Validity

Understand Validity

  • Validity is the extent to which an examination genuinely measures what it intends to. A valid result accurately reflects the concept under study.
  • In well-conducted research, every stage from design to data sampling to analysis must carefully consider the idea of validity.

Types of Validity

Construct Validity

  • Construct validity looks at how well the methods, tools, and procedures employed in a study accurately measure the constructs they are meant to.
  • This type of validity helps to fine-tune the methods used, minimising the risk of measurement errors.

Face Validity

  • Face validity tests if the research appears to examine the construct it purports to, at face value.
  • Though this only offers a preliminary check, face validity still forms a valuable part of good research design.

Criterion Validity

  • Criterion validity involves comparing the performance of operational definitions or tests against some other existing standard or ‘criteria’.
  • Predictive validity (if operational measures can accurately predict future results) and concurrent validity (how operational measures align with measures of the ‘criteria’ taken at the same time) are subsets of criterion validity.

Internal Validity

  • Internal validity considers how effectively an experiment has been carried out, especially in relation to eliminating confounding variables.
  • Establishing strong cause-and-effect relationships in an experiment is dependent on achieving high internal validity.

External Validity

  • External validity examines how applicable the results of a study are to other scenarios, individuals or environments.
  • This facet of validity is particularly relevant when attempting to apply research findings to the wider world. However, due to potentially uncontrolled variables, it is often difficult to achieve high external validity.

Improving and Maintaining Validity

  • By aiming to improve and ensure validity throughout their research, researchers can significantly enhance the quality of their work.
  • Applying methods such as pilot testing, peer review and replications can provide crucial insights into the validity of a study’s design and findings, mitigating possible biases and errors.