Journal of Nursing Education

Major Article 

Q-Factor Emotional Intelligence Profiles as an Area for Development in Graduate Nurse Anesthesia Students

Shawn Bryant Collins, PhD, DNP, CRNA; Duane Covrig, PhD; Isadore Newman, PhD

Abstract

Some fear that the added demands of the Affordable Care Act may worsen already high attrition rates among nurses. One potential solution is that graduate nursing programs must do a better job of selecting and retaining those who can persist in training to gain the clinical and leadership skills necessary to fill these new roles. Some believe emotional intelligence (EI) may help with the selection and retention of graduate nursing students. This study examined EI in 216 nurse anesthesia (NA) students. Q-factor analysis was used to create EI profiles of first-semester, 1-year, and last-semester NA students. It showed one EI type was consistent at each point in the program: High Facial Reader/Low EI Manager. [J Nurs Educ. 2014;53(9):501–510.]

Dr. Collins is Assistant Professor, Associate Dean of the College of Health and Human Sciences, and Director, Nurse Anesthesia Program, Western Carolina University, Cullowhee, North Carolina; Dr. Covrig is Professor of Leadership, Andrews University, Berrien Springs, Michigan; and Dr. Newman is Professor Emeritus, University of Akron, Akron, Ohio.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Shawn Bryant Collins, PhD, DNP, CRNA, Assistant Professor, Associate Dean of the College of Health and Human Sciences, and Director, Nurse Anesthesia Program, Western Carolina University, 28 Schenck Parkway, Suite 300, Asheville, NC 28803; e-mail: shawncollins@wcu.edu.

Received: February 10, 2014
Accepted: June 23, 2014

Abstract

Some fear that the added demands of the Affordable Care Act may worsen already high attrition rates among nurses. One potential solution is that graduate nursing programs must do a better job of selecting and retaining those who can persist in training to gain the clinical and leadership skills necessary to fill these new roles. Some believe emotional intelligence (EI) may help with the selection and retention of graduate nursing students. This study examined EI in 216 nurse anesthesia (NA) students. Q-factor analysis was used to create EI profiles of first-semester, 1-year, and last-semester NA students. It showed one EI type was consistent at each point in the program: High Facial Reader/Low EI Manager. [J Nurs Educ. 2014;53(9):501–510.]

Dr. Collins is Assistant Professor, Associate Dean of the College of Health and Human Sciences, and Director, Nurse Anesthesia Program, Western Carolina University, Cullowhee, North Carolina; Dr. Covrig is Professor of Leadership, Andrews University, Berrien Springs, Michigan; and Dr. Newman is Professor Emeritus, University of Akron, Akron, Ohio.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Shawn Bryant Collins, PhD, DNP, CRNA, Assistant Professor, Associate Dean of the College of Health and Human Sciences, and Director, Nurse Anesthesia Program, Western Carolina University, 28 Schenck Parkway, Suite 300, Asheville, NC 28803; e-mail: shawncollins@wcu.edu.

Received: February 10, 2014
Accepted: June 23, 2014

With the increased role that nurses are expected to play as a result of the changes in care delivery models, global payments, and the Affordable Care Act, it is important not only to retain the 2.8 million RNs in the United States but also to train advanced practice nurses who will be assuming increased responsibility in the new care-delivery models. This retention needs to take place in the context of an aging work force facing increased technological, political, institutional, and managed care stress.

To meet the retention demands in both academia and practice—and in support of allowing for full scope of practice—the Institute of Medicine (2010) reported that access to quality care can be expanded by using advanced practice nurses and that allowing these practitioners to practice to the full extent of their training results in better outcomes. Further support comes from an analysis of Medicare data for 1999–2005 that finds no evidence that opting out of the Centers for Medicare & Medicaid Services oversight requirement for nurse anesthetists resulted in increased inpatient deaths or complications. On the basis of the findings, the authors recommend that the Centers for Medicare & Medicaid Services allow certified registered nurse anesthetists (CRNAs) in every state to work without the supervision of a surgeon or anesthesiologist (Dulisse & Cromwell, 2010).

There are nearly 47,000 CRNAs and student RNAs nationwide. According to the American Association of Nurse Anesthetists Web site (2014), CRNAs are anesthesia providers who safely administer approximately 32 million anesthetics to patients in the United States each year. Furthermore, research demonstrates that CRNAs are the most cost-effective anesthesia providers with an exceptional safety record. In today’s changing health care environment, patients want health care delivered with personal care, at a lower cost, with a high degree of confidence. CRNAs deliver all of these.

Merwin, Stern, and Jordan (2006) supported the need for increasing numbers of advanced practice nurses such as CRNAs. Those authors suggested that the increasing supply of CRNAs would not outpace the market demand. This is due to multiple factors, including higher volume of surgery (2% increase per year) and increasing numbers of CRNAs moving into the nonsurgical work environment, such as pain management and radiology.

Another article reported that the anesthesia labor market has demonstrated a continually increasing demand for all types of anesthesia providers over the past 10 years (Schubert, Eckhout, Ngo, Tremper, & Peterson, 2012). That report also showed a steady increase of CRNAs graduating into the workforce over the same time period. In the early 2000s, a severe national shortage of CRNAs occurred. According to Schubert et al. (2012), this was due to a high number of retirements exceeding graduation of new CRNAs. Also, it has been demonstrated that nurse anesthetists are cheaper to train and have no difference in outcomes, compared with their contemporaries.

As graduate programs that train advanced practice nurses for these new delivery systems try to train larger numbers of qualified practitioners, pressure is being felt to facilitate progression of students to graduation. Perhaps just as challenging is the role that graduate school faculty have in setting standards that help to indicate the students who are ready for and subsequently withstand the intensive training programs. Program faculty need to provide every opportunity for individuals to successfully complete the program and then practice as advanced practice nurses. Some researchers point out that attrition of nurse anesthesia students negatively affects students, nurse anesthesia program viability, and consumers of health care (Andrews, Johansson, Chinworth, & Akroyd, 2006; Wilson, 2008). Mathis (1993) noted that although individuals will drop out or be pushed out for various reasons, programs that can maximize the effectiveness of who will most likely succeed may have an advantage. How can they more effectively assess and develop who will succeed in their programs—who will not only persist to completion but also develop the clinical and leadership skills needed for advanced nursing practice?

Literature Review

If advanced practice nurses are to be trained to become effective servant–leaders, who can move easily between leading and being effective followers of other health care leaders, sensitivity to feelings and needs of people may be a key factor. As Greenleaf (1977) noted, a “really good informative base” includes “both hard data and sensitivity to feelings and needs of people” (p. 23). Looking for the ability to be sensitive to others might be useful soft data to add to the already hard data of test scores and grade point averages used in admissions. That is why some are turning to such things as emotional intelligence (EI) to use as a multivariate prediction of success (Burns, 2009).

As defined by Mayer, Salovey, and Caruso (2004), EI is:

the capacity to reason about emotions, and of emotions to enhance thinking. It includes the abilities to accurately perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions so as to promote emotional and intellectual growth.

EI as set forth by Mayer et al. (2004) forms the basic theoretical framework for this study. EI from this theoretical perspective refers specifically to the cooperative interaction of cognitive intelligence and emotion (Ciarrochi, Cahn, & Caputi, 2000; Roberts, Zeidner, & Matthews, 2001).

Research has documented a relationship between EI and successful leadership (Codier, Kooker, & Shoultz, 2008; Connolly, 2002; Cox, 2002; Gewertz, 2006), education (Parker, Austin, Hogan, Wood, & Bond, 2005; Parker, Summerfeldt, Hogan, & Majeski, 2004; Petrides, Frederickson, & Furnham, 2004; Petrides & Furnham, 2000; Qualter, Gardner, & Whiteley, 2007; Qualter, Whiteley, Hutchinson, & Pope, 2007), and professional work situations (Bellack, 1999; Bellack et al., 2001; Chabeli, 2006; Freshwater & Stickley, 2004; Gooch, 2006; Kerfoot, 1996; McQueen, 2004; Reeves, 2005; Strickland, 2000), all of which impact graduate nursing student education. A study involving nurses showed positive correlations (p ⩽ 0.05) between clinical performance level and EI scores (Codier et al., 2008). Although these studies and others have provided valuable information on EI in relation to professional work and leadership, no studies were found that assess the relationship between EI and graduate nursing student program completion. As a first step in exploring EI, the researchers wanted to determine whether specific EI profiles or types existed within the graduate nursing student population. Understanding EI types in nurse anesthesia students is a first step in studying EI as it relates to their successful transition through a program and passing the National Certification Examination (NCE). There is no research or technique describing the different EI types or even whether different types exist at different stages of training. Therefore, the main purpose of this research was to identify whether different EI types existed.

The authors of the current article believed that EI might be a nonacademic measure useful to predict success in programs. The authors had seen that students in many types of other graduate programs, who had better emotional skills, seemed to survive tough training and difficult personal setbacks, and this was especially evident in advanced nursing programs. The authors realized that the measures might have to encompass different manifestations or measurements of EI at different stages of the graduate nursing program. This led to the use of a Q-factor analysis as a way to create group profiles of students using their EI measures. Q-factor analysis is a technique for the systematic study of subjectivity, which helped to identify possible common EI types among a group of individuals.

Method

In the current study, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) V2.0 was used to cross-sectionally collect EI data on graduate nurse anesthesia students at one specific point in time at three different stages in a nurse anesthesia program of study: matriculation, after 1 year of study, and in the last semester. The MSCEIT is a 141-item, ability-based measure that has good estimates of validity and reliability (Mayer, Salovey, & Caruso, 2002) and generates 15 scores per individual: two area scores, four branch scores (two for each area), and eight task scores) and an overall EI score. After obtaining institutional review board approval at each site, each research participant accessed the MSCEIT online ( http://www.mhs.com/msceit).

The MSCEIT framework uses an ability-based instrument to measure EI. The MSCEIT has been used to study performance in health care settings (Grewal & Davidson, 2008). Mayer et al. (2004) demonstrated that the MSCEIT can be objectively scored and its foundations reliably measured. Rossen and Kranzler (2009) stated that “the pattern of correlations (convergent and discriminant) that have been reported between the MSCEIT and other measures are generally logical and consistent with theory” (p. 60). In addition, moderate, but significant, correlations have been found between the MSCEIT and measures of cognitive ability (Verbal SAT, p < 0.001, r = 0.32) and two of the Big Five personality dimensions (Agreeableness and Intellect, p < 0.05, r = 0.24), suggesting that EI is related to but distinguishable from intelligence and personality (Bastian, Burns, & Nettleneck, 2005; Brackett & Mayer, 2003; Brackett, Mayer, & Warner, 2004)

Confidentiality was maintained by using a predetermined participant code number, group number, and password.Theresponseswere collected in the Multi-Health System’s (i.e., the MSCEIT rights owner and distributor) database. The confidential, password-protected scores and reports were accessed online. The purposive sample included all current students in each program, regardless of matriculation date. Purposive sampling is used when respondents were chosen based on some special purpose; in this case, the purpose was to measure the EI of nurse anesthesia students. The sample included all student RNAs from four accredited nurse anesthesia programs at universities in the southeastern United States, yielding a potential sample of 226 students.

An ex post facto survey design was used with a Q-factor analysis to determine the EI profiles of nurse anesthesia students. Q-factor analysis was used to create, simplify, and aggregate EI profiles of these graduate nursing students. Q-factor analysis was performed using a free program called PQMethod (Schmolck, 2012). PQMethod is a statistical program tailored to the requirements of Q studies. Resulting factors can be rotated either analytically (varimax rotation) or judgmentally, with the help of two-dimensional plots.

Statistical Analysis

Q-Factor Analysis

Q-factor analysis is a quantitative technique that is used to group people, but it does not include the sorting of items into a grid as a means of measuring subjectivity as Q methodology does. It is used to determine dimensions or profiles of people (Newman & Ramlo, 2010). According to Newman and Ramlo (2010), “Q-factor analysis groups people using data that may come from a variety of sources including interviews, observations, and demographic information” (p. 517). For the purposes of the current article and study, the researchers focused on Q-factor analysis to test the expectations that group profiles from the same year would match and that clinical skills would be rated higher for students in their final semester versus 1-year students.

Q-factor analysis is useful when researchers are looking for grouping differences that might explain social or psychological phenomena. This can be useful for identifying a group for interventions or for identifying individuals who, as a group, may be experiencing a program differently. Thus, the authors were looking for a resource that might help the directors to see how groups experience their program, specifically through the lens of EI. As such, the creation of profiles can be used to identify underlying constructs that can assist in grouping people in a meaningful way. The profiles can also be used in evaluation purposes, which could include admission criteria.

On the basis of the shape of their profiles of how the individual z scores are related to each other, Q-factor analysis is a clustering procedure that creates individual types (profiles or typologies), with the correlations reflecting the extent to which individuals perform similarly across measures (Morf, Miller, & Syrotuk, 1976; Rogers, 1995). Advantageously, a Q-factor analysis interprets the typologies based on which trait discriminates that typology.

In the current study, Q-factor analysis was used to determine whether a particular group of interrelated EI variables is more dominant among graduating nurse anesthesia students. Advantages of Q-factor analysis include a reduction of the number of variables by combining two or more variables into a single factor (type or typology). For example, the two area scores, four branch scores, and eight individual task scores of the MSCEIT could be combined into a single factor, such as overall EI. Usually, in an item-by-people matrix, factors are selected by grouping related items. In the Q-factor analysis technique, the matrix is transposed and factors are created by grouping people with similarly shaped profiles. Another benefit of Q-factor analysis is that identification of groups of interrelated variables can occur to show how they are related to each other.

Although two people may have the same overall score (as in total EI score), they do not necessarily represent the same type or profile. For example, an NCE has a certain average score the participant must achieve to pass the examination. However, the examination has six different subset scores (e.g. Pharmacology, Basic Nursing Principles, Advanced Nursing Principles, Physiology, Pathophysiology, and Equipment/Technology) that are used to compute the average score. Two students receiving the same average score does not mean they have the same profile, as one student may be high in the sciences and low in pharmacology, and vice versa for another student. Q-factor analysis uses the differences between z scores to determine which items differentiate between types and agreement items across types.

Results

A total of 216 RNA students (96%) completed the MSCEIT instrument. Their MSCEIT EI scores and clinical scores predictive of NCE success were placed into SPSS® software, and a Q-factor analysis was run on the EI; the results produced three profile types per group per year (split sample, as discussed in Statistical Analysis section; Table 1).

Emotional Intelligence (EI) Profile Types by Year and Group

Table 1:

Emotional Intelligence (EI) Profile Types by Year and Group

Grouping participants based on their similar z score patterns, the groups were then named based on their similarities, creating types. Of note, with Q-factor analysis, items sometimes do not load on a factor or some items that do load are not logical. In other words, no significant relationship existed between profiles of person 1 and person 2 to make a type. Table 2 represents the number of students for each type in each year and group and the percentage that did not load on a factor, and thus could not be typed for each year and group.

Numbers of Graduate Nurse Anesthesia Students and Percentages of Items That Could Not Factor Load for Each Profile Type

Table 2:

Numbers of Graduate Nurse Anesthesia Students and Percentages of Items That Could Not Factor Load for Each Profile Type

The variables included in the Q-factor analysis were all the EI scores (overall EI, two area scores, four branch scores, and eight task scores). The sample for each year was split into two groups to attenuate unstable factors and allow for use in linear regression models. Three EI types were created for each group of each year, which are presented in Table 1 and described in the Discussion section.

Q-factor analysis was performed, and three EI types were found for each of two groups for each of 3 years. Replicated types are presented (Table 1), with one consistent type in each group of each year (High Facial Reader/Low EI Manager). Each group of each year also has at least one unique type, whereas a new type is progressively introduced and repeated in successive years.

Discussion

This exploratory ex post facto study took some first steps in assessing the usefulness of EI as a possible tool in selecting RNA graduate students. It did so by trying to determine whether EI was useful in finding different profiles of these students to determine whether differences by group were present in this population. Although the groups appeared to be somewhat homogeneous in comparison and makeup, processing the groups identified differences between the NA students. These differences helped to make it clear that even within a specific field of work, there were different types of students (Table 1). These types should be considered EI types, but people can be typed in other ways (e.g., cognitive data), and this should be considered for future research related to the affective domain. The EI profiles change from year to year, with Highly Detached/Low Facilitator present in years 1 and 2, and High EI Understander/Low Experiencer present only in year 3. However, one type in particular, High Facial Reader/Low EI Manager, is consistently present in each group and year (Table 1).

The consistent type, the High Facial Reader/Low EI Manager type, shows that all RNA students in this study tend to be strong in perceiving others’ emotions, with a particular sensitivity to reading other people’s facial expressions, tone of voice, and artistic expressions. However, the High Facial Reader/Low EI Manager is not strong in managing his or her own and other’s emotions and is therefore unable to benefit by directing the emotions for long-term benefit and outcomes.

Overall, those in the final semester of their programs had lower EI scores than those at matriculation and 1 year of study. A possible explanation is the end of the program has a greater emphasis, almost myopically, on clinical technical issues, rather than on academic and interpersonal, caring education. This makes the year 3s better technicians, not necessarily better health care providers. Because this study was cross-sectional, the lower overall EI scores may be related to the individuals themselves and not to their programs nor their progression through the programs. Although no EI study of RNA students is available to compare with the current study, this finding is in contrast to a finding by Benson, Ploeg, and Brown (2010) that showed a statistically significant positive linear association (p < 0.05) between years in the program and higher EI functioning in baccalaureate nursing students.

The current study also showed that some components of EI (Facilitating Branch and Facilitating Area) were correlated with or predictive of NCE scores (Table 3). Future longitudinal studies need to be done to examine other possible factors.

Selected Emotional Intelligence Variables for Predicting National Certification Examination Scores

Table 3:

Selected Emotional Intelligence Variables for Predicting National Certification Examination Scores

Nooryan, Gasparyan, Sharif, and Zoladl (2011) concluded that the ability to effectively manage emotions in the workplace assists in coping with stress, and education in EI decreased anxiety in physicians and nurses—a finding corroborated by Montes-Berges and Augusto (2007) on the importance of EI for coping with stress in nursing. A study by Holahan and Moos (1991) demonstrated that EI reduces stress and predicts 66% of key success factors in health care. In other words, health care providers who score high in EI are far more effective in a number of key performance areas, including stress management. On the basis of these authors’ findings, it could be that if EI training were incorporated into the RNA graduate nursing curriculum, increased EI may play more of a role in predicting NCE scores and determining the role of stress in the intensive training of an RNA program.

A study by Weng et al. (2011) found that higher EI was significantly associated with less burnout (p < 0.001) and higher job satisfaction (p < 0.001) among doctors. This finding was duplicated among nurses (Montes-Berges & Augusto, 2007). If, as shown in the EI types described in the current study, students throughout the RNA curriculum are able to recognize emotions but are unable to manage their emotions and the emotions of those around them, training on emotion management during the curriculum may lead to better outcomes, including better stress management. It is important to determine whether these findings can be replicated in different programs with different participants.

Replicating this study in other areas of graduate nursing, such as other nurse practitioners, is an area to test for replication. Nurse anesthetists tend to have short, quick interactions with patients, and the EI profile may appear different than a graduate nursing student in primary care areas. An area of further study then may include researching EI differences and its predictions of success in areas that have different types or kinds of patient interactions.

Limitations

Two limitations of this study must be acknowledged. The first limitation was the study’s cross-sectional design. Although the data support changes in EI, as a cross-sectional design, the findings may not permit the conclusion that EI scores change over the 28 months of the program based on different participants, as the EI scores may have been different to begin with. Second, although the RNA programs came from the same geographical region, the similarities of the study populations (which allows for a smaller sample size) served to limit variability in responses.

Conclusion

Identifying EI types is a first step in understanding EI as it relates to graduate nursing students and future research on its use in enhancing student success. The results of this study have practical implications for graduate nurse educators. The current study identified a preliminary need for nurse anesthesia leaders to examine EI types of RNA students, with possible future use of EI as inclusion of EI training in the RNA curriculum.

Possible heuristic value was found that might help to identify different types of participants in a group, even though the groups appeared to be somewhat homogeneous in comparison and makeup. The emotional intelligence profiles are different among RNA classes, with Highly Detached/Low Facilitator present in years 1 and 2, and High EI/Understander/Low Experiencer present only in year 3. However, one type in particular, High Facial Reader/Low EI Manager, is consistently present in each group and year.

These findings could be used to guide research in several directions. This study found two EI types (Year 2, Group 1, Type 1 [Y2G1T1]—High Facial Reader/Low EI Manager—and Year 3, Group 1, Type 3 [Y3G1T3]—High EI Understander/Low Experiencer; Tables 48)—show predictive value for clinical scores that are predictive of NCE scores. The clinical scores for predicting NCE scores included the ability to transfer didactic knowledge to the clinical setting, clinical efficiency, the ability to troubleshoot equipment malfunctions, and technical skills (Table 9). The High Facial Reader/Low EI Manager is strong in perceiving others’ emotions, with a particular sensitivity to reading other people’s facial expressions, tone of voice, and artistic expressions. However, Type 1 High Facial Reader/Low EI Manager for Group 1 has a poor ability to know under what circumstances emotional intensity lessens and increases and how one emotional state changes into another. This type also has difficulty identifying the emotions that are involved in more complex affective states.

Q-Factor Year 2 Group 1 Type for Predicting Technical Skills Factor and the Corresponding Regression Model

Table 4:

Q-Factor Year 2 Group 1 Type for Predicting Technical Skills Factor and the Corresponding Regression Model

Q-Factor Year 2 Group 1 Type for Predicting Resource Management Factor and the Corresponding Regression Model

Table 5:

Q-Factor Year 2 Group 1 Type for Predicting Resource Management Factor and the Corresponding Regression Model

Q-Factor Type for Predicting Technical Skills Factor and the Corresponding Regression Model

Table 6:

Q-Factor Type for Predicting Technical Skills Factor and the Corresponding Regression Model

Q-Factor Analysis Z Scores for Year 3 Group 1 Types 1, 2, and 3

Table 7:

Q-Factor Analysis Z Scores for Year 3 Group 1 Types 1, 2, and 3

Q-Factor Analysis Z Scores for Each Year 2 Group 1 Types 1, 2, and 3

Table 8:

Q-Factor Analysis Z Scores for Each Year 2 Group 1 Types 1, 2, and 3

Selected Clinical Variables for Predicting National Certification Examination Scores and the Corresponding Regression Model

Table 9:

Selected Clinical Variables for Predicting National Certification Examination Scores and the Corresponding Regression Model

The High EI Understander/Low Experiencer is knowledgeable in complex emotions and how emotions combine and change over time and transition from one phase to another. Type 3 High EI Understander/Low Experiencer is low in the ability to perceive another’s emotions from visual stimuli and the ability to perceive, respond, and manipulate emotional information. Further research could explain what they would do to use these two types related to clinicals. Further EI research is needed from a longitudinal perspective to see how EI changes from the beginning of a graduate nursing program to the end of a program. Pre–post research could be done to determine whether EI training affects successful transition through the program, clinical success, and NCE scores.

Identifying the best criteria for training graduate nursing students in a way that enhances their success should be based on evidence, rather than using traditional variables, thus demonstrating movement toward best practice in education. Educators must be well informed of all the tools available to that end. EI shows promise as best practice.

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Emotional Intelligence (EI) Profile Types by Year and Group

Year (Y) and Group (G)TypeCommon Threada
Y1G1High Facial Reader/Low EI ManagerX
High Facial Facilitator/Low EI Understander*
Highly Detached/Low FacilitatorY
Y1G2High Facial Reader/Low EI ManagerX
High EI Manager/Low Facilitator*
Highly Detached/Low FacilitatorY
Y2G1High Facial Reader/Low EI ManagerX
Highly Detached/Low FacilitatorY
High EI Manager/Low Facial Reader*
Y2G2High EI Manager/Low EI Understander*
High Facial Reader/Low EI ManagerX
Perceiving Concrete Thinker/Low EI Understander*
Y3G1Highly Understanding Manager/Low Facilitator*
High Facial Reader/Low EI ManagerX
High EI Understander/Low ExperiencerZ
Y3G2High EI Understander/Low ExperiencerZ
Highly Facilitating Understander/Low Facial Emotion Manager*
High Facial Reader/Low EI ManagerX

Numbers of Graduate Nurse Anesthesia Students and Percentages of Items That Could Not Factor Load for Each Profile Type

Year (Y)/Group (G)/Type (T)N%Total N for Each Group% Could Not Be Typed% Variance Explained
Y1G1T11025.63929
Y1G1T21025.63919
Y1G1T3615.43933.316
Y1G2T110254028
Y1G2T28204019
Y1G2T3615404017
Y2G1T1927.33325
Y2G1T2824.23322
Y2G1T3515.23333.219
Y2G2T1616.73620
Y2G2T21027.83626
Y2G2T3719.43636.121
Y3G1T1823.53426
Y3G1T2823.53428
Y3G1T3514.73438.318
Y3G2T11029.43426
Y3G2T2617.63418
Y3G2T3823.53429.517

Selected Emotional Intelligence Variables for Predicting National Certification Examination Scores

Variablebtp
Facilitating task–4.527–2.8570.006*
Sensations task–5.249–2.5120.015*
Facilitating branch7.1782.5580.013*
Reasoning area0.3470.7860.435
ModelR2Adjusted R2df1/2FChangepSignificant
0.1520.0964/602.6970.039Yes

Q-Factor Year 2 Group 1 Type for Predicting Technical Skills Factor and the Corresponding Regression Model

Variableabtp
Y2G1T13.7171.8570.073*
Y2G1T2–1.477–0.7140.481
Y2G1T3–0.127–0.0530.958
ModelR2Adjusted R2df1/2FChangepSignificant
0.1830.0983/322.1600.114No

Q-Factor Year 2 Group 1 Type for Predicting Resource Management Factor and the Corresponding Regression Model

Variableabtp
Y2G1T10.6161.7870.084*
Y2G1T20.2270.6380.529
Y2G1T3–0.273–0.6590.515
ModelR2Adjusted R2df1/2FChangepSignificant
0.1540.0673/321.7620.177No

Q-Factor Type for Predicting Technical Skills Factor and the Corresponding Regression Model

Variableabtp
Y3G1T1–1.827–0.9210.365
Y3G1T2–1.452–0.7320.470
Y3G1T3–4.877–2.0990.044*
ModelR2Adjusted R2df1/2FChangepSignificant
0.1300.0433/331.4890.237No

Q-Factor Analysis Z Scores for Year 3 Group 1 Types 1, 2, and 3

StatementZ Scores (Rank)
Type 1Type 2Type 3
Faces task–0.74 (11)2.86 (1)–0.79 (12)
Facilitation task–1.76 (15)–0.09 (6)0.95 (4)
Changes task1.32 (1)–0.16 (8)1.12 (3)
Emotion manage task0.51 (7)–1.14 (14)0.26 (6)
Pictures task–0.48 (10)–0.32 (11)–0.79 (13)
Sensations task–0.21 (9)–1.21 (15)–1.60 (15)
Blends task0.78 (6)–0.15 (7)1.58 (2)
Social manage task0.96 (3)–0.30 (10)–0.50 (10)
EI perceiving branch–0.78 (12)1.14 (2)–0.79 (12)
EI facilitating branch–1.26 (14)–0.65 (12)–0.44 (9)
EI understanding branch0.90 (4)0.00 (5)1.63 (1)
EI managing branch0.88 (5)–0.73 (13)–0.21 (7)
EI experiencing area–1.22 (13)0.48 (4)–0.85 (14)
EI reasoning area1.12 (2)–0.25 (9)0.89 (5)
EI overall–0.03 (8)0.53 (3)–0.44 (8)

Q-Factor Analysis Z Scores for Each Year 2 Group 1 Types 1, 2, and 3

StatementZ Scores (Rank)
Type 1Type 2Type 3
Faces task2.54 (1)–0.28 (11)–1.90 (15)
Facilitation task0.02 (7)–1.73 (14)1.18 (2)
Changes task–1.17 (15)0.18 (9)–1.12 (13)
Emotion manage task0.36 (4)1.23 (2)1.15 (4)
Pictures task0.20 (5)1.24 (1)0.46 (6)
Sensations task–0.46 (10)–1.11 (13)0.03 (7)
Blends task0.80 (13)1.09 (3)–0.35 (10)
Social manage task–0.73 (12)0.36 (7)1.17 (3)
EI perceiving branch1.52 (2)0.38 (5)–0.96 (12)
EI facilitating branch–0.24 (8)–1.83 (15)0.52 (5)
EI understanding branch–1.11 (14)0.21 (8)–1.21 (14)
EI managing branch–0.35 (9)0.79 (4)1.37 (1)
EI experiencing area0.72 (3)–0.99 (12)–0.38 (11)
EI reasoning area–0.63 (11)0.38 (6)0.02 (9)
EI overall0.13 (6)0.08 (10)0.02 (9)

Selected Clinical Variables for Predicting National Certification Examination Scores and the Corresponding Regression Model

Variablebtp
Preoperative care–17.503–0.9440.350
Care plan22.2851.3170.194
Didactic transfer37.4833.4700.001**
Clinical judgment–22.186–1.4440.156
Skill mastery–20.814–1.6450.107
Data adjust care–14.014–0.8730.387
Recognize and respond to complications5.7960.3220.749
Efficient36.0582.2760.027*
Valid self-critique–23.849–1.9480.057
Independent communication–0.669–0.0580.954
Patient respect0.4330.0320.975
Stress management–3.668–0.3030.763
Budget25.9370.6990.488
Equipment malfunction–33.692–2.1740.035*
Standard precautions11.2180.7950.431
Peer comparison9.5360.8310.410
Resource management–16.127–0.8100.422
Technical skills factor3.2891.9780.050*
Patient-focused factor–7.352–1.5630.123
Resource management factor–15.655–1.7320.088
ModelR2Adjusted R2df1/2FChangepSignificant
0.4780.29017/62.5340.006Yes

10.3928/01484834-20140821-13

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