Addictive Behaviors 39 (2014) 341–344

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Addictive Behaviors

Short Communication

Psychometric properties of the Chinese version of the Gambling Related Cognitions Scale in Chinese mainland sample☆ Yuping Yang, Daxing Wu ⁎, Yi Wen, Xujing Lu, Mulei Li The Medical Psychological Institute, Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China

H I G H L I G H T S • We first to assess GRCS psychometric properties in the Chinese mainland sample. • We first to present normative data of GRCS-C of mainland Chinese people. • The GRCS-C has a good validity and reliability in non-clinical Chinese individuals.

a r t i c l e Keywords: Gambling cognition Psychometric property Chinese

i n f o

a b s t r a c t The English items of Gambling Related Cognitions Scale (GRCS) were first developed and validated by communitybased population in 2004. The scale is now becoming a validated and reliable instrument to assess gambling related cognitions in the gambling literature of the West. The present study recruited 730 general adult Chinese individuals to validate the Chinese version of Gambling Related Cognitions Scale (GRCS-C). The results of a confirmatory factor analysis of the Chinese data supported the second-order model with five major factors proposed by Oei and Raylu (2006). The overall scale and five factors demonstrated satisfactory internal consistency and test–retest reliability. Construct validity and concurrent validity of GRCS-C was also sound suggesting that the GRCS-C is a valid and reliable instrument for assessing gambling related cognition among non-clinical Chinese individuals. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Gambling cognitions have constantly been associated with the development and maintenance of problem gambling (Raylu & Oei, 2004a; Xian et al., 2008). The role of gambling cognitions received a lot of attention recently (Sharpe, 2002; Toneatto, Blitz-Miller, Calderwood, Dragonetti, & Tsanos, 1997). Several researches have shown that problem gamblers were more likely to display such thoughts than non-gamblers (Raylu & Oei, 2002; Toneatto et al., 1997). Some evidence indicated that the Chinese have higher rates of gambling and problem gambling (Blaszczynski, Huynh, Dumlao, & Farrell, 1998; Chen et al., 1993; Wong & So, 2003). But at present there remains a significant lack of information on the high gambling cognitions of Chinese who may be at high risk of gambling problems; especially the Chinese lived in Mainland China. It suggested that the dearth of information could be partially attributed to the lack of adequate measures for assessing gambling cognitions across cultures (Oei, Lin, & Raylu, 2007a, 2007b). ☆ All authors provided feedback on multiple versions of the manuscript and contributed to and have approved the final version of the manuscript. ⁎ Corresponding author at: 139 Middle Renmin Road, Changsha, Hunan 410011, China. Tel.: +86 731 85292126; fax: +86 731 85361328. E-mail addresses: [email protected] (Y. Yang), [email protected] (D. Wu), [email protected] (Y. Wen), [email protected] (X. Lu), [email protected] (M. Li). 0306-4603/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.addbeh.2013.09.021

Recently, Raylu and Oei (2004c) developed and validated a tool (the Gambling Related Cognitions Scale—GRCS) to assess gambling cognitions in non-clinical samples. The GRCS was first developed and validated by the community-based participants in Australia, then the GRCS was validated in a Chinese community sample from Chinese–Australian groups and Taiwan (Oei et al., 2007a), the result also showed that the psychometric properties were sound. Some research findings suggest that the culture variation of gambling related cognitions should be noted (Oei, Lin, & Raylu, 2008). The legitimacy of applying the gambling-related measurement instruments originated from the western culture and generalizing the results on Chinese gamblers are questionable (Oei et al., 2008; Raylu & Oei, 2004a, 2004b), and their psychometric properties should be properly tested before using, so we need to test the psychometric properties before use in Mainland China. The present study aimed to examine the psychometric properties of GRCS when it is translated for usage in mainland Chinese samples.

2. Method 2.1. Participants We used stratified sampling approach, and selected 730 residents (45.7% were male and 54.3% were female) from northeastern, north,

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central south, and south-eastern of Mainland China as the subjects of investigation according to different age groups, The mean age of the participants was 36.70 years (SD = 15.29; range = 18–85 years). A week later, 50 participants received a retest of GRCS-C. 2.2. Instrument 2.2.1. Demographics All respondents were asked questions on gender, education, age, marital status, and employment. 2.2.2. The Chinese version of Gambling Related Cognitions Scale (GRCS-C) (Raylu & Oei, 2004b) Professor Oei offered the Chinese version and agreed for it to be revised. The GRCS-C is a five factor, 23-item questionnaire designed to measure erroneous gambling cognitions, the five factors are: Illusion of control (IC), predictive control (PC), interpretive bias (IB), gambling expectancies (GE), and perceived inability to stop/ control gambling (IS). Participants respond by using a 7–point Likert scale (1 = strongly disagree, 7 = strongly agree). Higher overall scores indicate individuals held an increased number of cognitive distortions. 2.2.3. The Chinese version of Gambling Urge Scale (GUS-C) (Oei et al., 2007a) Participants respond by using a 7-point Likert-scale (1 = strongly disagree to 7 = strongly agree), with higher overall scores indicating a greater urge to gamble. An earlier study by the authors (Yang et al., 2012) reported a Cronbach's alpha of 0.834 for the GUS-C using a similar community-based population in Mainland China. 2.2.4. The Depression Anxiety Stress Scale — 21 (Lovibond & Lovibond, 1995) The questionnaire was grouped into three subscales: Depression, Anxiety, and Stress, participants respond by using a 4-point Likertscale ranging from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). An earlier study by our group (Wen et al., 2012) indicated that the psychometric properties of DASS-21 were sound among community-based population in Mainland China. 2.3. Procedures and data progressing Several trained researchers undertook the task of collecting data. Researchers went to different places and asked for people's collaboration. Participation in the study was totally voluntary and no monetary reward was given. As for the people who are illiterate, the researchers explain the meaning of items until he/she could understand. All participants were provided the above battery of questionnaires in the same order. The study was approved by the Second Xiangya Hospital Ethics Committees. The results were analyzed by using kinds of statistic tests with the SPSS16.0 and AMOS7.0 programs. 3. Results 3.1. Summary statistics and normative data for the GRCS-C The means, medians, SDs, and ranges of each of the five GRCS-C scales and total scale are presented in Table 1. The influences of relationship status, employment status, and education on GRCS-C scores had no significant influence on GRCS-C scores (p N 0.05). Kolmogorov–Smirnov tests revealed that the distributions of raw scores deviated significantly from a normal distribution (p b 0.001). Given the positive skew, using means and SDs from a normative sample is not useful when interpreting an individual's score, so we converted the raw scores of the GRCS-C to

Table 1 Raw scores on the GRCS-C converted to percentiles (N = 730). Percentiles

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 99 Means SD Median Range

Row score GRCS-GE

GRCS-IC

GRCS-PC

GRCS-IS

GRCS-IB

GRCS-TOT

0 0 0 0 4.1 4.3 4.5 4.7 4.9 5.4 6.4 7.3 8 8.8 9.5 10.4 11.7 13.8 22.1 7.38 4.42 5 4–27

0 0 4.4 4.8 5.8 6.9 7.8 8.7 9.5 10.1 10.8 11.8 13.0 14.1 15.2 16.1 17.3 18.6 23.9 10.83 5.54 10 4–28

4.5 5.1 5.8 6.2 6.4 6.7 7 8.0 9.2 10.7 11.9 12.9 14.3. 15.9 18.1 20.3 22.3 24.1 31.7 12.82 7.40 11 4–42

0 0 0 0 5 5.2 5.4 5.6 5.8 6.0 7.2 8.4 9.6 10.6 11.5 12.7 14.4 16.6 26.6 8.89 5.30 6 5–35

0 0 0 0 4 4.2 4.4 4.6 4.7 4.9 5.6 6.5 7.4 8.5 9.8 11.5 13.4 15.5 22.1 7.48 4.85 5 4–28

0 23.2 23.8 26.2 28.4 29.8 32.1 35.0 37.8 40.3 43.2 46.8 50.5 55.6 62.4 68.3 74.6 82.3 119.4 47.40 23.86 40 23–159

Note: IS, perceived inability to stop gambling; IB, interpretive bias; IC, illusion of control; GE, gambling expectancies; PC, predictive control; TOT, total score.

percentiles. The tabulation method in Table 1 was adopted to permit conversion from raw scores to percentiles in all five scales and the total scale using the same table. 3.2. Confirmatory factor analyses (CFA) A confirmatory factor analysis (CFA) was conducted to test the structure of GRCS-C utilizing AMOS17.0 maximum likelihood (ML) estimation. The most commonly used estimation method in CFA was applied to covariance matrices (Marsh, Hau, Balla, & Grayson, 1998). A number of goodness of fit indices (NFI, CFI, IFI, χ2/df, and RMSEA) were used in this study to investigate how well the data fit the models tested (Browne & Cudeck, 1993). In the present study the second-order factor model that was proposed by the Raylu & Oei (2004b) analyses showed that it was an acceptable fit of the data, the goodness of fit indices were in the acceptable range of χ2/df = 5.882, CFI = 0.886, RMSEA = 0.082, IFI = 0.886, and NFI = 0.866. The second model that we tested was the 5-factor model where the factors were allowed to inter-correlate, which was also proposed by Raylu & Oei (2004b). The result of CFA showed that the five factor model did not fit the data well. The χ2 (221) = 1527.807, p b 0.05, and all the indices were less than acceptable (χ2/df = 6.913, CFI = 0.817, RMSEA = 0.090, IFI = 0.862, and NFI = 0.842). For instance, the values of its fit indices (e.g. RMSEA N 0.08) consistently indicated a poor fit of the model (Hu & Bentler, 1999; Sayette et al., 2000). The third model to be examined was a 1-factor model where all the items were predicted to load on a single factor that reflected a general gambling cognition. The result of CFA also showed that the single factor model did not fit the data well, the findings indicated that most of the fit indices were less than acceptable (χ2 (230) = 1995.446, p b 0.05, χ2/df = 8.676, CFI = 0.761, RMSEA = 0.103, IFI = 0.813, and NFI = 0.794). 3.3. Reliability Cronbach's alpha for overall GRCS-C scale was high (α = 0.940). The Cronbach's alpha for the five subscales were also high: IB (α=0.849); IS

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(α = 0.729); PC (α = 0.857); IC (α = 0.714); GE (α = 0.764). A week later, the test–retest reliability for overall GRCS-C scale and the five subscales were as follows: TOT (α= 0.880); IB (α = 0.899); IS (α = 0.688); PC (α = 0.872); IC (α = 0.772); GE (α = 0.855). 3.4. Concurrent validity Previous researches on gambling suggests that a range of variables (e.g. anxiety, depression, stress, gambling behavior and gambling urge) were positively correlated with gambling cognitions (Anton, 1999; Bentler & Chou, 1987; Tiffany, 1999; Tiffany & Conklin, 2000), so in this study we just use the variables mentioned above, which were correlated with the GRCS-C, to investigate the concurrent validity of the GRCS-C. Previous research has shown that individuals that score higher on these variables tended to have higher gambling cognitions. In this study we used two questionnaires (i.e., GUS-C and DASS21) to assess these variables. The results showed that evidence of concurrent validity, the total GRCS-C score was significantly and positively correlated with all measured variables, the correlation coefficient ranges 0.304–0.570, all correlations were significantly at 0.01 level. 4. Discussion

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clinical samples. Furthermore, the self-report nature of the research design made the findings vulnerable to social desirability biases though the anonymity of the survey was highlighted and the respondents were encouraged to provide honest answers before they started accomplishing the survey (Wu & Tang, 2011). Despite of the limitations, the present findings suggest that the GRCS-C is an appropriate tool for assessing gambling related cognitions among non-clinical Chinese individuals and to understand the role of these cognitions in the development and maintenance of problem gambling. Role of funding source Our study was supported by the Philosophy and Social Science Fund Project of Hunan Province of China (12YBB275). In which 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.

Contributors (listed in order of authorship) a. Yuping Yang conducted statistical analyses and literature searches and wrote the first draft of the manuscript. b. Daxing Wu contributed to the study design, sampling, measure development, interpretation of results, and incorporated all revisions from co-authors into the final version of the manuscript. c. Yi Wen conducted statistical analyses and contributed to the interpretation of results.

The current study first usefully complements this by providing summary statistics and normative data for the GRCS-C from a sample broadly represented by the community-based Chinese in the Chinese mainland population. This is a first try to provide normative data of GRCS-C of mainland Chinese people, which also could provide a method for comparing with other studies in other countries. Second, confirmatory factor analysis (CFA) showed in the first tested model of GRCS-C, all five gambling related cognition factors were significantly correlated with each other, and a high level of covariation was found between the factors, which could be explained by a single higher order model in which first order factors of cognitive subtypes loaded on a factor of general cognition. Meanwhile, according to some researches, we added the concorrelation between the errors to make the model fit the data better (Bentler & Chou, 1987). The results suggested that the second-order model was also applicable among Chinese in Mainland China. NFI, CFI, and IFI are approximately 0.9, and the χ2/df being less than five, and RMSEA is approximately 0.08 indicated that the model was acceptable (Kline, 2005), which is in line with the findings of Raylu and Oei (2004a, 2008). A significant χ2 value is also a common statistic for an acceptable fit of the model to the data, because when a model is tested in very large sample sizes, non-significant χ2 value is usually difficult to achieve. In terms of the internal consistencies of the GRCS-C, the values found were acceptable, thus both the total score and the subscale score should be appropriate to be used for understanding Chinese people's gambling related cognitions. What's more, the present study was first investigated the test–retest reliability, and the test–retest reliability of the GRCS-C was sound, which the previous studies did not examine (Raylu & Oei, 2004a, 2004b). And thus both the total score and the subscale score should be appropriate to be used for understanding Chinese people's gambling related cognitions. Significant positive correlations were observed between the GRCS-C scores and other instruments assessing gambling related variables such as gambling urge and anxiety, depression, and stress. The results gave support to the validity of the GRCS-C, and these findings were in line with what was consistently observed in the previous studies (Oei et al., 2007b). We did not examine the discriminant validity of GRCS-C, which is one of the limitations in the present study. In addition, the findings provide important support for the GRCS-C as a measure of gambling related cognitions among Chinese individuals, but the generalizability is restricted to community samples, therefore, future studies should pay attention to assess the psychometric properties of the GRCS-C with

d. Xujing Lu contributed to the study design and coordinated all data collection efforts. e. Mulei Li contributed to eliminate grammar and spelling errors and corrected scientific English.

Conflict of interest There is no known conflict of interest. Acknowledgments We would like to express our gratitude to Professor Tian P. S. Oei from School of Psychology, University of Queensland, Brisbane, QLD 4072, Australia; thanks to him for offering GRCS-C and agreeing for it to be revised.

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