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Helping Behavior and Group Size
Initial registration date
May 12, 2018
June 23, 2020 3:10 PM EDT
Other Primary Investigator(s)
Additional Trial Information
Will a person in need of help be more likely to be helped when there are one or several potential helpers? Dozens of experiments have led social psychologists to conclude that the answer to this question depends entirely on the situation. This project uses game theory to predict in what situations one potential helper is more likely to provide help than a group of several potential helpers, and in what situations the opposite is true. The theoretical model concludes that in situations where few potential helpers are willing to help, then help is more likely to be provided when many people can help. However, in situations where most potential helpers are willing to help, help is more likely to be provided when only one person can help. I test this model in a lab experiment.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Help_second_1, Help_second_2, Help_second_3, Help_second_23 (between-test), Nethelp_second_23 (between-test), Help_third (main within-test).
Primary Outcomes (explanation)
Help_second_1 = Takes value 1 if the subject chose to Pay in the second stage when he is in a group with 1 AP and 0 otherwise.
Help_second_2 = Takes value 1 if the subject chose to Pay in the second stage when he is in a group with 2 AP and 0 otherwise.
Help_second_3 = Takes value 1 if the subject chose to Pay in the second stage when he is in a group with 3 AP and 0 otherwise.
Help_second_23 = Help_second_2 + Help_second_3.
Nethelp_second_23 = Help_second_23 - Help_first_2 - Help_first_3 (see secondary outcomes for the definition of Help_first)
Help_third = Includes all the variables about the decisions whether to Pay in the third stage, depending on group size and depending on the composition of the other AP in the group.
Secondary Outcomes (end points)
Help_first_1, Help_first_2, Help_first_3, Beliefs
Secondary Outcomes (explanation)
Help_first_1 = Takes value 1 if the subject chose to Pay in the first stage when he is in a group with 1 AP and 0 otherwise.
Help_first_2 = Takes value 1 if the subject chose to Pay in the first stage when he is in a group with 2 AP and 0 otherwise.
Help_first_3 = Takes value 1 if the subject chose to Pay in the first stage when he is in a group with 3 AP and 0 otherwise.
Beliefs = Belief elicitation across all stages.
There will be 378 subjects. Each session will consist of 18 subjects. Subjects are initially divided between Active Participants (AP) and Passive Participants (PP). The AP earnings depend on their decisions, while the PP earnings depend on the decisions of the AP. However, the PP do not know that they are PP until the end. They therefore make hypothetical decisions throughout the experiment.
The game: Subjects are placed in groups of one, two, or three AP and one PP. Within each group, the AP start with 10€ and the PP starts with 0€. The AP then play the following game: they can choose to Pay 3€ or to Not Pay. To Pay 3€ means to get 7€ instead of 10€. If at least one AP in the group chooses to Pay, the PP gets 5€. If none of the AP chooses to Pay, then the PP gets 0€. The experiment has three stages. In the first stage, subjects play this game in groups of one, two, and three AP (in random order). The decision that subjects make when they are in the group of one AP is called "Decision 1".
In the second stage, subjects play the same game but this time they get information about what other AP in their group chose in Decision 1. More concretely, they are told that the other AP in their group have been randomly selected from a "pool" of 6 AP. They are then told how many of these 6 AP of the pool chose to Pay in Decision 1.
The third stage uses the strategy method to elicit subjects' decision whether to Pay depending on how many of the other AP in their pool chose to Pay in Decision 1.
Experimental Design Details
Randomization done by computer. Note that randomization is endogenous: those who chose to (Not) Pay in Decision 1 are more likely to be in a pool in which most people chose to (Not) Pay. However, given that someone chose to (Not) Pay in Decision 1, that subject is effectively randomly assigned into either of the pools.
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Out of the 378 observations, 252 will be AP and 126 will be PP. The AP will be equally divided between those who see a pool in which most AP chose to Pay in Decision 1 and those who see a pool in which most AP chose to Not Pay in Decision 1. All the 126 PP will see situations in which most AP in their pool chose to Not Pay. Therefore, I expect about 252 observations where AP are in a pool in which most AP chose to Not Pay in Decision 1 and 126 where AP are in a pool in which most AP chose to Pay in Decision 1.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power analysis for the main between-subject hypothesis: the weak bystander effect (defined as the difference between the percentage of bystanders helping when alone and when in a group) increases as the percentage of bystanders who chose to Pay in Decision 1 increases. If, for example, subjects help with the same frequency when they are alone regardless of the pool composition, then this hypothesis means that subjects are more likely to choose to Pay when they are in a group in which most people chose to Not Pay in Decision 1 than when they are in a group in which most people chose to Pay in Decision 1. This hypothesis is tested between-subject (second stage) and within-subject (third stage).
I performed the power analyses through simulations (the STATA code is available upon request). I assume that 60% of the subjects choose to Pay in Decision 1. I only analyze the decisions of those subjects when they are in groups of 2 and 3 AP.
I assume that 55% (70%) of the subjects choose to Pay in the pool in which most AP chose to (Not) Pay in Decision 1. Therefore, I study an effect size of 15 percentage points. The test I perform is a Wilcoxon ranksum test.
Power to find a significant effect at the 5% level for the between-subject test: 85.3%.
Power to find a significant effect at the 5% level for the within-subject test: 99%.
INSTITUTIONAL REVIEW BOARDS (IRBs)
Post Trial Information
Is the intervention completed?
Intervention Completion Date
June 29, 2018, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
June 29, 2018, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms