J Gambl Stud DOI 10.1007/s10899-014-9473-2 ORIGINAL PAPER
Risk Gambling and Personality: Results from a Representative Swedish Sample Kristina Sundqvist • Peter Wennberg
Springer Science+Business Media New York 2014
Abstract The association between personality and gambling has been explored previously. However, few studies are based on representative populations. This study aimed at examining the association between risk gambling and personality in a representative Swedish population. A random Swedish sample (N = 19,530) was screened for risk gambling using the Lie/Bet questionnaire. The study sample (N = 257) consisted of those screening positive on Lie/Bet and completing a postal questionnaire about gambling and personality (measured with the NODS–PERC and the HP5i respectively). Risk gambling was positively correlated with Negative Affectivity (a facet of Neuroticism) and Impulsivity (an inversely related facet of Conscientiousness), but all associations were weak. When taking age and gender into account, there were no differences in personality across game preference groups, though preferred game correlated with level of risk gambling. Risk gamblers scored lower than the population norm data with respect to Negative Affectivity, but risk gambling men scored higher on Impulsivity. The association between risk gambling and personality found in previous studies was corroborated in this study using a representative sample. We conclude that risk and problem gamblers should not be treated as a homogeneous group, and prevention and treatment interventions should be adapted according to differences in personality, preferred type of game and the risk potential of the games. Keywords Gambling Risk gambling Problem gambling Gambling disorder Personality Big five
K. Sundqvist (&) P. Wennberg Centre for Social Research on Alcohol and Drugs (SoRAD), Stockholm University, 106 91 Stockholm, Sweden e-mail: [email protected]
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Introduction In Sweden approximately 2 % of the adult population are considered problem gamblers (Swedish National Institute of Public Health 2010). In addition to this group there is a larger group with risky gambling habits. Compared to substance use disorders, the research field of problem gambling is fairly unexplored. There are several terms for excessive gambling behavior. The psychiatric diagnosis in the Diagnostic and Statistical Manual of Mental Disorders is ‘‘gambling disorder’’ (5th ed.; DSM–5; (American Psychiatric Association 2013a, b). In the former edition the term was ‘‘pathological gambling’’ and was classified as an impulse control disorder (DSM-IV-TR, American Psychiatric Association 2000). The reason for the reclassification to a (behavioral) addiction disorder are findings from the growing research of problem gambling that reveal similarities with substance use disorders. In research the broader term problem gambling is often used to also include those that do not fill the criteria for a diagnosis but still suffer significant consequences from their gambling (Blaszczynski and Nower 2002). Risk gambling is the behavior that may lead to more severe consequences, a gambler being at-risk for developing gambling problems. In this paper the term risk gambling is used to cover the spectra from mild risk to an actual gambling disorder. There are several theories aiming at understanding the etiology of problem gambling. Personality differences between problem gamblers and non-problem gamblers is one of the factors in the well-known biopsychosocial pathway model (Blaszczynski and Nower 2002). A commonly used personality model is the five factor model (FFM), which consists of broad categories of personality dimensions; Neuroticism (prone to psychological distress), Extraversion (amount of energy directed outwards), Openness (active seeking of experiences), Agreeableness (characteristics perceived as warm and considerate) and Conscientiousness (degree of organization, persistence and control), (Costa and McCrae 1992). Some other common personality models used in the research of gambling are Eysenck’s Big Three (Eysenck 1990), Zuckerman and Kuhlmans Alternative Five model (Zuckerman et al. 1993) and Cloningers psychobiological model (Cloninger et al. 1993). Prior research findings concerning the personality of problem gamblers seems fairly inconsistent. A meta-analysis (Maclaren et al. 2011) on studies that compare personality traits of non-pathological and pathological gamblers, conclude that problem gambling was associated with traits corresponding to (Gore and Widiger 2013) high neuroticism, low agreeableness and low conscientiousness, together with facets of impulsivity in the FFM. In contrast to this, Bagby et al. (2007) did not find any correlations between problem gambling and agreeableness, and Myrseth et al. (2009) found that high scores on neuroticism and low scores on openness was related to problem gambling. Studies that have investigated sensation seeking (which is similar to the facet excitement-seeking in the dimension extraversion in FFM) have come to diverse conclusions. One study concluded that problem gamblers showed higher scores than non-problem gamblers on two of the subscales of sensation seeking, but not on the others (Fortune and Goodie 2010). Another study found that excitement seeking was higher for both problem and non-problem gamblers, suggesting that it might be a characteristic common to all who gamble (Bagby et al. 2007). Several studies have found impulsivity to be correlated with problem gambling (e.g. McDaniel and Zuckerman 2003; Walther et al. 2012), whereas Mishra et al. (2010) found lack of self-control, rather than impulsivity, to be a predictor of problem gambling. Based on Cloningers personality model, elevated levels of novelty seeking (particularly impulsivity) and harm avoidance, and lower levels of self-directedness and cooperativeness have been found in problem gamblers (Forbush et al. 2008).
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In prior research many of the study samples have consisted of either treatment seeking problem gamblers or students, both groups likely to be biased concerning personality traits. Overall, there are several potential confounders that have not always been taken into account. For instance, gender and age correlate with problem gambling (Johansson et al. 2009), but there are also consistent differences in personality, thus women score higher on neuroticism and agreeableness than men (Feingold 1994). Both of these FFM-dimensions have been found to correlate with problem gambling. Hence, findings of low agreeableness among problem gamblers may be due to over-representation of men in the study sample. Another variable that may have a mediating effect is type of game preferred. For example, Welte et al. (2004) found involvement in Casino gambling to be associated with a high risk of gambling pathology, whereas lottery and bingo were associated with lower risk of gambling pathology. Possibly personality affects what type of game one prefers which in turn influences the risk of gambling problems. This study examined the association between personality and gambling behavior in Sweden. More specifically we aimed at describing: 1. The association between risk gambling and personality 2. differences in risk gambling and personality contingent on preferred type of game activity 3. differences in personality between individuals with risk gambling behavior and the general Swedish population.
Methods Participants The participants were recruited through telephone interviews in the so called Swedish Monitoring project (Boman et al. 2006). The project aims at estimating alcohol- and tobacco habits in the Swedish population. Every month 1,500 randomly assigned respondents answer questions about their alcohol and tobacco habits. From April 2012 until May 2013 all participants were also screened for risk gambling using the Lie/Bet questionnaire (Johnson et al. 1997). Respondents that reported that they had, 1. lied to people important to them about how much they gambled and/or 2. felt the need to bet more and more money, were asked to participate in an upcoming study about gambling. In total 382 of those agreed to participate and were sent a postal questionnaire (see Fig. 1). Out of the 257 participants that completed the postal questionnaire, 78 were female (30 %) and 179 male (70 %). They were between 17 and 82 years old with a mean age of 49. In total 67 % had a steady income, and 77 % had a steady income during the period when they gambled the most. Further 66 % were married or living together with someone and 53 % were married or living together with someone when they gambled the most. Inventory Personality Personality traits were assessed with the Health relevant five-factor Personality inventory (HP5i; Gustavsson et al. 2008; Gustavsson et al. 2003), a self-rating instrument based on selected facets from the five-factor model. The development of HP5i aimed at generating
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J Gambl Stud Fig. 1 Recruitment flow chart
a model based on more specific traits found to be empirically or theoretically associated with health (e.g. predictors of outcome after therapy, interventions or rehabilitation). Twenty items were derived from a pool of 196 items, mainly from the Karolinska Scales of Personality (Gustavsson et al. 1997). The HP5i traits are: 1. Antagonism: a facet of Agreeableness (inversely related), addressing an overtly hostile interpersonal style or expressive hostility. 2. Impulsivity: a facet of Conscientiousness (inversely related), i.e. choosing rapidly with little thought and to have a non-planning tendency. 3. Hedonic capacity: a facet of extraversion. It addresses the emotional core of extraversion that defines positive emotionality as a motivation in daily life. 4. Negative affectivity: a facet of neuroticism. It represents nervous tension and distress. 5. Alexithymia: a facet of openness (inversely related). It is characterized by a disinterest in recognizing and understanding feelings. Gambling To assess life-time risk gambling a short version of the National Opinion Research Center DSM-IV Screen for Gambling Problems (NODS) was used. The short version NODS– PERC, consists of four of the originally 17 questions (Volberg et al. 2001). These authors found the combination of the four questions about preoccupation, escape, risked relationships and chasing (PERC) to best predict problem gambling. Based on the NODS– PERC, respondents were considered to be on a scale from low risk gambling to high risk gambling (Lie/Bet C 1 and NODS–PERC = 0, 1, 2, 3 or 4). The respondents were asked to list their three most frequently played games. The most frequent game was categorized into one of four preferred game types: card games (poker and black jack), sports games (e.g. betting on sports and horse betting), machines (e.g. ‘‘jack vegas’’ or one arm bandits) or games of chance (scratch cards, lottery, bingo and roulette). The respondents were also classified according to the risk potential of their frequently played
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games (Meyer et al. 2011; Swedish National Institute of Public Health 2010). The respondents most frequently played game was also classified according to the risk potential of the game (Meyer et al. 2011; Swedish National Institute of Public Health 2010). Low risk games (preferred by 12 %) were characterized by long time between stake and outcome, short duration time and low variability (e.g. scratch cards and lotto). Games that had a longer time between stake and outcome and with a gambling time that was able to extend to a suite of rounds were classified as medium risk games (preferred by 43 %; sports betting, live poker, bingo and gambling on horses). Finally, games that had a short time from bet to outcome, possibility of long gambling sessions and winnings quickly paid out were classified as high risk games (preferred by 45 %; Internet poker, casino games, machines). Dropouts Out of the 607 who screened positive on one of the questions in the Lie/Bet questionnaire, 219 declined participation. There were no differences between this group and the group accepting participation neither concerning age, gender, education, binge drinking last year nor number of positive answers on Lie/Bet. Of those 382 that received the postal questionnaire, 125 did not respond. There was a significant difference between responders and non-responders in the Lie/Bet questionnaire. Among the respondents, 11 % screened positive on both questions, whereas 22 % of the non-responders, v2 (1, N = 382) = 8.19, p = .004, did. Data Analyses Data were analyzed in SPSS, version 21. The association between level of risk gambling and personality was analyzed using Pearson’s correlations, and to control for age and gender Pearson’s partial correlations. To assess whether there were any differences in personality and risk level between the groups that preferred different types of games, analysis of variance (ANOVA) was conducted with each trait as a dependent variable, and analysis of covariance (ANCOVA) was used to control for age, gender and level of risk gambling. Bonferroni post hoc test was used to assess how the groups of preferred game differed from each other. To compare the study sample with norm data, one sample t test was conducted, with norm data for each trait and gender as test value.
Results Level of Risk Gambling and Personality Traits The level of risk gambling (NODS–PERC) was significantly correlated with four of the five personality traits (see Table 1). Antagonism, Impulsivity and Negative affectivity were positively correlated with level of risk gambling, whereas Hedonic Capacity was negatively correlated. After controlling for age and gender, with Pearson’s partial correlations, Impulsivity and Negative Affectivity remained significant.1 The correlations were weak (rxy.z ranging between .17 and .19).
1
Since data are on ordinal level Spearman’s correlations and Spearman’s partial correlations were conducted. Only small differences were found, and no differences in significance, and are therefore not reported.
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Type of Game Preferred and Personality Traits Next, respondents with different game preferences (card games, sports games, machines or games of chance) were compared regarding their personality. There was a significant difference in Alexithymia (F(3, 222) = 2.98, p = .032) across game preference groups. However after controlling for age, gender and level of risk gambling the significant effect did not remain (F(3, 216) = 2.53, p = .059). Respondents’ most frequently played game was classified with low, medium or high risk potential. Respondents playing games with different risk potential were compared regarding their personality. There was a significant difference in Antagonism (F(2, 223) = 4.21, p = .016) and Hedonic Capacity (F(2, 223) = 3.98, p = .020) across the groups preferring games with different risk potential. The differences did not remain significant after controlling for age, gender and level of risk gambling (F(2, 222) = 1.48, p = .23). There was a significant difference in the level of risk gambling between the preferred game groups (F(3, 219) = 4.14, p = .007). Bonferroni post hoc test showed that the group that preferred machines scored significantly higher (p = .018) on NODS–PERC (M = 1.72, SD = 1.67), than the group that preferred games of chance (M = 0.74, SD = 1.22). There was a significant difference in the level of risk gambling between groups playing games with different risk potential (F(2, 223) = 19.33, p \ .001). Bonferroni post hoc test showed that the group that preferred games with high risk potential scored significantly higher (p \ .001) on NODS–PERC (M = 1.52, SD = 1.28), than both the group that preferred games with medium risk potential (M = .80, SD = 1.10) and the group that played games with low risk potential (M = .15, SD = .78). Personality Profiles in the Study Sample Versus the General Population The study sample of risk gamblers was compared with the general Swedish population (norm group) regarding personality traits. The risk gamblers scored lower that the norm group with respect to Negative Affectivity; men, t(171) = -3.96, p \ .001 and women, t(171) = -4.58, p \ .001 (see Fig. 2). Men in the study sample scored significantly higher on Impulsivity then did the norm group, t(171) = 3.24, p = .001.
Discussion This study examined the association between personality and risk gambling behavior in a general Swedish population. Briefly our results can be summarized as: • There were weak, but significant correlations between the level of risk gambling and four of the personality traits (Antagonism, Impulsivity, Hedonic Capacity and Negative affectivity). However, after controlling for age and gender, only Impulsivity and Negative affectivity remained positively significant. • After controlling for age, gender and risk gambling, personality did not differ neither, between different type of preferred game (card games, sports games, machines or games of chance) nor between groups preferring games with different risk potential. • Respondents that played machines reported higher levels of risk gambling than respondents that played games of chance. Respondents that played games with high
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J Gambl Stud Table 1 Pearson’s correlations and Pearson’s partial correlations between personality traits, measured with the HP5i and level of risk gambling measured with NODS–PERC (N = 245) 1
2
1. Level of risk gambling
.16*
2. Antagonism
.09
3. Impulsivity
.19**
4. Hedonic capacity
2.12
3
4
.18**
-.16*
.16*
.03
.41**
-.18**
.31*
.20**
.01
.44**
.42** 2.14*
5
6
-.20**
.00
5. Negative affectivity
.17**
.34**
.43**
2.23**
6. Alexithymia
.02
.20**
.03
2.16*
.02 -.19** .13*
.17**
The numbers in the italicized area are unadjusted correlations, whereas the numbers in the bolded area are correlations after controlling for age and gender * p \ .05, ** p \ .01
Fig. 2 The charts shows comparisons between the study sample and norm data on self-scored personality traits; AA antagonism, CI impulsivity, EH hedonic capacity, NN negative affectivity, OA alexithymia. The left chart is for women and the right is for men. *p \ .05
risk potential reported significantly higher on NODS–PERC, than gamblers that played games with low and medium risk. • The study sample scored significantly lower on Negative affectivity then the general population, and male respondents scored higher on Impulsivity then men in the general population. In prior meta analytic research, neuroticism, agreeableness and conscientiousness have been found to correlate with problem gambling. This is in part supported by this study were impulsivity (a facet of conscientiousness) and negative affectivity (a facet of neuroticism) but not antagonism (a facet of agreeableness) were found to correlate with the level of risk gambling. This is consistent with the findings from Bagby et al. (2007). A reason for different findings across studies may be differences in populations studied. In the metaanalysis by MacLaren et al. (2011) most of the included studies, compared non-pathological gamblers to treatment seeking pathological gamblers, whereas this study as well as Bagby et al. (2007) used more representative samples. Taken together, this supports the theory that personality factors may have an implication on the etiology of problem gambling. It also seems that age and gender are confounders important to consider when studying problem gambling. The results did not support the notion that personality implicates the type of game preferred. An interesting result is that the study sample of risk
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gamblers scored lower on Negative Affectivity then did the general. We find no apparent explanation for this and we suggest that this could be further explored. A limitation of the study is the small N. Furthermore, the study design did not admit a full diagnostic procedure regarding risk gambling and personality. Hence we were restricted to rely on short inventories for assessing risk gambling, personality etc. A second limitation is the risk of sampling bias. Among the non-responders 22 % screened positive on both questions on the Lie/Bet questionnaire (compared to 11 % among responders), suggesting more severe gambling problems. Previous research (Marcus and Schu¨tz 2005) have shown that non-responders have slightly lower levels of openness and agreeableness than responders. This could have had implications for our results and could partly explain the discrepancy between our results on agreeableness and previous research on treatment seeking populations. A major strength of the study is the recruitment of a representative sample of risk gamblers from the general population. This is warranted as a complement to studies done on either students or clinical samples. An alternative approach to study the link between risk gambling and personality would be to explore patterns of traits rather than single traits. Further, the relationship between risk/problem gambling and the risk potential of the games played could be a direction for future research. In conclusion, the association between risk gambling and personality found in previous studies was corroborated in this study using a representative sample. We conclude that risk and problem gamblers should not be treated as a homogeneous group, and prevention and treatment interventions should be adapted according to differences in personality, preferred type of game and the risk potential of the games. Acknowledgments The study was funded by Svenska Spel. We declare no conflict of interest. We are grateful for valuable comments on the manuscript by professor Jan Blomqvist.
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Risk Gambling and Personality: Results from a Representative Swedish Sample Kristina Sundqvist • Peter Wennberg
Springer Science+Business Media New York 2014
Abstract The association between personality and gambling has been explored previously. However, few studies are based on representative populations. This study aimed at examining the association between risk gambling and personality in a representative Swedish population. A random Swedish sample (N = 19,530) was screened for risk gambling using the Lie/Bet questionnaire. The study sample (N = 257) consisted of those screening positive on Lie/Bet and completing a postal questionnaire about gambling and personality (measured with the NODS–PERC and the HP5i respectively). Risk gambling was positively correlated with Negative Affectivity (a facet of Neuroticism) and Impulsivity (an inversely related facet of Conscientiousness), but all associations were weak. When taking age and gender into account, there were no differences in personality across game preference groups, though preferred game correlated with level of risk gambling. Risk gamblers scored lower than the population norm data with respect to Negative Affectivity, but risk gambling men scored higher on Impulsivity. The association between risk gambling and personality found in previous studies was corroborated in this study using a representative sample. We conclude that risk and problem gamblers should not be treated as a homogeneous group, and prevention and treatment interventions should be adapted according to differences in personality, preferred type of game and the risk potential of the games. Keywords Gambling Risk gambling Problem gambling Gambling disorder Personality Big five
K. Sundqvist (&) P. Wennberg Centre for Social Research on Alcohol and Drugs (SoRAD), Stockholm University, 106 91 Stockholm, Sweden e-mail: [email protected]
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Introduction In Sweden approximately 2 % of the adult population are considered problem gamblers (Swedish National Institute of Public Health 2010). In addition to this group there is a larger group with risky gambling habits. Compared to substance use disorders, the research field of problem gambling is fairly unexplored. There are several terms for excessive gambling behavior. The psychiatric diagnosis in the Diagnostic and Statistical Manual of Mental Disorders is ‘‘gambling disorder’’ (5th ed.; DSM–5; (American Psychiatric Association 2013a, b). In the former edition the term was ‘‘pathological gambling’’ and was classified as an impulse control disorder (DSM-IV-TR, American Psychiatric Association 2000). The reason for the reclassification to a (behavioral) addiction disorder are findings from the growing research of problem gambling that reveal similarities with substance use disorders. In research the broader term problem gambling is often used to also include those that do not fill the criteria for a diagnosis but still suffer significant consequences from their gambling (Blaszczynski and Nower 2002). Risk gambling is the behavior that may lead to more severe consequences, a gambler being at-risk for developing gambling problems. In this paper the term risk gambling is used to cover the spectra from mild risk to an actual gambling disorder. There are several theories aiming at understanding the etiology of problem gambling. Personality differences between problem gamblers and non-problem gamblers is one of the factors in the well-known biopsychosocial pathway model (Blaszczynski and Nower 2002). A commonly used personality model is the five factor model (FFM), which consists of broad categories of personality dimensions; Neuroticism (prone to psychological distress), Extraversion (amount of energy directed outwards), Openness (active seeking of experiences), Agreeableness (characteristics perceived as warm and considerate) and Conscientiousness (degree of organization, persistence and control), (Costa and McCrae 1992). Some other common personality models used in the research of gambling are Eysenck’s Big Three (Eysenck 1990), Zuckerman and Kuhlmans Alternative Five model (Zuckerman et al. 1993) and Cloningers psychobiological model (Cloninger et al. 1993). Prior research findings concerning the personality of problem gamblers seems fairly inconsistent. A meta-analysis (Maclaren et al. 2011) on studies that compare personality traits of non-pathological and pathological gamblers, conclude that problem gambling was associated with traits corresponding to (Gore and Widiger 2013) high neuroticism, low agreeableness and low conscientiousness, together with facets of impulsivity in the FFM. In contrast to this, Bagby et al. (2007) did not find any correlations between problem gambling and agreeableness, and Myrseth et al. (2009) found that high scores on neuroticism and low scores on openness was related to problem gambling. Studies that have investigated sensation seeking (which is similar to the facet excitement-seeking in the dimension extraversion in FFM) have come to diverse conclusions. One study concluded that problem gamblers showed higher scores than non-problem gamblers on two of the subscales of sensation seeking, but not on the others (Fortune and Goodie 2010). Another study found that excitement seeking was higher for both problem and non-problem gamblers, suggesting that it might be a characteristic common to all who gamble (Bagby et al. 2007). Several studies have found impulsivity to be correlated with problem gambling (e.g. McDaniel and Zuckerman 2003; Walther et al. 2012), whereas Mishra et al. (2010) found lack of self-control, rather than impulsivity, to be a predictor of problem gambling. Based on Cloningers personality model, elevated levels of novelty seeking (particularly impulsivity) and harm avoidance, and lower levels of self-directedness and cooperativeness have been found in problem gamblers (Forbush et al. 2008).
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In prior research many of the study samples have consisted of either treatment seeking problem gamblers or students, both groups likely to be biased concerning personality traits. Overall, there are several potential confounders that have not always been taken into account. For instance, gender and age correlate with problem gambling (Johansson et al. 2009), but there are also consistent differences in personality, thus women score higher on neuroticism and agreeableness than men (Feingold 1994). Both of these FFM-dimensions have been found to correlate with problem gambling. Hence, findings of low agreeableness among problem gamblers may be due to over-representation of men in the study sample. Another variable that may have a mediating effect is type of game preferred. For example, Welte et al. (2004) found involvement in Casino gambling to be associated with a high risk of gambling pathology, whereas lottery and bingo were associated with lower risk of gambling pathology. Possibly personality affects what type of game one prefers which in turn influences the risk of gambling problems. This study examined the association between personality and gambling behavior in Sweden. More specifically we aimed at describing: 1. The association between risk gambling and personality 2. differences in risk gambling and personality contingent on preferred type of game activity 3. differences in personality between individuals with risk gambling behavior and the general Swedish population.
Methods Participants The participants were recruited through telephone interviews in the so called Swedish Monitoring project (Boman et al. 2006). The project aims at estimating alcohol- and tobacco habits in the Swedish population. Every month 1,500 randomly assigned respondents answer questions about their alcohol and tobacco habits. From April 2012 until May 2013 all participants were also screened for risk gambling using the Lie/Bet questionnaire (Johnson et al. 1997). Respondents that reported that they had, 1. lied to people important to them about how much they gambled and/or 2. felt the need to bet more and more money, were asked to participate in an upcoming study about gambling. In total 382 of those agreed to participate and were sent a postal questionnaire (see Fig. 1). Out of the 257 participants that completed the postal questionnaire, 78 were female (30 %) and 179 male (70 %). They were between 17 and 82 years old with a mean age of 49. In total 67 % had a steady income, and 77 % had a steady income during the period when they gambled the most. Further 66 % were married or living together with someone and 53 % were married or living together with someone when they gambled the most. Inventory Personality Personality traits were assessed with the Health relevant five-factor Personality inventory (HP5i; Gustavsson et al. 2008; Gustavsson et al. 2003), a self-rating instrument based on selected facets from the five-factor model. The development of HP5i aimed at generating
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J Gambl Stud Fig. 1 Recruitment flow chart
a model based on more specific traits found to be empirically or theoretically associated with health (e.g. predictors of outcome after therapy, interventions or rehabilitation). Twenty items were derived from a pool of 196 items, mainly from the Karolinska Scales of Personality (Gustavsson et al. 1997). The HP5i traits are: 1. Antagonism: a facet of Agreeableness (inversely related), addressing an overtly hostile interpersonal style or expressive hostility. 2. Impulsivity: a facet of Conscientiousness (inversely related), i.e. choosing rapidly with little thought and to have a non-planning tendency. 3. Hedonic capacity: a facet of extraversion. It addresses the emotional core of extraversion that defines positive emotionality as a motivation in daily life. 4. Negative affectivity: a facet of neuroticism. It represents nervous tension and distress. 5. Alexithymia: a facet of openness (inversely related). It is characterized by a disinterest in recognizing and understanding feelings. Gambling To assess life-time risk gambling a short version of the National Opinion Research Center DSM-IV Screen for Gambling Problems (NODS) was used. The short version NODS– PERC, consists of four of the originally 17 questions (Volberg et al. 2001). These authors found the combination of the four questions about preoccupation, escape, risked relationships and chasing (PERC) to best predict problem gambling. Based on the NODS– PERC, respondents were considered to be on a scale from low risk gambling to high risk gambling (Lie/Bet C 1 and NODS–PERC = 0, 1, 2, 3 or 4). The respondents were asked to list their three most frequently played games. The most frequent game was categorized into one of four preferred game types: card games (poker and black jack), sports games (e.g. betting on sports and horse betting), machines (e.g. ‘‘jack vegas’’ or one arm bandits) or games of chance (scratch cards, lottery, bingo and roulette). The respondents were also classified according to the risk potential of their frequently played
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games (Meyer et al. 2011; Swedish National Institute of Public Health 2010). The respondents most frequently played game was also classified according to the risk potential of the game (Meyer et al. 2011; Swedish National Institute of Public Health 2010). Low risk games (preferred by 12 %) were characterized by long time between stake and outcome, short duration time and low variability (e.g. scratch cards and lotto). Games that had a longer time between stake and outcome and with a gambling time that was able to extend to a suite of rounds were classified as medium risk games (preferred by 43 %; sports betting, live poker, bingo and gambling on horses). Finally, games that had a short time from bet to outcome, possibility of long gambling sessions and winnings quickly paid out were classified as high risk games (preferred by 45 %; Internet poker, casino games, machines). Dropouts Out of the 607 who screened positive on one of the questions in the Lie/Bet questionnaire, 219 declined participation. There were no differences between this group and the group accepting participation neither concerning age, gender, education, binge drinking last year nor number of positive answers on Lie/Bet. Of those 382 that received the postal questionnaire, 125 did not respond. There was a significant difference between responders and non-responders in the Lie/Bet questionnaire. Among the respondents, 11 % screened positive on both questions, whereas 22 % of the non-responders, v2 (1, N = 382) = 8.19, p = .004, did. Data Analyses Data were analyzed in SPSS, version 21. The association between level of risk gambling and personality was analyzed using Pearson’s correlations, and to control for age and gender Pearson’s partial correlations. To assess whether there were any differences in personality and risk level between the groups that preferred different types of games, analysis of variance (ANOVA) was conducted with each trait as a dependent variable, and analysis of covariance (ANCOVA) was used to control for age, gender and level of risk gambling. Bonferroni post hoc test was used to assess how the groups of preferred game differed from each other. To compare the study sample with norm data, one sample t test was conducted, with norm data for each trait and gender as test value.
Results Level of Risk Gambling and Personality Traits The level of risk gambling (NODS–PERC) was significantly correlated with four of the five personality traits (see Table 1). Antagonism, Impulsivity and Negative affectivity were positively correlated with level of risk gambling, whereas Hedonic Capacity was negatively correlated. After controlling for age and gender, with Pearson’s partial correlations, Impulsivity and Negative Affectivity remained significant.1 The correlations were weak (rxy.z ranging between .17 and .19).
1
Since data are on ordinal level Spearman’s correlations and Spearman’s partial correlations were conducted. Only small differences were found, and no differences in significance, and are therefore not reported.
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Type of Game Preferred and Personality Traits Next, respondents with different game preferences (card games, sports games, machines or games of chance) were compared regarding their personality. There was a significant difference in Alexithymia (F(3, 222) = 2.98, p = .032) across game preference groups. However after controlling for age, gender and level of risk gambling the significant effect did not remain (F(3, 216) = 2.53, p = .059). Respondents’ most frequently played game was classified with low, medium or high risk potential. Respondents playing games with different risk potential were compared regarding their personality. There was a significant difference in Antagonism (F(2, 223) = 4.21, p = .016) and Hedonic Capacity (F(2, 223) = 3.98, p = .020) across the groups preferring games with different risk potential. The differences did not remain significant after controlling for age, gender and level of risk gambling (F(2, 222) = 1.48, p = .23). There was a significant difference in the level of risk gambling between the preferred game groups (F(3, 219) = 4.14, p = .007). Bonferroni post hoc test showed that the group that preferred machines scored significantly higher (p = .018) on NODS–PERC (M = 1.72, SD = 1.67), than the group that preferred games of chance (M = 0.74, SD = 1.22). There was a significant difference in the level of risk gambling between groups playing games with different risk potential (F(2, 223) = 19.33, p \ .001). Bonferroni post hoc test showed that the group that preferred games with high risk potential scored significantly higher (p \ .001) on NODS–PERC (M = 1.52, SD = 1.28), than both the group that preferred games with medium risk potential (M = .80, SD = 1.10) and the group that played games with low risk potential (M = .15, SD = .78). Personality Profiles in the Study Sample Versus the General Population The study sample of risk gamblers was compared with the general Swedish population (norm group) regarding personality traits. The risk gamblers scored lower that the norm group with respect to Negative Affectivity; men, t(171) = -3.96, p \ .001 and women, t(171) = -4.58, p \ .001 (see Fig. 2). Men in the study sample scored significantly higher on Impulsivity then did the norm group, t(171) = 3.24, p = .001.
Discussion This study examined the association between personality and risk gambling behavior in a general Swedish population. Briefly our results can be summarized as: • There were weak, but significant correlations between the level of risk gambling and four of the personality traits (Antagonism, Impulsivity, Hedonic Capacity and Negative affectivity). However, after controlling for age and gender, only Impulsivity and Negative affectivity remained positively significant. • After controlling for age, gender and risk gambling, personality did not differ neither, between different type of preferred game (card games, sports games, machines or games of chance) nor between groups preferring games with different risk potential. • Respondents that played machines reported higher levels of risk gambling than respondents that played games of chance. Respondents that played games with high
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J Gambl Stud Table 1 Pearson’s correlations and Pearson’s partial correlations between personality traits, measured with the HP5i and level of risk gambling measured with NODS–PERC (N = 245) 1
2
1. Level of risk gambling
.16*
2. Antagonism
.09
3. Impulsivity
.19**
4. Hedonic capacity
2.12
3
4
.18**
-.16*
.16*
.03
.41**
-.18**
.31*
.20**
.01
.44**
.42** 2.14*
5
6
-.20**
.00
5. Negative affectivity
.17**
.34**
.43**
2.23**
6. Alexithymia
.02
.20**
.03
2.16*
.02 -.19** .13*
.17**
The numbers in the italicized area are unadjusted correlations, whereas the numbers in the bolded area are correlations after controlling for age and gender * p \ .05, ** p \ .01
Fig. 2 The charts shows comparisons between the study sample and norm data on self-scored personality traits; AA antagonism, CI impulsivity, EH hedonic capacity, NN negative affectivity, OA alexithymia. The left chart is for women and the right is for men. *p \ .05
risk potential reported significantly higher on NODS–PERC, than gamblers that played games with low and medium risk. • The study sample scored significantly lower on Negative affectivity then the general population, and male respondents scored higher on Impulsivity then men in the general population. In prior meta analytic research, neuroticism, agreeableness and conscientiousness have been found to correlate with problem gambling. This is in part supported by this study were impulsivity (a facet of conscientiousness) and negative affectivity (a facet of neuroticism) but not antagonism (a facet of agreeableness) were found to correlate with the level of risk gambling. This is consistent with the findings from Bagby et al. (2007). A reason for different findings across studies may be differences in populations studied. In the metaanalysis by MacLaren et al. (2011) most of the included studies, compared non-pathological gamblers to treatment seeking pathological gamblers, whereas this study as well as Bagby et al. (2007) used more representative samples. Taken together, this supports the theory that personality factors may have an implication on the etiology of problem gambling. It also seems that age and gender are confounders important to consider when studying problem gambling. The results did not support the notion that personality implicates the type of game preferred. An interesting result is that the study sample of risk
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gamblers scored lower on Negative Affectivity then did the general. We find no apparent explanation for this and we suggest that this could be further explored. A limitation of the study is the small N. Furthermore, the study design did not admit a full diagnostic procedure regarding risk gambling and personality. Hence we were restricted to rely on short inventories for assessing risk gambling, personality etc. A second limitation is the risk of sampling bias. Among the non-responders 22 % screened positive on both questions on the Lie/Bet questionnaire (compared to 11 % among responders), suggesting more severe gambling problems. Previous research (Marcus and Schu¨tz 2005) have shown that non-responders have slightly lower levels of openness and agreeableness than responders. This could have had implications for our results and could partly explain the discrepancy between our results on agreeableness and previous research on treatment seeking populations. A major strength of the study is the recruitment of a representative sample of risk gamblers from the general population. This is warranted as a complement to studies done on either students or clinical samples. An alternative approach to study the link between risk gambling and personality would be to explore patterns of traits rather than single traits. Further, the relationship between risk/problem gambling and the risk potential of the games played could be a direction for future research. In conclusion, the association between risk gambling and personality found in previous studies was corroborated in this study using a representative sample. We conclude that risk and problem gamblers should not be treated as a homogeneous group, and prevention and treatment interventions should be adapted according to differences in personality, preferred type of game and the risk potential of the games. Acknowledgments The study was funded by Svenska Spel. We declare no conflict of interest. We are grateful for valuable comments on the manuscript by professor Jan Blomqvist.
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