Predictive validity of an implicit association test measuring attitudes toward cannabis: Confirmatory data analytic plan
Creators: name: Howell, Jennifer
name: Lindgren, Kristen
name: Meese, William
name: Lindgren, Kristen P.
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology
subject: CUDIT
subject: cannabis
subject: health psychology
subject: implicit
subject: implicit attitudes
subject: substance use

description: Evaluating the predictive validity of an implicit association test that measures beliefs about cannabis presents a timely solution to contemporary issues surrounding complex attitudes and beliefs that are often difficult to assess through self-report measures. Specifically, measuring implicit beliefs about cannabis might help predict differences in the risk of hazardous cannabis use above and beyond what is captured by explicit self-report measures. This is important because implicit measures might be more sensitive to identifying and predicting risks that would otherwise go undetected using explicit self-report methods. Research on relatively more automatic and less consciously controlled measures of attitudes suggests that implicit associations tied to health predict health behavior intentions beyond explicitly assessed attitudes (Ratliff &amp; Howell, 2015) and predict health information avoidance beyond explicit reports (Howell et al., 2016). 
Existing work examining implicit attitudes and beliefs about cannabis is sparse. Yet, initial evidence suggests that implicit associations differ between users and non-users and, further, that stronger implicit associations of cannabis as harmful, as opposed to safe, predict lower current and future (3, 6 month) cannabis use (Ramirez et al., 2020). This work added to existing substance use research examining implicit cognition because it used both a longitudinal—rather than cross-sectional—design with a community—rather than online—sample. However, because they did not measure explicit attitudes toward cannabis and used a relatively unique, potentially less-generalizable sample (young people living in the Pacific Northwest), it remains unclear whether measuring implicit attitudes towards cannabis as safe or harmful explains unique variance in self-reported risk of hazardous cannabis use. The primary purpose of this study is to examine the internal reliability and predictive validity of an Implicit Association Test (Greenwald et al., 1998) designed by Ramirez and colleagues (2020) to measure attitudes toward cannabis as dangerous or safe. 
To extend prior work, the present study will use a large sample of adults (estimated sample size close to 10,000, see description of recruitment below) who reside in the United States to evaluate the unique contribution of implicit associations between cannabis and danger in predicting risk of hazardous cannabis use after controlling for explicit associations and to evaluate whether and how this relationship differs between different demographic groups and across different levels of self-reported physical and mental health.
We plan to split data that has already been collected (between August 2019 and August 2022) into an initial exploratory set (35% of observations) and a confirmatory set (65%). We have identified several theoretically-derived expectations to test in the exploratory data set (see below). After inspecting this initial exploratory model (e.g., regression diagnostics, model coefficients) we will revise and update this pre-registration with specific expectations regarding the analytic model, including code that we will use to examine the remaining 65% of extracted observations in a confirmatory model. 

Based on our preregistered exploratory analysis, we are preregistering a confirmatory plan that we will use to test the confirmatory data set. As such, we have uploaded the analysis code that we will use to examine our expectation that implicit attitudes will uniquely predict variance in cannabis use broadly and risk of hazardous use specifically, above and beyond explicit attitudes. Based on our evaluation of the exploratory analysis, we will use linear regression models entering CUDIT sum scores rather than using latent variable models because we could not justify this approach based on our results. As a supplement to our primary analysis, we plan to include analysis and accompanying code for latent variable models so that interested readers can examine them. Demographic variables did not meaningfully influence model results when entered as covariates, and we expect this to remain true in the confirmatory data; we will include these supplementary analyses for interested readers.