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A good quantitative design is one that adequately addresses internal validity, external validity, and statistical conclusion validity. A valid statistical conclusion results from a design that sufficiently shows a causal relationship between research variables so that there is absolute certainty about the conclusions concerning the nature of this relationship. External validity represents the applicability of conclusions to the general population or particular patients, other settings of care, slightly similar situations or related treatments. High external validity thus increases the usefulness of study results while also stimulating further research.  Internal validity refers to the degree of control over other variables besides the once relevant to the study. Control is carried out to make sure that observed outcomes are due only to causative variables and nothing else. This contributes to high statistical conclusion validity representing high-quality evidence.

One threat to internal validity in a quantitative design is instrumentation and the human errors associated with it. Examples of instruments are: the Braden Scale for pressure ulcer risk prediction, physical assessment, self-report questionnaire, laboratory study, and physiologic function tests such as spirometry. Instruments aiming to measure effects of one variable on another must be administered consistently based on measurement protocol. If compliance with the protocol is not stringent at all times, differences in applying the instrument can affect the values obtained. For instance, ECG changes, not due to the variables being examines, are noted when the researcher forgets to ask the patient to remove all metals from his or her person prior to the procedure. At the same time, equipment used must be calibrated and fully functional according to standards to ensure accurate measurement.

On the other hand, a threat to external validity concerns the possibility that results may have limited generalizability to the general population. This results from poor sampling. For example, if an intervention consisting of a drug is intended for use in the adult population, its generalizability to the older adult population is limited when participants from this segment are not actively included in the sample. At the same time, differences in response may exist between males and females owing to biological factors. Generalizability to both genders is limited if the sample consists of only males or vice versa.

Code: Sample20

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