Abstract
Objective:
This study aimed to determine the relationship between emotional intelligence (EI) and grade point average (GPA) in a cohort of sonography (DMS) or radiography (RT) students. Furthermore, this study explored whether an EI model, such as the Trait Emotional Intelligence Questionnaire (TEIQue), demonstrated a significant association with GPA and could potentially be used as a part of the admission process.
Materials and Methods:
The TEIQue questionnaire that collected participants’ EI and demographic data was administered to students in both educational programs. Data were obtained at the beginning of each respective program and 1 year later. Students’ GPA was examined over three college semesters.
Results:
In total, 26 participants enrolled. This cohort included 10 DMS and 16 RT students enrolled for an associate’s degree in an applied sciences program within a community college. No significant relationship was observed between academic GPA and global EI, as well as three subscales of trait EI. Global trait EI for DMS (P = .35) and RT students (P = .05) demonstrated nonsignificant relationships with students’ GPAs, respectively.
Conclusion:
These results demonstrated that there was no significant relationship between noncognitive variables, such as EI, among DMS or RT students and academic GPA. Further studies should investigate whether these findings were similar to those in other diagnostic imaging programs.
Keywords
Mayer et al. 1 defined emotional intelligence (EI), also known as emotional quotient, as “the ability to engage in sophisticated information processing about one’s own and others’ emotions and the ability to use this information as a guide to thinking and behavior.” The EI can be traced back to the 1920s. 2 Salovey and Mayer developed the first ability-based EI model in 1990. 3 Later, Goleman 4 claimed that EI could be the best predictor of success in life. Consequently, further in-depth research was conducted on EI, which led to the development of various EI models, such as mental ability, trait, and mixed models. 5 O’Connor et al. 6 emphasized that trait EI assessments offered a good prediction of actual behaviors in various situations as they measured typical behavior rather than best performance.
The EI, or noncognitive skills, includes critical thinking, problem-solving, and social skills, persistence, creativity, and self-control. These skills enable individuals to meaningfully contribute to society and succeed in their lives, workplaces, homes, and other social settings. 7 According to Kyllonen, behavioral science research suggests that noncognitive skills, such as motivation, work ethic, teamwork, organization, cultural awareness, and effective communication, play a further important role in determining academic and workplace success and compared with cognitive abilities. 7
Diagnostic medical sonographers (DMS) and radiographers (RT) are a major part of the imaging profession and provide unique contributions to health care. They provide specific diagnostic images for radiologists and must possess different sets of noncognitive skills. A reason why they must possess noncognitive skills is due to the limited time they have to connect with patients as that their exam times are short. Diagnostic imaging examinations can range anywhere from 10 minutes to an hour. Improper verbal and nonverbal communication before, during, and after an exam, such as body language, facial expressions, tone of voice, friendliness, and comfort level, could have major consequences on the patient’s and their family’s perception their health care. The Society of Diagnostic Medical Sonography (SDMS) standards 8 has emphasized the importance of the expected skills that fall into the category of “social” or “soft” skills. A diagnostic medical sonographer is expected to elicit patient’s cooperation, monitor their physical and mental status, adapt the protocol according to the patient’s condition and changes in their clinical status, work in partnership with other health care professionals, recognize their own strengths, communicate effectively with other members of the health care team, and provide patient care with respect. Therefore, learning these skills is crucial for imaging students to become proficient. Such behaviors can be assessed and measured via various EI models.
Rankin 9 determined that there was a significant predictive relationship between EI and practice performance, academic performance, and retention in nursing programs. The meta-analysis of MacCann et al. 5 revealed that EI had an association with academic performance. They determined that students with higher EI achieved higher grades and achievement test scores. Their most significant finding was that it was important for students to know of the causes and consequences of emotions and how to manage emotional situations. They concluded that this knowledge was potentially the most important aspect of EI in academic performance. Moreover, they asserted that being smart and hardworking was insufficient to be academically successful. Kyllonen 7 emphasized that personality factors related to EI, such as conscientiousness, predicted both workplace and academic success. Recent research also suggested that noncognitive factors, such as motivation, work ethics, teamwork, organization, cultural awareness, and effective communication, could be imperative in determining academic success. 7
The meta-analysis of Richardson et al. 10 found that noncognitive measures of school performance, such as academic self-efficacy, grade goals, and effort regulation, had a moderate correlation with grade point average (GPA). Likewise, Ramos-Villarreal and Holland 11 asserted that EI awareness was associated with the development of leadership capabilities and positive behaviors necessary for academic and career success. Similar results were reported by other health care professionals. Chew et al. 12 reported a positive relationship between EI and academic performance among medical students. Students in health care programs must possess certain skills required to take care of patients and serve them with compassion, dignity, and respect. Chew et al. 12 also reported that medical students with higher EI scores performed better on continuous assessments and professional examinations. In addition to academic abilities, it was beneficial to possess nonacademic abilities necessary to be successful in medical occupations. Vandewaa et al. 13 stated that nurses’ abilities, such as empathy, problem-solving, interpersonal relationships, and emotional self-awareness, were correlated with professionalism, performance level, and intuition. These abilities were some EI components and were beneficial for nurses in the workplace. Vandewaa et al. 13 found that nurses who were emotionally intelligent demonstrated increased levels of conscientiousness in performing nursing duties, participating in hospital activities as well as altruistic affairs, and voluntary organizing efforts at the workplace. According to Fernández-Berrocal and Ruiz 14 college students with higher EI showed fewer physical symptoms, lesser social anxiety, depression, rumination, and greater use of active coping strategies for problem-solving.
Collins 15 proposed a method to measure a candidate’s level of “sensitivity to feelings and needs” of the people around them as an admission criterion in addition to cognitive “hard data,” such as GPA. Collins 15 also suggested that an EI tool could be potentially used as an additional instrument in the future admission processes. Rankin 9 recommended that EI should be considered as an additional entrance criterion for nursing students’ recruitment and selection procedures. Unfortunately, there is limited literature on EI and its relationship between academic GPA among health science students. Furthermore, reports regarding DMS and RT students in the United States are scarce. This study aimed to determine the relationship between EI and academic GPA in a cohort of DMS and RT students.
Materials and Methods
This study was approved by the Institutional Review Board (IRB # 2019-20-081) in May 2020. The participants were students who actively attended a DMS or RT program that culminated in an Associate Degree in Applied Sciences. This study used a longitudinal quantitative correlational research design, which allowed for an 1-year evaluation of academic success. Data were collected over the semesters in the 2020–2021 school year. All the data gathered remained anonymized throughout the study. The curriculum for both the DMS and RT programs included didactic and clinical education. The EI concepts were not intentionally taught or discussed. Due to the COVID-19 pandemic, students attended virtual theory lectures via Microsoft Teams and in-person laboratory skill training. The professors who taught the programs were different and did not have any special EI training or certification.
Participant Selection
The research sample was a convenience sample of students enrolled in the DMS and RT programs. All the students were asked to voluntarily participate. The selection process did not include randomization. All the students from this cohort were included. Participants were students accepted into the programs via a point-ranking system through an anonymous selection process, which implied that the admissions committee had no access to the applicants’ personal or demographic information. All student ID numbers were replaced with alternate numbers during the selection process.
Research Design
The study followed a longitudinal, quantitative correlational research design. According to Creswell, 16 a longitudinal research design was used to collect data on trends within the same population and gradual changes in the cohort. A correlational research design helped predict or explain relationships among the variables. 16 Quantitative data were obtained from the EI and student demographic surveys from the same participants over 1 year. The Trait Emotional Intelligence Questionnaire (TEIQue) was used to measure students’ EI. The questionnaire was administered online due to the COVID-19 pandemic. A SurveyMonkey link was emailed to the students along with a consent form. The rate of return was 100% and 72.7% in the DMS and RT students, respectively.
Data Analysis
The TEIQue was administered at the beginning of the first semester and at the end of the third semester or after 1 year. Once three consecutive semesters were completed, EI scores were analyzed to examine whether there was a change in scores over time. The TEIQue consisted of 153 questions and included 15 subscales, four factors, and the global trait EI score. Of the 15 intelligence subscales, 13 were grouped into four factors: well-being, self-control, emotionality, and sociability. Only three were explored individually, optimism, stress management, and emotion expression, as they produced the most noticeable changes in EI scores at the completion of 1 year. The questions were concise and easy to understand, such as “I am usually able to control other people,” “Understanding the needs and desires of others is not a problem for me,” “I often find it difficult to recognize what emotion I am feeling.” Participants answered questions on a 7-point Likert scale that ranged from 1 (Completely Disagree) to 7 (Completely Agree). Academic GPA was measured as the average GPA of all the academic program courses attended by the students.
O’Connor et al. 6 emphasized that trait EI assessments offered a good prediction of actual behaviors in various situations as they measured typical behavior rather than the best performance. A reason for choosing this test was that the TEIQue test was available free of charge for academic and clinical research. 17 The TEIQue had an internal consistency (Cronbach’s alpha) of .89 for the global trait EI score and had superior predictive validity as well as psychometric properties. 6 All data from the TEIQue tests were entered by the researcher in an Excel spreadsheet template, which was downloaded directly from the London Psychometric Laboratory website. 17 Once the template was uploaded, their scoring engine produced a fully scored Excel file that included Cronbach’s alphas, a measure of internal consistency for all the variables. The London Psychometric Laboratory analyzed the data automatically online via their scoring engine.
Data were divided into two groups for analysis: DMS and RT students. The global EI score was calculated by averaging all the questionnaire responses. Global trait EI, four EI factors, and three EI subscales were obtained before and after three consecutive semesters for triangulation. The independent and dependent variables were EI scores after three consecutive semesters and students’ average academic GPA, respectively. All variables provided nominal data. The Statistical Package for the Social Sciences (SPSS) version 27 was used for data analysis. A Shapiro-Wilk test was used to determine whether the data were normally distributed. This test was selected due to the small sample size (n < 50). A parametric Pearson correlation and nonparametric Spearman’s rho correlation tests were used to measure the strength between global EI, the four factors as well as three subscales of trait EI, and academic GPA after three semesters. P value was set at .05.
Results
This study included 26 students. Of these, 10 and 16 were enrolled in the sonography and radiography programs, respectively.
Sonography Students
A test of normality was performed to determine whether all noncognitive variables, such as global EI, four factors, three subscales, and average academic GPA, were based on the DMS students’ responses (See Table 1). These findings indicated that the noncognitive variables, such as the three factors and three subscales of trait EI, and one cognitive variable of average academic GPA were normally distributed among DMS students. In addition, this analysis demonstrated statistical significance (P < .05) for global EI (P = .01) and one EI factor, well-being (P = .03), which indicated that it was not normally distributed.
Test of Normality Based on Sonography (DMS) Students’ GPA, Responses to Global EI, Four Factors, and the Three Subscales of Trait EI.
Abbreviations: DMS, diagnostic medical sonography; GPA, grade point average; EI, emotional intelligence. Statistical significance (P < .05).
The parametric Pearson correlation test was used to determine whether there was a significant relationship between the average academic GPA, three factors, and three subscales of trait EI among DMS students (See Table 2).
Pearson’s Correlation to Determine the Level of Association Between Sonography (DMS) Students’ Grade Point Average (GPA), Responses to Global EI, Three Factors, and the Three Subscales of Trait EI.
Abbreviation: EI, emotional intelligence.
Table 2 demonstrates that the level of association was not statistically significant (P< .05) for average GPA and all the EI variables. Hence, there was no significant relationship between the data collected from DMS students.
The Spearman rho test was used for nonparametric variables and to determine whether there was a significant relationship between DMS students’ GPA, global EI, and one EI trait: well-being (See Table 3).
Spearman’s Rho Correlation to Determine the Level of Association Between Sonography (DMS) Students’ Grade Point Averages (GPA), Responses to Global EI, and the Subscales of EI Trait: Well-being.
Abbreviation: EI, emotional intelligence.
Table 3 demonstrated no association between DMS students’ GPA and global EI. Likewise, there was no association between DMS students’ GPA and trait EI factor of well-being. Hence, there was no relationship between average GPA, global EI, or the well-being factor of trait EI among DMS students.
Radiography Students
The test of normality was performed to determine whether all noncognitive variables, such as global EI, four factors, three subscales, and average academic GPA, were normally distributed for RT students (See Table 4).
Test of Normality Based on RT Students’ GPA, Responses to Global EI, Four Factors, and the Three Subscales of Trait EI.
Abbreviations: EI, emotional intelligence; GPA, grade point average; RT, radiography. Statistical significance (P < .05).
These findings indicated that data were normally distributed. However, it was important to separately investigate the average GPA (P = .00) and EI trait factor of well-being (P = .04), which was not normally distributed.
Pearson’s correlation test was used to determine whether there was a significant relationship between the RT students’ global EI, three factors, and three subscales of trait EI (See Table 5).
Pearson’s Correlation to Determine the Level of Association Between Radiography (RT) Students ‘Responses to Global EI, Three Factors, and the Three Subscales of Trait EI.
Abbreviation: EI, emotional intelligence.
Table 5 demonstrated that the level of association was not statistically significant (P < .05) for average GPA and all the EI variables. This indicated that there was no significant relationship among the data collected from RT students.
Spearman rho correlation was used to determine an association between RT students’ average GPA and well-being, an EI factor (See Table 6).
Spearman Rho Correlation to Determine the Level of Association Between Radiography (RT) Students’ GPA and the Subscales of Trait EI: Well-being.
Abbreviations: EI, emotional intelligence; GPA, grade point average.
Table 6 demonstrated that there was no association between RT students’ average GPA and well-being factor.
Discussion
The cohort results did not show a significant relationship among academic GPA, global EI, the four factors, and three trait subscales of EI among DMS or RT students. These findings suggested that noncognitive variables, such as global, factors, and subscales on the TEIQue questionnaire, had no relationship with the students’ GPA after three consecutive semesters.
These results coincided with previous findings reported by Humphrey-Murto et al. 18 who concluded that EI, measured via the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) ability test, was not a reliable predictor of future academic performance in medical students. Cherry et al. 19 expressed concerns that use of EI assessments in the admission process could have inadvertent consequences. Most EI research studies in the United States were conducted with nursing students.9,20,21 Virtually, no previous studies ascertained EI or changes in EI in students enrolled in diagnostic imaging programs, such as DMS and RT students.18,12 This study’s outcomes could be attributed to the different styles of teaching, grading techniques, and didactic coursework in those programs.
These outcomes differed from those of previous studies. Some researchers claimed that EI could be a more accurate predictor of performance in professions that required social, emotional, and motivational skills.7,4,15 In contrast, Watson and Watson 22 discovered supportive evidence of a relationship between academic stress, coping self-efficacy. In addition, Kyllonen emphasized that behavioral science research suggested that noncognitive skills, such as motivation, work ethics, teamwork, organization, cultural awareness, and effective communication, played a further important role in determining academic and workplace success compared with cognitive abilities. 7
Literature review by Dugué et al. 21 concluded that a high level of EI was beneficial for students. They stated that students and health care professionals who were emotionally intelligent were more efficient and better at managing their stress and emotions. Such individuals had better health and relationships with patients and families, as well as within their health care teams. Zoromski 20 asserted that employees with high EI skills experienced better general well-being and less emotional exhaustion and burnout in service positions. Consequently, Sliter et al. 23 recommended that service industries should investigate the use of EI instruments for hiring selection to improve customer service performance and reduce health care expenses. O’Connor et al. 6 confirmed that people with high trait EI tended to have high levels of self-efficacy regarding emotion-related behaviors. These individuals were competent at managing and regulating their own and others’ emotions. Moreover, Gutiérrez-Cobo et al. 24 demonstrated that improved ability to manage emotions could improve undergraduate students’ capacity for cognitive control. Another study demonstrated that alexithymia, the inability to identify and describe emotions, was related to low EI and a significant predictor of low student success. 17 In contrast, Chapin 25 reported that students with high grades at the end of their first year achieved significantly higher EI scores than compared with groups with low grades and at risk of dropping out. However, this study did not reflect similar outcomes.
Goleman 4 emphasized that noncognitive skills in service occupations were significant. Therefore, it was important to determine whether students’ EI skills could be measured and improved during their health care programs to better prepare for their future work. Therefore, future studies should investigate whether these findings were similar to those obtained in other diagnostic imaging programs. This could lead to further studies on the relationship between EI and students’ GPA across various imaging and health care programs. Dugué et al. 21 stated that improving EI was beneficial for nursing students. Furthermore, EI training programs were effective in nursing. Goleman 26 affirmed that courses in social and emotional learning made sense due to neuroplasticity and since repeated experiences shaped the brain. Future research should also examine the implementation of dedicated EI education in the college curriculum or EI training for health care students and its implications was. Overall, there was limited research on EI and its relation to students’ success in diagnostic imaging programs. Hence, further research is essential.
Limitations
This cohort study had internal validity threats. Specifically, these were interactions with selection, maturation, mortality, and testing. Interactions with selection existed as although the participants were a student cohort, they belonged to two different professional programs. The threat of maturation was an issue as participants’ maturity levels may have changed during the length of the program. Some participants withdrew from their programs for various reasons, which resulted in mortality data. This particular RT programs’ attrition rate was approximately 22.7%, compared with the DMS programs’ average attrition rate of 0%.
This study also had threats to external validity. Specifically, these were interaction of setting and treatment, which made the results nongeneralizable. Students in this sample had different personalities and learning styles, among others, which may not be representative of students in other programs. The DMS program had mostly female students, whereas the RT program had a more even distribution. This study was conducted at a large suburban community college. Consequently, the findings may not apply to a small suburban community college or a large 4-year institution. Since data were collected based on a convenient sample, the small size was another important limitation.
Conclusion
These results suggested that there was no significant relationship between noncognitive variables, such as EI, compared with GPA among DMS or RT students. Further research should investigate whether these findings were similar to those in other diagnostic imaging student cohorts. This research could lead to further studies on the relationships between other noncognitive variables and students’ academic GPA in other imaging and health care programs.
Footnotes
Ethics Approval
This study was approved by the Institutional Review Board of the University of St. Francis, Joliet, IL (IRB # 2019-20-081).
Informed Consent
Written consent was obtained from all participants after they were informed of the study objective and design. They were free to withdraw from the study at any time.
Animal Welfare
Guidelines for humane animal treatment did not apply to the present study because because no animals were used.
Trial Registration
Not applicable.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.