A study of prosocial behaviour was carried out in a European city. Prosocial behaviour is doing something to benefit, help or care for someone else. A researcher approached pedestrians in a large mall (aged between 25 and 60 years, in the researcher’s judgment) and asked them for assistance with a bus fare. A well-designed protocol was used to select the individuals who were approached, to avoid bias on the researcher’s part.

On half of the occasions when a pedestrian was approached, the researcher was accompanied by a dog. In total, 160 pedestrians were asked for help. The research question of interest was whether the presence of the dog had an effect on people’s willingness to provide assistance. The data are in dogs.xlsx. The data can be read in using the R Markdown file provided for this quiz. In column 2 is the variable "Assistance" where 0 means 'no assistance was provided', and 1 means 'assistance was provided'.

Which of the four representations below is most helpful for addressing this question?

Question

A study of prosocial behaviour was carried out in a European city.  Prosocial behaviour is doing something to benefit, help or care for someone else.  A researcher approached pedestrians in a large mall (aged between 25 and 60 years, in the researcher’s judgment) and asked them for assistance with a bus fare.  A well-designed protocol was used to select the individuals who were approached, to avoid bias on the researcher’s part.

On half of the occasions when a pedestrian was approached, the researcher was accompanied by a dog.  In total, 160 pedestrians were asked for help.  The research question of interest was whether the presence of the dog had an effect on people’s willingness to provide assistance.  The data are in dogs.xlsx.  The data can be read in using the R  Markdown file provided for this quiz.  In column 2 is the variable "Assistance" where 0 means 'no assistance was provided', and 1 means 'assistance was provided'.

Which of the four representations below is most helpful for addressing this question?

BlackTom AI · Accepted Answer

The question presents a study where the outcome is binary (assistance provided: yes/no) and the main predictor is binary (presence of a dog vs no dog). The goal is to determine whether the presence of the dog affects the likelihood of providing assistance.

Option-by-option analysis is limited here because the actual text of options A, B, C, and D (beyond their letter labels) isn’t provided in the prompt. We only have the answer key indicating that option C is the correct choice.

What would constitute the most helpful representation in this scenario (given the design of the study) is typically a representation that directly compares proportions or counts across the two groups (dog present vs dog absent) for the binary outcome (assistance yes/no). Commonly useful representations include:
- A 2x2 contingency table showing the counts (or frequencies) of 0s and 1s for Assistance across the two levels of Dog presence. This yields the clearest, most interpretable summary of the association and is the natural basis for a chi-square test of independence or a Fisher exact test if counts are small.
- A bar chart or stacked bar chart displaying the proportion of participants providing assistance in each dog condition, which visually conveys potential differences between groups.
- If the data include additional stratification or covariates, a logistic regression model (presented via odds ratios) can be informative, but the simplest, most direct representation for the primary question remains the 2x2 table with raw counts and/or proportions.

Given these principles, the most helpful representation would directly contrast dog-present vs dog-absent against the binary outcome, typically via a 2x2 table and an accompanying visualization of proportions. Now, with the constraint that option C is the correct answer in this item, we can discuss the likely intent: option C probably corresponds to a representation that aligns with the 2x2 contingency framework (e.g., a 2x2 table or a bar chart of proportions) because that form is standard for questions about the effect of a categorical predictor on a binary outcome.

Why the other options would be unsuitable in general terms (assuming they represent common alternatives):
- If an option suggested a representation that only shows overall averages without distinguishing dog presence or lack thereof, it would obscure the key comparison needed to assess the dog effect. Such an approach mixes groups and hides potential differences, making it harder to answer the research question directly.
- If an option involved a time-series line plot or a single continuous measure not partitioned by dog presence, it would not tailor the visualization to a binary predictor and a binary outcome; this reduces clarity about the specific association of interest.
- If an option proposed a model or plot that only estimates a single proportion without a comparative reference (e.g., only the proportion of assistance across all pedestrians), it would fail to isolate the comparison between the two experimental groups (dog vs no dog) that the question seeks to address.

Note: Because the actual text of options A, B, and D isn’t provided here, this reasoning focuses on the typical rationale for selecting a 2x2 contingency-based representation in a study of a binary predictor (dog presence) and a binary outcome (assistance). If option C corresponds to that kind of representation, its correctness would align with standard best practices in presenting and analyzing such data.

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