Overview

• Qualtrics

  • What is it?
  • Setting up a survey
  • Navigating Qualtrics quirks
  • Downloading our data

• R

  • Reading in our data
  • Data exploration

• Data Wrangling

  • Recoding values
  • Fixing weirdness

Qualtrics

• Qualtrics is an online survey software where you can create/host questionnaires and collect data from participants

• It's used widely by researchers in the school, and will be used by a lot of you for your dissertations next year (a scary thought I know...)

• It's relatively straightforward to use but there are some 'quirks' 🙄

• So first, we're going to run through setting up a survey in Qualtrics and how we navigate these 'quirks'

• Then I'll demo how we get these data into R, how we can explore our data, and how we might go about preparing our data for factor analysis

Qualtrics

• Qualtrics is an online survey software where you can create/host questionnaires and collect data from participants

• It's used widely by researchers in the school, and will be used by a lot of you for your dissertations next year (a scary thought I know...)

• It's relatively straightforward to use but there are some 'quirks' 🙄

• So first, we're going to run through setting up a survey in Qualtrics and how we navigate these 'quirks'

• Then I'll demo how we get these data into R, how we can explore our data, and how we might go about preparing our data for factor analysis

Qualtrics Quirks

• We should check several parts of our Qualtrics survey before we send it out to participants:

• Practical considerations

  • Question wording
  • Response type, requirements & validation
  • Coding/scoring of responses

• Ethical considerations

  • Participant information sheet
  • Informed consent
  • Debrief
  • IP addresses

Qualtrics Quirks

• We should check several parts of our Qualtrics survey before we send it out to participants:

• Practical considerations

  • Question wording
  • Response type, requirements & validation
  • Coding/scoring of responses

• Ethical considerations

  • Participant information sheet
  • Informed consent
  • Debrief
  • IP addresses

Reading & Exploring Data

• Once our survey is ready and has been filled in by participants, we can export our data file in a .csv format

• We've worked with these types of files LOADS so it isn't anything new to us

• We can use the readr::read_csv() function to import our data into R, in the same way we have done previously

• We then should employ a range of functions() to explore our data:

  • We want to check everything has imported as we expected it to
  • We want to figure out what data wrangling we need to do
  • We want to take note of any weirdness from our checks

Data ExploRation

Data ExploRation

Keeping it simple

• When we download our data from Qualtrics, we'll often have a bunch of extra variables that we usually don't want or need

• These include things like: start date, end date, user language, duration, distribution channel...

• We might also get some extra rows that contain question labels or descriptions, and NOT data from our participants - we don't want this either!

• So, we can make our data easier to work with by using the slice(), filter(), and select() functions from dplyr::

• We can remove any extra rows, any variables we don't need, and any participants that didn't consent to our study

Help! I forgot to recode my items!

• After exploring your data, you might find that you didn't set up your survey properly

• But that's OK... we first need to figure out how the variables are currently measured/scored, and what they need to become

• Then we can wrangle our data and shape it into whatever we need using a variety of functions()

• The most useful functions for recoding come from Base R and the dplyr:

  • as.numeric()
  • replace()
  • dplyr::mutate()
  • dplyr::recode()

Help! I forgot to recode my items!

For recoding numeric data:

data <- data %>% dplyr::mutate(., col_name = dplyr::recode(col_name, `1` = 5))Copy Code

data <- data %>% dplyr::mutate(., col_name = dplyr::recode(col_name, "stduent" = "student"))Copy Code

data <- data %>% dplyr::mutate(col_name = as.numeric(col_name))Copy Code

data <- data %>% dplyr::mutate(col_name = replace(col_name, col_name > 10, NA))Copy Code

That's all - happy wrangling!