Validity refers to whether a measure actually measures what it claims to be measuring. Some key types of validity are explored below.
Face validity is a measure of whether it looks subjectively promising that a tool measures what it's supposed to
- e.g. It might be observed that people with higher scores in exams are getting higher scores on a IQ questionnaire; you cannot be sure that these are directly linked, but on the surface it appears that exam scores are a reasonable indication of IQ scores, so your measure shows good face validity.
Internal validity is a measure of whether results obtained are solely affected by changes in the variable being manipulated (i.e. by the independent variable) in a cause-and-effect relationship. Two key types of internal validity are:
- Construct validity – asks whether a measure successfully measures the concept it is supposed to (e.g. does a questionnaire measure IQ, or something related but crucially different?).
- Concurrent validity – asks whether a measure is in agreement with pre-existing measures that are validated to test for the same [or a very similar] concept (gauged by correlating measures against each other).
Internal validity can be assessed based on whether extraneous (i.e. unwanted) variables that could also affect results are successfully controlled or eliminated; the greater the control of such variables, the greater the confidence that a cause and effect relevant to the construct being investigated can be found.
External validity is a measure of whether data can be generalised to other situations outside of the research environment they were originally gathered in. Two key types of external validity are:
- Temporal validity – this is high when research findings successfully apply across time (certain variables in the past may no longer be relevant now or in the future).
- e.g. Changes in attitude towards gender roles over time could lower the temporal validity of data from past experiments when applied to modern day research.
- Ecological validity – whether data is generalisable to the real world, based on the conditions research is conducted under and procedures involved.
- e.g. Laboratory research can exert a high degree of control over extraneous variables that would otherwise vary in a natural environment, so results might be considered too ‘artificial’ and thus possess low ecological validity.
- However, mice, for example, might behave in the same way in a laboratory and in the wild, so laboratory experiments could arguably still maintain high ecological validity here.
The external validity of an experiment can be assessed and improved by replicating a study at different times and places, and obtaining similar results. For example, confidence in the generalisability [and in turn external validity] of results is increased when research is successfully replicated across different cultures.