Survey Data Basics

Ryan Clement

Middlebury College

Data Services Librarian

May 4, 2021

What are we covering?

  • Types of data
  • Survey data basics -- what to look at
  • Some useful techniques and visualizations for looking at your data
  • Questions about using Qualtrics to look at data
  • Suggestions for other tools

What are we NOT covering?

  • Rigorous statistical analysis
  • How to use R/other tools

Survey Data

what kind of data do we have?

Different types of data

  • Categorical/nominal data
  • Ordinal data
  • Ratio data
  • Real number data
  • Natural language

Categorical/nominal data

  • Constrained to "categories" -- can be characters or numbers
  • Race, gender, religion, yes/no, success/fail
  • Yes/no, True/false are a subset known as binary data
  • Cannot compare along a relative scale
  • Cannot use mean/median, must use mode
  • Very useful in creating crosstabs

Ordinal data

  • Ordered along a scale, but the distances between categories are not known
  • For instance, Likert scale questions -- 5 is higher than 4, but is my 5 the same amount higher than your 5?
  • Other examples: Income brackets, highest level of education
  • Can use to create a ranking, but cannot use other statistics -- What does "3.5 out of 5" mean?
  • Look at relative numbers of responses, like nominal data

Interval data

  • Order as well as the differences between responses (e.g. the "intervals") are known
  • Fahrenheit/Celsius temps, time/date, income vs. spending
  • No "true zero" -- negative values can exist, zero has meaning (it's not just "does not exist")
  • Can say one value is "higher" than the other, but not necessarily "twice" as high

Ratio data

  • Just like interval data, but now absolute zero has meaning and means "does not exist"
  • Kelvin temp, height/weight, number of children, years of education, income
  • Zero means "does not exist" -- so you can more easily say "X is 2x higher than Y"

Natural Language

  • Written by the respondent themselves in a free-text field
  • Usually needs to be coded in some way before it can be analyzed
  • Can possibly use text analysis tools on this type of data too

Some tricky ones for discussion

What do you think?

  • Age
  • Grades
  • Political party
  • Number of poor mental health days in the past week

Mean, median, mode, and standard deviation

Describing the tendencies of data


  • The value that occurs most often
  • Almost completely unaffected by outliers
  • Can be unimodal: [1, 1, 2, 2, 4, 4, 4, 5, 6, 6] or more
    • [1, 1, 1, 2, 2, 3, 4, 4, 4] (bimodal)
  • Often used for categorical data


  • We're talking about the arithmetic mean (there are others)
  • Often referred to as "the average"
  • Calculated by adding up the values and dividing by the number of values: (1+1+2+4+5+5)/6 = 3
  • Can be very sensitive to outliers:
    • (1+1+2+4+5+20)/6 = 5.5


  • The "middle" value in data -- the number that separates the top half from the bottom half
  • Calculated by lining up the values and finding the middle value: [1, 1, 2, 4, 4, 5, 5]
  • If you have an even number, take the mean of the middle two values:
    • [1, 1, 2, 4, 5, 5] -> (2+4)/2 = 3
  • Less sensitive to outliers than the mean:
    • [1, 1, 2, 4, 5, 5]
    • [1, 1, 2, 4, 5, 20]

Standard Deviation

  • Measure of how "spread out" the data is
  • Need to use the mean to calculate (we won't get into that)
  • Lower SD means numbers are mostly clustered around the mean, higher SD means they are more spread out (e.g. there is more variance)

A few useful visualizations + techniques

Look at summaries of your data

  • What are type of data are all of your variables?
  • How many "complete" cases do you have? (i.e. how much missing data do you have?) Is there meaning/pattern behind this?
  • Look at the central trends in your data (mode, mean) - do they make sense?

Distributions - histograms

Distributions - bar charts

Distributions - pie charts

2 categorical variables - crosstabs

Likert scale data -- stacked bar charts

Tool Options

  • Qualtrics -- just use the built-in visualizations and cross-tab tools!
  • Excel -- export your data from Qualtrics and use Excel to manipulate and visualize your data
  • Datawrapper, Infogram, Tableau (also has free student license) -- must use Excel (or something else) to clean and manipulate data first, but great for nicer/more complex visuals
  • Voyant -- useful for simple text visuals (if you have a lot of "natural language" data)
  • R + RMarkdown -- steeper learning curve, but great for cleaning/manipulating data, visualizing, and "writing" all in one


By Ryan Clement