Convert 27-item MCQ responses to a trial-level choice frame

Reshapes long-form Monetary Choice Questionnaire (MCQ) responses into the per-trial smaller-sooner versus larger-later choice frame consumed by fit_dd_choice(), joining each questionid to the canonical item design (immediate amount, delayed amount, delay) bundled in the lookup table (see get_lookup_table()) – Kirby, Petry, & Bickel (1999) for the 27-item MCQ, or Kirby & Maraković (1996, Table 1) for the 21-item MCQ.

Usage

mcq27_to_choice(
  responses,
  id_var = "subjectid",
  question_var = "questionid",
  response_var = "response"
)

Arguments

responses

Long-form data frame with one row per MCQ item per subject, holding the columns named by id_var, question_var, and response_var. response is 0 for the smaller-immediate reward (SIR/SS) and 1 for the larger-delayed reward (LDR/LL) – the same coding fit_dd_choice() expects, so no recoding is applied.

id_var, question_var, response_var

Column names in responses for the subject id, MCQ question id (1-27), and the binary choice. Defaults match the bundled mcq27 dataset ("subjectid", "questionid", "response").

Value

A tibble with columns id (character), ss_amount, ll_amount, delay (days), and choice (0/1, 1 = chose LL), in the input row order. Ready to pass to fit_dd_choice().

Details

Unknown or non-coercible question ids raise an error rather than silently producing unmatched rows. Question ids 1-21 are valid in both the 21- and 27-item designs, so passing the wrong items does not error – it silently returns the wrong amounts/delays for those ids; make sure items matches the instrument actually administered. Ragged input is allowed – subjects need not have all items – and NA responses are preserved (they are complete-cased by fit_dd_choice()). For the strict scorer see score_mcq().

See also

fit_dd_choice(), score_mcq27(), get_lookup_table(), mcq_to_choice()

Examples

ch <- mcq27_to_choice(mcq27)
head(ch)
#> # A tibble: 6 × 5
#>   id    ss_amount ll_amount delay choice
#>   <chr>     <dbl>     <dbl> <dbl>  <dbl>
#> 1 1            54        55   117      0
#> 2 1            55        75    61      0
#> 3 1            19        25    53      0
#> 4 1            31        85     7      1
#> 5 1            14        25    19      1
#> 6 1            47        50   160      0
# feeds directly into the structural choice model (requires TMB):
# fit_dd_choice(ch, mode = "structural", equation = "mazur")