#SURVEY DATA ANALYSIS EXCEL SOFTWARE#
The difference in point estimates and standard errors obtained using non-survey software and survey software with the design properly specified will vary from data set to data set, and even between analyses using the same data set. Ignoring the clustering will likely lead to standard errors that are underestimated, possibly leading to results that seem to be statistically significant, when in fact, they are not. The sampling weight will affect the calculation of the point estimate, and the stratification and/or clustering will affect the calculation of the standard errors. If you ignore the sampling design, e.g., if you assume simple random sampling when another type of sampling design was used, both the point estimates and their standard errors will likely be calculated incorrectly. This is because the sampling design affects both the calculation of the point estimates and the standard errors of those estimates.
When any sampling method other than simple random sampling is used, we usually need to use survey data analysis software to take into account the differences between the design that was used to collect the data and simple random sampling. Not only is it nearly impossible to do so, but it is not as efficient (either financially and statistically) as other sampling methods. However, very few surveys use a simple random sample to collect data. For experimental and quasi-experimental designs, this is exactly what we want.
Regular procedures in statistical software (that is not designed for survey data) analyzes data as if the data were collected using simple random sampling. Survey Data Analysis with R Why do we need survey data analysis software?
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