Conjoint experiment inquiries handler

Computes the true average marginal component effect (AMCE) of every attribute level relative to that attribute's first level. Each AMCE is the average change in the probability that the second profile in a task is chosen when the attribute moves from its first level to the given level, with the other attributes drawn at random by conjoint_assignment().

Usage

conjoint_inquiries(data, levels_list, utility_fn)

Arguments

data

A data frame with one row per profile and columns subject, task, and profile (1 or 2).

levels_list

A named list of attribute levels, as in conjoint_assignment(). The first level of each attribute is the reference.

utility_fn

A function that adds the utility column U, as in conjoint_measurement().

Value

A data frame with one row per non-reference level: attribute, reference, level, inquiry, and estimand.

Details

See https://book.declaredesign.org/experimental-descriptive.html#conjoint-experiments

Examples

set.seed(343)
levels_list <- list(gender = c("woman", "man"), age = c("young", "old"))

# 25 subjects, each choosing between two profiles in each of four tasks
profiles <- expand.grid(profile = 1:2, task = 1:4, subject = 1:25)

utility_fn <- function(data) {
  data$U <- 0.5 * (data$gender == "woman") - 0.2 * (data$age == "old") +
    rnorm(nrow(data))
  data
}

conjoint_inquiries(profiles, levels_list, utility_fn)
#> # A tibble: 2 × 5
#>   attribute reference level inquiry   estimand
#>   <chr>     <chr>     <chr> <chr>        <dbl>
#> 1 gender    woman     man   genderman  -0.0200
#> 2 age       young     old   ageold     -0.14