Introduction to qualtdict

qualtdict is a metadata and labelled-export companion to qualtRics for Qualtrics surveys. qualtRics owns API access: authentication, metadata retrieval, and response downloads from Qualtrics. qualtdict owns the package workflow that turns that metadata and those import-ID response exports into a Variable Dictionary, Validation Findings, and Labelled Survey Data.

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

Main Workflow

The core workflow is:

Use dict_generate() to build a Variable Dictionary.
Use dict_validate() to inspect Validation Findings.
Use fetch_labelled_survey_data() to create Labelled Survey Data.

A Variable Dictionary preserves several distinct identifiers:

response_column_id: the downloaded response column used to match raw Qualtrics response data to dictionary rows.
row_source: the Dictionary Row Source that identifies what kind of metadata fact produced the row. Current question-backed rows use row_source = "question".
qid: the bare Qualtrics question identifier, such as QID1.
question_name: the raw Qualtrics Question Name.
variable_name: the final export-safe Dictionary Variable Name used in Labelled Survey Data.
semantic_name: a generated naming reference, present only when requested.

This vignette avoids private survey data. The live workflow chunks use a placeholder survey ID and are not evaluated. The small table below shows the shape of a safe simulated Variable Dictionary.

example_dict <- tibble::tibble(
  response_column_id = c("QID1", "QID1_3_TEXT"),
  row_source = c("question", "question"),
  qid = c("QID1", "QID1"),
  question_name = c("Q1", "Q1"),
  variable_name = c("Q1", "Q1.1"),
  block = c("Main Block", "Main Block"),
  question = c("Choose one", "Choose one"),
  item = c(NA, NA),
  level = c("1", "3_TEXT"),
  label = c("Yes", "Other_TEXT"),
  type = c("MC", "MC"),
  selector = c("SAVR", "SAVR"),
  sub_selector = c("TX", "TX"),
  content_type = c(NA, NA)
)

example_dict
#> # A tibble: 2 × 14
#>   response_column_id row_source qid   question_name variable_name block question
#>   <chr>              <chr>      <chr> <chr>         <chr>         <chr> <chr>   
#> 1 QID1               question   QID1  Q1            Q1            Main… Choose …
#> 2 QID1_3_TEXT        question   QID1  Q1            Q1.1          Main… Choose …
#> # ℹ 7 more variables: item <lgl>, level <chr>, label <chr>, type <chr>,
#> #   selector <chr>, sub_selector <chr>, content_type <lgl>

Credentials

Live calls to dict_generate() and fetch_labelled_survey_data() require Qualtrics API credentials because they call Qualtrics through qualtRics. Your institution must support API access and it must be enabled for your account. The person who manages your Qualtrics account can help you find your API token and base URL.

library(qualtdict)

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

Validation, dictionary schema checks, block splitting, and labelled-export mechanics can be tested offline with synthetic dictionaries and synthetic metadata. Only live metadata and response downloads require credentials.

Variable Dictionary

Generate a Variable Dictionary from a Qualtrics survey ID:

survey_id <- "SV_XXXXXXXXXXXXXXXX"

dict <- dict_generate(
  survey_id,
  variable_name = "question_name"
)

With variable_name = "question_name", variable_name is derived from the raw Qualtrics Question Name and repaired as needed so Labelled Survey Data columns are export-safe and unique. The original question_name remains in the Variable Dictionary as a reference.

If Qualtrics Question Names are not suitable as analysis names, request Semantic Names:

block_pattern <- function(block) {
  substring(block, 1, 3)
}

dict <- dict_generate(
  survey_id,
  variable_name = "semantic_name",
  block_pattern = block_pattern,
  block_sep = "."
)

Semantic Names are best-effort readable conveniences generated from survey text, block information, and response metadata. They are not stable guarantees across package versions or survey text changes. For long text, Semantic Names select important words from ranked keywords and preserve those selected words in the order they appear in the naming text. Use them when they improve analysis ergonomics, but do not treat them as permanent identifiers.

You can also preprocess the text used only for Semantic Name generation:

drop_intro_text <- function(dict) {
  dict$question <- sub("^Please answer: ", "", dict$question)
  dict
}

dict <- dict_generate(
  survey_id,
  variable_name = "semantic_name",
  semantic_name_preprocess = drop_intro_text
)

semantic_name_preprocess receives the full post-normalisation dictionary and runs only on the Semantic Name path. Temporary helper columns created by the function are not included in the returned Variable Dictionary.

Findings

dict_validate() returns a stable validation object with two components: validation_findings and level_label_pairs.

validation <- dict_validate(dict)

validation$validation_findings
validation$level_label_pairs

Validation Findings cover consistency issues in the generated Variable Dictionary, including repaired variable_name values, non-unique or unsafe Dictionary Variable Names, and level-label coding findings.

Labelled Survey Data

Create Labelled Survey Data from a Variable Dictionary:

survey_dat <- fetch_labelled_survey_data(
  dict,
  extra_columns = c("externalDataReference", "startDate", "endDate"),
  exclude_findings = "none",
  unanswer_recode = -77,
  unanswer_recode_multi = 0
)

fetch_labelled_survey_data() calls qualtRics::fetch_survey() with the settings needed for reliable matching to response_column_id: import_id = TRUE, label = FALSE, convert = FALSE, and breakout_sets = TRUE. You may pass filtering arguments such as include_questions, but not arguments that would override the response-column naming, labelling, conversion, or breakout contract owned by qualtdict.

By default, variables with Validation Findings remain in the Labelled Survey Data. Use exclude_findings only when you explicitly want to drop those variables after download.

survey_dat_clean <- fetch_labelled_survey_data(
  dict,
  exclude_findings = "validation"
)

Survey Block Views

dict_generate() returns one Variable Dictionary and fetch_labelled_survey_data() returns one Labelled Survey Data object. Use explicit helpers when you want block-specific views: