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Course

Analyzing Survey Data in R

IntermediateSkill Level

4.8+

Updated 10/2022

Learn survey design using common design structures followed by visualizing and analyzing survey results.

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RProbability & Statistics4 hr14 videos49 Exercises3,950 XP15,276Statement of Accomplishment

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Course Description

You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Survey Data in R, you will work with surveys from A to Z, starting with common survey design structures, such as clustering and stratification, and will continue through to visualizing and analyzing survey results. You will model survey data from the National Health and Nutrition Examination Survey using R's survey and tidyverse packages. Following the course, you will be able to successfully interpret survey results and finally find the answers to life's burning questions!

Prerequisites

Introduction to the Tidyverse Foundations of Inference in R

1

Introduction to survey data

Our exploration of survey data will begin with survey weights. In this chapter, we will learn what survey weights are and why they are so important in survey data analysis. Another unique feature of survey data are how they were collected via clustering and stratification. We'll practice specifying and exploring these sampling features for several survey datasets.

What are survey weights?

Survey weights

Visualizing the weights

Specifying elements of the design in R

Designs in R

Stratified designs in R

Cluster designs in R

Comparing survey weights of different designs

Visualizing the impact of survey weights

NHANES weights

Tying it all together!

2

Exploring categorical data

Now that we have a handle of survey weights, we will practice incorporating those weights into our analysis of categorical data in this chapter. We'll conduct descriptive inference by calculating summary statistics, building summary tables, and constructing bar graphs. For analytic inference, we will learn to run chi-squared tests.

Visualizing a categorical variable

Summarizing a categorical variable

Interpreting frequency tables

Graphing a categorical variable

Exploring two categorical variables

Creating contingency tables

Building segments bar graphs

Summarizing with svytotal()

Interpreting svymean()

Inference for categorical variables

Running a chi squared test

Tying it all together!

3

Exploring quantitative data

Of course not all survey data are categorical and so in this chapter, we will explore analyzing quantitative survey data. We will learn to compute survey-weighted statistics, such as the mean and quantiles. For data visualization, we'll construct bar-graphs, histograms and density plots. We will close out the chapter by conducting analytic inference with survey-weighted t-tests.

Summarizing quantitative data

Survey statistics

Estimating quantiles

Visualizing quantitative data

Bar plots of survey-weighted means

Output of svyby()

Bar plots with error

Survey-weighted histograms

Survey-weighted density plots

Inference for quantitative data

Survey-weighted t-test

Tying it all together!

4

Modeling quantitative data

To model survey data also requires careful consideration of how the data were collected. We will start our modeling chapter by learning how to incorporate survey weights into scatter plots through aesthetics such as size, color, and transparency. We'll model the survey data with linear regression and will explore how to incorporate categorical predictors and polynomial terms into our models.

Visualization with scatter plots

Bubble plots

Survey-weighted scatter plots

Use of color in scatter plots

Visualizing trends

Line of best fit

Trend lines

Modeling survey data

Regression model

Regression inference

More complex modeling

Multiple linear regression

Tying it all together

Analyzing Survey Data in R

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FAQs

What R packages are used to analyze survey data in this course?

You use the survey package for handling weighted survey data and tidyverse packages for manipulation and visualization. Together they support the full analysis workflow.

What real survey dataset does this course use?

You model and analyze data from the National Health and Nutrition Examination Survey (NHANES), a well-known large-scale survey conducted in the United States.

Does this course explain survey weights and why they matter?

Yes. The first chapter focuses entirely on survey weights, what they are, why they are critical for unbiased analysis, and how to specify them alongside clustering and stratification.

Is this course appropriate for someone with only basic R skills?

No. This is an advanced course with ten prerequisites covering statistics, regression, hypothesis testing, sampling, inference, and data visualization in R.

What statistical tests and models are covered?

You learn survey-weighted chi-squared tests for categorical data, survey-weighted t-tests for quantitative data, and survey-weighted linear regression with categorical predictors and polynomial terms.

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