heck2017 | R Documentation |
Choice frequencies with multiattribute decisions across 4 item types (Heck, Hilbig & Moshagen, 2017).
heck2017
A data frame 4 variables:
B1
Frequency of choosing Option B for Item Type 1
B2
Frequency of choosing Option B for Item Type 2
B3
Frequency of choosing Option B for Item Type 3
B4
Frequency of choosing Option B for Item Type 4
Each participant made 40 choices for each of 4 item types with four cues (with validities .9, .8, .7, and .6). The pattern of cue values of Option A and and B was as follows:
Item Type 1: A = (-1, 1, 1, -1) vs. B = (-1, -1, -1, -1)
Item Type 2: A = (1, -1, -1, 1) vs. B = (-1, 1, -1, 1)
Item Type 3: A = (-1, 1, 1, 1) vs. B = (-1, 1, 1, -1)
Item Type 4: A = (1, -1, -1, -1) vs. B = (-1, 1, 1, -1)
Raw data are available as heck2017_raw
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
data(heck2017) head(heck2017) n <- rep(40, 4) # cue validities and values v <- c(.9, .8, .7, .6) cueA <- matrix( c( -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1 ), ncol = 4, byrow = TRUE ) cueB <- matrix( c( -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1 ), ncol = 4, byrow = TRUE ) # get predictions strategies <- c( "baseline", "WADDprob", "WADD", "TTBprob", "TTB", "EQW", "GUESS" ) strats <- strategy_multiattribute(cueA, cueB, v, strategies) # strategy classification with Bayes factor strategy_postprob(heck2017[1:4, ], n, strats)
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