Category: Anxiety

The challenges of sports presuppose some extent of anxiety and the urge for the victory in the name of a team or something/someone important for a sportsman. That is why arousal anxiety can be counted on when analyzing the examples of those eminent sportsmen who never stopped even with serious traumas or dysfunctions of organism. Moreover, the psychological constituent and the importance of cognition should be taken into account. Many of the researchers in medical sphere are intended to outline that cognitive anxiety is harmful in its ability to make a sportsman overloaded and a performance failed or reduced (Caruso, 2005). Nevertheless, physical activities contemplate the risk of being injured or to get traumas as well. This point is discussed between psychologists and therapists are rallying their thoughts over the question of why sportsmen are so relentless toward their health. This approach is highly depicted by Weinberg & Gould (2007) when they outline that when testing an athlete or a player coaches define the real capabilities of a person and the urge for competition, in particular. This is why there should be a structured plan of psychological trainings which can determine and develop in a sportsman a specific ability to pretend for more under difficult circumstances. This approach is rather effective and easy to imagine in theory, but in practice such steps to demonstrate endurance in competitions help distinctively analyze the whole picture about a sportsman.

The game six between the Los Angeles Lakers and the Philadelphia 76ers in 1980 can be evaluated as a perfect representation of how anxiety can be shown in a player. A badly sprained ankle of Kareem Abdul Jabbar in time when he had already scored 40 points did not stop him in making success for the team. Since a prevailing thought touched upon the idea that Kareem would rest during the game six and will be able to continue playing in the game seven, nobody thought of the team high spirits and anxiety at that time. They without Kareem were highly motivated to get more and more points. As a result, due to Jamal Wilkes, Michael Cooper and others, who helped in making LA Lakers the winners of that match.

Hanin’s IZOF model is outlined here in terms of a good play which contemplates in-time returning of the rest players playing in zones (Weinberg & Gould, 2007).

Tyson-Lewis fight on June 8, 2002 is another good example of anxiety processed in this event. Tyson’s words of blame and other curse-words did not affect Lewis and even made him more aroused about the event. Tyson, in return, wanted and desired to win due to various reasons one of which was his urge for money, and freedom. Tyson’s anxiety was stimulated also by mass media in constant discussions of his life problems. With the flow of time the arousal in Lewis could stop (Weinberg & Gould, 2007). Anxiety of Tyson did not leave him for a second due to perpetual needs.

Hanin’s theory points out here that Mike Tyson was out of his state of zone and emotional disorder was an obstacle in this case. Moreover, he was constantly near right zone of his, but impulsiveness was a bad helper for him at that time.

References

1. Weinberg, R. S. and Gould, D. (2007). Foundations of Sport and Exercise Psychology. Champaign, IL. Human Kinetics.
2. Caruso, A. (2005). Sports Psychology Basics. Spring City , PA: Reedswain Inc.

People with societal panic disorders frequently have a phobia of public communication. Glossophobia, or the anxiety that occurs when communicating in public, is a fairly widespread condition that impacts approximately seventy percent of the worldwide population (Aljabri, Rashwan, Qasem, Fakeeh, Albeladi et al., 2020) Some individuals experience severe pressure at the likelihood of delivering an oral presentation, while others endure completely developed terror and panic. Glossophobia is prevalent in youthful individuals than in elderly adults, and they may be more widespread in females than in males. Individuals who suffer from glossophobia may want to evade publicly communicating circumstances at all costs, and when compelled to do so, they experience shaking hands and a feeble, whispery voice. This raises the glucose levels, or activity levels, and the blood pressure and pulse rate, which increases blood circulation to the muscles (Aljabri, Rashwan, Qasem, Fakeeh, Albeladi et al., 2020). Glossophobia is usually treated with lifestyle modifications, counseling, and medicines. Relaxation approaches like deep breathing and meditation are frequently suggested.

Kausar Perveen, Yamna Hasan and Abdur Rahman Aleemi of the Department of Sociology at the University of Karachi researched on glossophobia. The study commenced in January 2018 and continued for three months. This study aimed to discover if females experience more tension than males when communicating in public and if learners experience more nervousness when speaking in front of the opposite sex. Furthermore, the research was trying to discover if there is a link between reduced public speaking panic and the adoption of impactful psycho-physiological techniques for learning languages. A total of one hundred and twenty-six undergraduate individuals from the University of Karachi were chosen in an equitable proportion of male and female students (Perveen, Hasan & Aleemi, 2018). The level of fear in public speaking among the male and female participants was determined using a percentage and frequency approach.

The intended participants were undergraduate learners at the University of Karachi, one of the nation’s biggest and most prominent universities. On the foundation of convenient selection, one hundred and twenty-six undergraduate individuals were sampled to participate in the study, with equivalent numbers of males and females (Perveen, Hasan & Aleemi 2018). Participants range in age from eighteen to twenty-two years old. For a comparison examination of tension rates in men and women when speaking in front of members of the different sex, multifactorial analysis of variance (ANOVA) was used. Furthermore, a paired T-test was used to see if psycho-physiological language learning procedures are beneficial in reducing glossophobia (Perveen, Hasan & Aleemi, 2018). When researchers want to compare the differences between two parameters for an identical subject, they utilize a paired t-test. The two parameters in this study were the male and female students.

The data was gathered by a systematic and self-reported questionnaire given to the respondents. The instrument comprised three components, each of which included pre-validated parts from various research. The personal report of public speaking anxiety (PRSPA) approach was employed in the study. This technique was used to assess the participant’s sentiments when communicating in front of an audience of the opposite gender (Mortberg, Jansson-Frojmark, Pettersson & Hennlid-Oredsson, 2018). Another method used to evaluate female and male nervousness levels when presenting in front of a predominantly female or male gathering was a seven-item assessment based on the Ethical Principles and Code of Conduct for Psychologists. A nine-item survey was utilized to determine the association between the adaption of psycho-physiological tactics for linguistic acquisition and the reductions in discomfort and fear of publicly communicating (Perveen, Hasan & Aleemi, 2018). All three instruments used a 5-point measure to respond, with answers ranging from strongly agree to disagree strongly.

Approximately thirty-three percent of the 63 male survey participants said they had a relatively significant public speaking phobia. Approximately 29% of the male study respondents experience moderate anxiety, twenty-seven percent have fairly low panic, and 6% have a severe feeling of fear. About five percent of male student respondents said they have no discomfort with public addressing. On the other hand, out of 63 female respondents, 41% have a relatively significant phobia of public communication, thirty-eight percent have moderate anxiety, and 20% have a severe sense of despair. On the other hand, female students did not express relatively low or poor levels of dread of speaking in public (Perveen, Hasan & Aleemi, 2018). As a result, it may be concluded that female learners in Karachi’s university have a greater phobia of the general populace speaking than male students.

The implementation of questionnaires by the researchers for data collection was the best strategy for dealing with the vast number of participants. Questionnaires are preferred survey methodologies since they offer a fast, effective, and low-cost manner to gather large information from huge representative sizes. Such techniques are significant for discovering a participant’s interests, emotions, and perspectives. Researchers would have been able to monitor the respondents’ behavior if they had conducted interviews with them. According to the study, individuals with socially irrational fears, such as glossophobia, have a greater than the standard increased danger of developing depressive symptoms or numerous different anxiousness disorders. This is most likely related to emotions of loneliness that might grow over time.

References

Aljabri, A., Rashwan, D., Qasem, R., Fakeeh, R., Albeladi, R., & Sassi, N. (2020). Overcoming speech anxiety using virtual reality with voice and heart rate analysis. International Conference on Developments in eSystems Engineering, 13(1), 311-316. Web.

Mortberg, E., Jansson-Frojmark, M., Pettersson, A., & Hennlid-Oredsson, T. (2018). Psychometric properties of the personal report of public speaking anxiety (PRPSA) in sample of university students in Sweden. International Journal of Cognitive Therapy, 11(1), 421-433. Web.

Perveen, K., Hasan, Y., & Aleemi, A. R. (2018). Glossophobia: The fear of public speaking in female and male students on University of Karachi. Pakistan Journal of Gender Studies, 16(1), 57-70. Web.

Anti-social behaviour among students is a pertinent societal issue that is a topic of discussion among many sociologists. According to Wowra’s (2007) findings, many students indulge in anti-social behaviour. In his works, the scholar establishes two explanations for why students indulge in malpractices; the Social anxiety hypothesis (SAH) and the moral anxiety hypothesis (MAH). Among the vital anti-social behaviours of focus in this study are academic dishonesty, social anxiety, and moral identity. Academic dishonesty is the involvement in dubious acts by people in a learning process and involves both learners and instructors, social anxiety is the phobia of being perceived negatively by others which often leads to having low self-esteem and a feeling of inadequacy and moral identity is the extent to which an individual morality influences their identity. Students can either be principled if they follow the rules or expedient if they usually stray from the expectations.

Several studies like those by Josephson (n.d) and Lapsley and Narvaez (2004) have established linkages between moral identities, social anxiety, academic dishonesty, and anti-social behaviour among students. Similarly, Wowra (2007) realized that students who recorded cases of anxiety were more likely to be involved in academic dishonesty, while those with commendable moral identities recorded fewer cases of indulging in the vice. In a snippet, principled students with high moral identities and less social anxiety are less likely to involve themselves in academic dishonesty.

The current study is complementary and not a substitute for the works of Wowra (2007). The research replicates the analysis further and supports MIH and SAH of academic cheating and anti-social behaviours. It strengthens the limitations of the previous studies. Some of the findings include a larger sample size, a wider range of ages, time spent in college, and considering participants beyond psych students. The research questions were to determine if there was a difference in recall of academic dishonesty between expedient students and principled students, to determine the difference in frequency of recalling other ASBS when comparing principled and expedient students, to establish the relationship between social anxiety and academic cheating, with academic cheating to find out if there was a relationship between social anxiety and other ASB such as lying. Lastly, it was to determine whether the expedient and principled samples showed any significant differences, and the hypothesis was that there were no differences between the samples.

Methods

The study employed random probabilistic sampling techniques where 1725 participants were involved, with 44.9 % being males and 55% of them being females. Their age ranges were 18-69, with 43.4 % below 21. Other data collected for the students included the number of semesters they had attended, their GPA, which ranged from 0 to 4, whether they were part-time (18%) or full-time students (82%), and whether they were principled (48.3%) or expedient students (50%). The data collection was done using questionnaires that were administered to learners. The responses from the participants were then keyed in SPSS software for analysis. Collected data was added to a previously collected data set to allow more participants to be included in the analysis.

Measures

Integrity Scale – High scores represent a strong endorsement of a principled ethics, whereas low scores represent a strong endorsement of an ethic of expediency (Schlenker, 2006). Integrity Scale items are measured on a 5-point agree-disagree scale with potential scores ranging from 18-90. In the current study, participants with scores less than or equal to 58 are labelled expedient, and those with scores greater than or equal to 59 are principled. The ALPHA for the current study is 0.05, which suggests that the study results will be dependable 95% of the time.

Social Phobia Symptoms Checklist – Differences in social anxiety were measured with the Social Phobia subscale of the Psychiatric Diagnostic Screening Questionnaire (Zimmerman, 2002), a self-report symptom checklist of emotional and behavioural problems defined by the Diagnostic and Statistical Manual of Mental Disorders. The social phobia subscale contains 15 true-false items that ask participants to recall symptoms of social anxiety they may have experienced over the past six months. Social anxiety scores were calculated by summing all true responses (range = 0 [low social anxiety] to 15 [high social anxiety]). The ALPHA for the current study is 0.05, which suggests that the study results will be dependable 95% of the time.

Anti-social behavior scale (ASBS) – The ASBS measures the frequency of committing unethical behaviors for the prior five years on 9 point scale: 0 (never), 1 (once or twice), 2 (about once a year), 3 (a few times a year), 4 (nearly every month), 5 (nearly every week), 6 (several times a week), 7 (nearly every day), and 8 (several times a day). The ASBS includes seven reliable subscales (Lying, Aggression, Broken Promises, Academic Dishonesty, Alcohol and Drugs, Stealing, and Fraud) and one item measuring infidelity. The Alphas for the current study are ASBS =.000, Lying =.000, Stealing =.000, Alcohol and Drugs =.000, Aggression =.000, Academic Dishonesty =.000, Fraud =.000, and Broken Promises =.000

Results

In determining the social anxiety hypothesis (SAH) and the moral anxiety hypothesis (MAH), several analyses included descriptive statistics, correlations, and ANOVA. The results are presented below.

Preliminary: Table 1 below shows the descriptive statistic of ASBS, and it suggests that almost all of the participants (99.1) had been involved in some form of ASBS. Some of the notable subsets of ASBS were realized lying (97%), aggression (82%), and broken promises (71%). A majority of the students had also been involved in examination irregularities, with three-quarters (75%) of the students reporting positive cases. About half (54%) of the participants admitted to having been involved in stealing, with the cases of fraud and infidelity reported being 39% and 36%, respectively.

Table #1 – Descriptives of ASBS

Table #2 below shows the correlations between academic dishonesty and other forms of ASBS in order to establish the linkages between the individual cases. The results suggest that there are weak positive correlations for all the antisocial behaviors that were studied. Academic dishonesty is positively correlated to ASBS (r =.685, p <.01), lying (r =.406, p <.01), stealing (r =.414, p <.01), alcohol and drugs (r =.481, p <.01), aggression (r =.527, p <.01), fraud (r =.586, p =.01), broken promises (r =.579, p =.001) and infidelity (r=.462, p=.01).

Table #2 – Correlations of individual scales on ASBS

MIH

Table #3 compares principle and expedient participants on the anti-social behaviour scales and the subscales. For all the observations, the expedient students registered higher scores than the principled students, and all the ANOVA were significant at the.000 level. In the various categories expedient students registered higher mean scores in comparison to principled students, ASBS (1.42,.96), lying (2.41, 1.84), stealing (.72,.35), alcohol and drugs (1.51, 1.18), aggression (1.22,.78) academic dishonesty (1.70, 1.10), broken promises (1.26,.76), infidelity (1.08,.48) and fraud (1.05,.49).

Table #3 – Group comparisons on ASB scales

SAH

Table #5 shows the correlations between ASBS and its subscales to social anxiety. In all the observations, there is a weak correlation between ASBS and its subscales to social anxiety. Academic dishonesty is positively correlated to social anxiety (r =.093, p <.000), lying (r =.229, p <.000), stealing (r =.102, p <.000), alcohol and drugs (r =.051, p <.000), aggression (r =.043, p <.000), fraud (r =.069, p =.000), broken promises (r =.093, p =.000) and infidelity (r=.068, p=.000).

Table #4 – Correlations of ASBS and subscales to Social Anxiety

ANOVA for Lying

Table #5 shows the ANOVA for the responses under the subscale ‘lying’. The sum of squares between groups is 133.762, whereas that for within groups is 3895.862, and the F-ration is 57.407

Table #5 – ANOVA for the cases of lying

Discussion

In investigating differences in recall of academic cheating between Principled and Expedient students, it was found that there were significant differences. This is consistent with the current hypothesis. It is also consistent with previous research done by Wowra (2007). These findings provide further support for the Moral Identity Hypothesis of Academic Cheating. Expanding the Moral Identity Hypothesis to other anti-social behaviours, the results indicate significant differences in how Expedient and Principled participants responded to the Anti-So cial Behavior Scale. Expedient students indicated higher levels of engaging in anti-social behaviours when compared to Principled. When looking at the subscales of the Anti-social Behavior scale, differences were found between Expedient and Principled participants on lying, stealing, alcohol and drugs, aggression, academic dishonesty, fraud, broken promises, and infidelity.

In investigating the relationship between social anxiety and academic cheating, it was found that there was a statistically significant relationship between scores on the Social Anxiety Scale and scores on the Academic Dishonesty Scale. The correlation between Social Anxiety and Academic Dishonesty was weak and positive. This is consistent with the current hypotheses and consistent with previous research done by Wowra (2007). These findings provide weak support for the Social Anxiety Hypothesis of Academic Cheating. Expanding the Social Anxiety Hypothesis to other Anti-social behaviours, the results indicate a statistically significant relationship between Social Anxiety and the Anti-Social Behavior Scale. The correlation between Social Anxiety and the Anti-social Behavior Scale was weak and positive. Looking at other individual subscales, all the scales were significantly related to social anxiety: lying, stealing, alcohol and drugs, aggression, academic dishonesty, fraud, broken promises, and infidelity.

ANOVA was performed on lying as a subscale of ASBS to test if there were any significant differences between the groups under study. The findings suggest that the samples expedient and principled participants were not significantly different. This is in tandem with the hypothesis and the findings by Wowra (2007). ANOVA was necessary to determine how much difference was a result of the grouping rather than the individual responses.

The data collection involved a single learning institution that might not represent other learning institutions. Moreover, adding the data to previously existing datasets can impact the reliability and validity of the study. Further studies should focus on diversifying their samples by including several institutions in the study. In addition, the current study only focused on establishing the correlation between the various parameters; future studies should be divergent to determine a cause-effect relationship. A causal relationship will be handy in finding the solution behind some of these pertinent societal issues.

References

Josephson, M. (n.d.). The ethics of American youth. Los Angeles, CA: Josephson Institute of Ethics.

Lapsley, D. K., & Narvaez, D. (Eds.). Moral development, self, and identity. Mahwah, NJ: Lawrence Erlbaum Associates. Web.

Schlenker, B. R. (2006). Principled and expedient ideologies: The Integrity Scale as a measure of ethical orientations. Unpublished manuscript, University of Florida, Gainesville.

Wowra, S. A. (2007). Moral identities, social anxiety, and academic dishonesty among American college students. Ethics & Behavior, 17(3), 303-321.

Zimmerman, M. & Sheeran, T. (2002). Social phobia: still a neglected anxiety disorder? The Journal of nervous and mental disease, 190(11), 786-788.

Phenobarbital Abuse

Phenobarbital is one of the first and most widespread drugs aimed at coping with anxiety issues. Many people who suffer from stress, panic attacks, and anxiety take the drug known under the titles Luminal and Nembutal. Although the medicine shows effectiveness in terms of stress resistance, the drawbacks of abuse are also considerable. Phenobarbital has a high level of addictiveness, along with side effects such as feeling tired or irritability.

As the effects of the drug are not long-lasting, people who take phenobarbital tend to use the medicine more often than it is allowed in the drug prescription. Thus, they become addicted to phenobarbital, and this drug dependence leads to people not realizing that they do not need antianxiety drugs in such amounts (“What is phenobarbital abuse?” 2019). At first, drugs such as phenobarbital were replaced with more modernistic versions of antianxiety medications known as benzodiazepines. Although they were supposed to become more efficient in the context of treatment, the risks of drug dependence became even higher.

Hence, people, especially older ones, who have been abusing phenobarbital for a long time, may not withdraw from the drug in order to use the modern ones. First of all, although the side effects of phenobarbital can be more evident than those of benzodiazepines, the process of withdrawal may be quite complicated for long-term abusers. The process of withdrawal is often followed by severe seizures, which should be controlled by specialists.

Hence, once people are to take antianxiety medications, they should be informed that the treatment process often requires some risk and side effects. In the case of phenobarbital abuse, patients who get used to the drug may not handle the medication withdrawal. For this reason, even if other antianxiety drugs seem to be safer or more effective in terms of disease, physicians should pay more attention to the general patient’s well-being and mental attributes.

Meprobamate vs. Benzodiazepines

Over the past decades, anxiety has become one of the major mental issues among the world and the American population, in particular. In the middle of the 20th century, when people only realized that the feeling they experienced is called anxiety, researchers came up with medications known as meprobamate (Levinthal, 2014). Although the medication proved to be highly effective in terms of anxiety handling, being a severe sedative, it had many side effects. People who used meprobamate struggled with a constant feeling of fatigue and lethargy. For this reason, scientists decided to discover a new kind of drug that would be as effective but focused directly on anxiety without having such sedative drawbacks.

Hence, meprobamate was replaced with benzodiazepines, drugs that helped people combat anxiety without feeling tired and apathetic. Benzodiazepines have become extremely popular with American residents over the past years, and such popularity later developed a full-scale epidemic in the country. Drugs like Valium or Xanax are highly addictive, so people who take the medication for more than five or six weeks become dependant on the drug for a long time (Schumann, 2018).

Moreover, benzodiazepines influence human metabolism so that the body requires more drug each time. Hence, the spreading of benzodiazepines has led to an increase in prescriptions by more than 60% (Garrison, 2018). Thus, when it comes to contrastive analysis of meprobamate and benzodiazepines, it is complicated to define the benefits and drawbacks of each of them.

For example, if meprobamates do not have such a predisposition to addictiveness, the efficiency of the drug is also lower than that of the benzodiazepines. All in all, it can be concluded that both types of antianxiety drugs are highly dangerous for the human body, and self-treatment should be forbidden. Once people are to take such medications, each decision on the treatment should be consulted with the specialists.

References

Garrison, A. (2018). Antianxiety drugs – often more deadly than opioids – are fueling the next drug crisis in US. Web.

Levinthal, C. F. (2014). Drugs, behavior, and modern society. London, UK: Pearson Education.

Schumann, J. H. (2018). Benzodiazepines: America’s “Other prescription drug problem.” Web.

What is phenobarbital abuse? (2019). Web.

The global pandemic has severely influenced the typical style of living people were used to before the strike of the harmful virus. The research paper focuses on the mental repercussions of the infection on students in Saudi Arabia. To be more precise, the authors aimed to investigate whether the transition to a new lifestyle due to the pandemic has impacted the anxiety levels of university youth.

Naturally, the researchers needed to convey practical analysis to obtain data to form conclusions regarding the alterations in anxiety levels among students. That is why Khoshaim et al. (2020) surveyed 400 students from different universities on an online platform, asking the participants about their mental state and whether they noticed any changes in their behavior or perception because of COVID-19. It seems significant to mention that questionnaires were sent to the youth from March to June in 2020, which is the pandemic’s peak around the globe (Khoshaim et al., 2020). Still, Khoshaim et al. (2020) concluded that 35% of the surveyed students were suffering from moderate or extreme anxiety, which severely complicated the course of their studies. By and large, this research is focused on an important topic, considering that students are still affected by the changes to global education as a result of COVID-19.

Unfortunately, the results of this research cannot be claimed to be reliable as the students could experience anxiety due to different reasons that could not be omitted in their answers to the online questionnaire. Still, this work is a solid foundation for further studies with more narrowed scopes that could investigate the mental issues among the youth as a result of the COVID-19 pandemic and the following restrictions. In addition, the government can use the obtained data to assist students in overcoming their anxiety to enhance their academic performance and well-being.

Reference

Khoshaim, H. B., Al-Sukayt, A., Chinna, K., Nurunnabi, M., Sundarasen, S., Kamaludin, K., Baloch, G. M., & Hossain, S. F. A. (2020). The anxiety level of university students during COVID-19 in Saudi Arabia. Frontiers in Psychiatry, 11. Web.

Globalisation and advances in transport and communication have led to an upsurge in international travel and migration. Furthermore, skirmishes, political strife and environmental calamities continue to generate large sporadic movements of people across national frontiers (Slewa-Younan et al., 2017; Salami, Salma, & Hegadoren, 2019). Migrant and refugee inhabitants go through stress attributed to problematic migration journeys as well as trials associated with moving from familiar grounds, adapting to strange nations, and a lack of support resources. Therefore, immigrants are highly predisposed to mental health disorders. Many studies have examined the plight of immigrants in foreign countries, particularly concerning access and utilisation of health services. Some of the notable challenges include learning foreign languages, seeking employment, traversing outlandish cultural and social systems as well as experiencing racism and prejudice (Dubus & LeBoeuf, 2019; Salami et al., 2019). The most common mental health disorders among refugees include post-traumatic stress disorder (PTSD), anxiety and depression (Kanagaratnam, Pain, McKenzie, Ratnalingam, & Toner, 2017; Giacco, Laxhman, & Priebe, 2018; Jongedijk, Eising, van der Aa, Kleber, & Boelen, 2020). This review appraises three studies examining the issue of anxiety among refugees and the role of professional interpreters in reducing anxiety.

The first study by Dong (2019) delved into the association between refugees and trauma. The author explained the factors that contribute to trauma, anxiety and mental distress among refugees. These include the need to face different cultures in foreign countries and poor living conditions due to the inability to secure employment. Language barrier hampers communication and leads to cultural isolation, which affect the refugees’ ability to interact with their surroundings. Adapting to a new culture entails disregarding one’s cultural identity and taking on new roles to fit into the new environment. Dong (2019) asserted that sharing similar cultures would make it easier for refugees to share their traumatic experiences without fear of judgement. It would also boost their resilience and create a sense of belonging. The suggested ways of lessening trauma in refugees include enhancing their comfort and developing a positive relationship between clients. Even though Dong (2019) mentions language barrier as a problem contributing to mental distress among refugees, he does not acknowledge the role of professional interpreters in improving the situation. Having professional interpreters could help circumvent the language barrier problem by helping refugees to express themselves freely and accurately (Jaeger, Pellaud, Laville, & Klauser, 2019), thereby creating a comfortable environment. Therefore, this study failed at exploring the contribution of professional interpreters in terms of anxiety because it only addressed traumatic experiences among refugees without an in-depth examination of anxiety in refugees, which is the main focus of this study.

The second study by Showstack (2019) examined the issue of language barriers in the healthcare system. Some of the common language barrier scenarios included speaking English to Limited English Proficiency (LEP) Hispanic patients, communicating with the patients using poor Spanish, using ad hoc interpreters, or facilitating communication through professional interpreters. The inability of patients to communicate effectively with their healthcare providers led to trauma. Showstack (2019) also highlighted the dangers of using ad hoc interpreters and stated that involving children as interpreters subjected them to trauma. Professional interpreters alleviate part of the anxiety experienced by refugees by facilitating effective communication. However, the scarcity of skilled interpreters has led to the use of non-professional interpreters (Celik & Cheesman, 2018). The risks of violation of confidentiality, translation slip-ups, and poor patient outcomes are higher when untrained interpreters are engaged (Eklöf, 2018; Hjern, 2018; Kasten, Berman, Ebright, Mitchell, & Quirindongo-Cedeno, 2020). Other options suggested by Showstack (2019) such as remote interpreting through audio and video devices cause trauma because patients prefer face-to-face communication. Furthermore, remote interpretation could fail in urgent situations. Using family members as interpreters could be effective. However, the main problem is maintaining the privacy and confidentiality of medical reports and safeguarding them from trauma and anxiety due to inadequate knowledge of medical terminology as opposed to professional interpreters. This study underscores the importance of professional interpreters in medical settings and highlights the challenges associated with using ad hoc interpreters. However, it does not address these factors in the context of refugees.

The third study by van Loon, van Schaik, Dekker and Beekman (2011) examined why Moroccan and Turkish migrants in the Netherlands abandoned treatment for anxiety and depressive disorders. Language barrier was the key problem responsible for dropping out, which could be solved by training therapists who share the same culture as the immigrants. Doing so would develop beneficial therapeutic relationships between clients and therapists, thereby enhancing treatment adherence. Migrant participants were to receive questionnaires to evaluate their responses, with the primary measure being staying on treatment. The article focuses on training of therapists to meet the cultural needs of Moroccan and Turkish immigrants. Intercultural training includes the proper use of professional interpreters among other cultural nuances that affect treatment. However, the trial does not involve the direct use of interpreters during treatment. If the migrant patients were allowed to have professional interpreters, trusting relationships would be built, which would encourage them to continue with treatment.

The review of the literature has shown that interpreters play a critical role in the provision of medical and mental health care to refugees. However, the direct effect of professional interpreters in reducing anxiety in refugees has not been studied extensively, which points to a gap that needs to be filled by conducting more primary studies on the area. Another gap that should be addressed is the effective strategies of training interpreters for medical and mental health settings.

References

1. Celik, F., & Cheesman, T. (2018). Non-professional interpreters in counselling for asylum seeking and refugee women Filiz Celik, Tom Cheesman. Torture Journal, 28(2), 85-98.
2. Dong, Y. (2019). Refugees and trauma. Asian Journal of Social Science Studies, 4(4), 79-85.
3. Dubus, N., & LeBoeuf, H. S. (2019). A qualitative study of the perceived effectiveness of refugee services among consumers, providers, and interpreters. Transcultural Psychiatry, 56(5), 827-844.
4. Eklöf, N. (2018). Somali asylum seekers and refugees in Finnish health care–Focus on privacy and the use of interpreters. Web.
5. Giacco, D., Laxhman, N., & Priebe, S. (2018). Prevalence of and risk factors for mental disorders in refugees. Seminars in Cell & Developmental Biology, 77, 144-152.
6. Hjern, A. (2018). Health of refugee and migrant children: Technical guidance. Web.
7. Jaeger, F. N., Pellaud, N., Laville, B., & Klauser, P. (2019). The migration-related language barrier and professional interpreter use in primary health care in Switzerland. BMC Health Services Research, 19(429), 1-10. doi:10.1186/s12913-019-4164-4
8. Jongedijk, R. A., Eising, D. D., van der Aa, N., Kleber, R. J., & Boelen, P. A. (2020). Severity profiles of posttraumatic stress, depression, anxiety, and somatization symptoms in treatment seeking traumatized refugees. Journal of Affective Disorders, 266, 71-81.
9. Kanagaratnam, P., Pain, C., McKenzie, K., Ratnalingam, N. & Toner, B. (2017). Recommendations for Canadian mental health practitioners working with war-exposed immigrants and refugees. Canadian Journal of Community Mental Health, 36(Special issue), 107-119.
10. Kasten, M. J., Berman, A. C., Ebright, A. B., Mitchell, J. D., & Quirindongo-Cedeno, O. (2020). interpreters in healthcare: A concise review for clinicians. The American Journal of Medicine, 133(4), 424-428.
11. Salami, B., Salma, J., & Hegadoren, K. (2019). Access and utilization of mental health services for immigrants and refugees: Perspectives of immigrant service providers. International Journal of Mental Health Nursing, 28(1), 152-161.
12. Showstack, R. (2019). Patients don’t have language barriers; the healthcare system does. Emergency Medicine Journal, 36(10), 580–581.
13. Slewa-Younan, S., Guajardo, M. G. U., Yaser, A., Mond, J., Smith, M., Milosevic, D.,… Jorm, A. F. (2017). Causes of and risk factors for posttraumatic stress disorder: The beliefs of Iraqi and Afghan refugees resettled in Australia. International Journal of Mental Health Systems, 11(4), 1-11. doi:10.1186/s13033-016-0109-z
14. van Loon, A., van Schaik, D. J., Dekker, J. J., & Beekman, A. T. (2011). Effectiveness of an intercultural module added to the treatment guidelines for Moroccan and Turkish patients with depressive and anxiety disorders. BMC Psychiatry, 11(1), 1-7.

Correlation Test (First Findings)

Some scholars have held over time that strong correlations between the levels of stress that can lead to depression associated with those experiencing emotional problems or Anxiety exist. Socio-psychological data can be analyzed statistically to establish correlation between two identified variables (Cohen J, Cohen P, West, & Alken, 2003). One variable of significance is the variable GHSEmPer. This variable describes the likelihood that a person may seek help for his or her emotional problems.

The degrees of the given problems are arranged in an ascending order using numbers. 154 subjects were involved in the study out of which there were 38 males and 116 females. The means by genders are summarised in the table below.

Table 1

On average, approximately 35.87 percent (SD 7.55) of males were found to either seek help for emotional problems or for their depression or anxiety. On the other hand, approximately 38.85 percent (SD 7.81) of the women in the study were found to either seek help for their emotional problems or for their depression or anxiety.

To test whether there was any difference between the mean stress levels by gender. The mean difference between males and females subjects had a statistical significance value of 0.872. Because the value was greater 0.05, the null hypothesis had to be rejected in favour of the alternative hypothesis and we conclude that they are from different gender with respect to the variable GHSEmPer. This situation implies that women are more likely to share their emotional problems or anxiety problems compared to their male counterparts (Zalta & Chambless, 2012), because the mean differences between the two groups are statistically significant.

When testing whether there was a difference in the levels of depression or anxiety affecting individuals by gender, a statistical significant value of 0.079 was found. Because the significant value was greater than 0.05, the null hypothesis was rejected in favour of the alternative hypothesis (Argyrous, 2011). Thus, it was concluded that there was variable difference by gender, as the findings indicate that females are more likely to share their depression or anxiety problems compared to their male counterparts (Zalta et al., 2012). Importantly, the means of the groups are significantly different from each other as the data analysis suggests.

The correlation analysis to determine whether the variables are correlated was performed. Consequently, the correlation value was determined. This correlation value was used to determine the nature and strength of the relationship. The correlation between the two variables is 0.769. This means that 76.9 percent of all the points are correlated to each other directly. So, if a line of best fit is drawn through these values (Rosenthal, 2011) approximately 77 percent of the points will lie on the line directly. The adjusted R square is 0.5913, which means that the variables have a strong positive relationship, and that the probability that a respondent experiences variable GHSEmPer is more likely to experience the second variable (Sharma, 2005); GHSAnx. Therefore, the two variables are highly correlated (Rice, 2007).

Test Analysis (Second Finding)

A study by Zalta et al. (2012) shows that a strong correlation exists between the level of anxiety that can lead to depression and those experiencing emotional problems and the tendency to share their experiences in relation to individual gender. This supposition could be verified by statistical analysis to test whether there is actually any relationship between the two variables with regard to gender (Cohen et al., 2003). The variable of interest in this case is GHSAnx. This variable describes the likelihood that an individual would seek help for his or her emotional problems, while the later describes the likelihood that a person would seek assistance if he or she indicates signs of depression or anxiety.

The degrees of the given problems are arranged in an ascending order using numbers. Like in the former case, 154 subjects were involved in the study of which 38 were males and 116 females. The means by genders are summarised in the following the table.

Table 2

On average, approximately 35.45 percent (SD 10.13) of males were found to seek help for either emotional problems or anxiety. On the other hand, approximately 39.21with (SD 8.89) per cent of the women in the study were found to either seek help informally for emotional problems or depression. This finding corresponds to those of the study conducted by Zalta et al., (2012).

When testing whether there is any difference in the means between the levels of anxiety by gender (Verma, 2013: Wood & Eagly, 2012), a significant value 0.030 with a t- statistic of -2.193 was found. This value being less than 0.05 led the researchers to verify the null hypothesis and reject the alternative hypothesis, and conclude that the variable was from the same population. As both groups’ means were not significantly different from each other, these variables do not depend on the gender of the subject (Lefebvre, 2006).

When testing whether there was any gender difference between the emotional problems that a person experiences and decision to get help in an informal way, a significant value of 0.041 was found and a t statistic of -2.061. The value being less than 0.05, the researchers accepted the null hypothesis and rejected the alternative hypothesis leading them to conclude that the second variable was from the same populations as of the first variable (Dowdy, Wearden, & Chilko, 2004). Because both groups’ means were not significantly different from each other (Lefebvre, 2006) then the variable does not depend on the gender of the subject.

Further, correlation analysis for the tendency to seek assistance and for sharing their experiences was done as explained in the first part of this assignment. The correlation value was determined and used to determine the nature and strength of the relationship. The correlation between the two variables was 0.769. This meant that 76.9 percent of all the points are correlated to each other directly. This meant that if the best line of fit would be drawn through all the possible points (Rosenthal, 2011) approximately 77 per cent of the points will lie on the line directly. This means the variables have a strong positive relationship. A strong positive relationship meant that the probability of a respondent experiencing the first variable to experience the second was high. Therefore, the two variables are positively correlated.

References

Argyrous, G. (2011). Statistics for Research (3rd Edition ed.). Singapore: SAGE Publications Ltd.

Cohen, J., Cohen, p., West, S. G., & Alken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. New Jersey: Lawrence Erlbaum Associates. Inc., Publishers.

Dowdy, S., Wearden, S., & Chilko, D. (2004). Statistics for Research (3rd Edition ed.). New Jersey: Johns Wiley & Sons, Inc.

Lefebvre, M. (2006). Applied Probability and Statistics. Montreal: Springer Science.

Rice, J. A. (2007). Mathematical Statistics and Data Analysis (3rd Edition ed.). Belmont, CA: Duxbury.

Rosenthal, J. A. (2011). Statistics and Data Interpretation for Social Work. London: Lippincott Williams & Wilkins.

Sharma, A. K. (2005). Text Book of Correlations and Regression. New Delhi: Discovery Publishing House.

Verma, J. P. (2013). Data Analysis in Management with SPSS Software. New Delhi: Springer.

Wood, W., & Eagly, A. H. (2012). Biosocial Construction of Sex Differences and Similarities in Behavior. Advances in Experimental Social Psychology, 46, 56-103.

Zalta, A. K., & Chambless, D. L. (2012). Understanding Gender Differences in Anxiety: The Mediating Effects of Instrumentality and Mastery. Psychology of Women Quarterly, 36 (4), 488-499.

Descriptive Statistics

Descriptive statistics (DS) help researchers to comprehend research data in details. By using DS, it is possible to learn about the data value, the character of variables, distribution range, etc. (McHugh, 2003a, p. 35). DS helps researchers to arrange data and summarize it. It may be used as a main instrument in the descriptive studies, or it may also be implemented as a complementary part of the research.

There are many types of DS (measures of shapes, measures of central tendency, measures of dispersion, percentile and quartile measures), and the selection of the right one is essential to the obtaining of accurate information (McHugh, 2003b, p. 111). For the increase in the reliability of statistics, it is important to ensure that the measurement criteria and the estimation of variables are matched.

It is also necessary to make certain that the statistics provide the information that is needed for particular research. In case a researcher knows how to choose the right levels of measurement, DS may help to reveal a significant amount of information about any kind of variable (Ho & Yu, 2015, p. 365). For example, for the typical cases, the mode, the median, and the mean measures are recommended; for exploring the nature of the distribution of the variable, the test of Skewness or Kurtosis is applied; and the measures of dispersion (range, variance, and deviation) are used to discover the mean variability (McHugh, 2003b, p. 115)

The practice of statistics includes the evaluation of the collected data; nevertheless, the DS measurements sometimes maybe “incomplete” (Conor, 1998, p. 268). For example, on the lower scales of measurement (nominal or ordinal scale), a researcher has less ability to rank and label the analyzed data; some measurements involve broader interpretation while others provide a higher level of precision. However, the profound understanding of how the DS measures work is necessary, otherwise, the validity of the results may be significantly reduced.

Statistics Anxiety

Statistics anxiety may be defined as a complex of the negative emotions encountered during learning or practicing statistical analysis (Baloglu, Deniz, & Kesici, 2011, p. 387). The feelings of anxiety or the adverse attitudes towards statistics interfere with the students’ efficacy and negatively affect their academic or professional performance. It is observed that anxiety may arise from the previous experience, negative perception of the mathematics, and the broader factors of personal characteristics such as self-perception or perfectionism (Perepiczka, Chandler, & Becerra, 2011, p. 100).

Statistics anxiety is a common phenomenon among the students, especially those who do not have a lot of experience in mathematics and who do not recognize the significance of analytical methods of research. Nevertheless, the course of statistics is a necessary part of the curriculum as it contributes to further professional development. When feeling statistics anxiety, a person cannot evaluate his/her abilities adequately because of the prejudiced and negative presumptions. In this way, negative sentiments interfere with the learning process.

Since many students may perceive statistics as something daunting and stressful, the positive climate in the class may help them to develop an adequate attitude towards the subject. It is observed, that when students feel support and are encouraged to achieve academic goals, the positive view of statistics starts to form (Perepiczka, Chandler, & Becerra, 2011, p. 105). However, fighting with negative perceptions requires the individual’s work and self-discipline as well. It is important to comprehend the origins and nature of statistics anxiety and make efforts to eradicate them. The statistics anxiety may be reduced in case the value of statistics is recognized by a student. DS is an important element of research, and by obtaining a necessary competence in the subject, a psychology practitioner increases his/her level of professional expertise.

References

Baloglu, M., Deniz, M., & Kesici, S. (2011). A descriptive study of individual and cross-cultural differences in statistics anxiety. Learning and Individual Differences, 21, 387–391. Web.

Conor, G. (1998). Understanding and using descriptive statistics. British Journal of Occupational Therapy, 61(6), 267-272. Web.

Ho, A., & Yu, C. (2015). Descriptive statistics for modern test score distributions: Skewness, Kurtosis, discreteness, and ceiling effects. Educational and Psychological Measurement, 75(3), 365–388. Web.

McHugh, M. L. (2003a). Descriptive statistics, part I: Level of measurement. Journal for Specialists in Pediatric Nursing, 8(1), 35-37. Web.

McHugh, M. L. (2003b). Descriptive statistics, part II: Most commonly used descriptive statistics. Journal for Specialists in Pediatric Nursing, 8(3), 111-6. Web.

Perepiczka, M., Chandler, N., & Becerra, M. (2011). Relationship between graduate students’ statistics self-efficacy, statistics anxiety, attitude toward statistics, and social support. The Professional Counselor, 1(2), 99–108. Web.

Abstract

There has been a lot of research exploring the phenomenon of statistics anxiety that is expected to be driven by various factors, including gender, age, inability to understand statistics concepts, mathematical capability, and others. It is argued that statistics anxiety adversely affects students’ performance, and there is a need to implement interventions that could help students lower their anxiety. The current study aims to determine the impact of statistics anxiety on academic performance of students. The data collected for this study comprises of views and results of 140 students. The primary research collects data on students’ demographics, academic performance, personality characteristics, and statistics anxiety. The univariate analysis showed that the anxiety of students reduced at the end of the course. The correlation analysis found significant relationships between total module marks and change in anxiety, A-level maths, and attendance. No personal characteristic has a significant impact on statistics anxiety. However, extraversion and agreeableness have significant effects on total module marks.

Introduction

Macher, Papousek, Ruggeri, and Paechte (2015) explain statistical anxiety as the situation where individuals feel pressurized when exposed to statistics information, methods, and instructions. It is indicated that students experience a high level of anxiety when they have to take the statistics course and face problems following the curriculum. The inclusion of statistics in different courses such as psychology, mathematics, and finance has increased their problems. It also affects their academic performance, and they are demotivated. Macher et al. (2015) critically discussed the findings of previous studies and highlighted that statistics anxiety is found to have varying effects on the academic performance of students. Moreover, it is suggested that statistics anxiety make students spend more time preparing for their assignments and exams, which is beneficial to them. Paechter, Macher, Martskvishvili, Wimmer, and Papousek (2017) also indicated that statistics anxiety indirectly and negatively affects the academic performance of students as they are found to experience increased levels of tension and anxiety in the statistics course and exam. Similarly, Siew, McCartney, and Vitevitch (2019) state that the indirect effects of statistics anxiety on students’ performance. The researchers highlighted that it is an incorrect perception that students need to have strong mathematical skills to perform well in the statistics course. They need effective support and teaching in classrooms to develop a clear understanding of statistical concepts and methods. Primi, Donati, and Chiesi (2018), Morsanyi et al. (2016)

and Bourne (2018) also found that there are cognitive factors such as willingness to learn, and the ability to follow instructions also affect the level of statistics anxiety experienced by students. The researchers also indicated that students with strong mathematical skills are expected to perform better in statistics.

Based on the review of previous studies, it could be indicated that the phenomenon of statistics anxiety needs further research that could help in devising in-classroom interventions for assisting students to overcome stress. Therefore, the current study is another attempt to explore this issue by analyzing the data collected from another source. The present study aims to investigate the impact of statistics anxiety on the academic performance of students enrolled in a statistics course. The three research questions set out for analysis and discussion are given below:

• RQ1: Does statistic anxiety affect the academic performance of students enrolled in a statistics course?
• RQ2: Which personality traits significantly affect the academic performance of students enrolled in a statistics course?
• RQ3: Which personality traits significantly affect the statistics anxiety of students enrolled in a statistics course?

These research questions are addressed in the discussion part of this report based on the results of the statistical tests performed in this study. The current study follows a deductive approach that draws a hypothesis from the review of the existing literature related to statistics anxiety and tests it based on the results of the tests performed in this report. The hypothesis set out to validate in this study is, “Anxiety about statistics is related to academic performance on an undergraduate statistics course.” This hypothesis is tested explicitly based on the results of the independent samples t-test correlation matrix and regression analysis.

Method

Design

The study performed in this report use the quantitative research methodology to collect numerical data and then analysing it using statistical techniques. The research design is cross-sectional as the data of students is collected at two different points in time.

Participants

A survey of 140 students was carried out, who were enrolled in the statistics course. Their views were collected in two sessions, i.e., before and after the completion of the course.

Materials

A questionnaire designed and used to collect data of 140 students, including their demographics (age, gender, A-level Maths, and attendance). Furthermore, the survey collects views of students about their personality based on the Ten Item Personality Inventory (TIPI), and averages of their responses are calculated to determine the scores of five dimensions including TIPI Extraversion, TIPI Agreeableness, TIPI Conscientiousness, TIPI Emotional Stability, and TIPI Openness to Experiences. Furthermore, statistics anxiety is measured before the start of the statistics course and after its completion based on the Anxiety with Statistics Scale (iBSc students, 2014) and

Procedures

The ASS scores of students before and after the course were calculated by recoding responses to a positive statement about comfortability to apply statistical knowledge and summing their response values for determining two variables, including ASSBase (Before) and ASSFollow (After). The change in statistical anxiety is calculated as a difference between ASSFollow and ASSBase values. The missing values of ASSFollow are coded -1 and are excluded from the analysis performed in this report. Moreover, the data of ASSBase and ASSFollow were grouped as 1 and 2, respectively, for carrying out the independent samples t-test. The confidence level assumed for testing the significance of the difference in mean values and also that of coefficients of the slope is 95%.

Statistical Analysis Plan

The analysis initiates with the calculation of descriptive statistics, including frequencies and other statistics such as mean, standard deviation, minimum, and maximum values of different variables based on their types. The results are summarized and also provided in appendices. The independent samples t-test is a univariate test to compare the score of individuals based on the Anxiety with Statistics Scale (ASS) at the beginning and end of the statistics course (Reinhart, 2015). The correlation matrix is constructed to determine the correlations between Score (Total Module Mark), ASSChange (ASSFPost– ASSBase), Attend (Attendance), and A-level Maths (George & Mallery, 2016).

In this analysis, two regressions have been performed. In the first regression, the dependent variable is the change in the ASS score, which is calculated the difference in the ASS scores at the base and after the completion of the assessment. On the other hand, Score is the dependent variable in the second regression model. The independent variables are the Ten Item Personality Inventory (TIPI), Age, and Gender of 140 students (Darlington & Hayes, 2017). All missing values are excluded from the analysis, and the normal distribution of data of variables (Abbott, 2017), which is a necessary assumption of the regression analysis, is also assessed by histograms provided in Appendix D.

Results

Descriptive Analysis

The data of 140 students was collected based on TIPI and ASS scales. The analysis also indicates the highest number of respondents were aged 22 years. There were 121 females in the sample. Moreover, 93 students had A-level Maths experience. Most of the respondents had a high attendance frequency of 10. These results, along with their graphical depiction, are provided in Appendix A. Table 1 indicates that the mean value of ASS before the course was 30.0643, whereas it decreased to 25.3509 at the end of the course, which shows that there was a decrease of 4.7134. It is noted that post-course estimates of anxiety had 26 missing values.

Table 1. ASS Before and After.

Furthermore, the personality characteristics assessment based on TIPP indicates that the mean value of all measures was more than 5, as shown in the table attached in Appendix B.

Inferential Analysis

The results of the independent samples t-test are summarized in the following table, and its detailed outcomes are provided in Appendix C.

Table 3. t-test.

It is noted that the mean difference between ASS before and after the course is negative. The difference was also found to be significant at 95% confidence level.

The results of the correlation matrix are summarized in the following table, and the detailed matrix is provided in Appendix E.

Table 4. Correlation.

It is seen that the correlation of Score is negative with ASSChange. It implies that students’ scores increase when they experience less anxiety. Furthermore, students with A-level Maths background and higher attendance achieve higher scores in the module. The strongest correlation is between Score and Attend. Moreover, the correlations of all three independent variables are found to be significant at the 0.05 level. There is no significant correlation between independent variables, which eliminates the issue of multicollinearity as required by the regression analysis.

The results of the regression models are provided in Appendix F and are summarized in the following.

Table 5. Regression.

The first regression model results show that the goodness of fit measured by R-square is weak, and the F-statistics is also not significant. It implies that the outcome of this analysis is not reliable to make definite conclusions about the relationships between variables. The coefficient of TIPI Extraversion and TIPI Agreeableness are found to be positive and insignificant as their p-value is not less than the alpha value. On the other hand, TIPI Conscientiousness, TIPI Emotional Stability, TIPI Openness to Experiences, Age, and Gender have negative coefficients which are insignificant as well. The second regression model results show that the goodness of fit measured by R-square is also weak. However, F-statistics is significant, which implies that the outcome of this analysis is reliable to make definite conclusions about the relationships between variables. The coefficient of TIPI Extraversion, TIPI Emotional Stability, and TIPI Openness to Experiences are found to be negative. However, the coefficient of TIPI Extraversion is significant as its p-value is less than 0.05. The results imply that introvert students and those who are emotionally more stable and open to experiences have lower scores. On the other hand, TIPI Agreeableness, TIPI Conscientiousness, Age, and Gender have positive coefficients. The coefficient of TIPI Agreeableness is significant as its p-value is less than 0.05.

Discussion

The analysis provided in this report focuses on determining the impact of statistics anxiety on academic performance of students. The mean value of statistics anxiety measured by the Anxiety with Statistics Scale was higher before the start of the course than after it. The results of the independent samples t-test show that students experience a higher level of anxiety at the start of the statistics course than after completing it, and this difference was found to impact their behaviour significantly. The possible explanations of this behaviour have also been discussed in previous studies, including Macher et al. (2015), Morsanyi et al. (2016), Bourne (2018), and Primi, Donati, and Chiesi (2018). Based on this analysis, it is clear that students were tensed before the start of this course, which implies that students’ statistics anxiety declined upon completion of the course.

Moreover, the correlation analysis showed that the Pearson Correlation of Total Module Score with the change in anxiety with statistics was negative and significant and that with A-level Maths education and attendance during the course was found to be positive and significant. This implies that students who manage to control or overcome their anxiety with statistics achieve higher marks as compared to those who feared this subject and were unable to form a good understanding of statistical methods and techniques. The results also imply that students who had taken Maths at A-level were in a better position, and they were able to score higher marks. Furthermore, the correlation between A-level Maths and change in anxiety with statistics is found to be negative, which implies that such students do not fear the subject, and this is the reason that they are able to grasp statistical knowledge quickly and effectively to score higher marks.

The correlation analysis also indicates that students who attend statistics course classes regularly scored more than those who skip classes. This helps them in gaining a better understanding of statistical concepts and overcome their anxiety. It is also noted that there is a negative correlation between Attend and AssChange, which is a measure of the difference between anxiety levels at the start and end of the course. It implies that those students who make an effort to attend statistics course classes are likely to overcome their fear and stress, which helps them perform in a better way. The results of the correlation analysis validate the research hypothesis, which states that anxiety about statistics is related to academic performance on an undergraduate statistics course based on the significant correlation found between them. It can also be inferred that statistic anxiety affects the academic performance of students enrolled in a statistics course.

The next two research questions are addressed based on the results of both regression models. It is noted that the two personality traits, including extraversion and agreeableness, have a positive relationship with the change in statistics anxiety. This means that students who are extroverted and show agreeableness have higher statistics anxiety at the end of the course. Moreover, it is noted that students who have conscientiousness, emotional stability, and openness to experiences experience a decline in their anxiety by the end of the course. However, no personality characteristics significantly affect the change in statistics anxiety. Finally, females and students of higher age are expected to have lower statistics anxiety.

The results of the second regression show that extraversion and agreeableness significantly affect the academic performance of students enrolled in a statistics course. It also implies that introvert students and those who are emotionally more stable and open to experiences have lower total module marks. The results show that students who exhibit more agreeableness and conscientiousness score higher than others. Moreover, students of higher age and female gender have better scores.

References

Abbott, M. L. (2017). Using statistics in the social and health sciences with SPSS and Excel. Hoboken, NJ: John Wiley & Sons.

Bourne, V. J. (2018). Exploring statistics anxiety: Contrasting mathematical, academic performance and trait psychological predictors. Psychology Teaching Review, 24(1), 35-43.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. New York, NY: Guilford Publications.

George, D., & Mallery, P. (2016). IBM SPSS Statistics 23 step by step: A simple guide and reference. New York, NY: Routledge.

iBSc students. (2014). Anxiety with statistics scale. Unpublished.

Macher, D., Papousek, I., Ruggeri, K., & Paechter, M. (2015). Statistics anxiety and performance: Blessings in disguise. (C. Primi, Ed.) Frontiers in Psychology, 5(1116), 1-4.

Morsanyi, K., Mammarella, I. C., Szücs, D., Tomasetto, C., Primi, C., & Maloney, E. A. (2016). Mathematical and statistics anxiety: Educational, social, developmental and cognitive perspectives. Frontiers in Psychology, 7(1083), 1-4.

Paechter, M., Macher, D., Martskvishvili, K., Wimmer, S., & Papousek, I. (2017). Mathematics anxiety and statistics anxiety. Shared but also unshared components and antagonistic contributions to performance in statistics. (J. De La Fuente, Ed.) Frontiers in Psychology, 8(1196), 1-13.

Primi, C., Donati, M., & Chiesi, F. (2018). The role of statistics anxiety in learning probability. In C. Batanero, & E. Chernoff, Teaching and learning stochastics. ICME-13 Monographs (pp. 145-157). Cham, Switzerland: Springer.

Reinhart, A. (2015). Statistics done wrong: The woefully complete guide. San Francisco, CA: No Starch Press.

Siew, C. S., McCartney, M. J., & Vitevitch, M. S. (2019). Using network science to understand statistics anxiety among college students. Scholarship of Teaching and Learning in Psychology, 5(1), 75-89.

Appendix A

Appendix B

Appendix C

Group Statistics

Independent Samples Test

Appendix D

Appendix E

Appendix F

First Regression

ANOVA^a

Coefficients^a

Second Regression

ANOVA^a

Coefficients^a

Statistical Test

The data analysis intends to employ SPSS in performing principal component analysis (PCA). Particularly, PCA aims to reduce the number of variables into principal components that explain most of the variation in SPSS anxiety among students. According to Field (2013), PCA eases exploration of data by establishing patterns, linear relationships, and the influence of each variable. In essence, PCA eliminates redundant variables and promotes dimensionality of variables (Jackson, 2015). For robust PCA, data ought to meet the assumptions of the multiplicity of continuous variables, the linearity of data, sample adequacy, and the absence of significant outliers. Elliott and Woodward (2015) elucidate that PCA applies orthogonal transformation in converting raw variables that highly correlate to principal components with low correlation coefficients. Thus, PCA enables the determination of principal components with major influence on the variation of data.

The Basis of Data

The data emanated from the questionnaire developed and utilized in collecting data regarding SPSS anxiety among 2571 students. The questionnaire comprises 23 closed-ended questions with a five-point Likert scale answers indicating the strength of agreement from 1 (strongly disagree) through strongly agree (5) (Field, 2013). Since students experience anxiety in the course of learning SPSS, the questionnaire aims to measure and ascertain the extent of SPSS anxiety. Through the interview of students with and without anxiety, the study formulated 23 questions with viability to measure SPSS anxiety. The examination of data indicates that they meet the assumptions of PCA because the questions are 23, variables have linear relationships, data do not have significant outliers, and the sample size is more than 300 (N = 2571). The use of the questionnaire would indicate the extent of anxiety in each student learning SPSS. Moreover, the questionnaire would reveal forms of SPSS anxiety existing among students. In essence, the study aims to use PCA in revealing principal variables that explain most of SPSS anxiety among students.

Research Questions

• What are the principal components that account for the variation in SPSS anxiety among students?
• What are the dominant themes in the extracted components?

Hypotheses

• H₀: The 23 questions in the questionnaire are not statistically significant variables in explaining the variation in SPSS anxiety among students.
• H₁: The 23 questions in the questionnaire are statistically significant variables in explaining the variation in SPSS anxiety among students.

Statistical Outcomes

Descriptive Statistics

The exploration of the data using descriptive statistics indicates that there are no missing data because 2571 students answered all the Likert items in the questionnaire. Denis (2016) asserts that descriptive statistics form the basis of data analysis for they reveal patterns and trends of data. Descriptive statistics also show that students neither agree nor disagree with most Likert statements (18) because their mean scores ranged from 2.23 to 2.89 as demonstrated in Table 1. Moreover, students agree with the remaining four Likert statements for their mean scores ranged from 3.16 to 3.62.

Table 1.

Assumption Tests

The study employed the correlation in testing the assumption of multicollinearity. The scrutiny of the correlation matrix shows that the 23 Likert items have both negative and positive relationships (Appendix A). The correlation analysis indicates that the relationships of the Likert items range from moderate to very weak positive and negative relationships. Further scrutiny of significance coefficients shows that all Likert items have statistically significant relationships with one or more Likert items (p < 0.001). According to Field (2013), Likert items with correlation coefficients greater than 0.9 exhibit multicollinearity. Therefore, as correlation coefficients are less than 0.6, it implies that no variables exhibit multicollinearity, and thus, no elimination of Likert items.

Regarding the assumption of the adequacy of sample size, KMO statistic (0.93) indicates that the sample size is adequate for principal component analysis (Table 2). Since KMO statistic is considerably greater than the threshold of 0.5, Field (2013) describes the adequacy of sample size as marvelous. Anti-image correlation matrices (Appendix B) indicates that individual OKM values of anti-image covariance and anti-image correlation are greater than 0.5, which means all Likert items are viable for PCA. As illustrated in Table 2, Bartlett’s test of sphericity is statistically significant (p = 0.000), and thus, it rejects the null hypothesis that the correlation matrix is similar to the identity matrix.

Table 2.

Factor Extraction

From Table 3, it is apparent that four factors out of 23 factors have eigenvalues greater than one while the remaining 19 factors have eigenvalues less than one

Table 3.

Before extraction, factors 1, 2, 3, and 4 had eigenvalues of 7.290, 1.739, 1.317, and 1.227, which accounted for 31.696%, 7.560%, 5.725%, and 5.336% of variance in that order. After extraction, factors 1, 2, 3, and 4 had the same eigenvalues that explained the same proportion of variance respectively. However, after extraction, factors 1, 2, 3, and 4 had eigenvalues of 3.730, 3.340, 2.553, and 1.950, which accounted for 16.219%, 14.523%, 11.099%, and 8.476% of variance correspondingly. Overall, the four factors cumulatively accounted for 50.317% of the variation in SPSS anxiety among students.

The table in Appendix C shows that shared variance of factors ranged from 0.343 to 0.739. The mean of common variance is 0.5032 (11.573/23), which is less than Kaiser’s threshold of 0.7. Component matrix (Appendix D) shows the distribution of factors’ loadings into the four components selected. According to Field (2013), factors with loadings greater than 0.4 are significant while factors with loadings less than 0.4 are not significant.

The scree plot demonstrates that the four components explain most of the variation in eigenvalues. According to McCormick, Salcedo, Peck, Wheeler, and Verlen (2017), the elbow of the scree plot provides a threshold of components in which further additions have no significant impact on the variation of data. Thus, the scree plot confirms the extraction of the four components in PCA.

The analysis of loadings in the rotated component matrix (Appendix E) shows themes of anxiety that are in each component. Pallant (2016) holds that the threshold of suppressing loading coefficients determines the emergence of these themes. The type of anxiety that loaded onto the first component relates to the fear of computers while the form of anxiety that loaded onto the second component relates to the fear of statistics. The same number of questions (8) loaded onto the second and third components. The questions that loaded onto the third component relate to the fear of mathematics whereas the questions that loaded onto the fourth component relate to the fear of social perception.

Interpretation

PCA was performed to determine which among the 23 Likert items considerably explain SPSS anxiety among students. The analysis showed the data met the assumptions of sample adequacy (OKM = 0.93), the absence of multicollinearity (r < 0.6), and significance of Bartlett’s test of sphericity. Factor extraction established four components with eigenvalues greater than one. Moreover, the four eigenvalues collectively accounted for 50.317% of the variation in SPSS anxiety among students. The scree plot confirms the extraction of the four components because the major inflection point occurred at the fourth factor. The analysis of how each question loaded onto different components revealed varied themes in the questions. Eight questions that loaded onto the first component relate to the fear of computers while another eight questions that loaded onto the second component relate to the fear of statistics. Whereas three questions that loaded onto the third component relate to the fear of mathematics, the remaining five questions that loaded onto the fourth component relate to the fear of social perception. Thus, in answering the research question, PCA shows that fears of computers, statistics, mathematics, and social perception are principal factors in the questionnaire that influence the occurrence of anxiety among students. Moreover, PCA rejects the null hypothesis for it demonstrates that the 23 questions in the questionnaire are statistically significant variables in explaining the variation in SPSS anxiety among students.

References

Denis, D. (2016). Applied univariate, bivariate, and multivariate statistics. Hoboken, NJ:Wiley.

Elliott, A. C., & Woodward, W. A. (2015). IBM SPSS by example: A practical guide to statistical data. Thousand Oaks, CA: SAGE Publications.

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4^th ed.). Los Angeles, CA: SAGE Publications.

Jackson, S. J. (2015). Research methods and statistics: A critical thinking approach (5^th ed.). Belmont, CA: Cengage Learning.

McCormick, K., Salcedo, J., Peck, J., Wheeler, A., & Verlen, J. (2017). SPSS statistics for data analysis and visualization. Indianapolis, IN: Wiley.

Pallant, J. (2016). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Sydney, Australia: Allen & Unwin.

Appendices

Appendix A: Correlation Matrix

Appendix B: Anti-Image Matrices

Appendix C: Communalities

Appendix D: Component Matrix

Appendix E: Rotated Component Matrix