Exploring the Relation between Marijuana Legality and Cannabis use While Gambling

Abstract

As cannabis becomes more socially acceptable, and more states continue to pass laws allowing medical or recreational marijuana usage, questions around how this legalization may impact harms relating to cannabis use while gambling arise. Gambling while under the influence of cannabis has been associated with risky gambling behavior and an increased risk of gambling disorder. In this study we recruited a sample of 769 US-based frequent gamblers and examined the differences in endorsement of gambling under the influence of cannabis, differences in the frequency, intensity, and duration of gambling behavior, and differences in gambling pathology based on the legal status of marijuana in the individuals’ state of residence. We found significant differences based on legality status, with the percentage of participants most likely to endorse gambling under the influence of cannabis living in jurisdictions where marijuana was medically permitted. There were no differences in self-reported gambling behavior or problem gambling severity based on the legal status of marijuana. We found no association between the legal status of marijuana and an increase in harm related to gambling under the influence of cannabis.

I. INTRODUCTION

The legalization of marijuana and gambling bear similar policy hallmarks in the United States.¹ Both are potentially addictive behaviors that have been taxed on the premise of reducing crime and redirecting monetary value from illegal streams to government revenue upon legalization.² Marijuana laws generally relieve restrictions on the access to various forms of cannabis within a specific jurisdiction while gambling laws typically legislate specific jurisdictional gambling options. As marijuana becomes increasingly legally accessible, questions have been raised about the impact of its use on gambling and problem gambling behaviors. A central point in this discussion is how marijuana-access laws relate to responsible gambling programs and policies. Specifically, is legal access to marijuana associated with an increase in gambling risk or the incidence of problematic gambling behaviors?

Prior research suggests that gambling while under the influence of cannabis is not uncommon among those who gamble regularly. In a US-based survey about concurrent engagement in cannabis use and gambling, individuals who gambled at least weekly were asked about their gambling and cannabis use. Thirty percent endorsed gambling under the influence of cannabis (GUIC) at least some of the time.³ In a Canadian study, 56.2% of cannabis users who gambled at least monthly endorsed engaging in both behaviors simultaneously.⁴ Finally, in a survey of gamblers, where most respondents were classified as high risk for a gambling disorder as defined by the symptom criteria in the DSM 5⁵ (94% met criteria for problem gambling), 48% of participants reported engaging in cannabis use while gambling.⁶ In each of these studies, GUIC was associated with an increased engagement in gambling and increased possibility of gambling disorder.

The substance use literature provides indirect research to support the view that individuals under the influence of cannabis are likely to experience impairments in executive functioning, which refers to the ability to coordinate thoughts and actions and direct them toward obtaining goals.⁷ The specific cognitive effects of cannabis may include interference with memory and learning, reduced attentional focus, impaired decision making, and increased risk taking.⁸ These types of impairments could affect gambling behaviors and put individuals who GUIC at a greater risk for gambling disorder.⁹

It is currently unknown whether GUIC and the incidence of gambling pathology are related to the legal status of marijuana. Investigations into the effects of the legal status of marijuana on cannabis use are relatively new, and no firm conclusions can be made regarding direct effects.¹⁰ Various researchers have documented mixed findings. Specifically, Smart and Pacula¹¹ found a minor increase in cannabis behaviors in states with recreational marijuana laws (RML) in adults but not in adolescents. Weinberger and colleagues¹² noted the most likely to be affected by the legalization of cannabis are adults with anxiety, who have increased cannabis use at a higher rate in states with medicinal marijuana laws (MML) and higher still in jurisdictions with RML. Another study found an increase in hospitalization rates relating to cannabis use in RML states, and a “moderate” increase in frequency of cannabis use in adults.¹³ A recent meta-analysis of research into cannabis use and legality in the United States found a correlation between cannabis availability and increased frequency of use, concluding that in states with MML or RML laws adults report higher frequencies of current cannabis usage but did not find a consistent relationship between cannabis legality and “risky” cannabis use.¹⁴

Taken together, these findings call for a clearer understanding of how gambling behavior differs in jurisdictions based on marijuana laws, particularly for those who might also meet criteria for a gambling disorder. The current article used a sample of weekly gamblers to explore these differences.

Toward this goal we examined:

(1)
Differences in endorsement of GUIC based on the legal status of marijuana in the individuals’ state of residence.
(2)
Differences in the frequency, intensity, and duration of gambling behavior based on the legal status of marijuana laws in their state of residence.
(3)
Differences in gambling pathology based on the marijuana laws in their state of residence in individuals who GUIC.

II. METHODS
A. Participants

Respondents were recruited via Amazon Mechanical Turk (Mturk). To be eligible, an individual had to reside in the United States, be at least 18 years old, and report gambling at least once per week. Of the 895 respondents who provided consent, 93 were removed because they did not pass the embedded instructional manipulation checks,^15,16 and 33 were removed for providing inconsistent responses. The final sample included 769 participants.

Participant demographics are displayed in Table 1. The mean age was 36.86 (SD = 10.83). Most identified themselves as male (65.6%), White (69.2%), heterosexual (88.2%), and holding bachelor's degrees or higher (72%). Over half were married (59.2%). They gambled an average of 3.85 (SD = 1.92) days per week, spent an average of 4.99 (SD = 4.13) hours per gambling episode, and risked an average of US$256.63 (SD = 276.91) per episode.

Table 1.
Demographic Variables of Sample as a Whole and Split by Marijuana Legality Status

Total sample Illegal (n = 228) Medicinal (n = 346) Recreational (n = 195) χ² p

N % n % n % n %

Race 6.82 0.033

Caucasian 532 69.2 149 65.4 256 74.0 127 65.1

Other 237 30.8 79 34.6 90 26.0 68 34.9

Sexual Orientation 3.23 0.199

Exclusively heterosexual 678 88.2 196 86.0 313 90.5 169 86.7

Not exclusively heterosexual 91 11.8 32 14.0 33 9.5 26 13.3

Education 11.62 0.020

No college 57 7.4 25 11.0 23 6.6 9 4.6

Some college 158 20.5 52 22.8 75 21.7 31 15.9

Bachelors or higher 554 72.0 151 66.2 248 71.7 155 79.5

Household Income 5.24 0.513

>$30,000 105 13.7 30 13.2 54 15.6 21 10.8

$30,000–$59,999 260 33.8 84 36.8 111 32.1 65 33.3

$60,000–$89,999 204 26.5 63 27.6 88 25.4 53 27.2

≥$90,000 200 26.0 51 22.4 93 26.9 56 28.7

Relationship status 7.66 0.022

Married 455 59.2 146 64.0 186 53.8 123 63.1

Other 314 40.8 82 36.0 160 46.2 72 36.9

Gender (n = 766) 2.44 0.295

Male 505 65.9 141 61.8 233 67.9 131 67.2

Female 261 34.1 87 38.2 110 32.1 64 32.8

M SD M SD M SD M SD F(2,766) P

Age 36.86 10.83 36.18 10.08 37.16 11.19 37.10 11.04 0.632 0.532

	Total sample	Illegal (n = 228)	Medicinal (n = 346)	Recreational (n = 195)	χ²	p
Race									6.82	0.033
Caucasian	532	69.2	149	65.4	256	74.0	127	65.1
Other	237	30.8	79	34.6	90	26.0	68	34.9
Sexual Orientation									3.23	0.199
Exclusively heterosexual	678	88.2	196	86.0	313	90.5	169	86.7
Not exclusively heterosexual	91	11.8	32	14.0	33	9.5	26	13.3
Education									11.62	0.020
No college	57	7.4	25	11.0	23	6.6	9	4.6
Some college	158	20.5	52	22.8	75	21.7	31	15.9
Bachelors or higher	554	72.0	151	66.2	248	71.7	155	79.5
Household Income									5.24	0.513
>$30,000	105	13.7	30	13.2	54	15.6	21	10.8
$30,000–$59,999	260	33.8	84	36.8	111	32.1	65	33.3
$60,000–$89,999	204	26.5	63	27.6	88	25.4	53	27.2
≥$90,000	200	26.0	51	22.4	93	26.9	56	28.7
Relationship status									7.66	0.022
Married	455	59.2	146	64.0	186	53.8	123	63.1
Other	314	40.8	82	36.0	160	46.2	72	36.9
Gender (n = 766)									2.44	0.295
Male	505	65.9	141	61.8	233	67.9	131	67.2
Female	261	34.1	87	38.2	110	32.1	64	32.8

	M	SD	M	SD	M	SD	M	SD	F(2,766)	P
Age	36.86	10.83	36.18	10.08	37.16	11.19	37.10	11.04	0.632	0.532

Using p value corrected with Bonferroni correction for multiple comparisons of p = 0.007; none of the above comparisons are considered significant.

A power analysis revealed approximately 80% statistical power or likelihood to detect very modest between-group differences (i.e., partial η² ≥ 0.01) at the standard α = 0.05. This sample size, therefore, is adequate to detect small associations between jurisdictional status of marijuana and the gambling by those in this sample.

B. Measures

1. Demographics

Participants were asked to identify their age, gender, race/ethnicity, sexual orientation, US State of residence, relationship status, if they have children in their household, income, and education.

2. Gambling behavior

Participants were asked how many days a week they gambled (1–7), how much they risk on an average gambling day (in US Dollars), and how long they typically gamble on a gambling day (in hours).

3. GUIC

Two items assessed participants’ GUIC. One item assessed the frequency of the behavior asking, “What percent of the time you gambled were you also under the influence of cannabis during the last year?” with response options ranging from 1 to 100.

4. Problem Gambling Severity Index

The 9-item Gambling Severity Index (PGSI) assessed gambling problem severity over the past year.¹⁷ Responses were indicated on a 4-point Likert-type scale, ranging from 0 = not at all to 3 = often. Item ratings were summed to yield a maximum score of 27. Individuals with a score below 8 can be classified as having a gambling problem or not likely to meet diagnosis for a gambling disorder. Those who reach a cutoff score of 8 are likely to meet diagnostic criteria. The PGSI is a screening measure, which indicates the risk level of an individual of having a diagnosable gambling disorder (as defined by the DSM). Individuals classified as problem gamblers on this measure are associated with a high likelihood of gambling pathology.¹⁸

C. Procedure

With Institutional Review Board approval, Mturk workers with a Human Intelligence Task approval rate of 80%, an index that helps ensure the quality of the respondent, were invited to complete an anonymous, self-report questionnaire packet about gambling and mental health. Those who provided consent and were found eligible were invited to complete a 30-minute packet of questionnaires that ended with a listing of resources (e.g., phone numbers and websites for finding local mental health providers and problem gambling treatment services) and a reimbursement code.

D. Data analytic plan

Statistical Package for the Social Sciences version 29 was used for all analyses. Chi-square and one-way analysis of variance (ANOVA) were completed to examine demographic differences between the three legality groups (i.e., nonlegal, MML, and RML). Demographic variables with significant differences were retained as covariates in subsequent analyses (Table 1).

To address the project's first question, 3 × 2 chi-square and ANOVA were used to explore whether endorsement of GUIC was different for those living in states with differing marijuana laws. The binary GUIC variable (i.e., yes vs. no) was created by classifying individuals who endorsed GUIC for any percentage of their gambling time into the GUIC group and individuals who reported not consuming cannabis while gambling as no GUIC. A 3-level categorical variable was created for the marijuana legal status variable (i.e., not legal, MML, and RML) based on the legal status of marijuana in participants’ self-reported state of residence. At the time of data collection, marijuana was illegal in 27 states, medical marijuana was legal in 12 states, and recreational marijuana was legal in 11 states.

The project's second question was to consider whether gambling behaviors were related to marijuana laws within those who GUIC. A one-way multivariate analysis of variance (MANOVA) was conducted with the 3-level legality variable as the independent factor and the gambling indices: gambling frequency (days per week an individual gambled), gambling duration (average number of hours gambling on a gambling day), gambling intensity (the average amount wagered during a gambling day), and percentage of gambling time spent GUIC as the dependent variable.

The project's final question was to explore if problem gambling was related to marijuana laws within those who GUIC. Participants’ total PGSI score was calculated and the cut-off of 8 or above was used to determine problem gambling risk status. A 2 × 3 chi-square analysis was used to determine if there was an association between individuals classified as at risk for having a gambling problem and states with different marijuana laws. For further investigation of the relationship, a one-way independent subjects ANOVA was run with the 3-level factor of legality as an independent variable and total PGSI score (continuous) as the dependent variable.

III. RESULTS

A. GUIC and marijuana laws

Table 2 presents the percentage of participants GUIC based on states’ marijuana legality laws. In the total sample, 46.2% (n = 355) of participants reported GUIC. In states where marijuana was illegal, 44.7% (n = 102) reportedly engaged in GUIC. In MML states, 42.2% (n = 146) engaged in GUIC. In RML states, 54.9% (n = 107) engaged in GUIC. These differences in percentages were significantly different (N = 769, χ² = 8.33, p = 0.016), indicating a difference in the number of people reporting any engagement of GUIC between states with differing marijuana laws.

Table 2.
3 × 2 Chi-Square Analysis of Marijuana Legality and GUIC

Total sample Illegal (n = 228) Medicinal (n = 346) Recreational (n = 195) χ² p

N % n % n % n %

GUIC 8.33 0.016

Yes 355 46.2 102 28.7 146 41.1 107 30.1

No 414 53.8 126 30.4 200 48.3 88 21.3

	Total sample	Illegal (n = 228)	Medicinal (n = 346)	Recreational (n = 195)	χ²	p
GUIC									8.33	0.016
Yes	355	46.2	102	28.7	146	41.1	107	30.1
No	414	53.8	126	30.4	200	48.3	88	21.3

GUIC, gambling under the influence of cannabis.

B. Gambling behavior and marijuana laws

Table 3 presents gambling behaviors and gambling harm, based on states’ marijuana laws, among participants who GUIC. On average, these participants gambled 4.22 (SD = 1.76) days per week, 6.87 (SD = 4.64) hours per gambling day, and 322.52 (SD = 289.27) dollars per gambling session. Participants reported that 46% of the time they gambled was under the influence of cannabis. The average score on the PGSI was 10.50 (SD = 5.72), and 72.4% (n = 257) were classified as at risk for gambling problems. There were no significant differences in these gambling behaviors or gambling harms based on state marijuana laws (all p > 0.05).

Table 3.
One-Way Multivariate Analysis of Variance Comparisons by Marijuana Legality

Multivariate result F(8,698) Wilk's λ p Partial η²

0.976 0.976 0.400 0.012

All GUIC (355) Illegal (n = 102) Medicinal (n = 146) Recreational (n = 107) F(2,352) p Partial η²

Univariate results M (SD) M (SD) M (SD) M (SD)

Gambling days per week 4.22 1.76 4.17 1.75 4.31 1.78 4.16 1.77 0.293 0.746 0.002

Gambling hours per day 6.87 4.64 7.45 4.83 6.38 4.57 6.95 4.51 1.793 0.168 0.010

Money risked in a gambling session 322.52 289.27 287.08 269.01 344.37 309.69 326.50 278.20 1.193 0.304 0.007

Proportion of gambling time GUIC 46.39 29.62 45.43 29.20 45.79 30.03 48.14 29.66 0.269 0.764 0.002

Multivariate result	F(8,698)	Wilk's λ	p	Partial η²
	0.976	0.976	0.400	0.012

	All GUIC (355)	Illegal (n = 102)	Medicinal (n = 146)	Recreational (n = 107)	F(2,352)	p	Partial η²
Gambling days per week	4.22	1.76	4.17	1.75	4.31	1.78	4.16	1.77	0.293	0.746	0.002
Gambling hours per day	6.87	4.64	7.45	4.83	6.38	4.57	6.95	4.51	1.793	0.168	0.010
Money risked in a gambling session	322.52	289.27	287.08	269.01	344.37	309.69	326.50	278.20	1.193	0.304	0.007
Proportion of gambling time GUIC	46.39	29.62	45.43	29.20	45.79	30.03	48.14	29.66	0.269	0.764	0.002

GUIC, gambling under the influence of cannabis.

A one-way MANOVA was conducted to determine whether there is a difference in gambling behavior between individuals who GUIC residing in states with differing marijuana laws. There was no significant difference in gambling behavior in those who GUIC in states with differing marijuana laws (F(8,698) = 1.046, p = 0.400, Wilk's λ = 0.976, partial η² = 0.012; see Table 3).

C. Problem gambling and marijuana laws

The third aim of this project was to consider whether participants who GUIC had different problem gambling severity based on the state marijuana laws. PGSI scores in the total sample ranged from 0 to 27 with an average score of 7.63. Within those who GUIC, scores had the same range as the full sample, with an average score of 10.50, and 72.4% (n = 257) classified as gamblers at risk for a diagnosis of gambling disorder. In those who did not GUIC, scores ranged from 0 to 23 with an average score of 5.17, and 27.5% classified as likely experiencing gambling disorder diagnosis.

A 3 × 2 chi-square was used to answer whether classification in the likely diagnosable range on the PGSI was associated with marijuana legality within those who GUIC. The result of this analysis was not significant (χ²(2, 355) = 3.63, p = 0.162), not supporting that marijuana laws were related to problem gambling while GUIC (Table 4).

Table 4.
Chi-square and One-Way ANOVA Comparisons with Marijuana Legality as Independent Variable and PGSI as Dependent Variable

All GUIC (355) Illegal (n = 102) Medicinal (n = 146) Recreational (n = 107) F(2,347.97) p Partial η²

M (SD) M (SD) M (SD) M (SD)

PGSI score 10.45 5.47 11.11 5.14 10.09 6.03 10.32 4.96 1.070^a 0.344 0.028

PGSI classification n % n % n % n % x² p

3.63 0.162

High risk 257 72.4 79 30.7 98 38.1 80 31.1

Not high risk 98 27.6 23 23.5 48 49.0 27 27.6

	All GUIC (355)	Illegal (n = 102)	Medicinal (n = 146)	Recreational (n = 107)	F(2,347.97)	p	Partial η²
PGSI score	10.45	5.47	11.11	5.14	10.09	6.03	10.32	4.96	1.070^a	0.344	0.028

PGSI classification	n	%	n	%	n	%	n	%	x²	p
									3.63	0.162
High risk	257	72.4	79	30.7	98	38.1	80	31.1
Not high risk	98	27.6	23	23.5	48	49.0	27	27.6

Degrees of freedom (df) for this item are (2, 347.97) due to a Brown–Forsythe correction.

ANOVA, ANALYSIS of variance; GUIC, gambling under the influence of cannabis; PGSI, Gambling Severity Index.

In addition, there was no significant effect of marijuana legality on PGSI score as a continuous variable of reporting gambling problems (F(2,352) = 1.07, p = 0.344, partial η² = 0.028; see Table 4).

IV. DISCUSSION

As cannabis becomes more socially acceptable, the continued changes in marijuana laws have raised the question of how marijuana legality is associated with gambling behavior and gambling problems. To answer this question, we first examined the differences in the percentage of frequent gamblers who GUIC based on whether they lived in US jurisdictions where marijuana was illegal, medically permitted, or recreationally available. We found significant differences based on legality status with the percentage of participants who GUIC highest for those living in jurisdictions where marijuana was medically permitted. However, there were no differences in self-reported gambling behavior (e.g., money risked, days per week), or problem gambling severity, based on the legal status of marijuana. We found no association between the legal status of marijuana and an increase in harm related to GUIC.

Our findings are consistent with the broader literature of the association between marijuana legislation and cannabis use. A recent review¹⁹ found a relationship between legality and frequency of use but did not find a strong relation between legality and risky cannabis behavior. The current study supplements these findings by showing that there were no differences in harm related to GUIC based on jurisdictions’ marijuana laws. As the current study was the first investigating the association between GUIC and jurisdictions’ marijuana laws, we are cautious regarding the wider interpretation of our results.

However, the legal status of marijuana being unrelated to the harms of cannabis while gambling, does not eliminate the finding that cannabis use is strongly linked with greater involvement in gambling and gambling harms. GUIC has previously been shown to be associated with more risky gambling behavior. Individuals who GUIC were found to gamble more often, for longer, and to risk more money than those who did not GUIC.²⁰ Individuals who GUIC were also much more likely to be classified as at a high risk for gambling disorder.²¹ That GUIC has been established as an activity associated with a significant increase in gambling-related harm highlights the need for the development of educational materials and responsible gambling policies that directly target this behavior. That this harm does not appear to be associated with the legality of marijuana puts this issue into focus. The harmful behavior is being engaged in irrespective of legality, and therefore regulations should be a priority, rather than something which can wait for the federal legalization of marijuana. Therefore, although we found no significant differences in gambling behavior within those who GUIC between states with differing marijuana laws, that there was an increase in GUIC engagement is still a cause for concern and should be a focus of regulation, irrespective of specific marijuana laws.

There are limitations to the current research. The sample, while adequate to detect small differences and explore a novel concept, is not a random sample representative of the US population. Adequately understanding the association between the prevalence of GUIC and its association with jurisdictions’ marijuana laws would require a larger, random sample of the US population that is demographically representative. Due to these restrictions, we have limited our analysis and interpretations of results to a comparison between groups of states based off their marijuana laws. This was appropriate for our exploratory study; however, future research on a larger, more representative sample would allow for a more granular examination of the issue, including analysis on a state level.

In addition, this study used participant's reported state of residence to determine which legality category individuals were assigned. This method is limited in that some people travel to gamble outside of their home state. Thus, this method could introduce inaccuracies about the legal status of the location in which participants are GUIC. Another limitation was that these cross-sectional data cannot causally link the legalization of cannabis and changes in gambling behavior. Data were collected through a crowdsourced convenience sample, which may have biased results, samples collected using Mturk tend to include respondents who are younger, employed less than full-time, and more politically liberal than the general US population.²² However, recent studies have suggested that Mturk may be useful for recruiting samples of people who regularly gamble and is acceptable for sampling those who use cannabis.²³

Consequently, the results should be interpreted with a degree of caution. To increase the field's confidence in the association between US jurisdictions’ marijuana laws and GUIC, future studies should collect a nationally representative sample to increase confidence in the findings reported here. Future studies should continue to investigate the details of the relationship between acute cannabis use while gambling and gambling-related harms with the specific goal of developing responsible gambling materials for integration with providers, therapists, and the general public, similar to what is already in place for alcohol use while gambling.

This research would suggest that this problem may need more attention before welcoming cannabis consumption in casinos. Most gaming institutes in the US have implemented responsible gambling practices, aiming to bring together key stakeholders (including industry members and clinicians) to help limit gambling-related harms.²⁴ The current research into GUIC suggests that GUIC is not an uncommon practice among frequent gamblers and is associated with an increased risk for harm.²⁵ Researchers have called upon regulators to ensure responsible gambling programs work to help gamblers in need of treatment get the best treatment possible and that the programs educate gamblers on risky gambling behavior. Indeed, researchers have previously suggested that alcohol intoxication should also be considered as part of a casinos’ responsible gambling plan,²⁶ and it may be productive for this conversation to include cannabis.

In conclusion, GUIC has been found to be related to increased gambling harms and higher incidences of problematic gambling behaviors.²⁷ In the current analysis, the legal status of marijuana does not appear to be related to the harms of GUIC. Individuals who GUIC in states where marijuana is illegal had similar rates of gambling-related harms as those in states where marijuana use was permitted in some capacity. This novel and exploratory finding highlights that more research is needed investigating GUIC to better inform clinicians when treating gambling clients and to potentially create educational messages warning of the risks of GUIC, if it is appropriate to do so. In addition, this research highlights that this harmful behavior is occurring at present and that the development and implementation of harm reduction strategies by the industry and public health stakeholders are needed imminently, rather than waiting for the federal legalization of marijuana. The regulation of cannabis by those who are gambling, and information promoting responsible usage, is required to help individuals who are experiencing increased harm due to GUIC.

Footnotes

1

Janne Nikkinen, The Legalization of Dangerous Consumption: A Comparison of Cannabis and Gambling Policies in Three US States, 25 Addiction Research & Theory 476 (2017).

2

Id.

3

Abby McPhail et al., Sweetening the Pot: Exploring Differences between Frequent Gamblers Who Do and Do Not Gamble under the Influence of Cannabis, 110 Addictive Behaviors 106531 (2020).

4

Daniel S. McGrath et al., Problem Gambling Severity, Gambling Behavior, Substance Use, and Mental Health in Gamblers Who Do and Do Not Use Cannabis: Evidence from a Canadian National Sample, 137 Addictive Behaviors 107520 (2023).

5

American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (5 ed. 2013).

6

E. Halle Smith et al., Expectations of How Acute Cannabis Use Affects Gambling Experiences and Behaviors, Cannabis (2024), (last visited Jun 6, 2024).

7

E. K. Miller & J. D. Wallis, Executive Function and Higher-Order Cognition: Definition and Neural Substrates, in Encyclopedia of Neuroscience 99 (Larry R. Squire ed., 2009), (last visited Dec 19, 2024).

8

Laura Dellazizzo et al., Evidence on the Acute and Residual Neurocognitive Effects of Cannabis Use in Adolescents and Adults: A Systematic Meta‐review of Meta‐analyses, 117 Addiction 1857 (2022).

9

Ken C. Winters & James P. Whelan, Gambling and Cannabis Use: Clinical and Policy Implications, 36 J Gambl Stud 223 (2020).

10

Rosanna Smart & Rosalie Liccardo Pacula, Early Evidence of the Impact of Cannabis Legalization on Cannabis Use, Cannabis Use Disorder, and the Use of Other Substances: Findings from State Policy Evaluations, 45 Am J Drug Alcohol Abuse 644 (2019).

11

Id.

12

Andrea H. Weinberger et al., Cannabis Use among US Adults with Anxiety from 2008 to 2017: The Role of State-Level Cannabis Legalization, 214 Drug Alcohol Depend 108163 (2020).

13

Wayne Hall & Michael Lynskey, Assessing the Public Health Impacts of Legalizing Recreational Cannabis Use: The US Experience, 19 World Psychiatry 179 (2020).

14

Jakob Manthey et al., The Impact of Legal Cannabis Availability on Cannabis Use and Health Outcomes: A Systematic Review, 116 International Journal of Drug Policy 104039 (2023).

15

David J. Hauser & Norbert Schwarz, Attentive Turkers: MTurk Participants Perform Better on Online Attention Checks than Do Subject Pool Participants, 48 Behav Res Methods 400 (2016).

16

Daniel M. Oppenheimer, Tom Meyvis & Nicolas Davidenko, Instructional Manipulation Checks: Detecting Satisficing to Increase Statistical Power, 45 Journal of Experimental Social Psychology 867 (2009).

17

J Ferris & Harold Wynne, The Canadian Problem Gambling Index: Final Report, (2001).

18

Shawn R. Currie et al., Defining a Threshold of Harm from Gambling for Population Health Surveillance Research, 9 International Gambling Studies 19 (2009).

19

Manthey et al., supra note 14.

20

McPhail et al., supra note 3; McGrath et al., supra note 4; Smith et al., supra note 6.

21

McPhail et al., supra note 3; McGrath et al., supra note 4; Smith et al., supra note 6.

22

Joseph K. Goodman & Gabriele Paolacci, Crowdsourcing Consumer Research, 44 Journal of Consumer Research 196 (2017).

23

Hyoun S. Kim & David C. Hodgins, Reliability and Validity of Data Obtained from Alcohol, Cannabis, and Gambling Populations on Amazon's Mechanical Turk, 31 Psychology of Addictive Behaviors 85 (2017).

24

See Robert Ladouceur et al., Responsible Gambling: A Synthesis of the Empirical Evidence, 25 Addiction Research & Theory 225 (2017).

25

McPhail et al., supra note 3; McGrath et al., supra note 4.

26

Alex Blaszczynski et al., Responsible Gambling: General Principles and Minimal Requirements, 27 J Gambl Stud 565 (2011).

27

McPhail et al., supra note 3; McGrath et al., supra note 4; Smith et al., supra note 6.

	All GUIC (355)		Illegal (n = 102)		Medicinal (n = 146)		Recreational (n = 107)		F(2,352)	p	Partial η²
Univariate results	M	(SD)	M	(SD)	M	(SD)	M	(SD)	F(2,352)	p	Partial η²
Gambling days per week	4.22	1.76	4.17	1.75	4.31	1.78	4.16	1.77	0.293	0.746	0.002
Gambling hours per day	6.87	4.64	7.45	4.83	6.38	4.57	6.95	4.51	1.793	0.168	0.010
Money risked in a gambling session	322.52	289.27	287.08	269.01	344.37	309.69	326.50	278.20	1.193	0.304	0.007
Proportion of gambling time GUIC	46.39	29.62	45.43	29.20	45.79	30.03	48.14	29.66	0.269	0.764	0.002

Exploring the Relation between Marijuana Legality and Cannabis use While Gambling

Abstract

I. INTRODUCTION

II. METHODS

1. Demographics

2. Gambling behavior

3. GUIC

4. Problem Gambling Severity Index

C. Procedure

D. Data analytic plan

III. RESULTS

A. GUIC and marijuana laws

Table 2. 3 × 2 Chi-Square Analysis of Marijuana Legality and GUIC Total sample Illegal (n = 228) Medicinal (n = 346) Recreational (n = 195) χ2 p N % n % n % n % GUIC 8.33 0.016 Yes 355 46.2 102 28.7 146 41.1 107 30.1 No 414 53.8 126 30.4 200 48.3 88 21.3

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Table 2.
3 × 2 Chi-Square Analysis of Marijuana Legality and GUIC

Total sample Illegal (n = 228) Medicinal (n = 346) Recreational (n = 195) χ² p

N % n % n % n %

GUIC 8.33 0.016

Yes 355 46.2 102 28.7 146 41.1 107 30.1

No 414 53.8 126 30.4 200 48.3 88 21.3