In the context of research design, two types of validity, which speak to the qua
ID: 131933 • Letter: I
Question
In the context of research design, two types of validity, which speak to the quality of different features of the research process, are considered: internal validity and external validity. Assuming that the findings of a research study are internally valid—i.e., the researcher has used controls to determine that the outcome is indeed due to manipulation of the independent variable or the treatment—external validity refers to the extent to which the findings can be generalized from the sample to the population or to other settings and groups. Reliability refers to the replicability of the findings.
For this Discussion, you will consider threats to internal and external validity in quantitative research and the strategies used to mitigate these threats. You will also consider the ethical implications of designing quantitative research.
With these thoughts in mind:
By Day 4
Post an explanation of a threat to internal validity and a threat to external validity in quantitative research. Next, explain a strategy to mitigate each of these threats. Then, identify a potential ethical issue in quantitative research and explain how it might influence design decisions. Finally, explain what it means for a research topic to be amenable to scientific study using a quantitative approach.
Be sure to support your Main Issue Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Explanation / Answer
Note: This response is in UK English, please paste the response to MS Word and you should be able to spot discrepancies easily. Also, I recommend that you add information at the end of the answer, based on your classwork just like our teacher has asked.
(Answer) Threat to internal validity: Whenever a research is conducted, the dependent variable is observed and analysed. This analysis will go on to either prove or disprove a hypothesis or even result in new findings. Therefore, the dependent variable should not be nudged in any direction directly. The experiment should be allowed to flow naturally. Therefore, the internal validity checks to see if the effects observed in a study are the result of tinkering with the independent variable and not the dependent variable. It is the job of the researcher to observe and document how the dependent variable behaves under different circumstances.
This internal validity can be threatened under a few circumstances. One criterion is ‘Maturation’.
Maturation: After an experiment has been conducted, the factors are observed and the results are published. Sometimes, there are confounding variables at play. These confounding variables are nothing but an odd factor that was the cause for the effect observed in “A” and “B.” However, at the time of research, it may have been documented that “A” causes “B.” After a certain amount of time or ‘Maturation’, the confounding variable shows up to disprove the causal relationship between “A” and “B.”
For instance, a researcher may observe that a certain topical pimple ointment is the reason that patients at a local dermatologist have declined. After a while, the researcher finds out that the pimple ointment didn’t really have an effect. The patients simply declined because they were not teenagers anymore and they stopped getting zits.
Solution: The solution for such a threat is simply to try and extend the observation time of an experiment. Even if a researcher is prudent enough to foresee other factors, it would still just be a guess. In other words, the researcher would only end up with another hypothesis and not a sharp result.
Threat to external validity: When an experiment is conducted, the situation and findings should be applicable to real-world scenarios. If these situations are true only in the environment of the laboratory, then the research will have no utility and perhaps no practicality.
One such threat to external validity is known as the ‘selection treatment interaction’.
Selection treatment interaction: takes place when the test subjects are either more or less sensitive to the experiment as compared to the normal population. When this happens, the results cannot be accurately applied to the general population.
For instance, if an experiment about the fears of bungee-jumping has several adrenaline junkies participating in it, the results will be biased. This is because not everyone in a general population is an adrenaline junky. In fact, the general population may have people who are far too afraid of heights.
Solution: To avoid inaccuracies that arise from selection treatment interaction, it is best to be inclusive when picking people to join the sample size. A researcher should include as many types of subjects within the required demograph as possible.