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Therefore, all threats to external validity can be described as statistical interactions. [6] Some examples include: Aptitude by treatment interaction: The sample may have certain features that interact with the independent variable, limiting generalizability.
The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
The major threats to internal validity are history, maturation, testing, instrumentation, statistical regression, selection, experimental mortality, and selection-history interactions. One way to minimize the influence of artifacts is to use a pretest-posttest control group design.
Over time, as the novelty wears off, the stress response decreases. This is a threat to external validity when individuals participating in a research study (a novel situation) perceive and respond differently than they would in the normal real world. [2]
A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors ...
It is a significant threat to a research study's external validity and is typically controlled for using blind experiment designs. There are several forms of reactivity. The Hawthorne effect occurs when research study participants know they are being studied and alter their performance because of the attention they receive from the experimenters.
This strategy is advantageous because it moderates several threats to validity, and history effects in particular. [2] [4] Concurrent multiple baseline designs are also useful for saving time, since all participants are processed at once. The ability to retrieve complete data sets within well defined time constraints is a valuable asset while ...
There are five key principles relating to internal validity (study design) and external validity (generalizability) which rigorous impact evaluations should address: confounding factors, selection bias, spillover effects, contamination, and impact heterogeneity. [5]