Internal and External Validity
I need a response to the instructor’s comment on my discussion answer.
Response from the instructor:
Your understanding of instrumentation as a threat to internal validity is sound. However, I think you don’t quite grasp the idea of temporal order. This suggests that the independent variable (IV) should supersede the dependent variable (DV) in a research design. So, weather may affect ice cream sales, but not the other way around. Also, I am confused by this statement: “External validity is the level at which specific outcomes can be comprehensive to other aspects.” so if you can address it and clarify what you mean, I would certainly appreciate it.
Discussion answer: Internal Validity
Internal validity is the level of confidence that the casual correlation being assessed is not affected by other variables or factors. There are several pressures to internal validity. The threats to internal validity are the indefinite temporal precedence, maturation, selection, regression effect, mortality, and testing (Mohajan, 2017). The indefinite temporal priority is mostly linked to the hardships in determining the specific variables and their implications. The internal validity threats may affect the accuracy of data in quantitative research. For instance, the instrumentation is the extent to which the various instruments used in the quantitative analysis are accurate in the measurements taken and the degree to which the tools used may be accountable for the results. Likewise, sampling effects, such as inadvertent selection discrimination, may significantly impact the research result. In most incidences, the impact of the statistical occurrence in the extreme outcomes tends to regress around the mean on the recurring testing occasions.
External Validity threats
External validity is the level at which specific outcomes can be comprehensive to other aspects. In this context, the validity of an experiment depends mostly on the experimental design. Coercion to external validity is the description of how one might be inaccurate while making a generalization based on the findings of a given study (Haegele & Hodge, 2015). Mostly, generalization is minimized when the impact of one of the involved factors is dependent on other factors. Some of the most significant risks to external validity include selection bias, history, maturation, and the real world versus the experimental globe. Based on the strategies used in addressing the various threats, they can lead to errors during quantitative researches.
Explanation of an Approach to Alleviate All of the Threats Associated
Several strategies can be used to lower or even mitigate the impact of the various threats in the qualitative approach. In this context, one can keep an eye and be vigilant in ensuring keen observation to test the study points. In most cases, the data collected to be ascertained through different processes to ensure accuracy (Halperin, Pyne & Martin, 2015). Usually, the impact of the statistical occurrence in the extreme outcomes tends to regress around the mean on the recurring testing occasions. In the same context, one should consider keeping consistency to minimize errors in data collection. For instance, instrumental threats can be successfully eliminated by making every effort to maintain consistency at all observation points.
Ethical Issue
In quantitative research, honesty is an ethical issue. Without honesty, one would be unable to collect sound data from people. Also, it would be hard to accomplish the intended missions towards achieving the goals of the research. Consequently, researchers have to consider being honest for easy achievement (Zyphur & Pierides, 2017). Thus, honesty is the potential ethical issue in quantitative research, for it can promote efficiency and accuracy in data designing and decision-making. When a research topic is said to be amenable, it means that it is relevant and agreeable, and the research can be conducted on it using the quantitative approach.
References
Haegele, J. A., & Hodge, S. R. (2015). Quantitative methodology: A guide for emerging physical education and adapted physical education researchers. The Physical Educator, 72(5).
Halperin, I., Pyne, D. B., & Martin, D. T. (2015). Threats to internal validity in exercise science: a review of overlooked confounding variables. International Journal of Sports Physiology and Performance, 10(7), 823-829.
Mohajan, H. K. (2017). Two criteria for proper measurements in research: Validity and reliability. Annals of Spiru Haret University. Economic Series, 17(4), 59-82.
Zyphur, M. J., & Pierides, D. C. (2017). Is quantitative research ethical? Tools for ethically practicing, evaluating, and using quantitative research. Journal of Business Ethics, 143(1), 1-16.
