8 Concluding remarks

This paper has outlined the appropriate steps to import, prepare and analyze datasets from CLSA using $\text{R}$, $\text{SAS}$, $\text{SPSS}$ and $\text{Stata}$. The data manipulation and analysis codes described in the paper can be applied to other datasets from surveys with a sampling scheme similar to the CLSA. From the comparisons presented in the paper, we see that $\text{R}$ provides accurate and reliable estimates and standard errors as an attractive statistical package.

This paper highlights the comparison of the codes between $\text{R}$ and other commercial statistical packages for different statistical procedures. The $\text{survey}$ packages in $\text{R}$ provides most of the procedures in which health policy researchers are interested. Compared to other packages, $\text{R}$ is easy to use, flexible and open-source. We recommend that health researchers choose $\text{R}$ as one of the statistical packages for data analyses.

This paper can also serve as a guide for the health policy analysts who can check the corresponding codes for {preforming and replicating } various statistical analyses with different statistical packages. This paper would be part of the groundwork for health-care survey administration organizations to include instructions and sample $\text{R}$ codes in their technical documentation.