SAS for Survey Data Analysis: Techniques and Best Practices

In order to properly gather and analyse data from a wide sample of the population, survey data analysis is an essential stage in every research project. Researcher’s may organise, analyse, and interpret complicated survey data effectively with the use of SAS (Statistical Analysis System), a potent tool for survey data analysis. This article will give a general introduction of SAS techniques and suggested practises for analysing survey data.

Data Management

Survey data management is a crucial stage in survey data analysis, and SAS provides numerous methods to make it simpler. Data can be imported into SAS in a number of different forms, including Excel, CSV, and text files. SAS also has built-in tools for combining, cleaning, and transforming data. Listed below are a few SAS data management best practices:

• Make a copy of the data for analysis while keeping the original data in a different file.
• To make the data easier to interpret, rename variables to names that make sense.
• To guarantee uniformity and clarity, recode and label variables.
• Applying the same format to several variables is made simpler by storing the format for variables in the SAS format library.

Sampling Procedures

In order to ensure that survey results are reliable and representative of the population, careful sampling strategies must be used while analysing survey data. Simple random sampling, stratified sampling, and cluster sampling are just a few of the sample methods offered by SAS. The following are some SAS sampling best practices:

• Prior to choosing the sampling technique, determine the target population and sampling frame.
• Depending on the research issue, the resources at hand, and demographic characteristics, choose the best sampling technique.
• To create random samples and to calculate the population’s mean and standard deviation, use SAS PROC SURVEYSELECT and PROC SURVEYMEANS, respectively.

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Weighting Strategies

If the sample used in the survey is not representative of the population, the data may be misleading. The sample data are modified using the weighting technique to represent the characteristics of the population. SAS offers a number of weighting methods, including propensity score weighting, raking, and post-stratification weighting. The following are some SAS weighting best practises:

• Recognize the elements that could distort the outcomes of the survey.
• Decide which weights should be used to transform the data.
• To determine weighted estimates and standard errors, use PROC SURVEYMEANS.

Descriptive Statistics

The survey findings are summarized by descriptive statistics, which enables researchers to spot patterns and trends. Descriptive statistics can be calculated using a number of SAS functions, including PROC MEANS, PROC FREQ, and PROC SURVEYFREQ. The following are some best practises for SAS’s calculation of descriptive statistics:

• To determine the frequency and percentage of categorical variables, use PROC FREQ.
• To determine the mean, median, and standard deviation of continuous variables, use PROC MEANS.
• To determine the weighted frequency and mean of survey data, use PROC SURVEYFREQ and PROC SURVEYMEANS.

Conclusion

In order to organise, analyse, and interpret complicated survey data, researchers might use SAS, a potent tool for survey data analysis. Researchers may make sure that their survey data analysis is accurate, dependable, and representative of the population by adhering to the best practices described in this article. Researchers can use SAS to get insightful analysis of their survey data and use the results to guide decisions.