Internal and external validity are significant factors in quantitative research and should always be considered before and during the experiment. The former is the level of control of the independent variable on the outcomes of the dependent variable (Cox, 2020). It implies that internal validity is responsible for the interrelationship of the two parameters of the study. However, there is a high number of internal and external factors that might severely affect the results of the experiment. In the academic community, such risks are generally referred to as threats, and one of the most significant objectives of the research is to minimize them.
Concerning internal validity, there are different threats for single and multi-group studies. For the former type, the primary risks are the intervention of unrelated events and the difference in instrumentation and assessment in pre-test and post-test context (Bhandari, 2020). For multi-group studies, the potential threats are selection bias and communication among the participants (Bhandari, 2020). Overall, there is a high number of potential risks that might affect the level of the interrelationship between the independent and dependent variables.
On the other hand, external validity concerns the overall level of generalization of the experiment that might be further applied to other target groups. Furthermore, the two types of validity are closely interconnected since the more control to the variables is assigned the less applicable the data is to other settings (Bhandari, 2020). Regarding the threats to external validity, they remain similar to the aforementioned ones and primarily concern sampling problems and the intervention of unintended events (Bhandari, 2020). Additionally, this type of control might be affected by experimenter and Hawthorne effects (Bhandari, 2020). Overall, the external validity is also subject to potential threats and disruptions.
Having acknowledged the impact of the threats of the validity models, it is essential to provide effective counter-measures. Concerning internal validity in single-group research, the authors of the experiment might have to add a second control group that would neglect most of the risks concerning sampling and pre and post-testing outcomes (Bhandari, 2020). For multi-group studies, it might be viable to assign participants in random order and also restrict their communication and other types of social interaction (Bhandari, 2020). Regarding external validity, the most prominent method is to replicate the experiment to assess its generalization capability (Bhandari, 2020). Ultimately, for functioning research, it is essential to attempt to mitigate potential threats.
One of the ethical issues that most researchers confront is the voluntary or, in some cases, potentially involuntary participation of the experiment subjects. Concerning this matter, Babbie states (2017), “A major tenet of medical research ethics is that experimental participation must be voluntary. The same norm applies to social research. No one should be forced to participate.” (63). Nevertheless, the concept of volunteering is complicated to control since the subjects of the study might believe that active participation might grant them additional benefits even if stated otherwise (Babbie, 2017). Furthermore, the instructors of the experiment do not always fully comprehend the concept of voluntary actions and peripherally affect the behavior of the participants (Kılınç & Firat, 2017). Overall, the current ethical issue needs to be properly monitored to avoid misleading outcomes of the research.
Amenability of a Research Topic
For a research topic to be amenable to scientific study using a quantitative approach, the subject and the method of the experiment need to be capable of numerical examination. For instance, social research concerning a large sample with distinct target groups and specific objectives is amenable to quantitative scientific study due to the mathematical and statistical outcomes. On the other hand, a qualitative experiment utilizing an interview with subjective results is of non-numerical nature, and, therefore, fails the amenability check concerning the quantitative approach.
Babbie, E. (2017). Basics of social research (7th ed.). Boston, MA: Cengage Learning.
Bhandari, P. (2020). Understanding external validity. Web.
Cox, K. A. (2020). Quantitative research designs. In G. J., Burkholder, K. A. Cox, L. M. Crawford & J. H. Hitchcock (Eds.), Research designs and methods: An applied guide for the scholar-practitioner (pp. 51-66). Thousand Oaks, CA: Sage.
Kılınç, H., & Firat, M. (2017). Opinions of expert academicians on online data collection and voluntary participation in social sciences research. Educational Sciences: Theory & Practice, 17(5), 5-30.