What are some common pitfalls and challenges of sensitivity analysis in Stata and how do you avoid them?

1 Data quality and preparation

Before conducting any sensitivity analysis, it is essential to ensure that the data is reliable, consistent, and suitable for the research question. Common data problems such as missing values, outliers, and measurement error can affect the sensitivity analysis and should be addressed. Missing values can be handled by dropping them, imputing them, or using a method such as multiple imputation. Outliers can be identified and dealt with through descriptive statistics, graphs, or tests, and either removed or adjusted. To account for measurement error, validation studies, reliability tests, or instrumental variables should be used. Reporting the results of the sensitivity analysis should take into account any differences caused by different methods of dealing with missing data or outliers.

2 Choice of parameters and scenarios

When conducting sensitivity analysis, it is essential to select the appropriate parameters and scenarios to vary and test. This can be difficult as you must justify the ranges and values used, based on theory, evidence, or expert opinion. Additionally, you should consider the correlation between parameters and use methods such as multivariate sensitivity analysis or copulas to account for this. Furthermore, you should make sure to choose scenarios that are meaningful and realistic for your research question and context. If done incorrectly, you risk masking or exaggerating the sensitivity of your results or confusing your analysis.

3 Presentation and interpretation of results

The final challenge of sensitivity analysis is to present and interpret your results clearly and accurately. To do this, you need to use graphs that can effectively display your results, such as tornado plots, spider plots, or fan charts. Additionally, you need to use statistics that can summarize your sensitivity analysis results, such as sensitivity coefficients, elasticities, or confidence intervals. When drawing conclusions, you should make sure they are consistent and supported by your sensitivity analysis results and acknowledge the limitations and uncertainties. Avoid overstating or understating the sensitivity of your results or cherry-picking or ignoring scenarios that suit your agenda. Sensitivity analysis is a powerful tool to enhance your quantitative analysis in Stata, but it must be done with careful planning, execution, and communication in order to ensure the quality and credibility of the results.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?