# Analyzing Survey Data in R

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

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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!

- 1
### Introduction to survey data

**Free**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?50 xpSurvey weights50 xpVisualizing the weights100 xpSpecifying elements of the design in R50 xpDesigns in R100 xpStratified designs in R100 xpCluster designs in R100 xpComparing survey weights of different designs100 xpVisualizing the impact of survey weights50 xpNHANES weights100 xpTying it all together!100 xp - 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 variable50 xpSummarizing a categorical variable100 xpInterpreting frequency tables50 xpGraphing a categorical variable100 xpExploring two categorical variables50 xpCreating contingency tables100 xpBuilding segments bar graphs100 xpSummarizing with svytotal()100 xpInterpreting svymean()50 xpInference for categorical variables50 xpRunning a chi squared test100 xpTying it all together!100 xp - 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 data50 xpSurvey statistics100 xpEstimating quantiles100 xpVisualizing quantitative data50 xpBar plots of survey-weighted means100 xpOutput of svyby()50 xpBar plots with error100 xpSurvey-weighted histograms100 xpSurvey-weighted density plots100 xpInference for quantitative data50 xpSurvey-weighted t-test100 xpTying it all together!100 xp - 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 plots50 xpBubble plots100 xpSurvey-weighted scatter plots100 xpUse of color in scatter plots100 xpVisualizing trends50 xpLine of best fit100 xpTrend lines100 xpModeling survey data50 xpRegression model100 xpRegression inference50 xpMore complex modeling50 xpMultiple linear regression100 xpTying it all together100 xpWrap-up50 xp

In the following tracks

StatisticianCollaborators

#### Kelly McConville

Assistant Professor of Statistics at Reed College

Kelly is a survey statistician and an assistant professor of statistics at Reed College where she teaches courses in statistics and data science. She uses R in all of her courses and considers the tidyverse to be a great introduction to data analysis! Whether it be assessing the impact of voter ID laws, quantifying changes in land use, or estimating occupational statistics, Kelly enjoys using data and R to better understand our world!

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