Append target characteristics to the targets table in the associatoR object. Use pre-defined characteristics from psycho-linguistic research or supply a data.frame as a user-defined look-up table.
Usage
ar_characterize_targets(
associations,
characteristics = c("valence", "arousal", "dominance", "concreteness",
"word_frequency"),
case_sensitive = FALSE
)Arguments
- associations
an
associatoRobject containing association data as generated by ar_import.- characteristics
a
characterstring or vector specifying the characteristic(s) containing one or more fromc("valence", "arousal", "dominance", "concreteness", "word_frequency")or adata.framecontaining characteristics. Thedata.framemust contain a columnwordto serve as the look up key and may contain additional columns with word characteristics.- case_sensitive
a
logicalspecifying whether to the case of targets. Default isFALSE.
Value
Returns an associatoR object containing a list of tibbles, with targets gaining additional columns including the target characteristics:
- participants
A tibble of participants including a participant
idand potential participant attributes.- cues
A tibble of cues including a
cuevariable and potential cue attributes.- responses
A tibble of responses including a participant id, the cues, the responses, the response level, and additional response attributes.
- targets
A tibble of targets including the specified analysis targets, and word characteristics
Details
Data on valence, arousal, and dominance are retrieved from Warriner et al. (2013). Concreteness and word frequency data are retrieved from Brysbaert et al. (2014).
References
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 1191–1207. https://doi.org/10.3758/s13428-012-0314-x
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904–911. https://doi.org/10.3758/s13428-013-0403-5
Examples
ar_import(intelligence,
participant = participant_id,
cue = cue,
response = response,
participant_vars = c(gender, education),
response_vars = c(response_position, response_level)) %>%
ar_set_targets(targets = "cues") %>%
ar_characterize_targets()
#>
#> ── An associatoR object ────────────────────────────────────────────────────────
#>
#> participants
#> # A tibble: 1,000 × 3
#> id gender education
#> <dbl> <chr> <chr>
#> 1 1 male high school
#> 2 2 male high school
#> 3 3 male high school
#> 4 4 male high school
#> 5 5 male high school
#> # ℹ 995 more rows
#>
#> cues
#> # A tibble: 804 × 1
#> cue
#> <chr>
#> 1 intelligence
#> 2 Einstein
#> 3 books
#> 4 IQ tests
#> 5 college
#> # ℹ 799 more rows
#>
#> responses
#> # A tibble: 29,882 × 5
#> id cue response response_position response_level
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 intelligence Einstein 1 1
#> 2 1 intelligence books 2 1
#> 3 1 intelligence IQ tests 3 1
#> 4 1 intelligence college 4 1
#> 5 1 intelligence smart people 5 1
#> # ℹ 29,877 more rows
#>
#> targets
#> # A tibble: 804 × 6
#> target valence arousal dominance concreteness word_frequency
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 intelligence 7.65 6.32 6.72 2.24 983
#> 2 Einstein NA NA NA NA NA
#> 3 books NA NA NA NA NA
#> 4 IQ tests NA NA NA NA NA
#> 5 college 6.44 4 5.89 4.62 4344
#> # ℹ 799 more rows
#>
ar_import(intelligence,
participant = participant_id,
cue = cue,
response = response,
participant_vars = c(gender, education),
response_vars = c(response_position, response_level)) %>%
ar_set_targets(targets = "cues") %>%
ar_characterize_targets(characteristics = data.frame(word = c("intelligence", "brain"),
message = c("Hello", "World")))
#>
#> ── An associatoR object ────────────────────────────────────────────────────────
#>
#> participants
#> # A tibble: 1,000 × 3
#> id gender education
#> <dbl> <chr> <chr>
#> 1 1 male high school
#> 2 2 male high school
#> 3 3 male high school
#> 4 4 male high school
#> 5 5 male high school
#> # ℹ 995 more rows
#>
#> cues
#> # A tibble: 804 × 1
#> cue
#> <chr>
#> 1 intelligence
#> 2 Einstein
#> 3 books
#> 4 IQ tests
#> 5 college
#> # ℹ 799 more rows
#>
#> responses
#> # A tibble: 29,882 × 5
#> id cue response response_position response_level
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 intelligence Einstein 1 1
#> 2 1 intelligence books 2 1
#> 3 1 intelligence IQ tests 3 1
#> 4 1 intelligence college 4 1
#> 5 1 intelligence smart people 5 1
#> # ℹ 29,877 more rows
#>
#> targets
#> # A tibble: 804 × 2
#> target message
#> <chr> <chr>
#> 1 intelligence Hello
#> 2 Einstein NA
#> 3 books NA
#> 4 IQ tests NA
#> 5 college NA
#> # ℹ 799 more rows
#>