'Longtermism, Climate Change Emotions and Coping'
(AsPredicted #150427)
Created: 11/09/2023 07:21 AM (PT)
This is an anonymized version of the pre-registration. It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.
1) Have any data been collected for this study already?No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? In previous studies we have found consistent evidence that longtermists feel concerned for climate change/global warming. In this investigation we wish to determine longtermists' emotional reactions to and coping with climate change, as well as reasons behind why they might cope or react in specific emotional ways.
First, we hypothesize that longtermists will report significantly higher legacy concerns (H1), and ability to generate environmental cognitive alternatives (H2) compared to the rest of the population.
Second, we hypothesize that because climate change is an extinction risk, longtermists will feel more angry about climate change (H3a), less climate contempt (H3b), more climate hope/enthusiasm (H3c), less climate powerlessness (H3d), more climate guilt (H3e), more climate anxiety (H3f), and more climate sorrow (H3g). We have varied predictions regarding how longtermists versus general population controls will score on climate isolation. On one hand, it's possible that longtermists may, on account of climate change representing an extinction threat, perceive their care regarding climate change as being higher than average, and consequently feel greater isolation. Conversely, longtermists may overestimate normative alignment with their own climate change attitudes and feel less isolation compared to general population controls as a result. Finally, because longtermists are taking active steps to address climate change, and they seek to protect future generations, we hypothesize that they will score higher on problem-focused coping with climate change (H3h), meaning-focused coping with climate change (H3i), and lower on avoidant coping with climate change (H3j)
We expect that higher scores in legacy concerns and ability to generate environmental cognitive alternatives will relate to more anger about climate change (H4a-H4b), less climate contempt (H5a-H5b), more climate hope/enthusiasm (H6a-H6b), less climate powerlessness (H7a-H7b), more climate guilt (H8a-H8b), more climate anxiety (H9a-H9b), more problem-focused coping (H10a-H10b), more meaning-focused coping (H11a-H11b), and less avoidant-coping (H12a-H12b).
Because of H1-H12, we also expect significant indirect effects of longtermism matching the aforementioned directional associations on climate emotions via higher legacy concerns and environmental cognitive alternatives (H13).
3) Describe the key dependent variable(s) specifying how they will be measured. Longtermism beliefs will be captured with a 7-item measure developed by the researchers. Importantly, each item will be completed simultaneously at four different timeframes/timepoints (1,000, 10,000, 100,000, and 1,000,000 years in the future). Scores will be captured on slider scales ranging from 0 = strongly disagree – 100 = strongly agree. Participants will be classified as longtermists if they score above 75 for the closest temporal timeframe, and they have the same score, or higher for future timeframes.
Legacy concerns will be captured using three items from Zaval et al. (2015) and measured on 7-point Likert scale.
Environmental Cognitive Alternatives will be captured with 10 items from Wright et al. (2020), on a 7-point Likert scale.
Climate emotions will be measured each with four items, developed by Marczak et al., (2023), on a 7-point Likert scale.
All different methods of coping with climate change (problem-focused, meaning-focused, avoidant) were taken directly or adapted from Ojala (2012). Each method of coping comprises 5 items, measured on a 7-point Likert scale.
4) How many and which conditions will participants be assigned to? No random assignment will take place.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. All analyses will be conducted with SAS or Rstudio.
H1, H2, H3a-H3j will be tested via independent sample t-tests.
H4-H4b, H5-H5b, H6-H6b, H7-H7b, H8-H8b, H9-H9b, H10-H10b, H11a-H11b, H12a-H12b will be tested via linear regressions.
H13 will be tested via indirect effect tests which will be evaluated using the PROCESS Macro (Hayes, 2013), Model 4 with 10,000 bootstrapped samples, with legacy and environmental cognitive alternatives as parallel mediators.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We will exclude participants who miss our attention check (selecting the correct choice in a multiple-choice question) and participants with duplicate IP addresses.
7) How many observations will be collected or what will determine sample size?
No need to justify decision, but be precise about exactly how the number will be determined. We will recruit a sample size of 800 participants from Prolific. Based on previous results, we expect that roughly 20%-25% of the population will score in the longtermist pattern for the LBS. This allows us to detect effect sizes as small as d = 0.20 with power set to 0.80, and alpha of 0.05.
8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) 1. All main analyses, will be estimated while controlling for age, gender, SES, and political orientation, expecting results to remain similar in terms of direction and statistical significance, but with decreases in the magnitude of differences/associations.
2. We will replicate the results of Table 5 (i.e., correlations between climate emotions) from Marczak et al., (2023).
