top of page

"Student Athletes' and Non-Student Athletes' Mental Health" by AnnaMaria Leal

The Effects of Personality and Locus of Control on a Student Athlete’s and Non-Student Athlete’s Mental Health Outcome, in Correlation with their Social Support

AnnaMaria Leal, St. Francis College

Abstract: The study aimed to observe the effect that a person’s personality and locus of control have on their mental health outcome especially when social support, or the lack thereof, is also considered as a factor in their mental health outcomes. This was done by grouping participants into three primary categories, non-athlete, individual sport athletes and team sport athletes. The participants were asked to take seven surveys to assess their mental health, personality with a focus on competitiveness, their locus of control, and how much social support they receive. Upon completion of these measures the participants were divided into two groups in order for them play a game to assess their competitiveness. The participants were randomly assigned to a competition group where they were told to beat a made-up high score of another participant or an average group where they were given a made-up average score of all the participants. The results showed that non-athletes scored higher in terms of chance on the locus of control survey against team sport athletes. They also scored higher against individual sport athletes in terms of impatience/irritability on the personality survey. However, in regard to competition, those assigned the competition group performed better than those in the average group with the exception of individual sport athletes who performed better when assigned to the average group. The results of this study display a difference in locus control amongst the non-athletes and student athlete groups, while also highlighting a need for more research that subdivides student athletes to assess their differences in regard to the teams they play on.



Competition and competitiveness are typically associated with athletes. However, how does the level of competitiveness in a student athlete measure up to that of a non-student athlete, and what factors come into play to affect these levels? Do their mental health outcomes and levels of social support impact their ability to excel in competition? Unfortunately, there is a lack of research on this specific topic and even less when student athletes as a population are sub-divided into members of individual sports and team sports.

Past research has shown that when non-student athletes are used as a control group when testing student athletes for anxiety, depression, alcohol use, and social support, female student athletes tend to have higher levels of anxiety or depression. Meanwhile, male student athletes had higher levels of alcohol issues (Storch et al., 2005). When taking into consideration that physical activity has been shown to help reduce mental health issues in a person, it may not come as a surprise that research has shown a significant difference in the mental health scores of student athletes and non-student athletes (Bano et al., 2014). This leads to the question of how much exercise really affects a person’s mental health and whether the pressures and demands of maintaining academics and athletic stature reduce the effects exercise should be having on the lives of student athletes. Armstrong et al. (2015) noted that student athletes are an at-risk group in their systematic literature review on depression. The results showed that student athletes are less likely to be depressed in comparison to their non-student athlete counterparts due to their self-esteem, confidence, social support, and feelings of connectedness. However, when depression did manifest there were factors of those students’ lives that served as barrier to seeking treatment. Thus, Armstrong et al. (2015) concluded that it is important for the support networks of student athletes to recognize aspects of their lives which may influence depression. Furthermore, Armstrong et al. (2015) allowed for the realization that they must determine what barriers may be in place that lead them to engage in risky behaviors, such as alcohol and drug use which may continue to cause them to avoid seeking treatment.

Social support is an important aspect of recovery for those suffering from mental health issues like depression (Armstrong et al., 2015). The relationship between mental health and social support was observed in a 2003 longitudinal study involving five hundred adults. The participants were asked to participate at two different points of the study: the six-month mark and then again at the twelve-month mark. The findings determined that there is a strong positive relationship between mental health and social support, and the impact that the two have on one another (Sanger et al., 2003). A more recent study (Fogaca et al., 2019) echoed the importance of social support. The purpose of the study was to observe the effect that an increase in coping skills and social support may have on the lives of student athletes when dealing with stressors that may exist in their life. In order to test the effect of these skills, two groups where formed. One group was taught coping skills, while the other group would not be taught coping skills. The results showed that those who were taught how to cope with stress had the potential to better their coping skills for dealing with athletic stressors – all of which increased their mental health related outcomes (Fogaca et al., 2019). Similarly, Rothon et al. (2012) determined that a good balance between one’s involvement in extracurriculars, social support and community social capital, and the involvement of family and friends in aspects of their life, are vital for adolescent students to reach their educational benchmarks. However, while the need for social support is continuously echoed through research, a study (Rankin et al., 2018) focused on college students and social support observed a discrepancy that exists between a person’s need for social support and the amount they receive. The data gathered by Rankin et al. (2018) suggests that the perceptions of needed support are much larger than the perceptions of received support (Rankin et al., 2018). This may suggest that part of the reason that collegiate student athletes may exhibit concerning levels of depression, anxiety and stress may be due in part to the lack of involvement they may have from their family and friends. If so, are these levels greater in the case of international students or students who go away for college, who may no longer have the necessary social support or community social capital necessary to help them cope with the stressors brought on by their academic and collegiate athletic careers?

These factors that cause for concern in student athletes may be due in part to aspects of their personality, as well as the different sports they are participating in. Steca et al. (2018) conducted a study involving 881 male athletes and non-athletes. Each participant completed a self-reported questionnaire that measured their personality traits. The results determined that athletes who were the most successful in their sport scored higher than non-athletes in each of the personality dimensions of the Big Five (extroversion, agreeableness, openness, conscientiousness, and neuroticism) (Steca et al., 2018), except for openness. Meanwhile, less successful athletes scored higher than non-athletes in extraversion and agreeableness. When compared to one another, the more successful athletes showed higher levels of agreeableness, conscientiousness, and emotional stability than the less successful athletes. When the athlete category was further broken down, those involved in individual sports were found to be more energetic and open in comparison to the athletes on team sports (Steca et al., 2018).

In 1954, Julian B. Rotter developed the concept of locus of control, which is the level of susceptibility a person believes they have to external forces when it comes to the outcomes of their lives. This concept helped influenced one of the proposed questions in this study: what effect does a person’s personality and locus of control have on their mental health outcome, especially when social support, or the lack thereof, is also considered as a factor behind their mental health outcomes?

The research will more specifically focus on the differences in these categories between student athletes, who will be divided by whether they are members of a team sport (basketball, soccer, volleyball, or water polo) and those who are members of individual sports (cross country, golf, swimming & diving, track & field, or bowling).

The predictions of this study are as follows:

I. Mental Health

· Student Athletes

· Student athletes will have fewer mental health issues because of their daily schedules, which involve a high amount of physical activity which has been shown to reduce stress and anxiety.

· Team Sports

· Members of team sports will have more mental health issues in comparison to members of individual sports because of the combined self-inflicted stress and pressure they place on themselves as well as the external stress and pressure placed on them by their coaches and teammates to do well.

· Individual Sports

· Members of individual sports will have lower mental health outcomes in comparison to members of team sports because of the individual nature of their sport, which may have external stresses and pressures from their coach, but come down to the athlete’s own self-inflicted stresses and pressure.

· Non-athletes

· Non-athletes will have higher mental health outcomes, because while they may do their own workouts, they are not exposed to the constant level of physical activity that student athletes endure.

II. Mental Health Outcome

· Personality

· Competitiveness

· Student athletes will have higher competitiveness scores than non-student athletes.

· Individual sport athletes will score higher on the competitiveness scale then team sport athletes, due to the fact that despite competing under a team name their success is tied to their own name more so then their team’s name. Whereas those on team sports compete and succeed as a unit before receiving recognition as an individual.

· Locus of Control

· Student athletes will showcase stronger feelings revolving around self-blame in comparison to non-student athletes.

· Social Support

· Student athletes will score higher in regard to the amount of social support they receive in comparison to non-athletes, where student athletes involved in team sports having more support than those involved in individual sports.

In order to research these questions, the study utilized a series of questionnaires that address the core issues being analyzed. The study also contained a form of manipulation, in that participants did not know that the study’s focus was to determine the effect of personality, competitiveness and locus of control on a person's mental health outcome and overall wellbeing. Instead, they will be told that the study’s purpose is to address the effects of personality on a person’s wellbeing. The participants were given one of two surveys at random. Both surveys consisted of the same measures and game (known as Switch Dash). However, they differed by one question included in the game section. When participants reach the question regarding the game in questionnaire number one, they were asked to beat a fake high score that was said to have been achieved by another participant. This was done in order to test if the directions referring to the success of another participant would trigger a competitive reaction from the participant to beat the score. Meanwhile, the second questionnaire did not refer to a high score, but rather a made-up average score of all the participants. Similar to the purpose of suggesting that a high score was achieved by another participant, the purpose of this format was to determine the competitive attitude towards success of one’s own game without the mention of success of another participant.



The sample consisted of 70 college students, 22 males (31.429%) and 48 females (68.571%), studying in the United States. The participants ranged in age from 18 to 27 (M = 20.3, SD = 1.8). The study more specifically focused on the distinctions between non-student athletes and student athletes. The student athlete sample was further divided into those who are members of individual sports and those who are members of team sports. In this regard the study consisted of 40 non-athletes (57.143%), 17 team sport athletes (18.571%) and 13 individual athletes (24.286%). In terms of their academic years, the distribution was relatively even, with the exception of the number of graduate students. There were 17 freshmen (24.286%), 17 sophomores (24.286%), 14 juniors (20.0%), 15 seniors (21.429%) and 7 graduate students (10.0%). The sample consisted of 35 white (50%) participants, with the second most prominent being Hispanic/Latino students with 16 participants (22.857%). The majority of the sample also consisted of American students, with 58 participants (82.857%) and only 12 international student participants (17.143%).


The study consisted of seven questionnaires that were formatted and administered by using Google Docs.

Demographic Information (see Appendix A)

Created with the purpose of gathering an overview of the participants in the study. It consists of 13 questions, regarding their age, grade level, ethnicity, international student status, and collegiate athletic status.

Mental Health Outcome (see Appendix A)

The Depression Anxiety Scale-21 (DASS-21) (Lovibond et al., 1995) is a 21-item questionnaire that measures the mental health outcomes of the participants. The items are grouped into three main dimensions: stress scale, depression scale, and anxiety scale. Participants evaluated their feelings about a certain statement on a 4-point Likert-scale ranging from 0 (Did not apply to me at all) to 3 (Applied to me very much or most of the time). Sample items include: “I was intolerant of anything that kept me from getting on with what I was doing” (stress scale), “ I felt that life was meaningless” (depression scale), and “I felt I was close to panic” (anxiety scale). The DASS-21 has been found to be reliable in previous research (Lovibond et al., 1995). The Cronbach’s α coefficient for the stress scale was .78. The Cronbach’s α coefficient for the depression scale was .81 and the anxiety scale was .89.

Locus of Control (see Appendix A)

The Levenson Multidimensional Locus of Control Scales (Levenson et al., 1974) is a 24-item questionnaire that measures the level of responsibility participants take for their actions. The items are grouped into three main dimensions: internal scale, powerful other scale, and chance scale. Participants will evaluate their feelings about a certain statement on a 6-point Likert-scale ranging from -3 (strongly disagree) to +3 (strongly agree). Sample items include: “Whether or not I get to be a leader depends on my ability” (internal scale), “I feel like what happens in my life is mostly determined by powerful people” (powerful other scale), “To a great extent my life is controlled by accidental happenings” (chance). The Levenson Multidimensional Locus of Control Scales have been found reliable in previous research (Levenson et al., 1974). The Cronbach’s α coefficient for the internal scale was .64. The Cronbach’s α coefficient for the powerful other scale was .77 and for the chance scale was .78.

Personality (Competitiveness) (see Appendix A)

The Multidimensional Type A Behaviour Scale (Burns et al., 1992) is a 24-item questionnaire that measures the type-a personality of each participant. The items are grouped into five dimensions: hostility, impatience-irritability, achievement striving, anger, and competitiveness. Participants evaluated their feelings about a certain statement on a 5-point Likert-scale ranging from 1 (strongly disagree) to 5 (strongly agree). Sample items include: “I express my anger” (hostility), “I feel infuriated when I do a good job and get a poor evaluation” (anger), “To be a real success I feel I have to do better than everyone I come up against” (competitiveness). The Multidimensional Type A Behaviour Scale has been used in previous studies; however no listed information can be found in regard to its reliability.

Wellness (see Appendix A)

The Perceived Wellness Survey (Adams et al., 2005) is a 36-item questionnaire that measures the overall wellbeing of the participant. The items are grouped into six dimensions: psychological, emotional, social, physical, spiritual, and intellectual. Participants evaluated their feelings about a certain statement on a 6-point Likert-scale ranging from 1 (very strongly disagree) to 6 (very strongly agree). Sample items include: “I am always optimistic about my future” (psychological), “There have been times when I felt inferior to most of the people I knew” (emotional), “Members of my family come to me for support” (social). The Perceived Wellness Survey has been found reliable in previous research (Adams et al., 2005). The Cronbach’s α coefficient for the scale was .91.

Social Support (see Appendix A)

The Multidimensional Scale of Perceived Social Support (Zimet et al., 2016) is a 12-item questionnaire that measures the level of social support being received by the participant. The items are grouped into three dimensions: significant other, family, and friends. Participants evaluated their feelings about a certain statement on a 7-point Likert-scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Sample items include: “There is a special person who is around when I am in need” (significant other subscale), “My family tries to help me” (family subscale), “My friends really try to help me” (friends subscale). The Multidimensional Scale of Perceived Social Support has been found reliable in previous research (Zimet et al., 2016). The Cronbach’s α coefficient for the scale was .88.

Post-Gameplay (see Appendix A)

The Post-Gameplay Competitiveness Questionnaire was created in order to be administered after each participant has completed the game, Switch Dash, in order to determine the participants’ level of competitiveness in relation to whether they are in the group being asked to beat another participant’s high score or if they are in the group only being given a made-up average score. It consists of the participant recording their scores from all three attempts at Switch Dash, as well as answering four questions regarding their competitive feelings while they played the game. These four questions were adapted from the competitiveness subscale of the Multidimensional Type A Behaviour Scale (Burns et al., 1992). The items are grouped into one dimension: competitiveness. Participants evaluated their feelings about a certain statement on a 5-point Likert-scale ranging from 1 (strongly disagree) to 5 (strongly agree). Sample items include: “I felt as though I had to do good in order to be successful,” “It was important for me to perform better than other participants on the task,” “I judged my performance on whether I could do better than others rather than on getting a high score.” While the Post-Gameplay Competitiveness Questionnaire has not been used in previous studies, the questionnaire in which this subscale was adapted from has been, since it is a part of the Multidimensional Type A Behaviour Scale (Burns et al., 1992). However, no listed information can be found in regard to its reliability.


After obtaining approval from the St. Francis College Institutional Review Board, the principal investigators recruited students through SONA, as well as through social media and outreach to their fellow peers. The participants were told they would be participating in a study about The Effects of Personality on a Person’s Overall Well Being, which would entail them answering a few questions, as well as playing a quick game. The study was conducted using Google Forms to administer the survey and was estimated to take thirty minutes to complete. The survey consisted of the informed consent, seven measures in order to gather information about each participant, as well as about their mental health outcome, locus of control, personality (competitiveness), wellness, social support, and their scores and feelings from the game they were asked to play, as well as the debrief stating the true purpose of the study. Participants were required to answer every survey question unless it only applied to student athletes. Participants completed the same survey up until the final section where they were asked to select the first choice out the two options presented to them, which then elicited a randomized game prompt to appear. Participants saw one of two prompts: one prompt explained that the participant before them received a (made-up) high score, while the other prompt stated a made-up average score of all the participants before them. Once participants completed the game, they were asked to report their scores from each of the three rounds, and then asked to fill out the final questionnaire to gage their feelings regarding the game they just played and their feelings of competitiveness.


In order to analyze the results, an ANOVA was conducted with student type (non-athlete, team sport athlete, and individual sport athlete) as the between-subjects factor and the chance subscale of the locus of control measure. Non-athletes (M = 24.20, SD = 8.83) scored significantly higher on the chance subscale than the team sport athletes (M = 18.294, SD = 7.380, p = .02), see Figure 1.

Figure 1. Student type (Non-athlete, Individual sport athlete, Team sport athlete) and the scores on the chance subscale of The Levenson Multidimensional Locus of Control Scales (Levenson et al., 1974).

A second ANOVA was conducted with student type as the between-subject factor and the impatience/irritability subscale of the personality measure. Non-athletes (M = 15.71, SD = 4.132) scored significantly higher on the impatience/irritability subscale than individual sport athletes (M = 11.60, SD = 3.406, p = 0.02). The scores between the non-athletes and the team sport athletes were not significant (M = 14.29, SD = 4.43, p = .02) see Figure 2.

Figure 2. Student type (Student type (Non-athlete, Individual sport athlete, Team sport athlete) and scores on the impatience/irritability subscale of The Multidimensional Type A Behaviour Scale (Burns et al., 1992)

A factorial ANOVA was conducted to assess the relationship between the independent variables, student types and the game categories (Average and Competition), and the scores on the Post-Gameplay Competition Measure. Overall, there was no difference in the scores between the student types (F(2, 1) = 0.66, p = .52) and the game categories (F(2, 1) = 7.68, p = .007) on the Post-Gameplay Competition Measure. However, it should be noted that there was an interaction between the data that showed how well a group did was dependent on their student type, see Figure 3.

Figure 3. Student type (Student type (Non-athlete, Individual sport athlete, Team sport athlete) and the relationship between the scores of the Post-Gameplay Competition Measure.

The scores of the individual sport athletes were significant; they scored higher when they were randomly assigned to the average game category (M = 3.714, SD = 0.636), rather than when they were assigned to the competition group (M = 2.417, SD = 1.252), t(11) = 2.42, p = .034.

A final T-test was conducted to analyze the relationship between each game category and their scores on the game used to assess competitiveness, Switch Dash. Neither the average group (M = 18.701, SD = 10.699) nor the competition group’s (M = 21.086, SD = 13.331), t(68) = -0.831, p = 0.409 scores on Switch Dash were significant.



The results show non-athletes scored higher on the chance subscale of The Levenson Multidimensional Locus of Control Scales (Levenson et al., 1974) in comparison to team sport athletes. This validates the hypothesis that team athletes tend to take on more responsibility for their actions potentially due to their performance in a game being connected to their team’s potential loss or triumph. The data showed that scores of individual sport athletes were close to that of the team sport athletes. Thus, it can be inferred that they too take on more responsibilities for their actions, potentially due to athletes often being condition to see their ability to train as the reasoning behind their success.

Non-athletes also scored higher on the impatience/irritability subscale of The Multidimensional Type A Behaviour Scale (Burns et al., 1992) in comparison to individual sport athletes. This could be due to patience being a skilled developed in athletes as part of their training, they must recognize they cannot win things right away or that training takes time to have long terms effects concerning their competitions. Team sport athletes scored between the two groups which could potentially be due to the group nature of their sports. Team sports could potentially demand quicker adjustments and adaptation as the ability of each person on the team is vital to their success within their sport.

The hypothesis that student athletes will have higher competitiveness scores than non-student athletes was supported. Those who participate in individual sports had higher competitiveness scores than those in team sports. Those assigned to beat a high score performed better on the game, than those who were just presented with an average score. Individual sport athletes scored higher when they were assigned the average score group than when assigned to the high score competition group. This could be due to the framework in which they typically perform. For example, during a swim meet an athlete is placed in a ‘heat’ which consists of several athletes that swim around the same time as one another. Their goal is not only to push past their own personal records, but to beat those who are ‘on average’ just as capable. In addition, athletes in individual sports not only compete against other teams but also compete within their own team. The within team goals are to beat other athletes on their team within the same events.

The hypothesis that non-athletes will have higher mental health outcomes than student athletes was not supported. This could potentially be because data was collected while the students where in quarantine due to COVID-19 and thus, they could possibly be feeling different then they normally would if they were following their typical schedule.

The hypothesis that student athletes receive more social support than non-athletes was not supported. This could also be due in part to the effects of isolation and lack of social interaction due to quarantine because of COVID-19.

The results of this study align well with the results of a study conducted by Evans et al. (2012), in which they observed the idea behind “we” and “me” in relation to sports, more specifically individual sports. Their study looked into the different typology of sports, from their research they determined there is a form of interdependence amongst athletes in individual sports teams. Where they may be a part of a team, in the sense that they train together and support one another especially when they compete under a school, or club name; however, they are also an individual with goals of their own that they strive to achieve (Evans et al, 2012). The study also determined that there is not much of a difference between adolescents who compete on a basketball team and those on a cross country team, the only point of distinction was that those on the basketball team had higher levels of task interdependence which may be due to the structure of their sport, yet their outcome interdependence perceptions where relatively the same.


Possible limitations of the study could be due the fact that sample size was not as balanced as hoped for; the unbalanced numbers within the non-athlete and athlete group could have skewed the data more in favor of the non-athlete’s results, regardless of the game category they were placed in. In terms of the athletes who participated in the study there was also a lack of individual sport athletes, which could have potentially skewed any data regarding the athlete group toward the team athletes or lead it to be insignificant.

Another limitation of the study can be due to the lack of diversity amongst the participants. The majority of the students who participated identified as white, with the second most responses coming from Hispanic/Latino students. Similarly, the sample also included an unequal distribution of International and American students, due to this lack in diversity amongst such categories it is difficult to rule out these differences in the sample as potential reasons behind a group being more or less competitive.

In regards to the method of testing people’s competitiveness using a game such as Switch Dash, a limitation occurred when setting up the parameters of what the researchers felt maybe a challenging high score for those assigned to the competitive category to work towards beating, as well as what would be an accurate average score one might get from this game. The limitation being that while the researchers correctly estimated a reasonable score to list for each category, there was concern for how their own poor skill level when testing the game may have made the numbers they set as harder to achieve then they really were. However, the majority of participants did fall short of the numbers that were set, with only a few outliers being able to surpass the estimated scores.

The largest limitation of the study was the onset of COVID-19 during the data collection process. Many of the measures used to collect data regarding aspects of the study, such as mental health and social support, asked participants to recall feelings from the last week or last month. Due to the interruption COVID-19 had on everyone’s typical daily life the responses to the surveys may have differed from the response they would have given before the onset of the pandemic. A participant may or may not being feeling as stressed as they once where during their normal day to day life. Thus, it is not possible to accurately gage if the responses to the surveys have been potential altered due to the onset of such a disruptive life event. Similarly, the onset and interruption of COVID-19 during the data collection period meant that some participants had been able to participate in the study before the move to a virtual modality which also interrupted athletic calendars for the remaindered of that school year. While others participated in the study after the pandemic had resulted in the cancelation of remaining athletic seasons and a move to virtual classes.

Directions for Future Research

Going forward this study can be duplicated with a more even, as well as diverse, distribution amongst the demographics of the sample in order to re-evaluate the hypothesis with an even number of participants to rule out the possibility of the data being skewed due to a lack of response from a specific category. Similarly, re-conducting the study under better circumstances, globally, would allow for a more accurate data that has not been altered by the potential psychological effects that living through a pandemic might cause a participant.

The data of this study can be used to help guide future studies that focus specifically on the role one’s locus of control plays on the lives of student athletes and non-athletes. Investigations could focus on areas such as academics or job searches. Another aspect that can be further investigated is an analysis of how student athletes who compete in two sports preform in comparison to those who only compete in one. This can be looked into deeper to see if those who compete on one individual sport and one team sport are more competitive than those on two individual sports or those on two team sports.


The results of this study exposed a need to conduct more studies on differences between student athletes and non-athletes, while also assessing the differences between athletes who compete on team sports and those who compete on individual sports. Going forward more studies should include an analysis of this potential difference amongst their sample as the mindsets of these athletes differs based on the demands of their sports. Similarly, the study has shown a difference in mindsets amongst student athletes and non-athletes in terms of their locus of control; this can lead to future studies that may delve deeper into an analysis of why this may be.

Appendix A

Using the Gallery below, you can see the various instruments used for this study.


Adams, T. (2005). Perceived Wellness Survey [Database record]. Retrieved from PsycTESTS. doi:

Armstrong, S. N., Burcin, M. M., Bjerke, W. S., Early, Jody. (2015). Depression in Student Athletes: A Particularly At-Risk Group? A Systematic Review of the Literature. Athletic Insight, 7(2), 177-193. Retrieved from

Bano, R. P. (2014). Physical activities and its effect on students’ mental health: A comparative study between athlete and non-athlete students. Indian Journal of Health and Wellbeing, 5(7), 34-37. Retrieved from

Burns, W., & Bluen, S. D. (1992). Multidimensional Type A Behaviour Scale [Database record]. Retrieved from PsycTESTS. doi:

Evans, M. B., Eys, M. A., Bruner, M.W. (2012). Seeing the “we” in “me” sports: The need to consider individual sport team environments. Canadian Psychology/Psychologie canadienne, 53(4), 301-308. DOI:10.1037/a0030202

Fogaca, J. L. (2019). Combining mental health and performance interventions: Coping and social support for student-athletes. Journal of Applied Sport Psychology, DOI: 10.1080/10413200.2019.1648326

Havard, C. T., Eddy, T., Reams, L., Stewart, R. L., & Ahmad, T. (2012). Perceptions and general knowledge of online social networking activity of university student-athletes and non-student-athletes. Journal of Applied Sport Management, 4(1), 14-31. Published online May 2012. Retrieved from

Hawley, L. R., Hosch, H. M., Bovaird, J. A. (2014). Exploring Social Identity Theory and the ‘Black Sheep Effect’ Among College Student-athletes and Non-athletes. Journal of Sport Behavior, 37(1), 56-76. Retrieved from

Levenson, H. (1974). Multidimensional Locus of Control Scales [Database record]. Retrieved from PsycTESTS. doi:

Lovibond, S. H. & Lovibond, P. F. (1995). Depression Anxiety Stress Scales [Database record]. Retrieved from PsycTESTs. doi:

Rankin, J. A., Paisley, C. A., Mulla, M. M., & Tomeny, T. S. (2018). Unmet social support needs among college students: Relations between social support discrepancy and depressive and anxiety symptoms. Journal of Counseling Psychology, 65(4), 34-37. DOI:10.1037/cou0000269

Rothon, C., Goodwin, L., & Stansfeld, S. (2012). Family social support, community "social capital" and adolescents' mental health and educational outcomes: A longitudinal study in England. Social Psychiatry and Psychiatric Epidemiology, 47(5), 697-709. doi:

Sanger, H. (2003). Linking social support and well-being: Testing the relationship between social support and mental health. Indiana University of Pennsylvania,

Shi, J., Yao, Y., Zhan, C., Mao, Z., Yin, F., & Zhao, X. (2018). The Relationship Between Big Five Personality Traits and Psychotic Experience in a Large Non-clinical Youth Sample: The Mediating Role of Emotion Regulation. Frontiers in psychiatry, 9, 648.

Steca, P., Baretta, D., Greco, A., D'Addario, M., & Monzani, D. (2018). Associations between personality, sports participation and athletic success. A comparison of big five in sporting and non-sporting adults. Personality and Individual Differences, 121, 176-183. doi:

Storch, E. A., Storch, J. B., Killiany, E. M., Roberti, J. W. (2005). Self-Reported Psychopathology in Athletes: A Comparison of Intercollegiate Student-Athletes and Non-Athletes. Journal of Sport Behavior, 28(1), 86-98. Retrieved from

Zimet, G. D., Dahlem, N. W., Zimet, S. G., Farley, & G. K. (2016). The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment 1988, 52, 30-41.


bottom of page