Abstract
Although little evidence supports cannabis-induced amotivational syndrome, sources continue to assert that the drug saps motivation [1], which may guide current prohibitions. Few studies report low motivation in chronic users; another reveals that they have higher subjective wellbeing. To assess differences in motivation and subjective wellbeing, we used a large sample (N = 487) and strict definitions of cannabis use (7 days/week) and abstinence (never). Standard statistical techniques showed no differences. Robust statistical methods controlling for heteroscedasticity, non-normality and extreme values found no differences in motivation but a small difference in subjective wellbeing. Medical users of cannabis reporting health problems tended to account for a significant portion of subjective wellbeing differences, suggesting that illness decreased wellbeing. All p-values were above p = .05. Thus, daily use of cannabis does not impair motivation. Its impact on subjective wellbeing is small and may actually reflect lower wellbeing due to medical symptoms rather than actual consumption of the plant.
Highlights
We focus on two extreme sub-samples, daily cannabis users and lifetime abstainers
Participants who used cannabis seven days a week demonstrated no difference from non-cannabis users on indices of motivation
While cannabis users were older than abstainers, age demonstrated no correlation with subjective wellbeing and a very weak correlation with motivation
Summary
Procedure Participants responded to an email request to complete an Internet survey on cannabis use and attitudes. By effectively removing 50% of the data from each tail of the distribution, the sample median demonstrates greater resistance to inflation due to outliers and heteroscedasticity [30]. Just as the median may offer a superior perspective by effectively removing 50% of the data from each end of the distribution, lower degrees of trimming may reveal more accurate estimates of central tendency. Trimming 20% offers numerous advantages over no trimming or the use of medians, including a smaller standard error [30] In this case, trimming 20% of extreme values removes all outliers, leaving a relatively normally distributed sample in tact. Yuen [32] derived a method of comparing trimmed means, while employing Welch's principles for heteroscedastic means comparison This approach can improve statistical power markedly. The resulting value, Q, indicates the separation of the distributions and strength of the effect
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