Testing Multiple Multi-Attribute Choice Models

2024-03-07

2024-03-29

Choices from the main choice task.

The experiment consists of two parts: (i) a quality elicitation step (Task 1) and (ii) a choice task (Task 2). In the quality elicitation step, we elicit subjective quality values of 24 consumer products using a multiple price list (MPL) method. Specifically, it measures the quality value of a product by asking a subject whether they prefer the product (Option A) to an amount of money (Option B). We ask this binary question multiple times by varying the amount of money in Option B ranging from $0.01 to $8 in increments of 1 cent. We expect that a subject chooses Option A in some initial questions, switches to Option B at a certain question, and chooses Option B for the remaining questions. We say that the dollar value at the switch point is the subjective quality of a product.

Before the main choice task, we select two sets of two-paired products in which (i) the elicited quality values are greater than or equal to $0.05, (ii) the elicited quality values are smaller than or equal to $6, and (iii) the difference in elicited quality values in each product set is greater than or equal to $0.5.

In the main choice task, a subject chooses the most preferred alternative from a choice set consisting of three alternatives. Note that the first and the second alternatives are bundles of a product and money while the third alternative is simply a money option, i.e., (Product 1, Money 1), (Product 2, Money 2), (Money 3). Product 1 and Product 2 are from a selected product set before the main choice task as described above. Money 3 is an endowment, and in this experiment, it can be either $8 or $10. In this case, the third alternative can be interpreted as an outside option, which is simply taking an endowment. Money 1 and Money 2 are calculated at the subject level based on the elicited quality values and the endowment so that we can distinguish and test the pairwise normalization model and the range normalization model. Specifically, once a product set and an endowment are fixed, we generate four choice sets in which the pairwise normalization model and the range normalization model provide different predictions. Remember that we have two product sets (i.e., product set 1 and product set 2) and two endowments (i.e., $8 and $10). Hence, each subject makes decisions for 16 choice sets.

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Randomization is done by the computer server that hosts the experiment.

Each subject faces 24 rounds in Task 1 and 16 rounds in Task 2. Remember that a subject makes a switch point decision in each round in Task 1, and a subject chooses the most preferred alternative in each round in Task 2. Each subject always starts with Task 1 and then Task 2. The order of rounds in Task 1 is randomized. In Task 2, the order of rounds is randomized, and the order of alternatives in each round is randomized as well. In the payment stage, one round in Task 1 and Task 2 is randomly chosen. If Task 1 is randomly selected, then one binary question is randomly selected to determine the payment.

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Based on a pre-analysis including power analysis by simulation, the target number of subjects is 80 students. However, we may have to exclude some observations if elicited quality values do not satisfy the criteria of choosing product sets as described above. To collect 80 subjects who pass the criteria, the actual number of collected subjects can be more than 80.

The main variable of interest is choices in the main choice task. If we have 80 subjects, then we have 1,280 choices since each subject makes 16 choices in the main choice task (80x16=1,280).

Based on a pre-analysis including power analysis by simulation, the target number of subjects is 80 students. However, we may have to exclude some observations if elicited quality values do not satisfy the criteria of choosing product sets as described above. To collect 80 subjects who pass the criteria, the actual number of collected subjects can be more than 80.