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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Addict Behav. Author manuscript; available in PMC 2014 May 1.
Published in final edited form as:
PMCID: PMC3654653

Are they all the same? An exploratory, categorical analysis of drinking game types[star]


Drinking games have become a ubiquitous part of the college student drinking culture and are associated with drinking to intoxication and increased alcohol consequences. Contemporary research commonly considers drinking games holistically, with little to no consideration to the different drinking game types. The current study describes the creation of a novel DG categorization scheme and reports differences between DG categories. Participants were 3421 college students (58% female) who completed online surveys. Based on participant responses, 100 distinct drinking games were identified and defined. Two student focus groups were conducted in which drinking game definitions and rules were verified by students. Drinking games were then categorized into five mutually exclusive categories: Targeted and Skill games, Communal games, Chance games, Extreme Consumption games, and Even Competition games. Finally, the frequency of games played in each category and typical player profiles were reported. Differences in peak drinks and frequency of specific alcohol consequences were documented according to game categories. The findings provide a novel drinking game categorization scheme and an exploratory analysis of basic differences between game categories.

Keywords: College student drinking, Drinking games, Drinking game types, Sex differences, Alcohol consequences

1. Introduction

Drinking games (DGs) are a fixture of the college social scene with 50 to 62% of students reported having played DGs in the past month (Borsari, 2004). Consistent research confirms that DGs encourage rapid and heavy alcohol consumption that is strongly associated with negative alcohol-related consequences such as poor grades, unplanned sexual activity, and interpersonal conflicts (for review, see Borsari, 2004). Despite this link between DG participation and consequences, student who play DGs hold positive alcohol expectancies, believing DG alcohol consumption will results in desirable outcomes such as tension reduction and liquid courage (Ham, Zamboanga, Olthuis, Casner, & Bui, 2010; Zamboanga, Schwartz, Ham, Borsari, & Van Tyne, 2010). Research strongly links drinking consequences to game participation (Johnson & Cropsey, 2000; Zamboanga, Schwartz, Ham, et al., 2010; Zamboanga, Schwartz, Van Tyne, et al., 2010), in one case even crediting DG participation with explaining as much as 50% of the variance in consequences resulting from alcohol consumption (Johnson, Wendel, & Hamilton, 1998). Research typically treats the over 500 types of DGs as one homogenous category (Borsari, Bergen-Cico, & Carey, 2003), although more recently, studies have indicated that DGs do vary on important alcohol-related factors such as level of intoxication (Cameron, Leon, & Correia, 2011; Zamboanga, Calvert, O’Riordan, & McCollum, 2007; Zamboanga, Leitkowski, Rodriguez, & Cascio, 2006). The current research seeks to establish a DG categorization system based on drinking behavior directly resulting from specific DG rules or procedures and to explore differences between different DG categories.

1.1. Drinking game consequences

Nagoshi, Wood, Cote, and Abbit (1994) explain the relationship between DG participation and consequences, suggesting that the act of getting drunk while playing DGs mediates the link between DG participation and resulting alcohol consequences. The finding of drunkenness as a mediator is significant as it is not uncommon for DG players to drink to extreme levels, evidenced by one study reporting 89% of participants either witnessing or experiencing loss of consciousness due to DG drinking (Polizzotto, Saw, Tjhung, Chua, & Stockwell, 2007). Moreover, increased DG frequency is related to increased negative drinking consequences (LaBrie, Hummer, Kenney, Lac, & Pedersen, 2011; Pedersen & LaBrie, 2006; Zamboanga, Schwartz, Ham, et al., 2010; Zamboanga, Schwartz, Van Tyne, et al., 2010). Considering the mediational role of intoxication between drinking game participation (Nagoshi et al., 1994) and the tendency for DG players to drink to intoxication (Polizzotto et al., 2007), the need to understand factors leading to high levels of intoxication is just as, if not more important to understanding resulting consequences than game participation alone.

1.2. Heterogeneous analyses of DGs

To our knowledge only three studies have investigated DGs heterogeneously (Cameron et al., 2011; Zamboanga et al., 2006, 2007). Two studies examined differences between specific DGs. The first study analyzed the heterogeneity of DGs by focusing on the popularity, type of beverage consumed, and intoxication level of participants for specific DGs (Zamboanga et al., 2006). Findings revealed differences between popularity of specific games, types of alcohol used for specific games (e.g., liquor versus beer), and level of participants’ intoxication, suggesting that DGs do vary. The second study investigated the structural heterogeneity of DGs by analyzing beverage type consumed, competitiveness, intoxication level, game duration, and participation motives (Zamboanga et al., 2007). Game categories were found to vary based on beverage type, competitiveness, game duration, intoxication levels, and DG participation motives. The third study considered the games beer pong, three man, and memory and reported similar findings that alcohol consumption and blood alcohol levels varied between the game types (Cameron et al., 2011). These three exploratory studies indicate that a homogenous approach to DG research fails to capture important differences between games. Although the three studies do support a heterogeneous approach to DGs, limitations of sample demographics necessitate further work in this area using larger representative samples. The two studies by Zamboanga et al. contained female-only samples and all studies were limited in size, with sample sizes of 303, 162, and 92 respectively.

1.3. Current study

Given the relative lack of empirical research, the current study used quantitative and qualitative procedures to investigate the heterogeneity of DGs with respect to relevant alcohol outcomes. The study utilized a novel categorization scheme based on different styles of drinking behaviors resulting from specific DGs (e.g., chugging versus competing against others). The current categorization scheme was inspired from Borsari’s (2004) six descriptive DG categories. Borsari’s DG categorization scheme describes the physical nature of game play (e.g., bouncing a quarter and rolling a die), but does not focus on in-game alcohol consumption. The currently proposed categorization scheme was designed to distinguish between different DGs with criteria more related to specific consumptions levels and the primary way in which the game promotes drinking.

DG categories were examined according to player demographics as previous research has consistently demonstrated drinking behaviors vary based on gender (Johnston, O’Malley, & Bachman, 2000; Wechsler et al., 2002), race (Centers for Disease Control, 1997; Pedersen & LaBrie, 2006), and membership in fraternities or sororities (hereafter referred to as Greek status) (Meilman, Leichliter, & Presley, 1999; Park, Sher, & Krull, 2008). Also examined were variations by peak drinks consumed and by specific consequences experienced. No specific a-priori hypotheses were proposed regarding differences between categories, allowing instead for an exploratory approach. Six goals were established prior to the study’s implementation: 1) Establish a comprehensive DG categorization scheme based on each game’s specific impetus for alcohol consumption; 2) Document the frequency of games played in each category; 3) Document the frequency of each category in which students reported peak DG drinking; 4) Report typical player profiles for each category; 5) Record differences between categories for number of peak drinks consumed when peak DG drinking occurred; 6) Document proportions of DG players experiencing specific alcohol related consequences as a function of category membership.

2. Method

2.1. Participants

A random sample of 11,069 undergraduate students stratified across class year and equally portioned from two west-coast campuses, one a large public university and the other a private mid-sized university was invited to complete a Web-based screening survey approximately one month into the fall term of the academic year. Local institutional review boards at each site approved the current study. Of these, 4984 (45.0%) completed the screening survey. For the purposes of the analyses, we were only interested in participants who reported consuming at least one alcoholic drink during a typical week. A total of 3421 participants met this criteria (68.3% of sample) and were selected for analyses. Recruitment rates were comparable to other large-scale studies among this population (e.g., Marlatt et al., 1998; McCabe, Boyd, Couper, Crawford, & D’Arcy, 2002).

The assessment took approximately 30 min to complete and participants received $25 for completion. Participants were informed that their responses were confidential and would not be connected to their name or e-mail address. Prior to answering questions related to drinking behavior, a standard drink was defined as a drink containing one-half ounce of ethyl alcohol — one 12 oz. beer, one 4 oz. glass of wine, or one 1.25 oz. shot of 80 proof liquor. Pictures of standard drinks accompanied these descriptions.

Analyses considered two distinct groups of participants: those who drank at least one standard alcoholic drink in a typical week but did not play a DG in the past 30 days and those who did play at least one DG in the past 30 days. Of the participants reporting drinking one or more standard alcoholic drinks in a typical week and no DG participation (n=1018; 29.9%), the mean age was 20.3 years (SD =1.42) and 70.4% were female. The distribution by class year was 12.3% first year, 17.7% sophomore, 28.2% junior, 41.3% senior and 45.5% of participants identified as Caucasian, 30.6% Asian, 10.8% Multiracial, 9.4% Other, and 3.7% African American. Among the group of participants who did play DGs (n=2403, 70.1%), the mean age was 19.9 years (SD=1.076) and 53.4% were female. The distribution by class year was 17.0% first year, 24.9% sophomore, 27.4% junior, and 30.8% senior. Over half (60.4%) of participants identified as Caucasian, 18.8% Asian, 10.9% multiracial, 8.4% other, 1.5% African American.

2.2. Measures

Participants first provided information regarding their age, birth sex, racial identity, and Greek status (membership in a fraternity or sorority).

DG behavior was assessed using four items. First, game participation was determined by asking participants “In the past 30 days, how many days did you play drinking games?” Participants responded with 0 to 30 days. Participants who reported playing at least one DG were then asked, “What drinking games did you play?” and were allowed to type in the names of the games they played. As a follow-up to that question, participants were then asked, “In which of the games listed above did you consume the most alcohol?” Participants were allowed to type in the name of only one DG. Peak alcohol consumption while playing DGs was assessed by asking “How many drinks did you typically consume when you played the above game?” (referring to the single game they listed in which they consumed the most alcohol while playing).

Alcohol consequences were assessed using the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989). The RAPI documented the occurrence of 25 negative alcohol-related consequences from one’s drinking (e.g., “Passed out or fainted suddenly” and “Noticed a change in your personality”). Two items assessing driving after two or more and four or more drinks were added to the standard 23-item measure. In order to capture more severe consequences that occur less frequently such as memory loss due to drinking, the RAPI measured consequences over the past three months. Participants rated each item on a scale from 1 (never) to 4 (more than 10 times). The RAPI was dichotomized with “0” indicating that that consequence was not experienced in the past three months and “1” indicating that it was experienced. Dichotomous scoring has been shown to be reliable and valid in identifying alcohol-related problems among college students (Martens, Neighbors, Dams-O’Connor, Lee, & Larimer, 2007). Inter-item reliability was very good for the dichotomized RAPI (α=.90).

2.3. Individual game descriptions

Specific DGs were sourced from the open-ended question that asked what DGs the participants played in the last 30 days. DG descriptions were obtained by conducting internet searches for rules and/or descriptions of the games (search terms: [name of game], drinking game, rules, description). After consolidating duplicate or extremely similar games and eliminating unidentifiable games (e.g., “that game last night” or “that one with cards”), exactly 100 distinct games were identified and described; these are reported in Table 1.

Table 1
DG definitions and reported DG.

2.3.1. Game description validation

Two focus groups (Ns=8 and 9) were conducted to verify the wording of the DG descriptions. Students were presented with each DG and its corresponding description and were then instructed to read each definition and voice any concerns regarding the game’s description or directions for play. Furthermore, students were directed to only critique the global nature of the game description and ignore minor rule nuances or personal variations to the game. The group discussed and agreed upon each suggested change for gameplay description and for the primary way in which the game prompts alcohol consumption.

2.4. Game category definitions

Once the DG descriptions were finalized, five distinct and mutually exclusive DG categories were established based on the commonalities between games and the defining characteristics of the games that precipitate alcohol consumption (e.g., games requiring that a participant drinks based on the roll of a die were grouped together into a single category). These five categories were inspired by Borsari’s (2004) six general, descriptive game types. The five categories that were established are as follows: (1) Targeted and Skill games, (2) Communal games, (3) Chance games, (4) Extreme Consumption games, and (5) Even Competition games (see Table 1).

Targeted and Skill games are games in which a single person uses skill to either avoid drinking or target others to drink. An example is “Speed Quarters”, in which an individual bounces a coin into a shot glass as quickly as possible. When the individual successfully bounces the quarter into the shot glass, he or she passes the shot glass to another player of his or her choice, thereby targeting another player and making the other player more likely to drink.

Communal games are group activities where there are no winners or losers. Drinking is prompted by external cues, such as phrases in a television show, with all group members taking a sip of an alcoholic drink each time the cue appears.

Chance games rely on random events such as the role of a die or the guess of a playing card value. An example is King’s Cup, where an individual must draw a card from a deck of cards and perform the drinking action associated with that card (e.g., “Three is me,” player takes a drink or “nine is rhyme,” players take turns rhyming until a player makes a mistake and must drink).

Extreme Consumption games are defined as games where one or more standard drinks are consumed rapidly; there is little else to the “game.” An example is drinking a whole beer as fast as possible (i.e., chugging a beer).

Even Competition games are games where individuals or teams of equal size compete against each other and the losing person or side will drink. A popular example is beer pong/Beirut, in which teams of two individuals attempt to throw ping pong balls into the opposing side’s cups, forcing them to drink that cup and remove it from the table. The team that makes all of their shots first wins and the losing side drinks as a result. Full definitions of each game category and example games are given in Table 1.

2.4.1. Individual game categorization

Game categorization was completed by four professional staff members at different times. Each staff member was instructed to read through the five category definitions and ask any questions that required clarification. Each staff member was then given five example DGs to categorize to confirm he or she understood the distinctions between the categories. After successfully completing the example coding, staff members were instructed to continue categorizing each game based on the primary way in which each DG prompts alcohol consumption (e.g., result of a die roll, players assigns a drink to another player). Fully 95% of the games had majority agreement between the four coders. Krippendorff’s alpha for interrater reliability was .88, indicating reliable coding (Hayes & Krippendorff, 2007; Krippendorff, 2004). Furthermore, steps were taken to resolve the disagreement on the categorization of the remaining five games by allowing all four coders to discuss the disagreed upon games and come to unanimous agreements on their final categorizations.

3. Results

3.1. Analytic plan

Frequency descriptives were computed to assess the percentage of game-playing drinkers who played each type of game category in the past month, as well as the percentage of game players who reported drinking the most while playing a game within each of the five categories. Only 2.2% of players reported peak DG drinking while playing Communal DGs. Because of the Communal category’s extremely low frequency it was left out of further analyses.

Next, group-based differences using z tests of independent proportions to examine the percentage of players that reported consuming their peak number of drinks while playing a game in a specific category were computed as a function of gender, race, and Greek-status (Table 2). For analytic purposes, race was dichotomized as 0 (White) and 1 (non-White). Additionally, the average peak amount of drinks typically consumed for each category was calculated overall, and by gender, race, and Greek status. Within each subgroup, one-way ANOVAs assessed differences in peak drinks reported between categories (Table 3). Finally, the percentages of participants experiencing each of the 25 alcohol-related consequences sourced from the RAPI were analyzed. Chi-square goodness-of-fit tests were run to determine if the percentage of players experiencing a consequence varied by game category. If a significant chi-square revealed which categories were different from one another, a series of z tests of independent proportions revealed which categories were different. Consequence analyses are reported in Table 4.

Table 2
Peak DG drinking independent proportions tests by gender, race, and Greek status.
Table 3
Peak DG drinking means and ANOVA comparisons overall and by gender, race, and Greek status.
Table 4
Significant overall and between groups differences in consequences experienced by game types.

3.2. Games played

In the past 30 days 72.8% of participants played an Even Competition game, 55.5% played a Chance game, 26.3% played a targeted or skill game, 7.2% played an Extreme Consumption game, and 4.7% played a Communal game. Furthermore, 54.4% of participants reported typically drinking the most while playing an Even Competition game, 29.8% while playing a Chance game, 9.6% while playing a targeted or skill game, 4.2% while playing an Extreme Consumption game, and 2.2% while playing a Communal game.

3.3. Participant characteristics

Chi-square goodness-of-fit analyses verified that there were significant differences within each subgroup of males, females, White students, non-White students, Greek students, and non-Greek students for the DG category in which students reported peak DG drinking. Each subgroup was unequally distributed between the four categories. Next, z tests for independent proportions were run between corresponding subgroup pairs for each category (e.g., males versus females). All proportions and comparisons are shown in Table 2. The proportion of students reporting peak DG drinking while playing a targeted and skill game did not vary by gender. For Chance games, a significantly greater proportion of females reported peak DG drinking. For Extreme Consumption and Even Competition games, a greater proportion of males reported peak DG drinking.

Considering race, for Targeted and Skill and Extreme Consumption games, the proportion of Whites and non-Whites reporting peak DG drinking did not vary. For Chance games, a greater proportion of non-Whites as compared to Whites reported peak DG drinking, and, for Even Competition games, a greater proportion of Whites as compared to non-Whites reported peak DG drinking.

Finally, for Targeted and Skills games and Extreme Consumption games, there was no difference in proportions by Greek status. For Chance games, a higher proportion of non-Greeks reported peak DG drinking than did Greeks. For Even Competition games, a greater proportion of Greeks reported peak DG drinking than non-Greeks.

3.4. Peak drinking

One-way ANOVAs assessed mean differences between the four categories for the peak number of drinks consumed while playing a DG overall, F (3,2178)=11.42, p<.001, and by the six sub-populations: males, F (3,1012)=7.06, p<.001, females, F (3,1162)=2.38, p=.07, Whites, F (3,1343)=6.43, p<.001, non-Whites, F (3,831)=4.94, p= .002, Greeks, F (3,695)=9.08, p<.001, and non-Greeks, F (3,1473)= 4.59, p=.003. Further, a series of Fisher’s least significant difference (LSD; Fisher, 1935) post-hoc analyses was used to determine which means were significantly different within each category, with the exception of females due to the non-significant ANOVA. Means and post-hoc analyses are reported in Table 3.

3.5. Consequences

For each of the 25 dichotomized RAPI items, chi-square goodness-of-fit tests were used to determine if the proportion of DG players reporting that specific consequence was equally distributed across the four categories. If a significant chi square was found, indicating that the proportion of DG players experiencing that consequence did vary by category, a series of z tests for two independent proportions were carried out to determine which proportions differed from each other. RAPI items that differed significantly from each other are presented in Table 4.

4. Discussion

The current study replicates previous research demonstrating that a large majority of college students participate in DGs (Borsari, 2004) and that women participated in DGs just as frequently as men (Newman, Crawford, & Nellis, 1991; Pedersen & LaBrie, 2006). Given the high prevalence and widespread participation in DGs, there is a need to better understand this phenomenon. To this end, the current study focused on exploring different types of DGs on college campuses by establishing a novel DG categorization scheme and performing preliminary analyses of relevant variables between DG categories. And the use of two campuses resulted in greater diversity of the demographic characteristics, thus increasing the generalizability of findings.

Previous research acknowledged many examples of DGs and suggested general game types (Borsari, 2004; Green & Grider, 1990; Zamboanga et al., 2007). This is the first study to create an empirically supported categorization system and then categorize 100 distinct DGs. The results produced five mutually exclusive DG categories: Targeted and Skill, Communal, Chance, Extreme Consumption, and Even Competition games (see Table 1 for category definitions and examples). Although conceptually similar to previously reported DG categories, the presented categorization scheme embodied two advantages over previous work. First, it utilized qualitative data from a large sample. Second, the categories are distinct and mutually exclusive from one another based on the manner and causes of in-game alcohol consumption, better allowing for empirical research.

4.1. Popularity of DG categories

When considering all types of DGs played in the past month, Even Competition games were most popular, with nearly three quarters of all DG players participating in an Even Competition game in the past month. This large percentage for Even Competition games is likely driven by the current popularity of Beer Pong. However, Chance and Targeted and Skill games were common as well, with about 50% and 25% of players respectively participating in one of these games types. Extreme Consumption and Communal games were played less frequently than the other categories.

4.1.1. Frequency of DG categories associated with peak drinking

DGs which resulted in a player’s peak drinking also varied with about half of the participants reporting that Even Competition games and about a third reporting that Chance games were responsible for peak drinking. Fewer participants reported that Targeted and Skill games or Extreme Consumption games were responsible for peak DG drinking, while still fewer reported that their peak drinking occurred during a Communal game. Regardless of sex, race, or Greek-status, the Even Competition games were reported most frequently as the DG category responsible for peak drinks followed by Chance, Targeted and Skill, and Extreme Consumption games.

4.1.2. Demographic differences in DG categories associated with peak drinking

Analyses also explored differences between which DG categories participants reported peak DG drinking by sex, race, and Greek status and found similar patterns. When compared to their respective counterparts, a significantly greater percentage of females, non-Whites, and non-Greek-affiliated students reported peak DG drinking during Chance games and a significantly greater percentage of males, Whites, and Greek-affiliated students reported peak DG drinking during Even Competition games. Finally, a significantly greater percentage of males as compared to females reported peak DG drinking during Extreme Consumption games. To summarize, the results suggest that males, Whites, and Greek-affiliated students have a greater tendency than their counterparts to engage in peak DG drinking during Even Competition games, while females, non-Whites, and non-Greek affiliated students have a greater tendency to engage in peak DG drinking during a Chance game. Although peak DG drinking is reported most commonly during Even Competition games, there are tendencies for certain groups to report peak DG drinking while playing games from other categories.

4.2. DG categories’ associations with number of peak drinks

Differences in the number of peak DG drinks consumed for each category were evidenced for all DG players, as well as by sex, race, and Greek status. Overall, players reporting peak DG drinking during an Extreme Consumption game drank the most, nearly six drinks. Players reporting drinking their peak drinks during Targeted and Skill games reported the second highest number of drinks, and players reporting both Chance and Even Competition games were not significantly different and were the lowest among the categories.

Slightly different results emerged when taking into consideration sex, race, and Greek status. When analyzed by sex, female drinking did not differ between categories while male drinking was highest for Extreme Consumption and Targeted and Skill games. For both Whites and non-Whites, Extreme Consumption and Targeted and Skill games were responsible for the highest number of peak DG drinks. For Greek-affiliated students, Extreme Consumption games were responsible for the highest peak drinks and for non-Greek-affiliated students, extreme and Targeted and Skill games were responsible for the greatest number of peak drinks.

Considering all players and players by sex, race, and Greek status, a general trend revealed that Extreme Consumption games were responsible for the greatest number of peak drinks. Targeted and Skill games also appeared to be responsible for elevated levels of peak drinking for some groups. Given that Extreme Consumption games are solely focused on heavy drinking to the exclusion of any other rules and requirements these findings make intuitive sense. Also, considering the rules of Targeted and Skill games allow players to ‘target’ other players to drink and that students often team up to target specific players (Green & Grider, 1990), it would be expected that this game style would also be responsible for elevated consumption. However, the range of peak drinks was often less than one drink and not greater than two drinks, indicating that although differences existed, they were subtle. Finally, females did not exhibit any differences between DG categories. The lack of a significant difference may in part be driven by females’ general tendencies to drink less than males (Ham & Hope, 2003).

4.3. DG categories and alcohol consequences

A preliminary analysis of past three month RAPI alcohol consequences revealed that only four of the consequences differed in their frequency between DG categories. For the consequences of “caused shame of embarrassment” and “felt feelings of going crazy,” players reporting peak DG drinking in Extreme Consumption games reported significantly higher frequencies of these consequences than those whose peak DG drinking occurred in the other categories. For “spent too much money on alcohol” players reporting peak DG drinking during Extreme Consumption and Chance games reported significantly higher frequencies than those in the other categories. Finally, for “memory loss,” players reporting peak DG drinking during Chance games reported significantly lower frequencies of this consequence. Although these results may suggest an elevated risk for Extreme Consumption games with almost half of players reporting peak DG drinking in this category also reporting shame and embarrassment and over a third reporting memory loss because of their drinking, it should be noted that for the majority of the consequences items considered, there were no significant differences between DG categories. Additionally, though some consequences are experienced at lower frequencies in certain game categories, this should not be taken as evidence that some DGs may be lower risk. Roughly 28–42% of DG players reported experiencing memory loss and shame and embarrassment as a result of drinking across all game types; a concerning statistic. It must be noted that these results are only a preliminary analyses of DG consequences since they cannot directly link DG participation to specific consequences. Future research can greatly expand on these results and draw more conclusive implications by documenting event-level DG specific consequences by category.

4.4. Implications

Although exploratory, the current study holds implications for educators, clinicians, and researchers working to address problematic alcohol behaviors. DG research has largely considered all DGs to be comparable across relevant variables. Whether this was because differences between types of games were previously unrealized or there was no categorization system available is unclear. The current study addresses both these points by establishing an empirically supported categorization system and determining that differences do exist between DG types. There are three primary benefits of the findings. First, the categorization system allows educators, clinicians, and researchers to better understand the exact nature of DGs with a specific focus on the manners and causes of in-game alcohol consumption. This can in turn allow for more tailored prevention and intervention programs. For example, specific behavioral strategies to avoid elevated alcohol consumption can be customized to each category instead of a one size fits all approach. Targeted and Skill game players may benefit more from a strategy such as counting drinks whereas in Even Competition games it is clearer how many drinks one has consumed per game round.

Second, a more general DG approach may work well with women who do not evidence any consumption level differences between categories while men may benefit from a more targeted effort addressing the games more likely to result in higher levels of peak drinking.

Third, this study calls for substantial future research on DG categories. Future research can utilize the categorization system to explore a number of potential hypotheses. Two areas ripe for exploration are exploring average drinks consumed per occasion and analyzing DG specific consequences to better understand average DG drinking behavior and directly resulting alcohol consequences.

4.5. Limitations and future directions

Despite the contributions the study provides, it should be viewed in the light of several limitations. DG rules and styles of play likely vary by region. Since our focus groups were conducted on one campus, it is possible some DG definitions were influenced by regional nuances. However, regional differences likely are not enough to shift a particular DG from one category to another. For example, Beer Pong, as it is known to most west coast college students or Beirut as it is known to most east coast students varies widely in regards to rules and styles of game play (Borsari, 2004). Yet, none of these variations change the Even Competition nature of the game, thus it would be consistently categorized based on the proposed categorization scheme.

The DGs reported by participants should also not be considered an exhaustive list, however, the proposed categorization scheme allows for individuals with knowledge of unlisted games to easily categorize them. Additionally, it has been suggested that DG players may refuse drinks or may have “side drinks” (i.e., drinks that are consumed while playing DG that are in addition to drinks they consume as a results of the game) (Correia & Cameron, 2010). Future research should try and account for refusals of game drinks or consumption of additional drinks. It may also be worthwhile to determine if the frequency of DG participation or the nature of DG playing changes at different points in the school year (e.g., after final examinations, during school holidays). Finally, the consequences examined were neither DG specific nor DG event specific. Although considerable evidence links DG participation to increased consequences (Nagoshi et al., 1994; Polizzotto et al., 2007), further research is needed to determine if specific consequences are a results of specific participation in certain DGs.

4.6. Conclusion

The current study extends research on DGs in several important ways and points to fruitful avenues for future research. DGs were shown to vary on a number of characteristics, but it is worth nothing that differences in peak drinks were not very large, often under two drinks. Furthermore, consequences were generally equivalent across game categories, indicating that all DGs are indeed risky. The analyses of differences between game categories are limited due to the exploratory nature of the study and lack of an experimental design. Nevertheless, utilizing the categorization system established in the current study, future research can better investigate important differences between DG categories to expand the initial work presented in the current study. The novel scheme also provides those working directly with college students and other youth for whom DG is a normative experience with an understanding of the games that are being played and with useful information to reduce risks associated with DGs.


  • Documented, defined, and categorized 100 distinct drinking games.
  • Recorded frequency of games played in each drinking game category.
  • Reported typical player profiles for each drinking game category.
  • Analyzed peak drinking differences between categories.


Role of funding sources

This research was funded by Grants R01 AA 012547-06A2 and R21AA020104-02 from the National Institute of Alcohol Abuse and Alcoholism (NIAAA). NIAAA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.


[star]This research was supported by National Institute on Alcohol Abuse and Alcoholism grants R01AA12547 and R21AA020104-02.


Joseph LaBrie, Phillip Ehret, and Justin Hummer have each contributed significantly to this manuscript. Specifically, Dr. LaBrie generated the idea for the study, designed the study, wrote the protocol, and contributed to all sections of the manuscript. Phillip Ehret developed the specific hypotheses tested, performed the literature review, performed the statistical analyses, and drafted all sections of the paper. Justin Hummer significantly assisted with drafting the introduction, discussion, and results section and revising/editing all sections of the paper.

Conflict of interest

All authors declare that they have no conflicts of interest.


  • Borsari B. Drinking games in the college environment: A review. Journal of Alcohol and Drug Education. 2004;48(2):29–51.
  • Borsari B, Bergen-Cico D, Carey KB. Self-reported drinking-game participation of incoming college students. Journal of American College Health. 2003;51(4):149–154. [PMC free article] [PubMed]
  • Cameron JM, Leon MR, Correia CJ. Extension of the simulated drinking game procedure to multiple drinking games. Experimental and Clinical Psychopharmacology. 2011;19(4):295–302. [PubMed]
  • Centers for Disease Control. Youth risk behavior surveillance: National college health risk behavior survey — United States, 1995. Morbidity and Mortality Weekly Report. 1997;46(SS-6):1–54. [PubMed]
  • Correia CJ, Cameron JM. Development of a simulated drinking game procedure to study risky alcohol use. Experimental and Clinical Psychopharmacology. 2010;18(4):322–328. [PubMed]
  • Fisher RA. The design of experiments. Oxford, England: Oliver & Boyd; 1935.
  • Green TA, Grider SA. Reversal of competence in college drinking games. Play & Culture. 1990;3(2):117–132.
  • Ham LS, Hope DA. College students and problematic drinking: A review of the literature. Clinical Psychology. 2003;23(5):719–759. [PubMed]
  • Ham LS, Zamboanga BL, Olthuis JV, Casner HG, Bui N. No fear, just relax and play: Social anxiety, alcohlol expectancies, and drinking games among college students. Journal of American College Health. 2010;58(5):473–479. [PubMed]
  • Hayes AF, Krippendorff K. Answering the call for a standard reliability measure for coding data. Communication Methods and Measures. 2007;1:77–89.
  • Johnson TJ, Cropsey JL. Sensation seeking and drinking game participation in heavy drinking college students. Addictive Behaviors. 2000;25(1):279–286. [PubMed]
  • Johnson TJ, Wendel J, Hamilton S. Social anxiety, alcohol expectancies, and drinking-game participation. Addictive Behaviors. 1998;23(1):65–79. [PubMed]
  • Johnston LD, O’Malley PM, Bachman JG. NIH Publication No 00–4803. Rockville, MD: National Institute on Drug Abuse; 2000. Monitoring the Future national survey results on drug use, 1975–1999 Volume II: College students and adults age 19–40.
  • Krippendorff K. Content analysis: An introduction to its methodology. 2. Thousand Oaks, CA: Sage; 2004.
  • LaBrie JW, Hummer J, Kenney S, Lac A, Pedersen E. Identifying factors that increase the likelihood for alcohol-induced blackouts in the prepartying context. Substance Use & Misuse. 2011;46(8):992–1002. [PMC free article] [PubMed]
  • Marlatt GA, Baer JS, Kivlahan DR, Dimeff LA, Larimer ME, Quigley LA, et al. Screening and brief intervention for high-risk college student drinkers: Results from a 2-year follow-up assessment. Journal of Consulting and Clinical Psychology. 1998;66(4):604–615. [PubMed]
  • Martens MP, Neighbors C, Dams-O’Connor K, Lee CM, Larimer ME. The factor structure of a dichotomously scored Rutgers Alcohol Problems Index. Journal of Studies on Alcohol and Drugs. 2007;68(4):597–606. [PubMed]
  • McCabe SE, Boyd CJ, Couper MP, Crawford S, D’Arcy H. Mode effects for collecting alcohol and other drug use data: Web and U.S. mail. Journal of Studies on Alcohol. 2002;63(6):755–761. [PubMed]
  • Meilman PW, Leichliter JS, Presley CA. Greeks and athletes: Who drinks more? Journal of American College Health. 1999;47(4):187–190. [PubMed]
  • Nagoshi CT, Wood MD, Cote CC, Abbit SM. College drinking game participation within the context of other predictors of alcohol use and problems. Psychology of Addictive Behaviors. 1994;8(4):203–213.
  • Newman IM, Crawford JK, Nellis MJ. The role and function of drinking games in the university community. Journal of American College Health. 1991;39(4):171–175. [PubMed]
  • Park A, Sher K, Krull J. Risky drinking in college changes as fraternity/sorority affiliation changes: A person–environment perspective. Psychology of Addictive Behaviors. 2008;22(2):219–229. [PMC free article] [PubMed]
  • Pedersen ER, LaBrie JW. Drinking game participation among college students: Gender and ethnic implications. Addictive Behaviors. 2006;31(11):2105–2115. [PMC free article] [PubMed]
  • Polizzotto MN, Saw MM, Tjhung I, Chua EH, Stockwell TR. Fluid skills: Drinking games and alcohol consumption among Australian university students. Drug and Alcohol Review. 2007;26(5):469–475. [PubMed]
  • Wechsler H, Lee JE, Kuo M, Seibring M, Nelson TF, Lee H. Trends in college binge drinking during a period of increased prevention efforts. Findings for 4 Harvard School of Public Health College Alcohol Study surveys: 1993–2001. Journal of Ameri-can College Health. 2002;50(5):203–217. [PubMed]
  • White HR, Labouvie EW. Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol. 1989;50(1):30–37. [PubMed]
  • Zamboanga BL, Calvert BD, O’Riordan SS, McCollum EC. Ping-pong, endurance, card, and other types of drinking games: Are these games of the same feather? Journal of Alcohol and Drug Education. 2007;51(2):26–39.
  • Zamboanga BL, Leitkowski LK, Rodriguez L, Cascio KA. Drinking games in female college students: More than just a game? Addictive Behaviors. 2006;31(8):1485–1489. [PubMed]
  • Zamboanga BL, Schwartz SJ, Ham LS, Borsari B, Van Tyne K. Alcohol expectancies, pregaming, drinking games, and hazourdous alcohol use in a multi-ethnic sample of college students. Cognitive Therapy and Research. 2010;34(2):124–133.
  • Zamboanga BL, Schwartz SJ, Van Tyne K, Ham LS, Olthuis JV, Huang S, et al. Drinking game behaviors among college students: How often and how much? The American Journal of Drug and Alcohol Abuse. 2010;36(3):175–179. [PubMed]