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An alternative approach to understanding doping behavior: A pilot study applying the Q-method to doping research
Performance Enhancement & Health 6 (2019) 139–147
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Performance Enhancement & Health journal homepage: www.elsevier.com/locate/peh
An alternative approach to understanding doping behavior: A pilot study applying the Q-method to doping research K. Gatterer a , M. Niedermeier b , B. Streicher c , M. Kopp b , W. Schobersberger a,d , C. Blank a,∗ a
Institute of Sports Medicine, Alpine Medicine & Health Tourism, UMIT, Eduard-Wallnöfer Zentrum 1, 6060 Hall in Tyrol, Austria Department of Sport Science, University of Innsbruck, Fürstenweg 185, 6020 Innsbruck, Austria Institute of Psychology, UMIT, Eduard-Wallnöfer Zentrum1, 6060 Hall in Tyrol, Austria d Tirol Kliniken, Anichstraße 35, 6020 Innsbruck, Austria b c
a r t i c l e
i n f o
Article history: Received 16 July 2018 Received in revised form 31 October 2018 Accepted 11 December 2018 Available online 18 March 2019 Keywords: Doping prevention Q-method Predictors
a b s t r a c t Background: Prevention plays an important role in the fight against doping. A lot of research in the field of doping and anti-doping has been conducted. Yet, there is still a lack of knowledge in understanding the various and multi-level impacting factors of doping behavior. Therefore, this paper aimed to apply the Q-method to doping research to identify items that potentially differentiate between athletes who dope and those who do not and to assess whether these items resemble constructs previously identified in socio-psychological literature to be predictive for doping intention, susceptibility, and behavior. As a secondary goal, we aimed to evaluate whether the Q-method method might be a suitable approach to doping prevention research. Methods: Five doped and five matched non-doped elite athletes from different sports were investigated. Each athlete completed three separate Q-sorts by indicating his/her degree of agreement with a total of 175 items on an 11-point Likert scale. Items stem from 13 different constructs such as attitudes towards doping, training/coach climate and moral disengagement. Results: Results showed that the Q-sorts clearly differentiate two types of athletes (non-doped and doped athletes). In total, only 15 (out of 175) items differentiated between the two groups, stemming from the constructs attitudes, sportspersonship, goal orientation and situational temptation/doping susceptibility. Conclusions: These results might be an initial indicator of items that differentiate between distinct types of athletes (doped and non-doped), and suggest that the Q-method might be a useful tool to differentiate these groups of athletes. However, further research to validate the items is needed before implementation into doping prevention strategies. © 2018 Elsevier Ltd. All rights reserved.
1. Introduction Identifying risk factors related to doping behavior (i.e. the violation of any rules set out in the World Anti-Doping Code article 2.1 – 2.10) was the focus of most research into doping prevention over the last decade (Blank, Kopp, Niedermeier, Schnitzer, & Schobersberger, 2016; Ntoumanis, Ng, Barkoukis, & Backhouse, 2014). Based on these findings, prevention strategies to decrease
∗ Corresponding author at: Department of Psychology and Medical Sciences, Institute of Sports Medicine, Alpine Medicine & Health Tourism, UMIT, EduardWallnöfer-Zentrum 1, 6060, Hall in Tirol, Austria. E-mail addresses: [email protected] (K. Gatterer), [email protected] (M. Niedermeier), [email protected] (B. Streicher), [email protected] (M. Kopp), [email protected] (W. Schobersberger), [email protected] (C. Blank). https://doi.org/10.1016/j.peh.2018.12.001 2211-2669/© 2018 Elsevier Ltd. All rights reserved.
doping incidence in adolescents and college student athletes were developed and implemented (Barkoukis, Kartali, Lazuras, & Tsorbatzoudis, 2016; Elliot, Goldberg, More, DeFrancesco, Durham, & Hix-Small, 2004; Goldberg, Elliot, Clarke, MacKinnon, Moe, Zoref et al., 1996; Goldberg, MacKinnon, Elliot, Moe, Clarke, Cheong, 2000). Programs have been proven to be successful in increasing anti-doping knowledge and decreasing vulnerability, intention to use and actual use of anabolic steroids (Barkoukis et al., 2016; Elliot et al., 2004; Goldberg et al., 1996, 2000). However, doping incidence does not seem to have decreased (de Hon, Kuipers, & van Bottenburg, 2015; World Anti-Doping Agency, 2016), which might be explained by two reasons. For one, it might be an indication that even though there is evidence for specific risk factors in doping behavior, anti-doping authorities lack to transfer this evidence into efficient prevention measure, considering that most prevention programs focus on knowledge-based interventions but lack to address value-based aspects. Secondly, it might be an indication
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that there still is a lack of knowledge in understanding the various and obviously multi-level influence factors of doping behavior. Critical evaluation of the anti-doping research performed in social sciences over the last few years outlines at least three potential limitations and thus, the possibility for a lack of successful transfer into prevention programs. The first limitation is of methodological nature and arises from the inconsistent use and operationalization of theoretical constructs. It should thus mainly be considered in terms of the comparability of academic studies. However, organizations in charge of doping prevention might struggle with developing evidence-based targeted prevention programs as a result of lack of clear implications from academics. Even though it is positive and important that numerous theoretical perspectives are applied to try to understand doping behavior, the according operationalization of the respective, predictive variables of different theoretical models was inconsistent across different studies. For example, attitudes were operationalized as ‘outcome expectancies’ (Gucciardi, Jalleh, & Donovan, 2011), differentiated semantically (i.e., ‘doping is good vs. bad’) (Barkoukis, Lazuras, Tsorbatzoudis, & Rodafinos, 2013) or by using the performance enhancement attitude scale (PEAS) of Petroczi and Aidman (2008) – and yet, always labelled as ‘attitudes’. It is questionable if the same dimension of attitudes was addressed and hence, if studies are comparable or, more precisely, if these results should for example be synergized. Also, outcome variables were sometimes labelled differently but measured with almost identical items such as the use of the concepts of ‘doping susceptibility’/’situational temptations’ (Lazuras, Barkoukis, Rodafinos, & Tzorbatzoudis, 2010) and the willingness to dope (Whitaker, Long, Petroczi, & Backhouse, 2014). In this case, it is not surprising that situational temptation is a strong predictor of doping susceptibility, but questionable if it is an artefact based on the operationalization. The second limitation relates to the investigated target groups and their different reasons to dope. Most studies focused on elite and/or semi-elite athletes of a given sport, country and setting (Blank et al., 2016; Ntoumanis et al., 2014). However, reasons for doping might differ by competition level and/or sport. In this context, Bilard, Ninot, and Hauw, (2011) investigated three sports (cycling, bodybuilding and football) and found that athletes in each sport had their own reasons to dope. Cyclists doped to maintain their health, bodybuilders wanted to increase muscular strength, and footballers used prohibited substances for personal recreation. In line with that, Overbye, Knudsen, and Pfister, (2013) showed that various circumstances influenced an athlete’s decision to dope and that these differ between athletes of different gender, age and sport type. Thus, it is again difficult to compare studies and draw general conclusions, as there are various reasons to dope that vary by competition level and sport. The third limitation concerns the classification of doped and non-doped athletes, which is mostly based on the self-appraisals of these athletes. Past research tried to identify risk factors for doping by questioning doped and non-doped athletes. However, in most studies it is unknown whether the athletes were doped or not. All athletes are considered clean if not proven otherwise (i.e., being detected as dopers).Thus, every study has to rely on the truthfulness of athletes’ self-declarations. However, self-reporting is considered to be methodologically problematic and can potentially lead to underestimated results due to the distorting effect of socially desirable responding (Petroczi & Nepusz, 2011). Consequently, there is the possibility that any group of non-doped athletes also includes doped athletes, albeit as-yet undetected. Conversely, as the term doping was rarely defined in individual studies, non-doped athletes might have indicated doping based on a misunderstanding. Consequently, it remains unclear whether the results of these studies are due to actual differences between the two groups or if they can be traced back to methodological artefacts.
These potential limitations become more visible when considering findings of predictive factors for doping behavior resulting from quantitative studies as well as findings of reasons for doping being researched in qualitative studies including convicted doped athletes, or athletes who had publicly admitted to doping (Engelberg, Moston, & Skinner, 2015; Kirby, Moran, & Guerin, 2011). Results of the quantitative approaches showed significant influences of attitudes, norms and morals on doping behavior (Blank et al., 2016; Ntoumanis et al., 2014). However, findings of qualitative studies including admittedly doped athletes do not suggest that any of those variables are reasons for doping behavior. They rather identified motives that are highly functional, for example the means to overcome an illness or injury (Engelberg et al., 2015; Kirby et al., 2011). However, these studies are small in number and suffer from the various difficulties that arise when trying to interview convicted, doped athletes (e.g., lack of interest of these athletes to participate in a study). We argue that the above-mentioned limitations must be addressed to gain new insights concerning the mechanisms driving doping. This might be achieved by applying an alternative approach that a) should be independent from labelling different constructs and therefore allow comparability of future studies and b) uses multiple methods to discriminate between dopers and non-dopers. To address these aspects, this paper opens a new avenue in doping research by applying the Q-method, that is, by combining the positive characteristics of quantitative and qualitative research (Brown, 1996). Applying the Q-method, the main aim of this paper was twofold and a) to identify whether items from previous research differentiate between athletes who dope and those who do not, by questioning doped athletes and matching them with non-doped controls and thus b) to confirm previous research findings based on classical research methods (questionnaire and interview) (aim 1). In this regard, a secondary aim was to evaluate whether the Q-method can be considered a suitable approach to doping prevention research by evaluating the hypothesis of two groups of athletes (doped and non-doped athletes) to be identified within this study (aim 2). To overcome the problem of heterogeneity in the operationalization of potential predictor variables, the focus was not set on any pre-defined construct, but rather on the items underlying these constructs. To address the self-report limitation, this pilot study included knowingly doped (convicted and previously banned) and non-doped control athletes (based on self-report). By applying this alternative approach, we aimed to identify specific items that differentiate between these two groups. The results could be taken further and used in future studies to test their validity and reliability, as well as in studies designed to allow conclusions to be drawn on cause-effect relationships.
2. Method The Q-Methodology was first used in 1935 by William Stephenson as an adaption of Spearman’s traditional factor analysis (Watts & Stenner, 2012). The main difference between the Q-method and the R-method (i.e., traditional factor analysis) is that the Q-method uses a specific survey and analysis procedure: instead of correlating statements or items (R-method), persons are correlated with the aim to identify groups of participants with similar overall attitudes or response-patterns (Watts & Stenner, 2012) and to find differences between these groups of participants. In view of the goal of identifying profiles or types of individuals that respond in a similar way to certain set of variables, the Q-method is sometimes compared to the cluster analysis. However, data analysis of the cluster analysis is more complex and the interpretation not as straightforward as with the Q-method (ten Klooster, Visser, & de Jong, 2008).
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In Q-type factor analysis, every participant is viewed as a different experimental case, representing a “factor entity” (Gabor, 2013, p.119). Thus, instead of presenting a low number of items to a high number of people (R-technique), in Q-method a high number of items are presented a low number of people that they should assess (Gabor, 2013). This is a major benefit of the Q-method in view of the difficulty of motivating doped athletes to partake in studies (He, Hu, & Fan, 2017; ten Klooster et al., 2008). The method only needs a small number of athletes as “the major relationships tend to stabilize with just a few cases” (Brown & Ungs, 1970, p.520). Although the Q-methodology has been applied to a number of scientific disciplines such as nursing (Ha, 2016) or medicine (Reid, Swift, & Mehanna, 2017), to our knowledge, it has never been used in the field of doping research. However, this method could be used as an alternative approach to understand doping behavior, eventually distinguishing doped from non-doped athletes based on pre-specified variables. It combines the strengths of qualitative and quantitative research, allowing athletes to articulate their personal opinion, and results can be generated into testable hypotheses (Brown, 1996; Valenta & Wigger, 1997). 2.1. Design Based on He et al. (2017), the procedure comprises five different steps that were applied to our study setting. An overview is presented in Fig. 1. To define the statement concourse (step 1), two meta-analyses addressing predictors of doping behavior, attitudes and intentions in elite sport were considered (Blank et al., 2016; Ntoumanis et al., 2014) and all items that were significantly associated with doping behavior, attitude and intention were included. The initial list of items and their respective constructs is available upon request. English items were translated into German, and then back into English, by a professional translating office, compared with the original version and adjusted accordingly (cf. van de Vijer & Hambleton, 1996). To develop the final Q-set, a representative sample of statements or items from the prior defined concourse (Durning & Brown, 2006) was defined (step 2). We therefore considered the views and opinions of experts in the field of doping research and sport by applying an adapted version of the Delphi-method as outlined in detail in Fig. 1 (for details concerning the Delphi-method, please refer to Hsu & Sandfort, 2007). Experts were specialists from the fields of sport science and coaching (n = 1), sport medicine (n = 2), sport and health psychology (n = 2), social psychology (n = 1) and a member of a sports federation entrusted with anti-doping matters (n = 1). All 13 initial psychological constructs were still present after applying the Delphi-method, yet with a reduced number of items. Based on the expert discussions and informed by prior evidence of qualitative research (Engelberg et al., 2015; Kirby et al., 2011) that external circumstances might play a specific role in the decision to dope, items generated by the meta-analyses were divided according to their association with external (situational) factors and internal factors. Subsequently, Q-set 1 consisted of 43 items that were associated with external variables, including the construct norms, training/coach climate, situational temptation and behavioral control. Q-set 2 would have consisted of 132 items all associated with internal variables. However, as single Q-sets typically contain 40–80 statements or items (Brown, 1980; Kelly, Moher, & Clifford, 2016), we considered 132 to be too many items. Thus, they were divided into 2 separate Q-sets to reduce cognitive complexity and enhance practicability for the participants. The allocation of the items to Q-set 2 and 3 was done randomly while making sure that each construct is represented in both Q-sets. Accordingly, Q-set 2 and 3 consisted of 66 items each including the constructs of attitude towards doping,
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beliefs about causes of success, goal orientation, performance orientation, motivation, behavioral regulation, moral disengagement, sportspersonship, anxiety and self-efficacy. The selection of the participant-set (step 3) included approaching non-doped and doped athletes, with the latter having used prohibited substances intentionally to enhance their performance. The intention underlying the doping offence was especially important, as we did not want to include so-called “unintentional dopers” - athletes who were convicted of doping but did not do so intentionally (e.g., had a positive test result due to contaminated food supplements). Athletes were recruited via personal contact by the study team. After agreeing to participate in the study, each doped athlete was matched with a non-doped control athlete from the same sport and the same gender. The main characteristic of the Q-method is that participants rank the pre-defined items (described above) according to their personal perspective (Burt & Stephenson, 1939). In this study, athletes were presented with the final three different Q-sets and were asked to rank them in a “forced or forced-choice distribution” (Watts & Stenner, 2012) of the shape of a (quasi-)normal distribution (Fig. 2) according to their level of agreement with each presented item (step 4). Short post-hoc interviews were conducted to obtain additional information if applicable. The procedure was observed by two members of the study team, after participants were informed about the study aims and signed a written informed consent form. The study was approved by the university’s ethical commission. For data protection reasons, no detailed information on gender, age or sport is presented. Data were analyzed using SPSS v. 24.0 (IBM, New York, United States; step 5). Two separate analyses were performed for Q-set 1 (external items) and for Q-set 2 and 3, which were merged (internal items). A centroid factor analysis with varimax rotation was applied using the Kaiser-Guttman criterion (eigenvalue ≥ 1.00) to extract the number of factors (groups of athletes). The regression factor score was calculated for each participant and factor and a significant regression factor score was used to allocate participants to factors (p < .01). Whenever a participant showed a significant loading on more than one factor, they were allocated to the factor with the higher regression factor score. Appropriate and inappropriate allocation of the participants to the factors (groups of athletes) was reported. Normalized average Q-sort factor scores were calculated for each factor. As indicated in the literature, items were considered distinctive for a factor (i.e., groups of athletes with similar responses in the items) “if they exceed 2.58 times the standard error of differences” (Brown, 1980, p.24; see also for a detailed description of the calculation of standard error for each factor). 3. Results 3.1. Q-set 1 – external items Regarding the external Q-set, the Kaiser-Guttman criterion revealed a 2-factor solution explaining 69.9% of the total variance. Normalized average Q-sort factor scores for each factor and item can be found in Appendix A. Nine out of ten athletes showed a significant factor loading on only one factor (A/doped athletes or B/non-doped athletes): three athletes on Factor A, six athletes on Factor B. One athlete from the group of doped athletes significantly loaded on both factors and was allocated to A (doped) based on the higher loading with that factor (cf. Table 1).1
1 From here on, all results relating to Q-set 1 will be referred to as Factor A1 and/or B1 ; and all results relating to Q-set 2 and 3 as Factor A23 and/or B23 .
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Fig. 1. 5 steps of Q-sort development. Table 1 Factor loadings of external and internal Q-set for each participant. External Q-set
Internal Q-set
Participant
Group
Factor loading A1
Factor loading B1
Assigned to factor
Factor loading A23
Factor loading B23
Assigned to factor
01 02 03 04 05 06 07 08 09 10
doped doped doped doped doped non-doped non-doped non-doped non-doped non-doped
0.58* 0.84* 0.84* 0.92* 0.30 −0.18 0.15 0.35 0.19 0.09
0.40* 0.17 0.07 0.00 0.57* 0.76* 0.89* 0.86* 0.84* 0.85*
A1 ˜ A1 A1 A1 B1 B1 B1 B1 B1 B1
0.58* 0.80* 0.30* 0.74* 0.50* 0.16 0.26* 0.51* 0.40* 0.09
0.37* 0.20 0.58* 0.06 0.37* 0.80* 0.79* 0.58* 0.68* 0.73*
A23 ˜ A23 B23 ˜ A23 A23 ˜ B23 B23 ˜ B23 ˜ B23 ˜ B23
*: p < .01. ˜: Assignment to factor based on higher factor loading due to significant loadings on both factors.
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Fig. 2. Prearranged distribution for a Q-sort with 48 items.
Table 2 Distinctive items external Q-sort differentiating between factor A1 (primarily doper) and factor B1 (primarily non-doper). Item #
Item wording
item 26
I would be tempted to use prohibited doping substances to enhance my performance this season when feeling disadvantaged. I would be tempted to use prohibited doping substances to enhance my performance this season when I believe that most colleagues use prohibited substances. I would be tempted to use prohibited doping substances to enhance my performance this season when I prepare for an important game/competition.
item 29
item 46
Factor A1 : The first group of participants consisted of doped athletes (eigenvalue 2.2, accounting for 22.1% of the variance). Four out of five doped athletes were assigned to factor A1 (and no nondoped athlete). Athletes assigned to this factor reported a high agreement with “I believe athletes in my sport engage in doping to enhance their performance” and “I would be tempted to use prohibited doping substances to enhance my performance this season when I believe that most colleagues use prohibited substances”. In addition, they reported a low agreement with: “My immediate family (parents, brothers, sisters) would approve of me using PES to improve my athletic performance” and “People who are important to me would approve of me using prohibited substances to enhance my performance during this season”. Factor B1 : The second group of participants consisted of primarily non-doped athletes (eigenvalue 4.8, accounting for 47.5% of the variance). Five out of five non-doped athletes, as well as one of the doped athletes, were assigned to factor B1 . Athletes assigned to this factor reported a high agreement with “I feel in complete control over whether I will use doping substances to enhance my performance” and “My coach rewards trying hard”. Furthermore, they reported a low agreement with “I would be tempted to use prohibited doping substances to enhance my performance this season when feeling disadvantaged.” and “I would be tempted to use prohibited doping substances to enhance my performance this season when I prepare for an important game/competition”. Three items (out of 43) were considered to distinguish between factor A1 and B1 whereas all athletes allocated to factor A1 reported a higher level of agreement in the distinctive items compared to the athletes allocated to factor B1 (refer to Table 2). In detail, the
items were as follows: “I would be tempted to use prohibited doping substances to enhance my performance this season when feeling disadvantaged”, “I would be tempted to use prohibited doping substances to enhance my performance this season when I believe that most colleagues use prohibited substances” and “I would be tempted to use prohibited doping substances to enhance my performance this season when I prepare for an important game/competition”.
3.2. Q-set 2 and 3 – internal items As described in the method section, Q-set 2 and 3 were merged prior to the analysis. The Kaiser-Guttman criterion revealed a 2-factor solution, which explained 56.0% of the total variance. Normalized average Q-sort factor scores for each factor and internal item can be found in Appendix B. Four out of ten athletes showed a significant factor loading on only one factor (A or B): two athletes on Factor A (doped) and two on factor B (non-doped). Six athletes significantly loaded on both factors and were allocated to the factor with the higher loading (two athletes on Factor A, doped; the remaining 4 on Factor B, non-doped) (cf. Table 1). Factor A23 : One group of participants consisted of doped athletes (eigenvalue 1.0, accounting for 10.3% of the variance). Four out of five doped athletes and none of the non-doped athletes were assigned to factor A23 . Athletes reported a high agreement with “There is no difference between drugs and the technical equipment that can be used to enhance performance (e.g., hypoxic altitude simulating environments)” and “Doping is an unavoidable part of competitive sport”. Furthermore, they reported a low agreement with “I respect all the rules of my sport” and “Considering difficult situations I am afraid of failing when a lot depends on my skills”. Factor B23 : The other group of participants consisted of primarily non-doped athletes (eigenvalue 4.6, accounting for 46.0% of the variance). Five out of five non-doped athletes and one out of five doped athletes were assigned to factor B23 . The doped athlete assigned to the group of primarily non-doped athletes was not the same athlete as the one in the analysis based on the external Q-set. Athletes reported a high agreement with “I respect all the rules of my sport” and “The use of illegal substances to improve sport performance or physical appearance would be bad”. Additionally, the reported a low agreement with “Performance-enhancing drugs/methods should be allowed for top level athletes” and “The use of illegal substances
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Table 3 Distinctive items internal Q-sort with higher level of agreement in athletes allocated to factor A23 (primarily doper). Item #
Item wording
item 118
Athletes in my sport are pressured to take performance-enhancing drugs. Doping is not cheating since everybody does it. Doping is necessary to be competitive in my sport. The use of illegal substances to improve sport performance or physical appearance would be useful. Athletes should not feel guilty about breaking the rules and taking performance-enhancing drugs. The only time I am satisfied is when I win. Doping is an unavoidable part of competitive sport. Using PES in the coming year would help me earn a living as a professional athlete. There is no difference between drugs and the technical equipment that can be used to enhance performance (e.g., hypoxic altitude simulating environments).
item 062 item 137 item 125 item 065 item 081 item 066 item 124 item 139
Table 4 Distinctive items internal Q-sort with higher level of agreement in athletes allocated to factor B23 (primarily non-doper). Item #
Item wording
item 167 item 055
I respect all the rules of my sport. It is important that my sport has an effective drug testing program. The use of illegal substances to improve sport performance or physical appearance would be bad.
item 057
to improve sport performance or physical appearance would be negative”. Twelve items (out of 132) were considered as distinctive between factor A23 and B23 . All athletes allocated to factor A23 reported a higher level of agreement to the following items: “Athletes in my sport are pressured to take performance-enhancing drugs”, Doping is not cheating since everybody does it”, “Doping is necessary to be competitive in my sport”, “The use of illegal substances to improve sport performance or physical appearance would be useful”, “Athletes should not feel guilty about breaking the rules and taking performanceenhancing drugs”, “The only time I am satisfied is when I win”, “Doping is an unavoidable part of competitive sport”, “Using PES in the coming year would help me earn a living as a professional athlete” and “There is no difference between drugs and the technical equipment that can be used to enhance performance (e.g., hypoxic altitude simulating environments)” (Table 3). All athletes allocated to factor B23 reported a higher level of agreement to the following items: “I respect all the rules of my sport”, “It is important that my sport has an effective drug testing program” and “The use of illegal substances to improve sport performance or physical appearance would be bad” (Table 4).
3.3. Additional comments by athletes Only four of the doped athletes felt the need to add additional comments. They agreed that it was challenging to keep to the forced choice normal distribution and tended more towards the right corner (agreement). None of them felt that the presented statements were irrelevant, but they added that their reasons to dope were mainly situational and influenced by the environment, in the sense of the sporting culture. One remark alluded to the importance of emotional dependency on the coach, meaning that he/she would not question instructions from the coach due to their dependency and personal lack of knowledge.
4. Discussion The aim of this paper was to identify items that differentiate between athletes who dope and those who do not and thus, analyze whether findings from previous research evaluating risk factors for doping behavior can be confirmed with a method new to doping prevention research. Additionally, we aimed to assess whether the application of the Q-method might be a suitable approach in doping prevention research while overcoming some limitations of traditional methods. Considering the results, there are two main findings to be highlighted. One, both Q-sorts identified a 2-factors solution, differentiating between the doped and non-dope athletes. In detail, the external factors seem to be stronger predictors for doping behavior as the external Q-sort differentiated more clearly between two groups. The internal Q-sort also differentiated two groups of athletes even though it was not particularly strong (i.e., only four of the ten athletes could be assigned by a unique significant regression factor). Beyond that, applying the Q-method outlined that there are still gaps in understanding doping behavior as we could not confirm most of the findings from previous research in terms of specific items predicting doping susceptibility and –behavior. Only 11% of all items (n = 15) differentiated between the groups of doped and non-doped athletes in the study at hand. Moreover, these items stem from only four constructs (as defined by the original work we retrieved them from) and addressed attitudes, goal orientation, sportspersonship and situational temptation. In terms of the feasibility of applying the Q-method, none of the athletes indicated major constraints. Yet, future studies would need to prove the validity of the method in regard to anti-doping research. 4.1. External Q-sort: distinctive items The external Q-sort differentiated more clearly between two groups compared to the internal Q-sort. This could support evidence from previous research that external factors seem to be strong predictors for doping behavior (Engelberg et al., 2015; Kirby et al., 2011). However, considering the items, only 3 out of 43 items could be considered as distinctive and to all of them, doped athletes reported a higher agreement. These three items all referred to being tempted to doping in threatening situations (e.g. feeling disadvantaged, believing that most colleges dope, facing an important game etc.) and thus belong to situational temptation/doping susceptibility (as used by Lazuras et al., 2010). None of the other external constructs such as norms and/or training/coach climate could be confirmed by this study as distinctive between doped and non-doped athletes as none of them distinguished between the two groups – thus, doped and non-doped athletes had a similar agreement to these. Consequently, we could not verify that norms and/or training/coach climate are risk factors for doping susceptibility as none of the items associated with these construct differentiated between the two groups. This might be considered a surprising finding as previous research with methods not using Q-methodology suggested an association or predictive value of these (Barkoukis, Lazuras, & Harris, 2015; Barkoukis, Lazuras, Tsorbatzoudis, Rodafinos et al., 2013; Gucciardi et al., 2011; Hodge, Hargreaves, Gerrard, & Lonsdale, 2013; Jalleh, Donovan, & Jobling, 2014; Whitaker et al., 2014). However, also in previous research, effect sizes for situational temptation were higher (r > .47; ß > 0.49) compared to any of the other constructs (r < .36) (Barkoukis, Lazuras, Tsorbatzoudis, Rodafinos et al., 2013; Lazuras et al., 2010; Ntoumanis et al., 2014). 4.2. Internal Q-sort: distinctive items The internal Q-sort also differentiated two groups of athletes; however, only four of the ten athletes could be assigned by a
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unique significant regression factor. All distinctive items belong to only three different psychological constructs: attitudes (n = 10), goal orientation (n = 2) and sportspersonship (n = 1). Interestingly, of the 10 attitude items, only one originates from the method of semantic differentials. The remainder stem from the “Performance Enhancement Attitude Scale” (PEAS) (Petroczi, 2007). In sum, the internal Q-sorts were weaker in distinguishing between the two groups. Thus, we could not support previous findings of an association or predictive value of beliefs about causes of success in sport, performance orientation, motivation, behavioral regulation, moral disengagement and/or self-efficacy (Chan et al., 2015; Barkoukis, Lazuras, & Tsorbatzoudis, 2013; Barkoukis, Lazuras, Tsorbatzoudis, Rodafinos et al., 2013; Lucidi, Grano, Leone, Lombardo, & Pesce, 2004; Ntoumanis et al., 2014) as no items of these constructs were distinctive. Similar to the external Q-sort, those items that distinguished in internal Q-sort belong to those constructs that showed the highest effects also in previous research. In detail, attitudes had the biggest effects sizes among the socio-psychological variables in previous research (Lazuras et al., 2010; Ntoumanis et al., 2014). In addition, sportspersonship and goal orientation were found to have small to moderate negative effects on doping behavior (Blank et al., 2016; Ntoumanis et al., 2014). Interestingly, moral norms proved to be a relatively important predictor as well, displaying moderate to high effect sizes (0.36 < r > 0.53) (Ntoumanis et al., 2014). However, we cannot confirm this result, as none of the items assigned to the constructs of norms were distinctive in our study. 4.3. Are external variables risk- and internal variables protective factors for doping susceptibility? – Grounds for a new perspective in doping prevention Combining the findings of external and internal Q-sort, a tentative interpretation could be that facing a stressful situation and feeling disadvantaged might be the main driver of the decision to dope, especially in view of the more clearly distinction of the external Q-sort. Considering these factors as risk factors, preventive actions would need to aim to minimize these kinds of situations. However, minimizing these situations is closely bound to the system of elite sport in which everything from sponsors, financial support and squad affiliation is associated with athletes’ performance (Overbye, 2018). In line, doping was previously discussed as a symptom of professional elite sport (D’Angelo & Tamburrini, 2010). Additionally, it is associated with the respective sport culture due to constant competition and stress, and, in the case of team sport such as cycling also upholds team bonding and group cohesion (Schnell, Mayer, Diehl, Zipfel, & Thiel, 2014). Preventing athletes from facing such situations would mean structural prevention measure changing the core characteristic of the elite sport system and the sports culture, which in our view is difficult to implement and not yet thoroughly researched. Additionally, one could include an additional perspective to doping prevention that originally stems from health sciences and psychology and focuses on the perspective of health promotion instead of prevention (Hurrelmann, Klotz, & Haisch, 2009) and the term ‘positive psychology’ (Gable & Haidt, 2005). Applied to doping prevention research before (Englar-Carlson, Gleaves, Macedo, & Lee, 2016), we could also focus on protective factors that might explain why some athletes in the elite sport system dope and others do not, even though they all face similar pressure points. Thus, research and prevention in the future could address those factors that make athletes resilient against the pressure they face being an elite athlete as already proposed elsewhere (Blank, 2017). Findings from the internal Q-sort might deliver first indications concerning this new perspective: The fact that the allocation of the athletes was somehow confounded (i.e., athletes loading significantly on more than one factor) might be an indicator that there is no’ dop-
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ing personality’ in the sense of specific personality characteristics as direct risk factor for doping susceptibility or -behavior. Possibly, these internal variables might rather function as effect modifiers and thus, impact on the associations between specific risk factors and doping behavior. In line with this thought, it was proposed earlier (Petroczi & Aidman, 2008) that doping is not the surrogate end-point to be analyzed but it is rather a coping strategy to reach a desirable end-point: satisfy the elite sport system and be successful. Our assumption thus would be to consider internal factors as modulating factors that, if pronounced, protect athletes in pressure situations to refrain from doping and use a different coping strategy. Interestingly, in contrast to previous research, the only distinctive internal items addressed attitudes, goal orientation and sportspersonship (as not defined by us but by previous researchers) and according to our assumptions, these are no risk- but protective factors in the decision to dope. Thus, all other psychological constructs that are currently basis of prevention measures (especially knowledge-based) could be questioned if the results of our study can be proven valid in future studies repeating this design. 4.4. Limitations One limitation that must be addressed in this study is the number of participants. Since convicted doped athletes often do not want to further discuss their doping offence, it was challenging to recruit doped athletes willing to participate in the study. However, even though the total number of participants was only 10 (5 doped and 5 non-doped athletes), the Q-method is suitable for such a small number of participants. Its representativeness is not dependent on a large sample of participants as the diversity of the participants is of higher importance than the number, all the method requires is enough participants to identify viewpoints and perspectives as factors for the purpose of comparison (Brown, 1980; ten Klooster et al., 2008; Valenta & Wigger, 1997). According to Brown (1980), it is sufficient if each identified factor includes at least four of five people; each additional participant would add very little. A further limitation is the fact that we were dependent on the control athletes’ self-declaration of being clean. We tried to minimize this limitation by clearly explaining to the athletes the nature of the study. Nonetheless, we cannot be sure that they were clean in the sense of not taking any prohibited substance neither at the time of the study nor at any time during their career. Additionally, the retrospective aspect of the study could be a limitation to be addressed. The doped athletes all retrospectively indicated their opinion on the specific items, putting themselves into the position they were at the time they committed to doping. It might thus be the case that some of them could not completely remember their thoughts and opinions that the time. To encounter this limitation, the study team specifically instructed them to think back to the time when they decided to use doping and then did use it. 5. Conclusion This study represented a first attempt to overcome some of the challenges encountered by previous studies that render the identification of implications very difficult by applying a new method to doping prevention research. Even though the conclusions of this pilot study must be considered as ideas and indications that need further validation and discussion, some useful information and new insights in doping research was provided. Overall, this study shows that we still know very little about why some athletes dope while others do not. Additionally, the finding that the external Q-sort seems to be more robust compared to the internal Q-sort might indicate that external
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factors are a stronger driver of the decision to dope than internal factors, and thus should also be addressed in future prevention strategies. Additionally, we propose that internal factors be considered as modulating factors that, if pronounced, protect athletes to refrain from doping in pressure situations. In this case, doping prevention should address two levels: a) increase the expression of the protective factors by especially addressing attitudes, goal orientation and sportspersonship and b) decrease risk situations, which would include structural intervention measures and should be addressed to sports federations, sports policies and politics. However, this is so far only a hypothesis based on the findings of this and previous studies and needs to be validated in future research and scientific exchange. This kind of research might be challenging as it warrants a big number of athletes to gather enough data to test such a complex statistical hypothesis and integrate the different perspectives. After all, future research should also take into account the voice of the athletes themselves. If external difficult situations can hardly be changed because they are the core of elite sport, than we should also consider athletes’ opinions whether the provided prevention prepares them to deal with these situations without applying a bad coping strategy such as doping. Declaration of interest The authors report no conflicts of interest. Acknowledgements The authors would like to thank the respondents of the study and the experts helping in developing the Q-sorts. Furthermore, we thank the NADA Austria and the Tyrolean Science Fund (TWF; Grant number: UNI-0404/1870) for their financial support. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.peh.2018.12. 001. References Barkoukis, V., Kartali, K., Lazuras, L., & Tsorbatzoudis, H. (2016). Evaluation of an anti-doping intervention for adolescents: Findings from a school-based study. Sport Management Review, 19(1), 23–34. http://dx.doi.org/10.1016/j.smr.2015. 12.003 Barkoukis, V., Lazuras, L., & Harris, P. R. (2015). The effects of self-affirmation manipulation on decision making about doping use in elite athletes. Psychology of Sport and Exercise, 16(2), 175–181. http://dx.doi.org/10.1016/j.psychsport. 2014.02.003 Barkoukis, V., Lazuras, L., & Tsorbatzoudis, H. (2013). Beliefs about the causes of success in sports and suceptibility for doping use in adolescent athletes. Journal of Sports Sciences, 32(2), 212–219. http://dx.doi.org/10.1080/02640414. 2013.819521 Barkoukis, V., Lazuras, L., Tsorbatzoudis, H., & Rodafinos, A. (2013). Motivational and social cognitive predictors of doping intentions in elite sports: An integrated approach. Scandinavian Journal of Medicine & Science in Sports, 23(5), 330–340. http://dx.doi.org/10.1111/sms.12068 Bilard, J., Ninot, G., & Hauw, D. (2011). Motives for illicit use of doping substances among athletes calling a national antidoping phone-help service: An exploratory study. Substance Use & Misuse, 46(4), 359–367. http://dx.doi.org/ 10.3109/10826084.2010.502553 Blank, C. (2017). A road to a new perspective in doping prevention – One size does NOT fit all. In INDR commentary.. December. http://ph.au.dk/en/research/ research-areas/humanistic-sport-research/research-unit-for-sport-and-bodyculture/international-network-of-doping-research/newsletters/december2017/indr-commentary-cornelia-blank/ Blank, C., Kopp, M., Niedermeier, M., Schnitzer, M., & Schobersberger, W. (2016). Predictors of doping intentions, susceptibility, and behaviour of elite athletes: A meta-analytic review. SpringerPlus, 5(1), 1333. http://dx.doi.org/10.1186/ s40064-016-3000-0 Brown, S. R. (1980). Political subjectivity. In Applications of Q methodology in political science. New Haven, CT: Yale University Press.
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Performance Enhancement & Health journal homepage: www.elsevier.com/locate/peh
An alternative approach to understanding doping behavior: A pilot study applying the Q-method to doping research K. Gatterer a , M. Niedermeier b , B. Streicher c , M. Kopp b , W. Schobersberger a,d , C. Blank a,∗ a
Institute of Sports Medicine, Alpine Medicine & Health Tourism, UMIT, Eduard-Wallnöfer Zentrum 1, 6060 Hall in Tyrol, Austria Department of Sport Science, University of Innsbruck, Fürstenweg 185, 6020 Innsbruck, Austria Institute of Psychology, UMIT, Eduard-Wallnöfer Zentrum1, 6060 Hall in Tyrol, Austria d Tirol Kliniken, Anichstraße 35, 6020 Innsbruck, Austria b c
a r t i c l e
i n f o
Article history: Received 16 July 2018 Received in revised form 31 October 2018 Accepted 11 December 2018 Available online 18 March 2019 Keywords: Doping prevention Q-method Predictors
a b s t r a c t Background: Prevention plays an important role in the fight against doping. A lot of research in the field of doping and anti-doping has been conducted. Yet, there is still a lack of knowledge in understanding the various and multi-level impacting factors of doping behavior. Therefore, this paper aimed to apply the Q-method to doping research to identify items that potentially differentiate between athletes who dope and those who do not and to assess whether these items resemble constructs previously identified in socio-psychological literature to be predictive for doping intention, susceptibility, and behavior. As a secondary goal, we aimed to evaluate whether the Q-method method might be a suitable approach to doping prevention research. Methods: Five doped and five matched non-doped elite athletes from different sports were investigated. Each athlete completed three separate Q-sorts by indicating his/her degree of agreement with a total of 175 items on an 11-point Likert scale. Items stem from 13 different constructs such as attitudes towards doping, training/coach climate and moral disengagement. Results: Results showed that the Q-sorts clearly differentiate two types of athletes (non-doped and doped athletes). In total, only 15 (out of 175) items differentiated between the two groups, stemming from the constructs attitudes, sportspersonship, goal orientation and situational temptation/doping susceptibility. Conclusions: These results might be an initial indicator of items that differentiate between distinct types of athletes (doped and non-doped), and suggest that the Q-method might be a useful tool to differentiate these groups of athletes. However, further research to validate the items is needed before implementation into doping prevention strategies. © 2018 Elsevier Ltd. All rights reserved.
1. Introduction Identifying risk factors related to doping behavior (i.e. the violation of any rules set out in the World Anti-Doping Code article 2.1 – 2.10) was the focus of most research into doping prevention over the last decade (Blank, Kopp, Niedermeier, Schnitzer, & Schobersberger, 2016; Ntoumanis, Ng, Barkoukis, & Backhouse, 2014). Based on these findings, prevention strategies to decrease
∗ Corresponding author at: Department of Psychology and Medical Sciences, Institute of Sports Medicine, Alpine Medicine & Health Tourism, UMIT, EduardWallnöfer-Zentrum 1, 6060, Hall in Tirol, Austria. E-mail addresses: [email protected] (K. Gatterer), [email protected] (M. Niedermeier), [email protected] (B. Streicher), [email protected] (M. Kopp), [email protected] (W. Schobersberger), [email protected] (C. Blank). https://doi.org/10.1016/j.peh.2018.12.001 2211-2669/© 2018 Elsevier Ltd. All rights reserved.
doping incidence in adolescents and college student athletes were developed and implemented (Barkoukis, Kartali, Lazuras, & Tsorbatzoudis, 2016; Elliot, Goldberg, More, DeFrancesco, Durham, & Hix-Small, 2004; Goldberg, Elliot, Clarke, MacKinnon, Moe, Zoref et al., 1996; Goldberg, MacKinnon, Elliot, Moe, Clarke, Cheong, 2000). Programs have been proven to be successful in increasing anti-doping knowledge and decreasing vulnerability, intention to use and actual use of anabolic steroids (Barkoukis et al., 2016; Elliot et al., 2004; Goldberg et al., 1996, 2000). However, doping incidence does not seem to have decreased (de Hon, Kuipers, & van Bottenburg, 2015; World Anti-Doping Agency, 2016), which might be explained by two reasons. For one, it might be an indication that even though there is evidence for specific risk factors in doping behavior, anti-doping authorities lack to transfer this evidence into efficient prevention measure, considering that most prevention programs focus on knowledge-based interventions but lack to address value-based aspects. Secondly, it might be an indication
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that there still is a lack of knowledge in understanding the various and obviously multi-level influence factors of doping behavior. Critical evaluation of the anti-doping research performed in social sciences over the last few years outlines at least three potential limitations and thus, the possibility for a lack of successful transfer into prevention programs. The first limitation is of methodological nature and arises from the inconsistent use and operationalization of theoretical constructs. It should thus mainly be considered in terms of the comparability of academic studies. However, organizations in charge of doping prevention might struggle with developing evidence-based targeted prevention programs as a result of lack of clear implications from academics. Even though it is positive and important that numerous theoretical perspectives are applied to try to understand doping behavior, the according operationalization of the respective, predictive variables of different theoretical models was inconsistent across different studies. For example, attitudes were operationalized as ‘outcome expectancies’ (Gucciardi, Jalleh, & Donovan, 2011), differentiated semantically (i.e., ‘doping is good vs. bad’) (Barkoukis, Lazuras, Tsorbatzoudis, & Rodafinos, 2013) or by using the performance enhancement attitude scale (PEAS) of Petroczi and Aidman (2008) – and yet, always labelled as ‘attitudes’. It is questionable if the same dimension of attitudes was addressed and hence, if studies are comparable or, more precisely, if these results should for example be synergized. Also, outcome variables were sometimes labelled differently but measured with almost identical items such as the use of the concepts of ‘doping susceptibility’/’situational temptations’ (Lazuras, Barkoukis, Rodafinos, & Tzorbatzoudis, 2010) and the willingness to dope (Whitaker, Long, Petroczi, & Backhouse, 2014). In this case, it is not surprising that situational temptation is a strong predictor of doping susceptibility, but questionable if it is an artefact based on the operationalization. The second limitation relates to the investigated target groups and their different reasons to dope. Most studies focused on elite and/or semi-elite athletes of a given sport, country and setting (Blank et al., 2016; Ntoumanis et al., 2014). However, reasons for doping might differ by competition level and/or sport. In this context, Bilard, Ninot, and Hauw, (2011) investigated three sports (cycling, bodybuilding and football) and found that athletes in each sport had their own reasons to dope. Cyclists doped to maintain their health, bodybuilders wanted to increase muscular strength, and footballers used prohibited substances for personal recreation. In line with that, Overbye, Knudsen, and Pfister, (2013) showed that various circumstances influenced an athlete’s decision to dope and that these differ between athletes of different gender, age and sport type. Thus, it is again difficult to compare studies and draw general conclusions, as there are various reasons to dope that vary by competition level and sport. The third limitation concerns the classification of doped and non-doped athletes, which is mostly based on the self-appraisals of these athletes. Past research tried to identify risk factors for doping by questioning doped and non-doped athletes. However, in most studies it is unknown whether the athletes were doped or not. All athletes are considered clean if not proven otherwise (i.e., being detected as dopers).Thus, every study has to rely on the truthfulness of athletes’ self-declarations. However, self-reporting is considered to be methodologically problematic and can potentially lead to underestimated results due to the distorting effect of socially desirable responding (Petroczi & Nepusz, 2011). Consequently, there is the possibility that any group of non-doped athletes also includes doped athletes, albeit as-yet undetected. Conversely, as the term doping was rarely defined in individual studies, non-doped athletes might have indicated doping based on a misunderstanding. Consequently, it remains unclear whether the results of these studies are due to actual differences between the two groups or if they can be traced back to methodological artefacts.
These potential limitations become more visible when considering findings of predictive factors for doping behavior resulting from quantitative studies as well as findings of reasons for doping being researched in qualitative studies including convicted doped athletes, or athletes who had publicly admitted to doping (Engelberg, Moston, & Skinner, 2015; Kirby, Moran, & Guerin, 2011). Results of the quantitative approaches showed significant influences of attitudes, norms and morals on doping behavior (Blank et al., 2016; Ntoumanis et al., 2014). However, findings of qualitative studies including admittedly doped athletes do not suggest that any of those variables are reasons for doping behavior. They rather identified motives that are highly functional, for example the means to overcome an illness or injury (Engelberg et al., 2015; Kirby et al., 2011). However, these studies are small in number and suffer from the various difficulties that arise when trying to interview convicted, doped athletes (e.g., lack of interest of these athletes to participate in a study). We argue that the above-mentioned limitations must be addressed to gain new insights concerning the mechanisms driving doping. This might be achieved by applying an alternative approach that a) should be independent from labelling different constructs and therefore allow comparability of future studies and b) uses multiple methods to discriminate between dopers and non-dopers. To address these aspects, this paper opens a new avenue in doping research by applying the Q-method, that is, by combining the positive characteristics of quantitative and qualitative research (Brown, 1996). Applying the Q-method, the main aim of this paper was twofold and a) to identify whether items from previous research differentiate between athletes who dope and those who do not, by questioning doped athletes and matching them with non-doped controls and thus b) to confirm previous research findings based on classical research methods (questionnaire and interview) (aim 1). In this regard, a secondary aim was to evaluate whether the Q-method can be considered a suitable approach to doping prevention research by evaluating the hypothesis of two groups of athletes (doped and non-doped athletes) to be identified within this study (aim 2). To overcome the problem of heterogeneity in the operationalization of potential predictor variables, the focus was not set on any pre-defined construct, but rather on the items underlying these constructs. To address the self-report limitation, this pilot study included knowingly doped (convicted and previously banned) and non-doped control athletes (based on self-report). By applying this alternative approach, we aimed to identify specific items that differentiate between these two groups. The results could be taken further and used in future studies to test their validity and reliability, as well as in studies designed to allow conclusions to be drawn on cause-effect relationships.
2. Method The Q-Methodology was first used in 1935 by William Stephenson as an adaption of Spearman’s traditional factor analysis (Watts & Stenner, 2012). The main difference between the Q-method and the R-method (i.e., traditional factor analysis) is that the Q-method uses a specific survey and analysis procedure: instead of correlating statements or items (R-method), persons are correlated with the aim to identify groups of participants with similar overall attitudes or response-patterns (Watts & Stenner, 2012) and to find differences between these groups of participants. In view of the goal of identifying profiles or types of individuals that respond in a similar way to certain set of variables, the Q-method is sometimes compared to the cluster analysis. However, data analysis of the cluster analysis is more complex and the interpretation not as straightforward as with the Q-method (ten Klooster, Visser, & de Jong, 2008).
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In Q-type factor analysis, every participant is viewed as a different experimental case, representing a “factor entity” (Gabor, 2013, p.119). Thus, instead of presenting a low number of items to a high number of people (R-technique), in Q-method a high number of items are presented a low number of people that they should assess (Gabor, 2013). This is a major benefit of the Q-method in view of the difficulty of motivating doped athletes to partake in studies (He, Hu, & Fan, 2017; ten Klooster et al., 2008). The method only needs a small number of athletes as “the major relationships tend to stabilize with just a few cases” (Brown & Ungs, 1970, p.520). Although the Q-methodology has been applied to a number of scientific disciplines such as nursing (Ha, 2016) or medicine (Reid, Swift, & Mehanna, 2017), to our knowledge, it has never been used in the field of doping research. However, this method could be used as an alternative approach to understand doping behavior, eventually distinguishing doped from non-doped athletes based on pre-specified variables. It combines the strengths of qualitative and quantitative research, allowing athletes to articulate their personal opinion, and results can be generated into testable hypotheses (Brown, 1996; Valenta & Wigger, 1997). 2.1. Design Based on He et al. (2017), the procedure comprises five different steps that were applied to our study setting. An overview is presented in Fig. 1. To define the statement concourse (step 1), two meta-analyses addressing predictors of doping behavior, attitudes and intentions in elite sport were considered (Blank et al., 2016; Ntoumanis et al., 2014) and all items that were significantly associated with doping behavior, attitude and intention were included. The initial list of items and their respective constructs is available upon request. English items were translated into German, and then back into English, by a professional translating office, compared with the original version and adjusted accordingly (cf. van de Vijer & Hambleton, 1996). To develop the final Q-set, a representative sample of statements or items from the prior defined concourse (Durning & Brown, 2006) was defined (step 2). We therefore considered the views and opinions of experts in the field of doping research and sport by applying an adapted version of the Delphi-method as outlined in detail in Fig. 1 (for details concerning the Delphi-method, please refer to Hsu & Sandfort, 2007). Experts were specialists from the fields of sport science and coaching (n = 1), sport medicine (n = 2), sport and health psychology (n = 2), social psychology (n = 1) and a member of a sports federation entrusted with anti-doping matters (n = 1). All 13 initial psychological constructs were still present after applying the Delphi-method, yet with a reduced number of items. Based on the expert discussions and informed by prior evidence of qualitative research (Engelberg et al., 2015; Kirby et al., 2011) that external circumstances might play a specific role in the decision to dope, items generated by the meta-analyses were divided according to their association with external (situational) factors and internal factors. Subsequently, Q-set 1 consisted of 43 items that were associated with external variables, including the construct norms, training/coach climate, situational temptation and behavioral control. Q-set 2 would have consisted of 132 items all associated with internal variables. However, as single Q-sets typically contain 40–80 statements or items (Brown, 1980; Kelly, Moher, & Clifford, 2016), we considered 132 to be too many items. Thus, they were divided into 2 separate Q-sets to reduce cognitive complexity and enhance practicability for the participants. The allocation of the items to Q-set 2 and 3 was done randomly while making sure that each construct is represented in both Q-sets. Accordingly, Q-set 2 and 3 consisted of 66 items each including the constructs of attitude towards doping,
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beliefs about causes of success, goal orientation, performance orientation, motivation, behavioral regulation, moral disengagement, sportspersonship, anxiety and self-efficacy. The selection of the participant-set (step 3) included approaching non-doped and doped athletes, with the latter having used prohibited substances intentionally to enhance their performance. The intention underlying the doping offence was especially important, as we did not want to include so-called “unintentional dopers” - athletes who were convicted of doping but did not do so intentionally (e.g., had a positive test result due to contaminated food supplements). Athletes were recruited via personal contact by the study team. After agreeing to participate in the study, each doped athlete was matched with a non-doped control athlete from the same sport and the same gender. The main characteristic of the Q-method is that participants rank the pre-defined items (described above) according to their personal perspective (Burt & Stephenson, 1939). In this study, athletes were presented with the final three different Q-sets and were asked to rank them in a “forced or forced-choice distribution” (Watts & Stenner, 2012) of the shape of a (quasi-)normal distribution (Fig. 2) according to their level of agreement with each presented item (step 4). Short post-hoc interviews were conducted to obtain additional information if applicable. The procedure was observed by two members of the study team, after participants were informed about the study aims and signed a written informed consent form. The study was approved by the university’s ethical commission. For data protection reasons, no detailed information on gender, age or sport is presented. Data were analyzed using SPSS v. 24.0 (IBM, New York, United States; step 5). Two separate analyses were performed for Q-set 1 (external items) and for Q-set 2 and 3, which were merged (internal items). A centroid factor analysis with varimax rotation was applied using the Kaiser-Guttman criterion (eigenvalue ≥ 1.00) to extract the number of factors (groups of athletes). The regression factor score was calculated for each participant and factor and a significant regression factor score was used to allocate participants to factors (p < .01). Whenever a participant showed a significant loading on more than one factor, they were allocated to the factor with the higher regression factor score. Appropriate and inappropriate allocation of the participants to the factors (groups of athletes) was reported. Normalized average Q-sort factor scores were calculated for each factor. As indicated in the literature, items were considered distinctive for a factor (i.e., groups of athletes with similar responses in the items) “if they exceed 2.58 times the standard error of differences” (Brown, 1980, p.24; see also for a detailed description of the calculation of standard error for each factor). 3. Results 3.1. Q-set 1 – external items Regarding the external Q-set, the Kaiser-Guttman criterion revealed a 2-factor solution explaining 69.9% of the total variance. Normalized average Q-sort factor scores for each factor and item can be found in Appendix A. Nine out of ten athletes showed a significant factor loading on only one factor (A/doped athletes or B/non-doped athletes): three athletes on Factor A, six athletes on Factor B. One athlete from the group of doped athletes significantly loaded on both factors and was allocated to A (doped) based on the higher loading with that factor (cf. Table 1).1
1 From here on, all results relating to Q-set 1 will be referred to as Factor A1 and/or B1 ; and all results relating to Q-set 2 and 3 as Factor A23 and/or B23 .
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Fig. 1. 5 steps of Q-sort development. Table 1 Factor loadings of external and internal Q-set for each participant. External Q-set
Internal Q-set
Participant
Group
Factor loading A1
Factor loading B1
Assigned to factor
Factor loading A23
Factor loading B23
Assigned to factor
01 02 03 04 05 06 07 08 09 10
doped doped doped doped doped non-doped non-doped non-doped non-doped non-doped
0.58* 0.84* 0.84* 0.92* 0.30 −0.18 0.15 0.35 0.19 0.09
0.40* 0.17 0.07 0.00 0.57* 0.76* 0.89* 0.86* 0.84* 0.85*
A1 ˜ A1 A1 A1 B1 B1 B1 B1 B1 B1
0.58* 0.80* 0.30* 0.74* 0.50* 0.16 0.26* 0.51* 0.40* 0.09
0.37* 0.20 0.58* 0.06 0.37* 0.80* 0.79* 0.58* 0.68* 0.73*
A23 ˜ A23 B23 ˜ A23 A23 ˜ B23 B23 ˜ B23 ˜ B23 ˜ B23
*: p < .01. ˜: Assignment to factor based on higher factor loading due to significant loadings on both factors.
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Fig. 2. Prearranged distribution for a Q-sort with 48 items.
Table 2 Distinctive items external Q-sort differentiating between factor A1 (primarily doper) and factor B1 (primarily non-doper). Item #
Item wording
item 26
I would be tempted to use prohibited doping substances to enhance my performance this season when feeling disadvantaged. I would be tempted to use prohibited doping substances to enhance my performance this season when I believe that most colleagues use prohibited substances. I would be tempted to use prohibited doping substances to enhance my performance this season when I prepare for an important game/competition.
item 29
item 46
Factor A1 : The first group of participants consisted of doped athletes (eigenvalue 2.2, accounting for 22.1% of the variance). Four out of five doped athletes were assigned to factor A1 (and no nondoped athlete). Athletes assigned to this factor reported a high agreement with “I believe athletes in my sport engage in doping to enhance their performance” and “I would be tempted to use prohibited doping substances to enhance my performance this season when I believe that most colleagues use prohibited substances”. In addition, they reported a low agreement with: “My immediate family (parents, brothers, sisters) would approve of me using PES to improve my athletic performance” and “People who are important to me would approve of me using prohibited substances to enhance my performance during this season”. Factor B1 : The second group of participants consisted of primarily non-doped athletes (eigenvalue 4.8, accounting for 47.5% of the variance). Five out of five non-doped athletes, as well as one of the doped athletes, were assigned to factor B1 . Athletes assigned to this factor reported a high agreement with “I feel in complete control over whether I will use doping substances to enhance my performance” and “My coach rewards trying hard”. Furthermore, they reported a low agreement with “I would be tempted to use prohibited doping substances to enhance my performance this season when feeling disadvantaged.” and “I would be tempted to use prohibited doping substances to enhance my performance this season when I prepare for an important game/competition”. Three items (out of 43) were considered to distinguish between factor A1 and B1 whereas all athletes allocated to factor A1 reported a higher level of agreement in the distinctive items compared to the athletes allocated to factor B1 (refer to Table 2). In detail, the
items were as follows: “I would be tempted to use prohibited doping substances to enhance my performance this season when feeling disadvantaged”, “I would be tempted to use prohibited doping substances to enhance my performance this season when I believe that most colleagues use prohibited substances” and “I would be tempted to use prohibited doping substances to enhance my performance this season when I prepare for an important game/competition”.
3.2. Q-set 2 and 3 – internal items As described in the method section, Q-set 2 and 3 were merged prior to the analysis. The Kaiser-Guttman criterion revealed a 2-factor solution, which explained 56.0% of the total variance. Normalized average Q-sort factor scores for each factor and internal item can be found in Appendix B. Four out of ten athletes showed a significant factor loading on only one factor (A or B): two athletes on Factor A (doped) and two on factor B (non-doped). Six athletes significantly loaded on both factors and were allocated to the factor with the higher loading (two athletes on Factor A, doped; the remaining 4 on Factor B, non-doped) (cf. Table 1). Factor A23 : One group of participants consisted of doped athletes (eigenvalue 1.0, accounting for 10.3% of the variance). Four out of five doped athletes and none of the non-doped athletes were assigned to factor A23 . Athletes reported a high agreement with “There is no difference between drugs and the technical equipment that can be used to enhance performance (e.g., hypoxic altitude simulating environments)” and “Doping is an unavoidable part of competitive sport”. Furthermore, they reported a low agreement with “I respect all the rules of my sport” and “Considering difficult situations I am afraid of failing when a lot depends on my skills”. Factor B23 : The other group of participants consisted of primarily non-doped athletes (eigenvalue 4.6, accounting for 46.0% of the variance). Five out of five non-doped athletes and one out of five doped athletes were assigned to factor B23 . The doped athlete assigned to the group of primarily non-doped athletes was not the same athlete as the one in the analysis based on the external Q-set. Athletes reported a high agreement with “I respect all the rules of my sport” and “The use of illegal substances to improve sport performance or physical appearance would be bad”. Additionally, the reported a low agreement with “Performance-enhancing drugs/methods should be allowed for top level athletes” and “The use of illegal substances
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Table 3 Distinctive items internal Q-sort with higher level of agreement in athletes allocated to factor A23 (primarily doper). Item #
Item wording
item 118
Athletes in my sport are pressured to take performance-enhancing drugs. Doping is not cheating since everybody does it. Doping is necessary to be competitive in my sport. The use of illegal substances to improve sport performance or physical appearance would be useful. Athletes should not feel guilty about breaking the rules and taking performance-enhancing drugs. The only time I am satisfied is when I win. Doping is an unavoidable part of competitive sport. Using PES in the coming year would help me earn a living as a professional athlete. There is no difference between drugs and the technical equipment that can be used to enhance performance (e.g., hypoxic altitude simulating environments).
item 062 item 137 item 125 item 065 item 081 item 066 item 124 item 139
Table 4 Distinctive items internal Q-sort with higher level of agreement in athletes allocated to factor B23 (primarily non-doper). Item #
Item wording
item 167 item 055
I respect all the rules of my sport. It is important that my sport has an effective drug testing program. The use of illegal substances to improve sport performance or physical appearance would be bad.
item 057
to improve sport performance or physical appearance would be negative”. Twelve items (out of 132) were considered as distinctive between factor A23 and B23 . All athletes allocated to factor A23 reported a higher level of agreement to the following items: “Athletes in my sport are pressured to take performance-enhancing drugs”, Doping is not cheating since everybody does it”, “Doping is necessary to be competitive in my sport”, “The use of illegal substances to improve sport performance or physical appearance would be useful”, “Athletes should not feel guilty about breaking the rules and taking performanceenhancing drugs”, “The only time I am satisfied is when I win”, “Doping is an unavoidable part of competitive sport”, “Using PES in the coming year would help me earn a living as a professional athlete” and “There is no difference between drugs and the technical equipment that can be used to enhance performance (e.g., hypoxic altitude simulating environments)” (Table 3). All athletes allocated to factor B23 reported a higher level of agreement to the following items: “I respect all the rules of my sport”, “It is important that my sport has an effective drug testing program” and “The use of illegal substances to improve sport performance or physical appearance would be bad” (Table 4).
3.3. Additional comments by athletes Only four of the doped athletes felt the need to add additional comments. They agreed that it was challenging to keep to the forced choice normal distribution and tended more towards the right corner (agreement). None of them felt that the presented statements were irrelevant, but they added that their reasons to dope were mainly situational and influenced by the environment, in the sense of the sporting culture. One remark alluded to the importance of emotional dependency on the coach, meaning that he/she would not question instructions from the coach due to their dependency and personal lack of knowledge.
4. Discussion The aim of this paper was to identify items that differentiate between athletes who dope and those who do not and thus, analyze whether findings from previous research evaluating risk factors for doping behavior can be confirmed with a method new to doping prevention research. Additionally, we aimed to assess whether the application of the Q-method might be a suitable approach in doping prevention research while overcoming some limitations of traditional methods. Considering the results, there are two main findings to be highlighted. One, both Q-sorts identified a 2-factors solution, differentiating between the doped and non-dope athletes. In detail, the external factors seem to be stronger predictors for doping behavior as the external Q-sort differentiated more clearly between two groups. The internal Q-sort also differentiated two groups of athletes even though it was not particularly strong (i.e., only four of the ten athletes could be assigned by a unique significant regression factor). Beyond that, applying the Q-method outlined that there are still gaps in understanding doping behavior as we could not confirm most of the findings from previous research in terms of specific items predicting doping susceptibility and –behavior. Only 11% of all items (n = 15) differentiated between the groups of doped and non-doped athletes in the study at hand. Moreover, these items stem from only four constructs (as defined by the original work we retrieved them from) and addressed attitudes, goal orientation, sportspersonship and situational temptation. In terms of the feasibility of applying the Q-method, none of the athletes indicated major constraints. Yet, future studies would need to prove the validity of the method in regard to anti-doping research. 4.1. External Q-sort: distinctive items The external Q-sort differentiated more clearly between two groups compared to the internal Q-sort. This could support evidence from previous research that external factors seem to be strong predictors for doping behavior (Engelberg et al., 2015; Kirby et al., 2011). However, considering the items, only 3 out of 43 items could be considered as distinctive and to all of them, doped athletes reported a higher agreement. These three items all referred to being tempted to doping in threatening situations (e.g. feeling disadvantaged, believing that most colleges dope, facing an important game etc.) and thus belong to situational temptation/doping susceptibility (as used by Lazuras et al., 2010). None of the other external constructs such as norms and/or training/coach climate could be confirmed by this study as distinctive between doped and non-doped athletes as none of them distinguished between the two groups – thus, doped and non-doped athletes had a similar agreement to these. Consequently, we could not verify that norms and/or training/coach climate are risk factors for doping susceptibility as none of the items associated with these construct differentiated between the two groups. This might be considered a surprising finding as previous research with methods not using Q-methodology suggested an association or predictive value of these (Barkoukis, Lazuras, & Harris, 2015; Barkoukis, Lazuras, Tsorbatzoudis, Rodafinos et al., 2013; Gucciardi et al., 2011; Hodge, Hargreaves, Gerrard, & Lonsdale, 2013; Jalleh, Donovan, & Jobling, 2014; Whitaker et al., 2014). However, also in previous research, effect sizes for situational temptation were higher (r > .47; ß > 0.49) compared to any of the other constructs (r < .36) (Barkoukis, Lazuras, Tsorbatzoudis, Rodafinos et al., 2013; Lazuras et al., 2010; Ntoumanis et al., 2014). 4.2. Internal Q-sort: distinctive items The internal Q-sort also differentiated two groups of athletes; however, only four of the ten athletes could be assigned by a
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unique significant regression factor. All distinctive items belong to only three different psychological constructs: attitudes (n = 10), goal orientation (n = 2) and sportspersonship (n = 1). Interestingly, of the 10 attitude items, only one originates from the method of semantic differentials. The remainder stem from the “Performance Enhancement Attitude Scale” (PEAS) (Petroczi, 2007). In sum, the internal Q-sorts were weaker in distinguishing between the two groups. Thus, we could not support previous findings of an association or predictive value of beliefs about causes of success in sport, performance orientation, motivation, behavioral regulation, moral disengagement and/or self-efficacy (Chan et al., 2015; Barkoukis, Lazuras, & Tsorbatzoudis, 2013; Barkoukis, Lazuras, Tsorbatzoudis, Rodafinos et al., 2013; Lucidi, Grano, Leone, Lombardo, & Pesce, 2004; Ntoumanis et al., 2014) as no items of these constructs were distinctive. Similar to the external Q-sort, those items that distinguished in internal Q-sort belong to those constructs that showed the highest effects also in previous research. In detail, attitudes had the biggest effects sizes among the socio-psychological variables in previous research (Lazuras et al., 2010; Ntoumanis et al., 2014). In addition, sportspersonship and goal orientation were found to have small to moderate negative effects on doping behavior (Blank et al., 2016; Ntoumanis et al., 2014). Interestingly, moral norms proved to be a relatively important predictor as well, displaying moderate to high effect sizes (0.36 < r > 0.53) (Ntoumanis et al., 2014). However, we cannot confirm this result, as none of the items assigned to the constructs of norms were distinctive in our study. 4.3. Are external variables risk- and internal variables protective factors for doping susceptibility? – Grounds for a new perspective in doping prevention Combining the findings of external and internal Q-sort, a tentative interpretation could be that facing a stressful situation and feeling disadvantaged might be the main driver of the decision to dope, especially in view of the more clearly distinction of the external Q-sort. Considering these factors as risk factors, preventive actions would need to aim to minimize these kinds of situations. However, minimizing these situations is closely bound to the system of elite sport in which everything from sponsors, financial support and squad affiliation is associated with athletes’ performance (Overbye, 2018). In line, doping was previously discussed as a symptom of professional elite sport (D’Angelo & Tamburrini, 2010). Additionally, it is associated with the respective sport culture due to constant competition and stress, and, in the case of team sport such as cycling also upholds team bonding and group cohesion (Schnell, Mayer, Diehl, Zipfel, & Thiel, 2014). Preventing athletes from facing such situations would mean structural prevention measure changing the core characteristic of the elite sport system and the sports culture, which in our view is difficult to implement and not yet thoroughly researched. Additionally, one could include an additional perspective to doping prevention that originally stems from health sciences and psychology and focuses on the perspective of health promotion instead of prevention (Hurrelmann, Klotz, & Haisch, 2009) and the term ‘positive psychology’ (Gable & Haidt, 2005). Applied to doping prevention research before (Englar-Carlson, Gleaves, Macedo, & Lee, 2016), we could also focus on protective factors that might explain why some athletes in the elite sport system dope and others do not, even though they all face similar pressure points. Thus, research and prevention in the future could address those factors that make athletes resilient against the pressure they face being an elite athlete as already proposed elsewhere (Blank, 2017). Findings from the internal Q-sort might deliver first indications concerning this new perspective: The fact that the allocation of the athletes was somehow confounded (i.e., athletes loading significantly on more than one factor) might be an indicator that there is no’ dop-
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ing personality’ in the sense of specific personality characteristics as direct risk factor for doping susceptibility or -behavior. Possibly, these internal variables might rather function as effect modifiers and thus, impact on the associations between specific risk factors and doping behavior. In line with this thought, it was proposed earlier (Petroczi & Aidman, 2008) that doping is not the surrogate end-point to be analyzed but it is rather a coping strategy to reach a desirable end-point: satisfy the elite sport system and be successful. Our assumption thus would be to consider internal factors as modulating factors that, if pronounced, protect athletes in pressure situations to refrain from doping and use a different coping strategy. Interestingly, in contrast to previous research, the only distinctive internal items addressed attitudes, goal orientation and sportspersonship (as not defined by us but by previous researchers) and according to our assumptions, these are no risk- but protective factors in the decision to dope. Thus, all other psychological constructs that are currently basis of prevention measures (especially knowledge-based) could be questioned if the results of our study can be proven valid in future studies repeating this design. 4.4. Limitations One limitation that must be addressed in this study is the number of participants. Since convicted doped athletes often do not want to further discuss their doping offence, it was challenging to recruit doped athletes willing to participate in the study. However, even though the total number of participants was only 10 (5 doped and 5 non-doped athletes), the Q-method is suitable for such a small number of participants. Its representativeness is not dependent on a large sample of participants as the diversity of the participants is of higher importance than the number, all the method requires is enough participants to identify viewpoints and perspectives as factors for the purpose of comparison (Brown, 1980; ten Klooster et al., 2008; Valenta & Wigger, 1997). According to Brown (1980), it is sufficient if each identified factor includes at least four of five people; each additional participant would add very little. A further limitation is the fact that we were dependent on the control athletes’ self-declaration of being clean. We tried to minimize this limitation by clearly explaining to the athletes the nature of the study. Nonetheless, we cannot be sure that they were clean in the sense of not taking any prohibited substance neither at the time of the study nor at any time during their career. Additionally, the retrospective aspect of the study could be a limitation to be addressed. The doped athletes all retrospectively indicated their opinion on the specific items, putting themselves into the position they were at the time they committed to doping. It might thus be the case that some of them could not completely remember their thoughts and opinions that the time. To encounter this limitation, the study team specifically instructed them to think back to the time when they decided to use doping and then did use it. 5. Conclusion This study represented a first attempt to overcome some of the challenges encountered by previous studies that render the identification of implications very difficult by applying a new method to doping prevention research. Even though the conclusions of this pilot study must be considered as ideas and indications that need further validation and discussion, some useful information and new insights in doping research was provided. Overall, this study shows that we still know very little about why some athletes dope while others do not. Additionally, the finding that the external Q-sort seems to be more robust compared to the internal Q-sort might indicate that external
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factors are a stronger driver of the decision to dope than internal factors, and thus should also be addressed in future prevention strategies. Additionally, we propose that internal factors be considered as modulating factors that, if pronounced, protect athletes to refrain from doping in pressure situations. In this case, doping prevention should address two levels: a) increase the expression of the protective factors by especially addressing attitudes, goal orientation and sportspersonship and b) decrease risk situations, which would include structural intervention measures and should be addressed to sports federations, sports policies and politics. However, this is so far only a hypothesis based on the findings of this and previous studies and needs to be validated in future research and scientific exchange. This kind of research might be challenging as it warrants a big number of athletes to gather enough data to test such a complex statistical hypothesis and integrate the different perspectives. After all, future research should also take into account the voice of the athletes themselves. If external difficult situations can hardly be changed because they are the core of elite sport, than we should also consider athletes’ opinions whether the provided prevention prepares them to deal with these situations without applying a bad coping strategy such as doping. Declaration of interest The authors report no conflicts of interest. Acknowledgements The authors would like to thank the respondents of the study and the experts helping in developing the Q-sorts. Furthermore, we thank the NADA Austria and the Tyrolean Science Fund (TWF; Grant number: UNI-0404/1870) for their financial support. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.peh.2018.12. 001. References Barkoukis, V., Kartali, K., Lazuras, L., & Tsorbatzoudis, H. (2016). Evaluation of an anti-doping intervention for adolescents: Findings from a school-based study. Sport Management Review, 19(1), 23–34. http://dx.doi.org/10.1016/j.smr.2015. 12.003 Barkoukis, V., Lazuras, L., & Harris, P. R. (2015). The effects of self-affirmation manipulation on decision making about doping use in elite athletes. Psychology of Sport and Exercise, 16(2), 175–181. http://dx.doi.org/10.1016/j.psychsport. 2014.02.003 Barkoukis, V., Lazuras, L., & Tsorbatzoudis, H. (2013). Beliefs about the causes of success in sports and suceptibility for doping use in adolescent athletes. Journal of Sports Sciences, 32(2), 212–219. http://dx.doi.org/10.1080/02640414. 2013.819521 Barkoukis, V., Lazuras, L., Tsorbatzoudis, H., & Rodafinos, A. (2013). Motivational and social cognitive predictors of doping intentions in elite sports: An integrated approach. Scandinavian Journal of Medicine & Science in Sports, 23(5), 330–340. http://dx.doi.org/10.1111/sms.12068 Bilard, J., Ninot, G., & Hauw, D. (2011). Motives for illicit use of doping substances among athletes calling a national antidoping phone-help service: An exploratory study. Substance Use & Misuse, 46(4), 359–367. http://dx.doi.org/ 10.3109/10826084.2010.502553 Blank, C. (2017). A road to a new perspective in doping prevention – One size does NOT fit all. In INDR commentary.. December. http://ph.au.dk/en/research/ research-areas/humanistic-sport-research/research-unit-for-sport-and-bodyculture/international-network-of-doping-research/newsletters/december2017/indr-commentary-cornelia-blank/ Blank, C., Kopp, M., Niedermeier, M., Schnitzer, M., & Schobersberger, W. (2016). Predictors of doping intentions, susceptibility, and behaviour of elite athletes: A meta-analytic review. SpringerPlus, 5(1), 1333. http://dx.doi.org/10.1186/ s40064-016-3000-0 Brown, S. R. (1980). Political subjectivity. In Applications of Q methodology in political science. New Haven, CT: Yale University Press.
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