The article "Validity and Reliability of Measurement Instruments Used in Research" by Carole L. Kimberlin and Almut G. Winterstein emphasizes the importance of reliability and validity in research instruments for accurate and consistent data collection. It emphasizes the development and validation process, reliability estimates, validity, responsiveness to change, and data accuracy, particularly in healthcare and social science research, where accurate and reliable instruments are crucial for quality research.
It doesn't matter what you are studying. If your procedures and results are not reliable (consistent and predictable) or valid (truthful), then any conclusions or actions you take from them are worthless at best and possibly harmful.
A long-time adage in IT is "GIGO", or "Garbage IN, Garbage OUT". No amount of "statistics" can make incorrect data correct.
Steven Cooke, I really agree with your statement. The reliability and validity of techniques and outcomes are critical in all fields of research, including information technology. The 'GIGO' principle serves as a fantastic reminder of how important it is to ensure the quality of input data. In addition, I believe it is critical to evaluate the possible ramifications of incorrect or misleading results. Could you please explain how you ensure the reliability and validity of your research techniques and outcomes? Are you using any special strategies or tools to validate your findings?
Hachemi Inas, There are many good sources for techniques defining measurement reliability, validity, and accuracy. They are more specific in the physical sciences, with entire subspecialties in metrics. In the social sciences “measuring instruments” usually refer to methods of data collection. Most frequently the initial data is obtained from personal interviews or surveys. Some other studies may be done by simple observation.
In all cases, validity (the most important factor) can be difficult to establish in social sciences because of the variability in populations and sub-groups. It is often best to simply admit to whatever “error” margin may exist than to try to refine a set of questions to improve validity. Usually, a smaller, more easily defined, and validated sub-group is used, with the understanding that the results may not apply to other groups.
Reliability always refers to the consistency and repeatability of results from any particular experimental conditions. Social science surveys are often developed with “Focus groups” and then tested in wider populations to ensure that questions are understood and answered properly. This is all quite well-known, researched, and discussed in many journals and books.
Your initial and subsequent questions are about the evaluation of the “downside” of statistical analyses. I commend you for that, as it is too often ignored by everyone in the misunderstanding of statistical “validity”. What statistics can tell us confidently is ONLY if results are “random” in occurrence. Every tweak beyond that conclusion is just a study in risk assessment and acceptance.
Certainly, a more thorough study or examination of the consequences of “guessing wrong” (despite statistical indicators) may be useful to a general public who is told that statistics are accuracies instead of probabilities. But, evaluating that effect is independent of any initial study. You only need to know the “error” probability (usually the inverse of the claimed reliability of a study) to develop a “failed case” scenario of outcomes. It is those risks that need to be understood and accepted if the argument for proceeding with any action is to be reasonable.
Dear Steven Cooke, I'm writing to thank you for taking the time to respond so thoroughly and informatively to my inquiries about measuring reliability, validity, and accuracy in the social sciences. It is clear that your expertise in this field has allowed you to convey a thorough comprehension of these complex issues.
I look forward to incorporating this newfound knowledge into my own research pursuits.
Warm regards,