Surveys provide you with insights into the behavior of individuals and populations. They can be managed in various formats, ranging from paper questionnaires to telephone surveys, online surveys, and computer-assisted surveys. Once you have collected the data, the real challenge begins.

Pooling multiple surveys, especially in healthcare research, often involves complex sampling designs, such as strata, PSU (Primary Sampling Unit), and sampling weights. The availability of these variables varies across different datasets, as many include all variables, some omit a few, while others exclude all of them. However, it can make analysis challenging; worry not, as R functions can simplify data analysis, saving both your time and effort. If you are curious about how to analyze health surveys using functions in R, keep reading!

What Is Survey Data Analysis Software, And Why Do You Need It?

Organizations use survey analysis software to convert survey responses into actionable insights. Survey analysis software automates tax analysis, statistical analysis, and data cleaning. They also enable teams to quickly identify patterns, trends, and customer feedback. The key features of survey software include:

• Qualitative data analysis, which includes text analytics, open-ended coding, and AI theme discovery.
• Integration that connects with survey platforms like SurveyMonkey, Qualtrics, and Google Forms.
• They support dashboards, visualization charts, word clouds, and more.
• Quantitative data analysis, such as statistical testing, cross-tabs, and segmentation.

You cannot put rows of messy and inconsistent data into Excel. You may miss important patterns, and manual sorting can take hours. Survey analysis software addresses these problems by enhancing data quality, streamlining workflows, and yielding more in-depth insights. Many sectors utilize survey data analysis; for example, the healthcare sector employs this software to identify gaps in care.

Sampling Designs

Many people use agencies or companies to conduct surveys instead of conducting their own. It is crucial to determine the type of sampling used when collecting the data, as estimates and standard errors may vary depending on the sampling design.

Some Common Sampling Designs

• PSU: It is the first unit sampled in the design and is referred to as the primary sampling unit. For example, hospital districts from Texas may be sampled, and then hospitals within districts may be sampled. In this case, the hospital district would be the PSU.
• Stratification: the method of dividing a population into groups, often based on age, or other demographic factors, is a form of stratification.
• Sampling Weights: A sampling weight is a probability weight. It has undergone one or more adjustments. Both a probability weight and a sampling weight are used to reweight the sample to reflect the population from which it was drawn.

Steps To Analyze Health Data With Functions In R

Follow the steps mentioned below to analyze health data with functions in R:

Deal With The Missing Values

When you apply the functions of R to a data set, it will exclude missing observations, provided that the number of missing values is different from the total number of observations in the dataset. For example, suppose the data set contains 200 people, and 50 observations are missing the PSU variable. In that case, those 50 observations will be dropped. In contrast, if 200 people were missing, the PSU would be able to, and then you would keep the data set as it is. According to this approach, it is assumed that if all observations had missing data for the PSU variable, it is likely because this was the intended design of the case. The information was also not included in the dataset. Whatever the case may be, there is little or nothing you can do. However, you can still utilize any available information to your advantage.

Flagging The Datasets

You have excluded the few observations with the missing data for the PSU variable. Alternatively, you may have retained some data with the variables stratum and sampling weights, but without the PSU variable. Using the collected data, you will create a new variable that flags each data set according to the variables it contains, whether it includes PSU or not, or whether it has stratum and sampling weights instead. For example, you will identify if the data has all three, PSU, stratum, and sampling weights, or just 2 of them, or only one. You can name anything in the new variables you created. You will use these new variables to compute the results later on. For example, suppose the data set is flagged as only having PSU and sampling weights. In that case, you will compute the results using only the two variables, and so on.

Computing Results

It is now time to compute the result using variables. You can compute the survey-adjusted need for a variable, in this example, 'age'. You will calculate the mean and the 95% confidence intervals using the complex sampling design for this numeric variable. It means that your results may represent the underlying population. To apply the function:

• First, examine the variable’s type and understand the available sampling design variables.
• In the second step, the function will compute the results of interest, which, for this example, is the mean age.
• In the third step, the results will be stored in a data frame, which will be returned. You have the option to calculate the metric of interest, such as gender.

Put It All Together

Let’s call the pool data set containing many health surveys ‘pooleddata' and the variable that identifies each survey, 'study_id’. You will apply the function ‘cleaning_svy' to each dataset in the list. You will now create the variable in each data set inside the list. You will have the same list, containing the unique service, each with a new variable name of your choice, which will be flagged according to the available variables. You can now apply the function 'results_svy_mean_age' to all the data sets in the list. You will get your desired output, which is the mean age of patients in a specific region.

Conclusion

Survey analysis software is used to convert survey responses into actionable insights. The common sampling designs include probability sampling, stratified sampling, and sampling weights. If you have pooled many health surveys with complex sampling designs, there may be a chance that some of those surveys do not have all the variables or are missing some. But worry not, you can still analyze each survey independently, rather than dropping surveys with missing variables, by using the functions in R.