Reliability and validity

Reliability and validity are pivotal concepts in research. Reliability refers to the degree of stability and consistency in results when measurements are repeated. In essence, it denotes the absence of random errors in data. On the other hand, validity assesses the correctness of the results. Using target practice diagrams as illustrations, reliability can be likened to precision while validity parallels accuracy. Although reliability is crucial, validity is more significant; unreliable studies can be rectified through repetition, but invalid ones present larger issues.

In the realm of quantitative research, the emphasis on reliability is predominantly about measurement. Validity, however, is categorized into four distinct types:

1) Measurement Validity: Ensures data accurately reflects what it is intended to measure.
2) Statistical Conclusion Validity: Determines if identified associations in data are accurate or mere chance.
3) Internal Validity: Evaluates if an association provides valid evidence of a causal relationship, ensuring the proper sequence of cause and effect and excluding other potential explanations.
4) External Validity: Examines the generalizability of results, determining how broadly a claim can be applied beyond the study's context.

While qualitative studies might employ similar terminology, their evaluation methods for these concepts are distinct from those in quantitative research.

[LlewellynVanZyl]

Thanks for all your videos Mikko! Really interesting and always well explained. Very consistent