
Historic validity questions whether if a measurement was used again in the future it would produce the same results. If the results are similar it means there is external high validity, as the tool is not susceptible to change.
Population validity can be tested by repeated an investigation on a different population/culture to decipher whether the findings can be generalised to different groups.
Ecological validity assess whether different settings and situations affect the findings.
To improve external validity, many investigators focus on the sample, and try taking larger, more varied samples, to try make the findings more applicable to the wider population. Additionally ecological validity is improved by carrying out the investigation in different, more realistic settings to try make the findings more relevant to everyday life.
Validity is essential because it is the basis of our conclusion – if there is low validity we cannot easily trust the conclusion of an investigation. An investigation that has no external validity should not be generalised to a wider population, as it may have just been the situation, timing or sample that produced the results. Validity is important, especially internal validity, because without it our findings would not mean anything, as the investigator may not even be measuring what he sent out to, but instead a confounding variable.