write results based on this data, What were your findings? First, present descriptive statistics (mean, SD of each condition). Then, explain what statistical analyses you performed on your data, and the results of the inferential statistical tests as they relate to your research question and hypothesis. Did they support your hypothesis? Use tables or graphs to present results. If space is limited, you can show tables and graphs in the appendix, but make sure to describe your results in this section.

Reference:

BIKE CHOICE

The Pearson's Chi-squared test showed there was no significant difference in food choice between the happy and doomsday conditions (² = 0.61, p = .43, Cramer's V = 0.04)

After applying the Bonferroni correction, the results remained non-significant (² = 0.61, p_adj. = 1, Cramer's V = 0.04).

FOOD CHOICE

The Pearson's Chi-squared test showed there was no significant difference in travel choice between the two conditions (² = 0.35,

p = .55, Cramer's V = 0.03)

After applying the Bonferroni correction, the results remained non-significant (² = 0.35, p_adj. = 1, Cramer's V = 0.03)

E-MAIL

The Pearson's Chi-squared test showed there was no significant difference in number of participants sending the email between the two conditions (² = 3.91, p = .05, Cramer's V = 0.09)

After applying the Bonferroni correction, the results remained non-significant (² = 3.91, p_adj. = 1, Cramer's V = 0.09)

Condition	Sent e-mail	Did not sent e-mail							Condition	Free bike membership for one year	Free Uber gift card						Condition	A week of free plant-based meals	A week of free regular meals
Happy	29.76%	70.24%							Happy	30.56%	69.44%						Happy	38.17%	71.83%
Doomsday	21.76%	78.24%							Doomsday	31.60%	66.95%						Doomsday	20.50%	79.50%

Hypotheses

Hypothesis 1: There will be a difference in how many participants choose the climate friendly options between the two conditions. Hypothesis 2: There will be a difference in how many participants send an email to the Minister between the two conditions.

Design Plan

Study type

Experiment - A researcher randomly assigns treatments to study subjects, this includes field or lab experiments. This is also known as an intervention experiment and includes randomized controlled trials.

Blinding

• For studies that involve human subjects, they will not know the treatment group to which they have been assigned.

Is there any additional blinding in this study?

No response

Study design

We will use a between-subjects design with 2 conditions from a randomized design. 1. Condition 1: participants will read a message that frames climate change information in a negative way (doomsday frame). 2. Condition 2: participants will read a message that frames climate change information in a happiness-inducing way (happy climate frame).

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Randomization

Participants will be randomly assigned to 1 of 2 conditions at the beginning of the study using simple randomization using Qualtrics.

Sampling Plan

Existing Data

Registration prior to creation of data

Explanation of existing data

No response

Data collection procedures

Inclusion criteria: Participants must meet the following criteria to be included in our sample: Language - Participants must be able to speak English. Student - Participants must be a UBC student registered in the HSP participation pool. Age - Participants must be at least 18 years of age. Exclusion criteria: Participants will be excluded according to the following criteria: If they withdraw before completing the survey. If their responses are incomplete (e.g., missing answers to required questions). If their responses to all questions are the same (e.g., answering "3" for every question). Attrition: Participants who are excluded from the study will be replaced.

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Sample size

We will recruit at least N=197 participants total. The units are individual participants.

Sample size rationale

We assume a small effect size of w=0.2, alpha=0.05, power=0.8, with 2 between-subjects conditions for a chi-square test, so we need a minimum of N=197 participants total.

Stopping rule

No response

Variables

Manipulated variables

The study involves manipulating one variable (framing) across two conditions. 1. Condition 1: participants will read a message that frames climate change information in a negative way (doomsday frame). 2. Condition 2: participants will read a message that frames climate change information in a happiness-inducing way (happy climate frame).

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Measured variables

Primary outcome measure: Travel prize - Participants are asked to choose between a free bike membership for one year (climate-friendly option) or a free Uber gift card. The choice will be measured. Food prize - Participants are asked to decide between a week of free plant-based meals (climate-friendly option) or a week of free regular meals. The choice will be measured. Email the Minister - Participants are asked whether they would like to send an email to the Minister of Environment and Climate Change Canada to support a climate policy. Whether they send an email will be measured.

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Indices

No response

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Analysis Plan

Statistical models

To examine Hypothesis 1: There will be a difference in how many participants choose the climate friendly options between the two conditions, we will conduct a chi-square test for the travel prize and another chi-square test for the food prize to see if there is a difference in how many participants choose the climate friendly options between the two conditions. To examine Hypothesis 2: There will be a difference in how many participants send an email to the Minister between the two conditions, we will conduct a chi-square test to see if there is a difference in how many participants send an email between the two conditions.

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Transformations

No response

Inference criteria

We will use the standard two-tailed, alpha=0.05 criteria for determining the significance of the effect.