Inter- and Intra-Gender Similarities and Differences in Motivations for Casino Gambling
Notice bibliographique
Résumé
Abstract The two objectives of this study were to examine if motivations for casino gambling vary by gender and, based on motivations for casino gambling, to ascertain different types of male and female gamblers. To accomplish these objectives, five casino motivation scales were developed. Nine hundred male and female casino patrons living in two major Canadian metropolitan areas completed a telephone questionnaire. Male study participants rated risk-taking/gambling as a rush and learning/cognitive self-classification as being more important than did female participants. Two types of male casino gamblers existed: men who gave primacy to risk-taking/gambling as a rush and emotional self-classification, and men who gave primacy to communing. Three types of female casino gamblers existed: women who gave primacy to emotional self-classification and escaping everyday problems, women who gave primacy to communing and emotional self-classification, and women who gave primacy to communing alone. Gender theory was used to explain these findings, and study limitations and future research recommendations also were discussed. Keywords: casino gamblingcluster analysisgendermasculinitymotivation This research was supported by a grant from the Alberta Gaming Research Institute. The authors would like to thank the University of Alberta Population Research Lab staff for their assistance collecting the data. Correspondence concerning this article should be addressed to Gordon J. Walker, E-424 Van Vliet Centre, Faculty of Physical Education and Recreation, University of Alberta, Edmonton, Alberta, Canada, T6G 2H9. E-mail: gordon.walker@ualberta.ca Notes 1For example, a search of Leisure Sciences (using the PsycINFO database and the keyword "gambling") did not uncover any papers published on this topic between 1985 and September 2003. Note: S-C stands for self-classification. 2The decision to conduct the study in two western Canadian metropolitan areas was based on (a) the growth of gambling and casino gambling in Canada, (b) the location of the study researchers, and (c) the provision of research funding by a provincial agency. 3Although an attempt was made to develop and test items that would measure Cotte's (1997) self-definition motivation, it was unsuccessful due in part to the number of different roles casino gamblers can select from (e.g., rebels, casino pros, variety seekers). 4For comparative purposes, the median age in the province where the study was conducted is 34 years for males versus 36 years for females, with 15% of males and 22% of females have completed community college or the equivalent, and 21% of males and 22% of females having attained a Bachelors degree or greater (Statistics Canada, 2003). 5For comparative purposes, in Ontario Wiebe, Single, and Falkowski-Ham (2001) found that people who gambled at in-province casino tables did so approximately 6.3 times per year while those who gambled at out-of-province casinos did so approximately 1.7 times per year. In contrast, Welte et al. (2002) found that U.S. casino gamblers averaged 11 visits per year, although this figure is nearly twice the average (5.7 visits) reported in another study (Profile of the American Casino Gambler: Harrah's Survey 2002) of American casino visitors. Note: Cog. S-C stands for cognitive self-classification. Only items having loadings ≥ |.55| on one factor and having loadings ≤ |.32| on the other factors are shown. * p < .0001. 6Because participants were selected based on gender as well as—in the case of 400 individuals—having visited a distant casino (i.e., greater than 80 kilometers), a MANOVA was first conducted on the five casino gambling motivation scales using gender, type of visits (i.e., only local casinos, only distant casinos, both), and their interaction. Because the interaction was not significant [Wilk's Λ = 0.99, F (10, 1734) = 1.19, p > .2902], only the effect of gender was subsequently examined. 7The cluster order is not important as the first cluster seed is simply a function of which observation is read first by the SAS FASTCLUS program (Hair & Black, 2000). Note: Scales have been standardized within-case. Motivations that have a mean with a superscript are significantly (p < .01) different than 0.00 for that cluster. 8The effectiveness of casino gambling as a long-term coping strategy remains open to debate however. A study by Potenza et al. (2001, p. 1504) has raised the possibility that women, once they begin gambling, may develop gambling problems at a more rapid rate than men.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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