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Asian J Kinesiol > Volume 26(1); 2024 > Article
Lee, Kang, An, and Kim: A Meta-Analysis of Attitudes toward Doping among Korean Athletes



The purpose of the current study was to conduct a meta-analysis to comprehensively analyze and identify research on doping attitudes studied in Korea.


Twelve articles were selected for analysis by searching for ‘doping attitudes’ and ‘doping PEAS’ in Korean academic databases. The meta-analysis was conducted using the meta-analysis package in the R program (ver.4.3.2).


Results indicated that 12 literatures on Korean athletes’ doping attitudes revealed an average PEAS mean score of 37.20 (95% CI = 34.29, 40.11). Moreover, results found that 10 articles on gender differences in Korean athletes’ doping attitudes showed a small effect size of -0.23 (95% CI = -0.34, -0.12), and a significant difference (p < .0001), with that female had higher mean PEAS scores than males. Results indicated that 4 studies on the difference in doping attitudes based on doping experience were found to have publication bias, so we meta-analyzed two additional studies using a trim-and-fill algorithm and found a moderate effect size of -0.64 (95% CI = -1.23, -0.05), with a significant difference (p = .0343). Additionally, Korean athletes with doping experience had higher mean PEAS scores than those without doping experience.


This study suggests that attitudes may be important in predicting doping intentions or doping behavior.


The World Anti-Doping Agency (WADA) defines doping as the occurrence of one or more anti-doping rule violations contained in Article 2 of the World Anti-Doping Code (WADC) and refers to the collective behavior of prohibited substances and methods used by athletes to enhance their performance [1]. Doping is generally unsportsmanlike conduct that undermines the value of sport by creating an unfair advantage [2]. In addition, most drugs used for doping are believed to cause short- and long-term side effects [3]. Therefore, the WADA [1] regulates doping in sports from a legal perspective in order to preserve the intrinsic value of sports, protect the health of athletes, and ensure fair sports competition.
Athletes’ use of banned substances is driven by a desire to improve their performance and achieve better results, and the search for ways to do so [4]. With rapid advances in biomedicine, athletes increasingly have the option and opportunity to use banned substances and methods to illegally enhance their performance [5, 6]. To combat this, the WADA leads the anti-doping movement, primarily using detection and punishment strategies through anti-doping codes, laws, and penalties [1]. However, anti-doping codes, legislation, and doping tests are considered to be one step behind the developers and users of doping substances or methods [6]. There is also growing criticism that anti-doping organizations are unable to detect all cases of doping that directly or indirectly affect an athlete’s performance, and that they focus primarily on doping testing and punitive strategies because the number of doping violations shows no sign of decreasing [7]. Current anti-doping education programs focus on awareness and knowledge of prohibited performance-enhancing substances, reporting and testing requirements, and penalties for violations [8]. These education programs may include sporting values to help athletes resist tendencies or inducements to use prohibited performance-enhancing substances [9]. However, these educational approaches ignore psychosocial variables related to attitudes toward performance-enhancing substance use [8].
The doping behavior literature is replete with studies based on the belief that poor attitudes are responsible for athletes’ poor choices (doping use) [10]. Research has shown that attitude is a significant predictor of doping susceptibility and behavior [2], with attitude predicting intention and indirectly predicting doping behavior [11]. Thus, while doping behavior is a complex psychological process, attitude is an important factor.
The Performance Enhancement Attitude Scale (PEAS) [12,13], the most widely used standard measure of doping attitudes among athletes, was developed in 2000 to facilitate attempts to explain doping behavior with psychological factors in an environmental context [14]. The PEAS has been used to assess personality and other socio-cognitive factors to predict doping intentions or behaviors [10]. It has been 10 years since Kim & Kim [15] translated and began using the PEAS in Korea. In that time, a number of studies have been conducted using the PEAS in Korea. There is a need to synthesize and analyze the research on doping attitudes conducted in Korea. Meta-analysis is an approach to statistically synthesize individually conducted studies and has the advantage of further organizing and generalizing knowledge in a particular field [16]. In particular, the results of statistical hypothesis testing in most quantitative studies are significantly affected by sample size, and many researchers are facing criticism for arbitrary interpretation of significance, so meta-analysis is gaining attention as a new research method that can overcome the shortcomings of individual studies [17].
The purpose of the current study was to conduct a meta-analysis to comprehensively analyze and identify the research on doping attitudes studied in Korea. Specifically: (a) analyzing the overall mean of Korean athletes’ doping attitudes; (b) comparing gender differences in Korean athletes’ doping attitudes; and (c) comparing doping attitudes according to doping experience among Korean athletes.


Data Search

The literature search covered academic articles and theses published in Korea until October 31, 2023. The academic databases used for the literature search were DBpia (, Korea Citation Index (, Korean studies Information Service System (, National Assembly Library (, and Research Information Sharing Service ( The search terms ‘doping attitudes’ and ‘doping PEAS’ were used in each database for the literature search. This research was approved from the Institutional Review Board of Seoul National University of Science and Technology.

Selection Criteria

The following criteria were used to select articles for analysis: (a) theses and dissertations published in Korean academic databases; (b) articles with full-text available; (c) articles that measured all 17 items of the PEAS; and (d) articles that presented the mean PEAS scores and standard deviation.

Selection Process

To select the literature for analysis, we searched for ‘doping attitude’ and ‘doping PEAS’ in each database, and found 32 journal articles and 12 dissertations in DBpia, 33 journal articles and 20 dissertations in Korea Citation Index, 20 journal articles and 16 dissertations in Korean studies Information Service System, 16 journal articles and 2 dissertations in National Assembly Library, and 35 journal articles and 24 dissertations in Research Information Sharing Service, for a total of 136 journal articles and 38 dissertations. Of these, we excluded 101 duplicate journal articles and 15 dissertations, and 18 duplicate journal articles and 15 dissertations by title and abstract. Finally, after a detailed review, we excluded one duplicate dissertation published in a journal, one journal article that used the same data in each journal article, two journal articles that used only part of the PEAS questions, and eight literatures with missing PEAS scores, and selected 12 literatures. The process of selecting articles for analysis (PRISMA flow diagram) [18] is shown in <Figure 1>.


The authors, publication type, participants, number of participants (male/female), and PEAS mean score ± SD (male/female) of the analyzed articles were coded as shown in Table 1. The PEAS consists of 17 statements, which are answered on a 6-point Likert scale from 1 strongly disagree to 6 strongly agree. It ranges from a low of 17 to a high of 102, with higher scores indicating more tolerant attitudes toward doping. In order to conduct a meta-analysis, clear criteria for coding must be provided, and agreement and reliability among coders must be verified [19]. To this end, data coding of the selected articles was performed by one professor and two PhDs majoring in sports psychology, and in case of discrepancies during the data coding process, one PhD majoring in sports psychology was consulted.

Statistical Analysis

The meta-analysis was performed using the meta-analysis package in the R program (ver.4.3.2). For the meta-analysis, the PEAS mean scores, standard deviations, and number of participants of the 12 articles selected for analysis on Korean athletes’ doping attitudes were analyzed to obtain the average PEAS mean score. To compare gender differences in Korean athletes’ doping attitudes, the effect size was calculated as a standardized mean difference using the PEAS mean scores, standard deviations, and number of participants in the 10 studies. To compare doping attitudes according to doping experience, the effect size was calculated as a standardized mean difference using the PEAS mean scores, standard deviations, and number of participants in the 4 studies.
The operational definition of standardized mean difference used by Cochrane Reviews is the effect size, known in the social sciences as Hedges’s g [20]. Because Cohen’s d tends to overestimate effect sizes when samples are small, it must be converted to Hedges’s g, which corrects for the presence of studies with large and small samples [21]. The confidence interval (CI) for the effect size is 95%, and an effect size of 0.2 to 0.5 was interpreted as a small effect size, 0.5 to 0.8 as a moderate effect size, and greater than 0.8 as a large effect size [22].
The I2 statistic and p value were used to test the heterogeneity of the collected articles. If the I2 is 40% or less, the heterogeneity may not be significant. 30-60% suggests moderate heterogeneity. 50-90% suggests real heterogeneity. 75-100% indicates significant heterogeneity. And if the p value was less than .10, the effect size heterogeneity was considered substantial. We chose a random-effects model if the analysis showed high heterogeneity among the studies, and a fixed-effects model if the analysis showed low heterogeneity. To verify the validity of each literature, we conducted a funnel plot and Egger’s regression analysis [23] to check for publication bias. If publication bias was found, we adjusted the asymmetry to symmetry using a trim-and-fill algorithm.


Participant characteristics of the selected articles

A total of 12 studies were selected for analysis: 11 journal articles and 1 dissertation published between 2014 and 2023. The total number of participants in the 12 studies was 3558. Ten studies presented data separately for men and women, with 2080 men and 1087 women, a total of 3167 participants. There were four studies that examined PEAS and doping history, with 117 of the 2029 participants having doped. Of the 12 studies, 9 were conducted on athletes, 1 on bodybuilding coaches, 1 on bodybuilding club members, and 1 on students at Korea National Sport University.

Publication Bias

The funnel plot and Egger’s regression test were conducted to check for publication bias. The funnel plot of the articles selected for the meta-analysis of Korean doping attitudes is shown in <Figure 2(a)>, and the regression analysis shows that there is no publication bias with bias = -3.6890 (t = -0.60, df = 10, p = 0.5644). The funnel plot of the literature selected for the meta-analysis of gender differences in Korean doping attitudes is shown in <Figure 2(b)>, and the regression analysis shows that there is no publication bias with bias = -1.2756 (t = -1.19, df = 8, p = 0.2689). However, the regression analysis of the articles selected for the meta-analysis of the difference in doping attitudes by doping experience, bias = -4.3642 (t = -8.39, df = 2, p = 0.0139), showed publication bias, so two articles were added using the trim-andfill algorithm to adjust the publication bias to bias = -0.8449 (t = -0.35, df = 4, p = 0.7463). The funnel plot is shown in <Figure 2(c)>.


Results of the meta-analysis of Korean doping attitudes are shown in <Figure 3>. Due to the significant heterogeneity among the studies (I2 = 98%, p < .01), the analysis was conducted with a random effects model. The average PEAS mean score was 37.20 (95% CI = 34.29, 40.11).
Furthermore, results of the meta-analysis of gender differences in Korean doping attitudes are shown in <Figure 4>. As there may be heterogeneity among the studies (I2 = 41%, p = .08), we analyzed them with a random-effects model. The effect size was -0.23 (95% CI = -0.34, -0.12), indicating a small effect size and a significant difference (z = -4.15, p < .0001). The PEAS mean score for males was 36.58 (95% CI = 33.17, 40.00) and the PEAS mean score for females was 39.84 (95% CI = 37.21, 42.46).
<Figure 5> showed the results of the meta-analysis of differences in doping attitudes by doping experience. Given the substantial heterogeneity among the studies (I2 = 81%, p < .01), a random effects model was used. The effect size was -0.64 (95% CI = -1.23, -0.05), indicating a moderate effect size and a significant difference (z = -2.12, p = .0343). The PEAS mean score of the four articles analyzed was 40.68 (95% CI = 39.27, 42.09), and the PEAS mean score of athletes with doping experience was 54.35 (95% CI = 47.89, 60.82).


The current study conducted a meta-analysis of Korean athletes’ doping attitudes through 12 literatures. As a result, the average PEAS mean score was 37.20. In a systematic review and meta-analysis of PEAS using several international index databases, [10] interpreted PEAS results as 17-32.9 as very negative, 33-39.9 as negative, 40-45.9 as slightly negative, 46-59.9 as slightly positive, and 60 and above as positive, and the average PEAS mean score was 39.18 in a meta-analysis of 44 studies. Comparing the results of this study with those of [10] it can be concluded that Koreans have similarly negative attitudes towards doping.
A number of international studies have shown that male athletes are generally perceived to have more permissive doping attitudes than female counterparts, while male athletes have higher PEAS scores than female athletes [24-27]. The rationale for this is that in many sports, male athletes outnumber female athletes and are relatively more competitive [24, 26-28]. However, in this study, which compared gender differences in Korean athletes’ doping attitudes through 10 studies, the PEAS mean score of males was 36.58 and the PEAS mean score of females was 39.84, indicating that female athletes had a higher PEAS mean score than male peers. In addition, according to <Table 1>, female participants had higher PEAS scores in all Korean studies. This result is contrary to international studies. The gender differences in the PEAS scores between Korean and International studies can be understood in the viewpoint that athletes’ attitudes toward doping and potential doping behaviors are influenced by various factors such as the diversity of the sporting environment in each country, participation rates, and competitive structures related to performance [29]. If it is accepted, it is plausible to explain that various environmental and contextual factors may affect attitudes toward doping, such as the increasing performance of female athletes in various sports and the intensification of competition [30, 31]. In a previous meta-analysis of literature from various countries, male athletes had a PEAS mean score of 40.84 and female athletes had a PEAS mean score of 39.65, indicating that male athletes were more tolerant of doping [10]. However, when looking at the individual studies used in the meta-analysis, not all of them had higher PEAS scores for male athletes, and some of them had higher PEAS scores for female athletes. As previous studies revealed mixed findings on gender differences in doping attitudes, further replications of this finding are needed.
Having used a banned substance predisposes one to a more lenient attitude toward doping [28]. Athletes who have used a prohibited substance exhibit a false consensus effect, in which they tend to overestimate the prevalence of drug use among other athletes in the same sport [32]. The false consensus effect is important because an athlete’s decision to use a banned substance may be influenced by the assumption that other competitors are also taking drugs, and athletes who exhibit this effect may be more prone to doping behavior than other athletes [33, 34]. In this study, we examined doping attitudes according to doping experience in Korea through four studies and found that athletes who have never doped had a PEAS mean score of 40.68 and athletes who have doped had a PEAS mean score of 54.35, suggesting that athletes who have doped are more tolerant of doping. A meta-analysis of PEAS in doping and non-doping athletes found that doping athletes had a PEAS mean score of 45.38 and non-doping athletes had a PEAS mean score of 35.88, indicating that doping athletes were more likely to be tolerant of doping [10]. As mentioned earlier, athletes with a history of banned substance use were more likely to be tolerant of doping than athletes without a history of banned substance use. These results suggest that the PEAS is a valid tool for measuring doping attitudes and may help predict doping behavior, assuming that athletes answered truthfully about their use of banned substances.
In an environment of winning and record-breaking, athletes who want to exceed their physical capabilities are easily tempted to dope, rationalize that doping is necessary, and develop a tolerant attitude toward doping [30]. From a psychosocial perspective, lenient attitudes toward doping are considered to be associated with a higher likelihood of using banned substances [13]. The literature is consistent in its view that attitudes towards banned substances are strongly associated with doping behavior [14, 35, 36]. Research by the WADA and other doping organizations have shown that deviant behavior is partly influenced by an athlete’s attitude toward doping [24]. And studies examining doping behavior often include attitudes toward doping alongside knowledge and beliefs about doping. However, the actual construct measured by attitude is not always an attitude per se, but a mixture of views, beliefs, expressed values, and hypothetical will [10]. Since the PEAS has been shown to measure the moral aspect of doping attitudes, rather than attitudes toward one’s own behavior, it cannot be assumed to be a reliable predictor of doping behavior [10]. Therefore, the PEAS should be interpreted with caution at the level at which it is measured.
In order to help athletes overcome the temptation of doping and effectively prevent doping behavior, it is necessary to identify and analyze the various factors that influence doping, along with changing ethical perceptions. Previous research on doping in sport has focused primarily on understanding why athletes dope, how they do it, and providing educational anti-doping programs. However, doping attitudes have been shown to be influenced by a variety of factors, including country, sport, sport level, experience, gender, age, education, supplement intake, anti-doping education, and doping use, and athletes’ doping behavior has a complex causal relationship with psychological factors such as individual attitudes, motivation, confidence, and beliefs [37, 38]. Therefore, it is necessary to study social psychological factors to understand doping behavior in athletes and develop anti-doping programs.
This study conducted a meta-analysis of overall mean, gender differences, and doping experience in Korean doping attitudes. The results showed that Korean attitudes toward doping are generally negative, with women being more tolerant of doping than men, and athletes with doping experience being more tolerant of doping than athletes without doping experience. Therefore, when developing anti-doping programs, the KADA should consider gender when developing anti-doping programs to prevent doping behavior. In addition, since athletes who have doped before having been found to be more tolerant of doping, they should receive anti-doping education as early as possible to prevent doping. Also, doping can be done by anyone. Nowadays, not only athletes, sports enthusiasts, students majoring in sports, students preparing to major in sports in college, but also ordinary middle and high school students are using illegal drugs such as stimulants and narcotics for academic purposes without realizing the risks. Therefore, there is a need for anti-doping education for the entire population.
There are several limitations to be addressed for further research. All included studies that reported on doping behavior measured behavior via self-report rather than objective measures (e.g., hair analyses, etc.). Therefore, the potential for inaccurate self-reports may lead to biased results. A possible solution to this problem is offered by using either experimental designs or randomized response techniques. The diverse definitions of performance enhancing substances might be another limitation. Not all of the included studies presented a PES definition to their respondents. This might have led to under- and overestimation of the figures in the original studies, as not all participants might be correctly informed about whether or not substances, they consume are PES as defined by the WADA. Finally, included studies were limited to a narrow target population of Korean elite athletes, and further research is needed to validate these results in additional target samples.


The current study conducted a meta-analysis of the Korean literature measuring PEAS to comprehensively analyze and identify Korean athletes’ doping attitudes. The conclusions are as follows. First, Korean athletes’ attitudes toward doping are generally negative. Second, Korean female athletes are more tolerant of doping than men. Third, athletes with a history of doping were more tolerant of doping than those without a history of doping. This study suggests that attitudes may play a role in predicting doping intentions or doping behavior. Negative attitudes toward doping may manifest as anti-doping behavior and lead to clean sport. However, attitudes should not be equated with doping behavior and need to be validated by further research.


This study was supported by the Research Program funded by the Korea Anti-Doping Agency.

Conflicts of Interest

The authors declare no conflict of interests.

Figure 1.
PRISMA flow diagram for identifying and selecting studies using the PEAS.
Figure 2.
Funnel Plot. (a) funnel plot of selected articles for meta-analysis of Korean athletes’ doping attitudes; (b) funnel plot of selected articles for meta-analysis of gender differences in Korean athletes’ doping attitudes; (c) adjusted funnel plot of selected articles for meta-analysis of differences in doping attitudes based on doping experience.
Figure 3.
Forest plot of the literatures selected for the meta-analysis of Korean athletes’ doping attitudes.
Figure 4.
Forest plot of the literature selected to conduct a meta-analysis on gender differences in doping attitudes in Korea athletes.
Figure 5.
Forest plot of the literature selected for meta-analysis of differences in doping attitudes based on doping experience.
Table 1.
Characteristics of studies.
Authors (year) Publication Type Participants n (male/female) Mean PEAS score ± Sd (male/female)
An et al., 2015 Journal article National disabled athletes 211 (141/70) 41.12 ± 13.68 (39.68 ± 13.60/44.13 ± 13.83)
Chu et al., 2018 Journal article Elite handball players 385 (193/165) 31.13 ± 11.99 (- , -)
Kim & Choi, 2015 Journal article Korea national sport university students 190 (145/45) 41.11 ± 13.82 (40.28 ± 14.60/43.80 ± 10.62)
Kim & Kim, 2014a Journal article National players 315 (214/101) 38.85 ± 13.40 (39.66 ± 14.11/40.24 ± 11.80)
Kim & Kim, 2014b Journal article Elite athletes & elite adolescent athletes 438 (292/146) 39.35 ± 12.87 (38.90 ± 13.62/40.26 ± 11.23)
Kim & Kim, 2023 Journal article Bodybuilding club members 114 (83/31) 35.65 ± 12.59 (34.99 ± 12.37/37.65 ± 13.16)
Kim et al., 2016a Journal article Elite racket sports players 33 (- / -) 39.27 ± 13.41 (- / -)
Kim et al., 2016b Journal article Elite golf players 136 (75/61) 33.24 ± 11.46 (30.93 ± 11.75/35.77 ± 11.08)
Kim, 2020a Journal article Adolescent athletes 246 (183/63) 26.45 ± 10.48 (24.89 ± 10.23/31.86 ± 11.17)
Kim, 2020b Journal article Bodybuilding coaches 111 (77/34) 34.46 ± 11.44 (33.41 ± 11.73/36.44 ± 10.74)
Lim & Jeon, 2016 Journal article Taekwondo players 1086 (693/393) 42.26 ± 13.51 (41.15 ± 14.10/43.76 ± 12.38)
Shin, 2017 Dissertation Elite swimming athletes 320 (177/143) 42.78 ± 14.85 (41.86 ± 16.15/43.52 ± 13.07)


1. World Anti-Doping Agency (WADA). World Anti-Doping Code. Montreal, Canada; 2021.

2. Blank C, Kopp M, Niedermeier M, Schnitzer M, Schobersberger W. Predictors of doping intentions, susceptibility, and behavior of elite athletes: A meta-analytic review. Springerplus. 2016; 5(1): 1–14.
pmid pmc
3. Reardon CL, Creado S. Drug abuse in athletes. Subst Abuse Rehabil. 2014; 5:95–105.
crossref pmid pmc
4. Petróczi A, Norman P, Brueckner S. Can We Better Integrate the Role of Anti-Doping in Sports and Society? A Psychological Approach to Contemporary Value-Based Prevention. Medi Sport Sci. 2017; 62:160–76.

5. Stewart B, Smith AC. Drug use in sport: Implications for public policy. J Sport Soc Issues. 2008; 32(3): 278–98.

6. Waddington I, Smith A. Sport, health and drugs: A critical sociological perspective. Routledge; 2013.

7. Chan DKC, Dimmock JA, Donovan RJ, Hardcastle SARAH, Lentillon-Kaestner V, Hagger MS. Self-determined motivation in sport predicts anti-doping motivation and intention: A perspective from the trans-contextual model. J Science Med Sport. 2015; 18(3): 315–22.
8. Jalleh G. Testing the Sport Drug Control Model of Factors Influencing Performance-enhancing Substance Use (Doctoral dissertation) University of Western Australia, Perth, Western Australia, Australia. 2013.

9. Lippi G, Banfi G, Franchini M, Guidi GC. New strategies for doping control. J Sports Sci. 2008; 26(5): 441–5.
crossref pmid
10. Folkerts D, Loh R, Petróczi A, Brueckner S. The performance enhancement attitude scale (PEAS) reached ‘adulthood’: Lessons and recommendations from a systematic review and meta-analysis. Psychol Sport Exerc. 2021; 56:101999.
11. Ntoumanis N, Ng JY, Barkoukis V, Backhouse S. Personal and psychosocial predictors of doping use in physical activity settings: A meta-analysis. Sports Med. 2014; 44(11): 1603–24.
crossref pmid pdf
12. Petróczi A. Exploring the doping dilemma in elite sport: Can athletes’ attitudes be responsible for doping? (Doctoral dissertation) University of Northern Colorado, Greeley, Colorado, United States. 2002.

13. Petróczi A, Aidman E. Measuring explicit attitude toward doping: Review of the psychometric properties of the Performance Enhancement Attitude Scale. Psychol Sport Exerc. 2009; 10(3): 390–6.
14. Petróczi A. Attitudes and doping: a structural equation analysis of the relationship between athletes’ attitudes, sport orientation and doping behavior. Subst Abuse: Treat Prev Policy. 2007; 2(1): 1–15.
pmid pmc
15. Kim TG, Kim EK. Attitudes and dispositions toward doping in adolescent elite athletes. Korean J Sports Med. 2013; 31(2): 99–106.
16. Jackson GB. Methods for integrative reviews. Rev Educ Res. 1980; 50(3): 438–60.
17. Halsey LG, Curran-Everett D, Vowler SL, Drummond GB. The fickle P value generates irreproducible results. Nat Methods. 2015; 12(3): 179–85.
crossref pmid pdf
18. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern. 2009; 151(4): 264–9.
19. Schmitt N, Klimoski RJ, Ferris GR, Rowland KM. Research methods in human resources management. Cincinnati, OH: South-Western Pub; 1991.

20. Higgins JPT, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.4 (updated September 2023). Cochrane; 2023.

21. Bernard RM, Abrami PC, Borokhovski E, et al. A meta-analysis of three types of interaction treatments in distance education. Rev Educ Res. 2009; 79(3): 1243–89.
crossref pdf
22. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988.

23. Egger M, Smith GD, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997; 315(7109): 629–34.
crossref pmid pmc
24. Alaranta A, Alaranta H, Holmila J, Palmu P, Pietilä K, Helenius I. Self-reported attitudes of elite athletes towards doping: differences between type of sport. Int J Sports Med. 2006; 27(10): 842–6.
crossref pmid
25. Lucidi F, Zelli A, Mallia L, Grano C, Russo PM, Violani C. The social-cognitive mechanisms regulating adolescents’ use of doping substances. J Sports Sci. 2008; 26(5): 447–56.
crossref pmid
26. Peretti-Watel P, Guagliardo V, Verger P, Mignon P, Pruvost J, Obadia Y. Attitudes toward doping and recreational drug use among French elite student-athletes. Sociol Sport J. 2004; 21(1): 1–17.
27. Waldron JJ, Krane V. Whatever it takes: Health compromising behaviors in female athletes. Quest. 2005; 57(3): 315–29.
28. Moran A, Guerin S, Kirby K, MacIntyre T. The development and validation of a doping attitudes and behavior scale. Unknown Publisher; 2008.

29. Won YI, Kim JT, Hong SM. A Study on the Attitudes and Dispositions of Doping in Professional and University Soccer Players. Korean J Sports Sci. 2019; 28(1): 1093–102.
30. Kim EK, Cha JH, Choi H. Relationship between attitudes toward doping and sports orientations in achievement motivation among racket sports players. Korean J Sport. 2016; 14(4): 875–83.

31. Park J, Choi H, Kim T. The Investigation of Influencing Factors to Attitude toward Doping in Korean Athletic Players. J Digit Converg. 2019; 17(5): 391–8.

32. Dunn M, Thomas JO, Swift W, Burns L. Elite athletes’ estimates of the prevalence of illicit drug use: evidence for the false consensus effect. Drug Alcohol Rev. 2012; 31(1): 27–32.
crossref pmid
33. Kim EK, Choi HK. Beliefs, Knowledge and Attitudes of Doping among Korea National Sport University Students. Sport Sci. 2015; 32(2): 43–50.

34. Kim EK, Kim TG. Relation and Difference in Attitudes, Beliefs and Knowledge for Doping between Elite and Elite Adolescent Athletes in Korea. Korean Soc Sports Sci. 2014; 23(4): 377–89.

35. Hauw D, Mohamed S. Patterns in the situated activity of substance use in the careers of elite doping athletes. Psychol Sport Exerc. 2015; 16:156–63.
36. Wiefferink CH, Detmar SB, Coumans B, Vogels T, Paulussen TGW. Social psychological determinants of the use of performance-enhancing drugs by gym users. Health Educ Res. 2008; 23(1): 70–80.
crossref pmid
37. Sas-Nowosielski K, Świątkowska L. The knowledge of the world anti-doping code among Polish athletes and their attitudes toward doping and anti-doping policy. Human; 2007.

38. Wolff W, Schindler S, Brand R. The effect of implicitly incentivized faking on explicit and implicit measures of doping attitude: when athletes want to pretend an even more negative attitude to doping. PloS One. 2015; 10(4): e0118507.
crossref pmid pmc


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