This article will discuss the power analysis for showing both the dark (limitations) and bright side (benefits) of this process. Let’s get started without taking too much space for setting the stage.
Power analysis- A brief introduction
Power analysis is the process of calculating the size of the sample to carry out research, especially primary research. Suppose you are planning to collect data from the target population in a controlled or natural setting, then the first thing to decide on is the smallest number of participants. The selection of a sample size that is neither too long nor too short to be handled effectively is essential. This is because the generalisation of a study is mainly determined by the sample size. However, conducting a power analysis has four main steps;
Statistical power:
It is the probability of accepting an alternative hypothesis. In another context, statistical power is the likelihood of a test detecting the effect of a sample size if there is one set that can account for 80 % or higher.
Sample size:
It is a relatively common term that refers to the smallest number of observations in order to see the effect of a certain phenomenon. Usually, in power analysis, this factor remains unknown, and by calculating the relationship between the other three components, its value can be calculated.
Significance level:
It is the minimum risk of rejecting a null hypothesis that is often denoted by ‘α’ and taken as 5%.
Expected sample size:
It is the quantified size of a population. In other words, it is the expected results of the power analysis. Its value can be estimated based on the results of a pilot study.
If you closely see these four components of a power analysis, you will come to know that all these are interconnected with one another. For example, if the number of scholarly articles increases for conducting a systematic literature review, then the statistical power increase and the expected sample size also increases. But the major problem lies in the fact that too many scholarly articles then required may cause data handling problems, and too few will be problematic for giving credible results. Thus, power analysis by calculating all these components help the researcher to decide on the sample size that will be sufficient to give valid results, or that can be reached within the available resources.
Time to conduct power analysis:
There is no hard and fast rule to conduct this analysis. A researcher can conduct it prior to data collection or even after collecting it. But the point to understand here is if this analysis is conducted at the start or before collecting data, then it will be called power analysis. However, even with quite similar steps, if you measure the sample size after collecting the data, the process will be called retrospective power analysis and post hoc analysis.
Benefits of conducting power analysis:
As described earlier, the main benefit of conducting this analysis is that it helps researchers in measuring the accurate size of the target population to conduct a study. Without performing it, the approximation of the accurate sample size will be difficult to measure. It helps researchers save energy and time by only reaching the desired number of participants. In this way, whether you want to manage the resources or make a research plan in a limited time frame, this type of analysis is extremely important.
The limitation that everyone must know before conducting this analysis:
Reviewing a picture from all possible aspects is essential to make a valid decision. Thereby, it is important to see the limitations of this type of analysis to understand it properly. One of the biggest limitations of this type of analysis is it usually does not generalise the information very well.
In case you performed the analysis before collecting the data or later on due to any reason you have to modify or change the methodology, then you have to re-conduct the analysis by taking new statistical power to achieve the aims. Likewise, if you perform it after collecting the data, then it may result in a power approach paradox. In simple words, in post- hoc analysis, the results of a study, despite having a smaller p-value give more statistical power. Conducting this analysis for measuring the smallest sample size is not at all a difficult process, but still, those who have any problem can Buy Dissertation Online.
Final Thoughts:
In a nutshell, the analysis of power refers to the act of calculating a smaller sample size to conduct research effectively. It can be performed before and after collecting data, but in both ways, you have to face certain limitations. Contrary to this, you can get specific benefits from each as well.
