Reliability refers to the measure of degree accuracy with which a research process succeeds in describing properties of the research subjects in accordance to aims of a particular research. Such accuracies are significant in both a research’s design and implementation of…
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A research that lacks either form of validity communicates possible deviations from actual properties of the research subjects and can therefore not be relied upon. Both external and internal validities are also susceptible to threats that must be monitored for a desired level of accuracy. The two forms of validities are therefore important in developing confidence in drawn conclusions and made inferences from a research initiative. They are however different in their specific scopes of applicability, and their threats. Internal validity for instance defines a research process’ independence from confounds that may influence observations contrary to the treatment’s causal effects while external validity defines the degree of confidence in inferring research results to a population. Another difference between internal and external validity is their sets of threats. Threats to internal validity such as “maturation,” “selection,” “instrumentation,” “statistical regression,” and “attrition” induces bias on the causal effect relationship to impair accuracy of observation on treatment effect. Threats to external validity however include “reactive effects of testing,” “interactive effect of selection,” “reactive effect of innovation” and “multiple program interface” and induces barriers between properties of the used sample and other population segments (Fink, 2004, 78, 79).
Research questions to which external validity is of primary concern are those questions that seek to establish relationships that are generally applicable to an entire population. Example is a research question to establish the relationship between gender and students performance in sciences that is psychologically hypothesized to be uniform across populations. Internal validity, however, is primary to research questions that seek to establish existence of a relationship between two
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(“Samples, Power Analysis, and Design Sensitivity Statistics Project”, n.d.)
Samples, Power Analysis, and Design Sensitivity Statistics Project. Retrieved from https://studentshare.org/statistics/1603598-samples-power-analysis-and-design-sensitivity
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Samples, Power Analysis, and Design Sensitivity Statistics Project. https://studentshare.org/statistics/1603598-samples-power-analysis-and-design-sensitivity.
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y on the other hand refers to the degree to which results and conclusions of a research can be inferred to other settings and is important in validating inference of a sample’s conclusions to a population (Jackson, 2011).
Similarities in within- subjects design and matched-
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