Dear all,
In social sciences, it is often recommended to determine the sample size we need with an a priori power analysis. This analysis requires to provide, among others, an expected effect size, which is usually provided by prior works similar to our own. However, this index is not always reported. In this context, according to Perugini et al. (2018), it is possible to use a sensitivity power analysis to determine the minimum effect size that can be reliably detected. This analysis is computed from, among others, the available sample size for the study.
The problem i'm facing is the following: do I have to compute a sensitivity power analysis before each statistical analysis? If not, and assuming that 3 distinct analyses must be conducted, from which analysis should I determine the appropriate effect size for my study?
Thank you for considering my request.
Best,
Kévin
According to J.B. Maverick, sensitivity analysis is an analysis method used to identify how much variations in the input values for a given variable will impact the results for a mathematical model. Sensitivity analysis can be applied in several disciplines, including business analysis, investing, environmental studies, engineering, physics, and chemistry.
All models and studies executed to draw conclusions or inferences for policy decisions are based on assumptions regarding the validity of the inputs used in calculations. Sensitivity analysis is concerned with the uncertainty inherent in mathematical models where the values for the inputs used in the model can vary. It is the companion analytical tool to uncertainty analysis, and the two are often used together.
The conclusions drawn from studies or mathematical calculations can be significantly altered depending on how a certain variable is defined or the parameters chosen for a study. When the results of a study or computation do not significantly change due to variations in underlying assumptions, they are considered robust. If variations in foundational inputs or assumptions significantly change outcomes, sensitivity analysis can be employed to determine how changes in inputs, definitions, or modeling can improve the accuracy or robustness of any results.
Sensitivity analysis can be helpful in various situations, including forecasting or predicting as well as identifying where improvements or adjustments need to be made in a process. However, the use of historical data can sometimes lead to inaccurate results when forecasting since past results don't necessarily lead to future outcomes. Below are a few common applications of sensitivity analysis.
Return on Investment
In a business context, sensitivity analysis can improve decisions based on certain calculations or modeling. A company can use sensitivity analysis to identify the inputs which have the biggest impact on the return on a company's investment (ROI). The inputs that have the greatest effect returns should be considered more carefully. Sensitivity analysis can also be used to allocate assets and resources.
One simple example of sensitivity analysis used in a business is an analysis of the effect of including a certain piece of information in a company's advertising, comparing sales results from ads that differ only in whether or not they include the specific piece of information.
Dear Jorge Morales Pedraza,
Above all, many thanks for taking the time to answer the question raised here. Your answer is really clear regarding situations in which it is appropriate to use a sensitivity analysis and its implications beyond social sciences.
However, I fail to understand how your answer could help me to solve the methodological problem I'm facing. Indeed, after re-reading, it seems that the sensitivity analysis to which your referred differs from the sensitivity power analysis. The sensitivity power analysis concerns (to the best of my knowledge) only methodological issues about how to determine the minimum effect size that can be reliably detected knowing the available sample size for our study.
Please, let me ensure you I am very grateful to you for answering this question.
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