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Further Briggs (2008)
adds weight to the argument saying although internal validity and the statistical conclusion adds weight to the causal effect, external validity and construct validity are essential for the causal effects and generalization purposes to many settings. To ensure concrete construct validity level on a study's dependent variable, re-examination for reliability is noteworthy not just the causal effect. Details on the sample and sampling procedures should be analyzed thoroughly for rigorous data Creswell, 2008()
Threats to external validity
Non-representative sampling
It implies a situation where sample picked to act as a representative of the population is insufficient to cover all facets of the population. In this case, the sample is inadequate to act as a representative and is expected to obscure generalization of research result.
Non-representative research context
It describes a situation where the research was set out of context. Example if the survey conducted tried to evaluate the reaction of persons with high intelligence quotient while generalization is subjected to the average person. This question obscures the research results from generalization to a population of persons with an average intelligence quotient.
Threats to construct validity
Inadequate operationalization...
Lack of reliability of the variables implying they vary from time to time then the construct validity may fail. If the variables are not representative then they will compromise on content validity and thus threaten to construct validity. The impact of the independent variable failing to be realistic enough will compromise construct validity. Should the dependent variable fail to be sensitive enough to enable the detection of changes in the independent variable construct validity is threatened Creswell, 2008()
Conclusion
To avoid research being rebuked, researchers need to identify the potential threats to the validity aspect discussed above. The validity aspects need to be central in doing research because with sound validity, result from a study will stand out and be embraced. They will also support theory interpretation with higher degree of confidence.
References
Briggs, D.C. (2008). Comments on Slavin: Synthesizing causal inferences. Educational Researcher, 37, 15-22.
Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand-McNally.
Creswell, J.W. (2008). Research design: qualitative, quantitative, and mixed method approaches: Sage Publications.
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