In lyh970817/qualtdict: Generating Variable Dictionaries and Labelled Data Exports of Qualtrics Surveys

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Qualtrics is an online survey and data collection software platform. The qualtdict R package builds on the qualtRics R package which implements the retrieval of survey data using the Qualtrics API and aims to reduce the preprocessing steps needed in analyzing such surveys. The current package makes more comprehensive use of the survey metadata (retrieved via functions in qualtRics) and generates a variable dictionary including most of the information essential for data processing and analysis. It also uses a modified version of the RAKE algorithm by [Rose. et al.] (https://media.wiley.com/product_data/excerpt/22/04707498/0470749822.pdf) implemented in the package slowraker to generate meaningful names for all variables in the survey, as well as adding a comprehensive set of metadata attributes that uniquely identifies each variable.

Note that your institution must support API access and that it must be enabled for your account. Whoever manages your Qualtrics account can help you with this. Please refer to the Qualtrics documentation to find your API token.

The authors and contributors for this R package are not affiliated with Qualtrics and Qualtrics does not offer support for this R package.

Usage

• dict_generate to generate a qualtdict, a variable dictionary dataframe that can be later used to download actual survey data, or exported into a table file for provide straightforward overview of the survey variables.

• dict_validate to check for potential mistakes in the dictionary.

• get_survey_data to retrieve sjlabelled survey data, where each variable is labelled with question, recodes (levels) and recode labels.

Registering Qualtrics credentials

You need to first register your Qualtrics credentials with the function qualtrics_api_credentials exported from the package qualtRics.

library(qualtdict)

qualtrics_api_credentials(
  api_key = "<YOUR-QUALTRICS_API_KEY>",
  base_url = "<YOUR-QUALTRICS_BASE_URL>",
  install = TRUE
)

Generating a dictionary

You can then generate a variable dictionary.

mydict <- dict_generate("SV_4YyAHbAxpdbzacl", var_name = "question_name")

Question names for each variable as set on Qualtrics will be used as variable names

You may wish to generate meaningful variable names (if you don't already have them in the survey) in the dictionary. If doing so, preferably you would also want to define a function that extracts block prefixes from block names.

# Define a block prefix extraction function
block_pattern <- function(x) {
  # Use the first three characters of a block name
  substring(x, 1, 3)
}

mydict <- dict_generate("SV_4YyAHbAxpdbzacl",
  var_name = "easy_name",
  block_pattern = block_pattern,
  block_sep = "."
)

These "easy names" are generated through a modified version of the RAKE algorithm implemented in the package slowraker. The RAKE algorithm was originally proposed as a method to extract keywords from documents Rose. et al.. It works by first selecting a series of candidate keywords, where each candidate keyword is a set of contiguous words that doesn’t contain a phrase delimiter or a meaningless stop word. Each single word within a keyword is then scored by degree/frequency, where degree equals the number of times that the word co-occurs with another word in another keyword, and frequency is the total number of times that the word occurs overall. Each candidate keyword is then scored by summing the scores of its member words.

The key modification of the algorithm in this package is that while the candidate keywords for each variable are extracted from text components in the survey associated with that variable (e.g. the overarching question as well as the sub-question it belongs to, and for questions allowing for selecting multiple choices , the choice labels), word frequency is calculated as the number of times a word appears in the entire survey.

Dictioanry validation

After downloading the dictionary, You might want to check for potential mistakes in the dictionary with dict_validate, which returns a list of qids with potential mistakes and mistake codes (explained in the function documentation of dict_validate, as well as a list of unique level-label pairing (numerals v.s. text labels), so that you can check that across a number of questions measured on supposedly the same scale, there isn't a question with distinct levels or labels to the others.

dict_validate(mydict)

Downloading survey with dictionary

And use the dictionary to download sjlabelled survey data labelled with the associated text components from the survey (e.g. question, recode labels and replacement strings in loop and merge questions).

Extra parameters from fetch_survey in qualtRics can be specified (e.g. unanswer_recode and unanswer_recode_multi).

survey_dat <- get_survey_data(mydict,
  unanswer_recode = -77,
  unanswer_recode_multi = 0
)

lyh970817/qualtdict documentation built on April 8, 2023, 9:26 p.m.