Elaboration Likelihood Model Analysis
Caitlin Reeves
Rationale
The Elaboration Likelihood Model (ELM) proposes that individuals who carefully process high-quality persuasive information may experience longer-lasting attitude change than individuals who rely on peripheral cues.
This investigation examines whether the amount of time individuals spend viewing high-quality persuasive information predicts their likelihood of favoring marijuana legalization one month later.
Hypothesis
Participants who spend more time viewing high-quality persuasive information will have greater odds of favoring marijuana legalization one month later.
Variables and Method
The study involved 200 participants who initially reported having no strong opinions about recreational marijuana legalization.
Participants browsed a persuasive website for 30 minutes while eye-tracking technology recorded how long they viewed high-quality persuasive information.
The independent variable, Minutes, measures the amount of time spent viewing this information.
The dependent variable, Favor_1, indicates whether participants subsequently favored legalization (1) or did not express support (0).
Binary logistic regression was selected because the dependent variable is dichotomous.
Data and Analysis
| Predictor | Estimate | SE | z | p |
|---|---|---|---|---|
| (Intercept) | -2.3125 | 0.3897 | -5.9348 | 0 |
| Minutes | 0.1545 | 0.0241 | 6.4107 | 0 |
Results
The dataset contained 200 participants. Participants spent an average of 14.7 minutes viewing high-quality persuasive information. Approximately 49% subsequently favored legalization.
The logistic regression produced a coefficient of 0.155 for Minutes, with a standard error of 0.024 and a z statistic of 6.411.
The corresponding p-value was <0.001.
The odds ratio was 1.167, indicating that each additional minute of viewing was associated with a 16.7% change in the odds of favoring legalization.
The relationship was statistically significant at the 0.05 significance level.
Visualization
Interpretation
The analysis examined whether increased exposure to high-quality persuasive information predicted support for marijuana legalization.
The results indicate a statistically significant positive relationship between viewing time and support for legalization. Therefore, the hypothesis was supported.
These findings provide evidence relevant to the Elaboration Likelihood Model. However, viewing time is only an indirect measure of cognitive processing.
Additionally, the investigation did not establish participants’ initial positions on the specific follow-up question. Consequently, the results cannot conclusively demonstrate that the website caused long-term attitude change.
Conclusion
This investigation used binary logistic regression to examine the relationship between exposure to high-quality persuasive information and subsequent support for marijuana legalization.
The results contribute to understanding whether increased attention to persuasive information is associated with attitudes measured one month after exposure.
Future research could incorporate direct measures of cognitive processing, initial attitudes and multiple follow-up measurements to investigate long-term attitude change more thoroughly.