**Planned Sample**
We plan to collect a minimum of 500 American residents or citizens from the Project Implicit research website (https://implicit.harvard.edu). 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.
The ST-IAT will be scored with the IAT D algorithm adapted for the ST-IAT (Greenwald, Nosek, & Banaji, 2003).
**Main hypotheses**
H1: Explicit and implicit identification with environmentalism will be weakly or moderately correlated.
H2: Explicit and implicit identification with environmentalism will each uniquely predict individual environmental behaviors and environmental policy opinions.
**Supplemental hypotheses**
H3: Implicit identification will uniquely predict policy attitudes better when explicit identification with environmentalism is moderate compared to high or low, (Hawkins & Nosek 2012).
Analytic note: Individuals who have ambivalent explicit identification with environmentalists may show more implicit-explicit divergence. When an explicit attitude is less important and less ingrained, there can be more noise in implicit attitudes, leading to less correspondence with explicit reports. We assume this will generalize to the current measures of identity. Tests for heteroscedasticity (the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it) will reveal if this emerges in the current study for identity.
**Analysis plan**
H1: Correlation will test the within-subjects relationship between explicit and implicit identification.
H2: Explicit and implicit identification will be regressed onto:
A) Individual environmental behaviors
B) Each environmental policy separately, unless they correlate above r = .75 in which case the policies will be combined into a composite.
H3: Moderate explicit identification will be defined as a mean of 3.5-4.5 on the 1-7 4-item scale. Moderate vs. non-moderate explicit identifiers will be separated using a dummy variable in a regression including implicit identification to predict policy opinions. See H2:B above about policy analysis.
ST-IAT counterbalancing 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:A and H2:B to reduce noise in the implicit ratings of identification.
