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<p><strong>Planned sample</strong></p> <p>We plan to collect 513 American residents or citizens that are 18 years old or older from the Project Implicit research website (<a href="https://implicit.harvard.edu" rel="nofollow">https://implicit.harvard.edu</a>). Based on Study 1's ST-IAT, we predict that around 13 (2.6%) participants will be excluded for misbehavior on the ST-IAT, leaving a final sample of about 500. 500 participants will afford 80% power to detect r = .124 and 95% power to detect r = .160. Participants will be excluded from all analyses if more than 10% of the trials on the ST-IAT were faster than 400 milliseconds.</p> <p><strong>Unanticipated error during data collection</strong></p> <p>Due to a programming error, our original sample (Study 3A) consisted of 446* 18 year olds (instead of people who are 18 or older) who completed the study. We saw this as an opportunity to compare the psychology of college-age participants with participants from a more age-diverse sample. So, we put the study up again (Study 3B) to collect the sample we originally pre-registered (i.e., 513 American residents or citizens that are 18 years old or older). </p> <p>The analysis plan remains unchanged except with one extra provision. We will compare Studies 3A and 3B to see if they differ in ways directly relevant to the hypotheses below, for example, if in 3b there is a positive relationship between identity and behavior and and in 3a there is a negative relationship.</p> <p>If they differ, we will report them separately within the paper and focus on the differences between them. We will also put up an online supplement showing the analyses combined across studies.</p> <p>If they do not, we will combine Studies 3A and 3B for reporting and put up an online supplement with analyses done separately by study, with any differences highlighted in the main text.</p> <p>*We had collected 350 participants when we originally detected the error. By the time the study was taken down, it had collected 94 more participants. </p> <p><strong>Confirmatory hypotheses</strong></p> <p>Each hypothesis is followed directly by the planned statistical analyses.</p> <p><em>H1</em>: Explicit and implicit environmentalist identification will be weakly or moderately positively correlated. The relationship between explicit and implicit identification will be tested using Pearson's r correlation.</p> <p><em>H2</em>: Explicit and implicit identification will each uniquely positively predict environmental behaviors (21) and environmental policy opinions (2 questions after the vignette). The two policy questions are expected to correlate highly and be combined. Therefore, two multiple linear regressions will use explicit and implicit identification to predict (1) behaviors and (2) policy opinions from the vignette.</p> <p><em>H3a-c</em>: Implicit identification will be positively predicted from trait awe, internal motivation, and environmentalist social status. Implicit and explicit environmentalism will be entered into regressions predicting each of these separately.</p> <p><em>H4</em>: If any of H3a-c holds that implicit identification is positively predicted from those constructs, then mediation is expected [H3a-c predictor] → I → (behavior, policy vignette). Two mediation models will be tested through multiple regression for direct, indirect, and total paths for the effect of [H3a-c predictor] on (behavior, policy vignette) through implicit identification with environmentalists.</p> <p><strong>Analysis Plan - Exploratory analyses</strong></p> <ul> <li>If any of the scales expected to become composites are not reliable based on Cronbach's alpha &lt; .60, exploratory analyses will be used on various combinations to work towards scoring better indices for these variables.</li> <li>Do any of trait awe, internal motivation, and environmentalist social status predict behavior and policy vignette independently of explicit and implicit identity?</li> <li>Correlate rural/urban location with explicit and implicit identification and political orientation. Does rural/urban location add unique variance to the prediction of civic behavior or policy attitudes using regression above and beyond the other predictors?</li> <li>Demographics such as age, gender, and ethnicity will also be correlated with study variables.</li> </ul> <p><strong>Analysis Plan - General Notes, Scoring, and Composites.</strong></p> <ol> <li> <p>Alpha = .05. Continuous predictors will be standardized (z-score) before analyses.</p> </li> <li> <p><em>Note</em>. The ST-IAT is counterbalanced by random assignment: half of the participants get Self + Environmentalist first, and the other half will get Other + Environmentalist first. This random condition will be used as a covariate in the regressions for H2-4 to reduce noise in the implicit ratings of identification.</p> </li> <li> <p>Each of explicit identification with environmentalism; awe; internal motivation; external motivation; environmental behavior; and policy opinions (3) will be combined into composites.</p> </li> <li> <p>If correlation &lt; .60 for the policy items, the items will be looked at separately. </p> </li> <li>If Cronbach's alpha &lt; .60 for the awe items, items 1-3 and 4-6 will be examined separately. If those subscales aren't reliable, then just the first item will be used.</li> <li>If Cronbach's alpha &lt; .60 for the internal or external motivation scales, or for environmental behavior, then items with poor reliability will be removed until scale reliability &gt; .60.</li> <li>The ST-IAT will be scored with the IAT D algorithm adapted for the ST-IAT (Greenwald, Nosek, & Banaji, 2003).</li> </ol>
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