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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. [2] [3] Generalizability refers to the applicability of a predefined sample to a broader population while transportability refers to the applicability of one sample to another target population. [2]
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.
Generalizability theory acknowledges and allows for variability in assessment conditions that may affect measurements. The advantage of G theory lies in the fact that researchers can estimate what proportion of the total variance in the results is due to the individual factors that often vary in assessment, such as setting, time, items, and raters.
For example, high school grades have moderate ecological validity for predicting college grades. [2] Hammond [ 3 ] argued that the now common use of the term to refer to generality of research results to the "real world" is inappropriate because it robs the original usage of its meaning.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
For example, if a pharmaceutical company wishes to explore the effect of a medication on the common cold but the data sample only includes men, any conclusions made from that data will be biased towards how the medication affects men rather than people in general. That means the information would be incomplete and not useful for deciding if the ...
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
An example is the Hawthorne effect: in a 1925 industrial ergonomics study conducted at the Hawthorne Works factory outside Chicago, experimenters observed that both lowering and brightening the ambient light levels improved worker productivity. They eventually determined the basis for this paradoxical result: workers who were aware of being ...