heck2017_raw | R Documentation |
Raw data with multiattribute decisions (Heck, Hilbig & Moshagen, 2017).
heck2017_raw
A data frame with 21 variables:
vp
ID code of participant
trial
Trial index
pattern
Number of cue pattern
ttb
Prediction of take-the-best (TTB)
eqw
Prediction of equal weights (EQW)
wadd
Prediction of weighted additive (WADD)
logoddsdiff
Log-odds difference (WADDprob)
ttbsteps
Number of TTB steps (TTBprob)
itemtype
Item type as in paper
reversedorder
Whether item is reversed
choice
Choice
rt
Response time
choice.rev
Choice (reversed)
a1
Value of Cue 1 for Option A
a2
Value of Cue 2 for Option A
a3
Value of Cue 3 for Option A
a4
Value of Cue 4 for Option A
b1
Value of Cue 1 for Option B
b2
Value of Cue 2 for Option B
b3
Value of Cue 3 for Option B
b4
Value of Cue 4 for Option B
Each participant made 40 choices for each of 4 item types with four cues
(with validities .9, .8, .7, and .6).
Individual choice freqeuncies are available as heck2017
Heck, D. W., Hilbig, B. E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26-40. doi: 10.1016/j.cogpsych.2017.05.003
heck2017
for the aggregated choice frequencies per item type.
data(heck2017_raw) head(heck2017_raw) # get cue values, validities, and predictions cueA <- heck2017_raw[, paste0("a", 1:4)] cueB <- heck2017_raw[, paste0("b", 1:4)] v <- c(.9, .8, .7, .6) strat <- strategy_multiattribute( cueA, cueB, v, c( "TTB", "TTBprob", "WADD", "WADDprob", "EQW", "GUESS" ) ) # get unique item types types <- strategy_unique(strat) types$unique # get table of choice frequencies for analysis freq <- with( heck2017_raw, table(vp, types$item_type, choice) ) freqB <- freq[, 4:1, 1] + # reversed items: Option A freq[, 5:8, 2] # non-rev. items: Option B head(40 - freqB) data(heck2017) head(heck2017) # same frequencies (different order) # strategy classification pp <- strategy_postprob( freqB[1:4, ], rep(40, 4), types$strategies ) round(pp, 3)
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