Search results
Results from the WOW.Com Content Network
External validity is the validity of applying the conclusions of a scientific study outside the context of that study. [1] In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times.
In other words, the relevance of external and internal validity to a research study depends on the goals of the study. Furthermore, conflating research goals with validity concerns can lead to the mutual-internal-validity problem, where theories are able to explain only phenomena in artificial laboratory settings but not the real world. [13] [14]
Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity. In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the study.
Cross-validation, [2] [3] [4] sometimes called rotation estimation [5] [6] [7] or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose.
In qualitative research, a member check, also known as informant feedback or respondent validation, is a technique used by researchers to help improve the accuracy, credibility, validity, and transferability (also known as applicability, internal validity, [1] or fittingness) of a study. [2]
The normal distribution is NOT assumed nor required in the calculation of control limits. Thus making the IndX/mR chart a very robust tool. This is demonstrated by Wheeler using real-world data [4], [5] and for a number of highly non-normal probability distributions.
The goal for all data collection is to capture evidence that allows data analysis to lead to the formulation of credible answers to the questions that have been posed. Regardless of the field of or preference for defining data ( quantitative or qualitative ), accurate data collection is essential to maintain research integrity.