Date: 03/09/2015
Replication of Walasek and Stewart (2014) and Additional Tests
Investigators:
Journal Club Members of the Haas School of Business, University of California, Berkeley; Minah Jung, Clayton Critcher, Phoebe
Wong, Leif Nelson, and Don Moore
We will conduct a study to partially replicate the findings from Walasek and Stewart (2014) and to test potentially confounding
factors.
We will follow the same paradigm used in the original article with the following modifications:
Participants (N=1,060) recruited at Amazon Mechanical Turk will be randomly assigned to one of the four conditions.

The first two conditions were drawn from the original paper; $20(gain)-$40(loss) and $40(loss)-$20(gain). Participants will
see pairs of lotteries in a randomized order. These pairs of lotteries involve values that span different ranges and that move up
by different increments: $2 increments for $20 (ranging from $6 to $20) and $4 increments for $40(ranging from $12-$40).
The only difference between the original study and our study in these two conditions is that participants see each lottery
twice—meaning they will see a total of 128 pairs instead of 64, as in the original. This is to roughly equate the number of
lotteries participants see in these conditions as in our new two conditions.

We added the 3rd and 4th conditions in which participants see pairs of lotteries in which increments are always $2 in both $6$20 and $12-40 ranges. The purpose of these two conditions is to test whether or not loss aversion or its reversal is driven by
how far apart two values are ranked (as the original authors’ account predicts) or by how high or low a specific value is with
reference to the total range of gain or loss values in that condition.

We have also increased the sample size to 1,060 (roughly 2.5 times the largest sample size used in the original study [Study 1a]
with four cells).
Table 1. The lottery values used for loss and gain in our study.
Original
Asymmetric
Gain
($2)
Loss
($4)
20-40
Total
lotteries =
8 10 12 14 16 18 20
12 16 20 24 28 32 36 40
64(will be
repeated;
total=128)
Gain
($4)
Loss
($2)
40-20
Total
lotteries =
New
20-40
6
12 16 20 24 28 32 36 40
6
8 10 12 14 16 18 20
64(will be
repeated;
total=128)
Total
lotteries =
40-20
Total
lotteries =
Data Analyses Plan:
Gain($2) 6 8 10 12 14 16 18 20
Loss($2) 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
120
Gain($2) 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Loss($2) 6 8 10 12 14 16 18 20
120
We will conduct the same analysis (logit regression) in the original article as well as “the old school” null hypothesis significance
testing. We will compare the relative sensitivity to losses vs. gains in the original conditions to the relative sensitivity to losses vs.
gains in the new conditions. The decision by sampling account of loss aversion suggests that the new 20(gain)-40(loss) condition will
show relatively more sensitivity to losses than the old 20(gain)-40(loss) condition. Furthermore, the decision by sampling account of
loss aversion predicts that the new 40(gain)-20(loss) will involve relatively more sensitivity to gains than the old 40(gain)-20(loss)
condition. The alternative account—which emphasizes that people are sensitive to shifts in gains or losses proportional to the range of
gains or losses, respectively, to which they are exposed—predicts that the new conditions will look like the old conditions. We will
also follow the same exclusion rules used in the original paper but will report the results both with and without exclusions.
References
Walasek, L., & Stewart, N. (2014). How to Make Loss Aversion Disappear and Reverse: Tests of the Decision by Sampling
Origin of Loss Aversion.