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article review

article review

The influence of emotional intelligence, cognitive test anxiety, and coping

strategies on undergraduate academic performance

Christopher L. Thomas a,

?, Jerrell C. Cassady a

, Monica L. Heller b

a Department of Educational Psychology, Ball State University, 2000 W. University Avenue, Muncie, IN 47306, USA

b Department of Behavioral and Social Sciences, University of Saint Francis, 2701 Spring StreetFort Wayne, IN, 46808, USA

article info abstract

Article history:

Received 23 February 2016

Received in revised form 21 February 2017

Accepted 3 March 2017

This study explored factors with the potential to exert facilitative and debilitative influence on undergraduate

students’ academic performance. Participants responded to the Schutte Emotional Intelligence Scale, COPE inventory, and Cognitive Test Anxiety Scale-Revised and agreed to have their responses paired with institutional performance data. Analyses tested the iterative and collective influence of the identified variables on four-year GPA

after controlling for previous academic performance (first-year GPA). The examination revealed cognitive test

anxiety and use of emotion-focused coping strategies were significant predictors of students’ long-term academic

outcomes such that increased cognitive test anxiety and increased use of emotion-focused coping strategies were

associated with decreases in four-year GPA. The results inform the nature of the influence these student factors

have on long-term academic outcomes and highlight the importance of developing a multifaceted intervention

model that supports emotion regulation and self-regulation skill development to buffer the impact of cognitive

test anxiety on achievement.

© 2017 Elsevier Inc. All rights reserved.


Emotional intelligence


Cognitive test anxiety

Academic performance

Academic anxiety

1. Introduction

It is well established in the psychological literature that standard

cognitive processing differences are insufficient to capture the full

range of variability observed in academic performance (e.g.,

Duckworth, Peterson, Matthews, & Kelly, 2007; Schunk & Zimmerman,

2003; Snow, Corno, & Jackson, 1996). The classic approach to this

work has primarily adopted a deficit orientation and has focused on

identifying constructs that exert a debilitative influence on performance. For instance, it has been effectively summarized that student

performance can be adversely impacted by stressors within (e.g., task

difficulty, academic overload, academic anxiety) and beyond the academic setting (e.g., financial obligations, family, and personal needs).

Alternatively, many contemporary conceptual orientations have

adopted a positive psychology perspective concerned with the identification of facilitative influences of affective constructs such as grit

(Duckworth et al., 2007), a sense of purpose (Kuh, Cruce, Shoup,

Kinzie, & Gonyea, 2008), and emotional intelligence (Perera &

Digiacomo, 2013). We advocate for a broader perspective when examining students’ academic abilities and self-regulatory tendencies that

acknowledge both the adaptive and maladaptive influences of constructs in the affective domain. While information related to both

supportive and debilitative influences on student performance hold

value in isolation, it is only with attention to multiple factors in concert

that the true operations of the factors may be realized. Therefore, the

purpose of the current examination was to explore the viability of a theoretically based framework for explaining the influences of supportive

and debilitative factors on undergraduate students’ GPAs over the typical four-year time interval.

1.1. Emotional intelligence

Emotional intelligence (EI) is an expansive construct consisting of

mental skills, abilities, and capacities that both process and draw from

emotions (Salovey & Mayer, 1990; Mayer, Salovey, & Caruso, 2000).

Dominant theoretical orientations assume these tendencies allow individuals to accurately assess, regulate, and express their emotional states

as well as to perceive and assess the emotional states of others

(Ciarrochi et al., 2001; Mayer & Salovey, 1997). Further, EI appears to

be a multidimensional construct characterized by bidirectional influences among familial, environmental, and cognitive factors. Moreover,

as such, EI has the potential to influence the expression, interpretation,

and impact of emotional responses in all phases of human experience

(Mayer, Roberts, & Barsade, 2008).

Over the past 20 years, the field has clarified a distinction between

two common constructs found within EI literature, commonly referred

to as trait and ability EI. Trait EI can be conceptualized as individuals’

perceptions of their emotional world and emotional self-efficacy

Learning and Individual Differences 55 (2017) 40–48

? Corresponding author.

E-mail addresses: (C.L. Thomas), (J.C. Cassady), (M.L. Heller).

1041-6080/© 2017 Elsevier Inc. All rights reserved.

Contents lists available at ScienceDirect

Learning and Individual Differences

journal homepage:

(Petrides et al., 2016; Petrides & Furnham, 2000). That is, trait EI refers

to perceptions of the behavioral dispositions and abilities that allow individuals to effectively assess, regulate, and express emotional states

(Petrides & Furnham, 2000). Given the subjective nature of the construct, trait EI is commonly assessed within empirical investigations

via self-report measures (Mavroveli, Petrides, Rieffe, & Bakker, 2007).

Conversely, ability EI is conceptualized as the actual cognitive abilities

that allow individuals to identify, understand, and manage emotions

(Bar-On, 2010; Mavroveli et al., 2007). Consequently, research examining ability EI have commonly assessed the construct using performancebased assessments (Petrides, Pita, & Kokkinaki, 2007). While these constructs differ in their operationalization, both have shown strong predictive utility in regards to numerous academic, career, and life

outcomes (Petrides et al., 2016; Amdurer, Boyatzis, Saatcioglu, Smith,

& Taylor, 2014).

Negative associations between EI and various psychological traits

(e.g., anxiety, depression) are generally explained by the rather simple

premise that one or more emotional processing dimensions (e.g., perception/clarity, management/regulation) are flawed. That is, individuals

experience negative psychological states – in part – because they ineffectively interpret emotional stimuli, set inappropriate goals, implement ineffective coping strategies, or fail to employ appropriate

emotion regulation skills (e.g., Salovey et al., 2008; Yusoff et al., 2013).

For instance, prior studies have demonstrated that individuals with anxiety have difficulty engaging in strategies that will help them manage or

change their emotional states due to low emotional clarity, inability to

process emotions, and deficient emotional regulation (Fisher et al.,

2010; Fernández-Berrocal, Alcaide, Extremera & Pizarro, 2006,

Southam-Gerow & Kendall, 2000). Perhaps paradoxically, empirical investigations have also indicated that high levels of specific dimensions

of EI may backfire and heighten individuals’ risk for negative affective

outcomes. As explained by Ciarrochi et al. (2001), individuals with

high levels of emotional perception may become more aware of environmental stressors and sources of struggle in their lives, contributing

to higher levels of perceived stress.

Investigations stemming from a positive psychology perspective

have highlighted the facilitative influence of EI within academic settings. For instance, researchers that have modeled “thriving” with respect to trait and ability EI have demonstrated that students’ levels of

EI are positively associated with numerous adaptive outcomes including: psychological wellbeing (Salami, 2011), quality of interpersonal relationships (Afolabi, Okediji and Ogunmwonyi, 2009), conflict

resolution skills (Salovey, Mayer, Caruso, & Yoo, 2008), year retention

at the university level (Parker, Hogan, Eastabrook, Oke, & Wood, 2006;

Qualter, Whiteley, Morley, & Dudiak, 2009), and academic achievement

(e.g., standardized test scores, grade point average, graduation;

Fernández, Salamonson, & Griffiths, 2012; Hogan et al., 2010; Jaeger &

Eagan, 2007; Keefer, Parker, & Wood, 2012; MacCann et al., 2011;

Mayer et al., 2008; Mestre, Guil, Lopes, Salovey, & Gil-Olarte, 2006;

Perera & Digiacomo, 2013).The facilitative influence of EI within academic domains has traditionally been attributed to students’ abilities

to “successfully navigate” the complex social-emotional environment

imposed by academic environments (Matthews, Zeidner, & Roberts,

2002). More specifically, EI has been linked to psychological

constructs that are believed to directly or indirectly contribute to academic success — such as need for achievement (Afolabi et al., 2009),

adaptive coping strategies (MacCann et al., 2011; Tugade &

Frederickson, 2008), and positive peer interactions (Mavroveli,

Petrides, Rieffe, & Bakker, 2010; Petrides et al., 2008).

1.2. Test anxiety

Test anxiety is a pervasive form of academic anxiety that generally

has a negative impact on patterns of beliefs and behaviors common to

testing situations (Cassady, 2010). Traditionally, test anxiety has been

conceptualized as a multidimensional construct consisting of two

broad dimensions, commonly referred to as worry and emotionality

(Liebert & Morris, 1967). Emotionality – or affective test anxiety – is

characterized by the physiological reactions to evaluative situations

that are consistent with more “traditional” anxiety responses (e.g.,

headaches, dry mouth). Worry – or cognitive test anxiety – includes beliefs and behaviors associated with evaluation events that impair optimal performance (e.g., avoidance, poor study skills, cognitive

interference; Zeidner & Matthews, 2005).

Research in the domain of test anxiety has repeatedly linked the experience of cognitive test anxiety to performance outcomes in academic

settings, with consistent findings illustrating a negative impact on student performance for high stakes tests (Cruz, 2010; DeCaro, Thomas,

Albert, & Beilock, 2011; Lowe, Grubein, & Raad, 2011), typical classroom

exams (Zeidner & Matthews, 2005), and even laboratory-based assessment measures that have no evaluative impact (Cassady, 2004a;

Naveh-Benjamin, 1991).

Contemporary orientations have expanded upon the traditional

view that test anxiety influences performance by generating cognitive

interference or distraction while students are taking exams (e.g.,

Sarason, 1984). These updated orientations (Zeidner & Matthews,

2005; Sommer & Arendasy, 2014) propose a variety of viable explanations for “types” of test anxiety (von der Embse, Mata, Segool, & Scott,

2013), but generally support the position that test anxiety is ubiquitous

operating as a trait-like anxiety. Learners encounter the influence of test

anxiety across all phases of the learning-testing cycle, with investigations noting test anxiety related impairment during test preparation

(Cassady, 2004b), test performance (Ramirez & Beilock, 2011), and

test reflection phases (Sommer & Arendasy, 2014; Thomas & Gadbois,

2007). The synthesis of results in this domain suggests that a complete

understanding of test anxiety will only be realized when researchers

and clinicians recognize there are varied manifestations of the construct

that are dependent upon the individual strengths and weaknesses of the


Available evidence suggests that manifestations of test anxiety

across the learning-testing cycle share a rather complex relationship

with learners’ level of EI. Fundamentally, students with high levels of

skill in emotional perception and emotional regulation should be better

equipped to effectively identify and respond to sources of emotional

distress (Gohm et al., 2005; Sanchez-Ruiz, Pérez-González & Petrides,

2010). However, this does not mean that students with high levels of

EI are necessarily predisposed to low levels of test anxiety. To the contrary, individuals with high skills in emotional perception may be

more likely to identify emotional markers for stressors, increasing the

overall level of perceived anxiety (Ciarrochi et al., 2001).

1.3. Coping with academic stressors

Coping strategies form a constellation of behaviors that learners employ in response to their individual-specific interpretations of external

and internal threats they face in academic settings (Fletcher &

Cassady, 2010). A classic and illustrative representation for the relationships among perceived stressors and coping tendencies is the Transactional Stress and Coping framework (Lazarus & Folkman, 1984). This

model of coping posits that individuals’ cognitive appraisals of stressors

are influenced by both personal characteristics (e.g., personality characteristics, emotional intelligence, personal history) and environmental

factors (e.g., academic environment, social pressures, challenging

tasks; Lazarus, 1993a, 1993b; Lazarus & Folkman, 1984, 1987). Based

upon this appraisal, individuals develop either a positive or negative affective emotional response to the context, establish goals for the situation, and employ coping strategies aimed at managing the perceived

stressors and achieving established goals (Cassady & Boseck, 2008).

Generally, coping responses can be characterized as falling within one

of three broad domains: (1) active behavioral responses that aim to

adapt to and manage sources of stress (i.e., problem-focused coping;

Folkman & Lazarus, 1985; Zeidner & Saklofske, 1996); (2)

C.L. Thomas et al. / Learning and Individual Differences 55 (2017) 40–48 41

disengagement from the source of stress as a means of escape (i.e.,

avoidance-coping; Billings & Moos, 1981; Parker & Endler, 1996); and

(3) adjusting one’s interpretation of the situation or emotional disposition (i.e., emotion-focused coping; Austin, Saklofske, & Mastoras, 2010).

Given the dynamic nature of the coping response, it is no surprise

that learners’ appraisals of sources of stress and their responses to

these perceptions can vary dramatically. However, empirical investigations in this domain have repeatedly demonstrated that students who

employ adaptive (problem-focused) coping strategies in response to

stressful academic conditions often navigate perceived challenges

more effectively, attain higher levels of achievement, and report greater

success overall (Dyson & Renk, 2006; McNamara, 2000; Sasaki &

Yamasaki, 2007; Struthers, Perry, & Menec, 2000). By contrast, those individuals prone to employing maladaptive, emotion-focused, avoidant,

or contextually inappropriate coping strategies in response to perceived

stressors tend to exacerbate negative performance and affective outcomes (Austin et al., 2010; Dyson & Renk, 2006; Giacobbi et al., 2004;

Park, Armeli, & Tennen, 2004; Turner, Thompson, Huber, & Arif, 2012).

1.4. Current study

Our interpretation of this body of literature suggests that effectively

understanding the influences of broad emotional functioning (i.e., emotional intelligence) and more specific cognitive and affective responses

to environmental stressors (i.e., test anxiety and coping strategies) on

academic success cannot be achieved through isolated investigations

of individual variables. In light of the connections among student appraisals of stressful events, the coping strategies they are likely to select,

and their general abilities in perceiving and regulating their emotional

states, we propose that research in this domain requires examination

of multiple contributing variables simultaneously.

When examining the relationships among test anxiety, EI, and coping it is important to identify that we adhere to theoretical orientations

that consider both EI and test anxiety to be trait-like constructs (e.g.,

MacCann et al., 2011) and recognize that they are likely highly correlated constructs. Prior research suggests the strong association among the

variables is likely due to EI being a hierarchically superior construct in

the personality space that dictates the manifestation of test anxiety

(Abdollahi & Talib, 2015). Further, consistent with existing models personality processes, we acknowledge superordinate dispositional constructs – such as test anxiety and EI – exert a dramatic influence on

learners’ appraisals of – and reactions to – stressful life events

(Matthews, Zeidner, & Roberts, 2006).

Therefore, the present investigation was designed to explore the collective influences of EI, cognitive test anxiety and coping strategies on

the academic performance of undergraduate learners. Prior research examining the relationships among these variables has traditionally focused on short-term academic performance (e.g., course or semester

grades; Austin et al., 2010; Saklofske et al., 2012). The current examination is unique in that we are examining the iterative and collective influence of these variables on predicting the undergraduates’ cumulative

GPA values at the conclusion of their university experiences. To bolster

this study’s focus on the operation of the primary independent variables, we have also controlled for initial indicators of student competence by including their GPA from the end of their first year. This

design enables exploration of both individual variable influences on

performance, as well as the collective impact of EI, test anxiety, and coping strategies on student performance patterns during the period between the end of their first year in college to the end of their fourth year.

Based on the prior research, we hypothesized that four-year GPA

would be positively associated with first year GPA, emotional intelligence, and tendency to employ adaptive coping strategies. That is, we

anticipated these variables would predict higher levels of four-year

GPA. We also hypothesized that there would be a detrimental impact

on four-year GPA exerted by cognitive test anxiety and avoidance coping strategies. The limited findings related to emotion-focused coping

led to low expectations for that variable as a meaningful predictor of

four-year GPA. Finally, we predicted that the predictive power captured

by emotional intelligence for four-year GPA would be influenced by

adding cognitive test anxiety and coping strategies to the model. That

is, we expected that while positive emotional intelligence would likely

be related to performance (as demonstrated in prior research), that

the addition of cognitive test anxiety (which demonstrates a higher

level of perceived academic stress) and coping strategies would provide

a more durable representation for learner success predictions.

2. Method

2.1. Participants

Data were collected from undergraduate students attending a midsize public university in the Midwestern United States. In compliance

with the approval for research obtained through the University Institutional Review Board, all participants provided informed consent and

were able to receive course credit as part of their involvement in an undergraduate subject pool.

The participants (N = 534) completed the Schutte Emotional Intelligence Scale (SEIS; Schutte et al., 1998), the COPE Inventory (Carver,

Scheier, & Weintraub, 1989), and the Cognitive Test Anxiety Scale-Revised (CTAR-25; Cassady & Finch, 2015) during either their second or

third year at the university in a single administration setting. At that

time, participants also gave permission to access university records for

this research study. Data collected with the support of the institutional

assessment office included gender, ethnicity, 1st year cumulative GPA,

and graduating cumulative GPA. After merging collected and institutional level data, 141 participants had complete data for all variables

of interest. The attrition in this sample was due to three primary causes

(in order of magnitude): (a) many students provided inaccurate schoolspecific identification numbers precluding effective matching with demographic files, (b) others who provided accurate information had

yet to reach the 4-year GPA mark by the time of data collection; and

(c) student withdrawal from the university.

Given the rather large reduction in sample size following the merging of collected and institutional level data, it was decided to compare

participants with complete and incomplete data on the primary variables of interest. Results of a series of independent samples t-tests

with the Bonferroni correction revealed participants with complete

data (81% female & 93% Caucasian) did not significantly differ from

those with incomplete data on levels of reported EI, cognitive test anxiety, problem-focused coping, social-focused coping, avoidant coping, or

emotion-focused coping.

2.2. Measures

2.2.1. Cognitive test anxiety

Cognitive test anxiety levels were assessed using the Cognitive Test

Anxiety Scale-Revised (CTAR; Cassady & Finch, 2015). The CTAR is a

25-item revision to the original Cognitive Test Anxiety Scale (CTAS;

Cassady & Johnson, 2002), and uses a four-point Likert-type scale common to several test anxiety measures (1 = not at all like me,4= very

much like me; e.g., Sarason, 1984). Revisions to the original scale were

driven by measurement issues that indicated a problem with the reverse-coded items in the original CTAS. Removal of the reverse-coded

items resulted in a more parsimonious measure of cognitive test anxiety

while maintaining the indicators of internal consistency and construct

validity that were demonstrated with the original measure (Cronbach’s

? = 0.96; Cassady & Finch, 2015).

2.2.2. Emotional intelligence

The Schutte Emotional Intelligence Scale (SEIS; Schutte et al., 1998)

is a 33-item measure designed to assess trait emotional intelligence.

Using a standard Likert scale participants indicated the extent to

42 C.L. Thomas et al. / Learning and Individual Differences 55 (2017) 40–48

which each item described them. Prior investigations have demonstrated the reliability of the measure for use among university students

(Cronbach’s ? = 0.90; Schutte et al., 1998). Subsequent attempts to validate a factorial solution for the SEIS have generated significant divergence in the literature. Studies by Petrides & Furnham (2000) as well

as Saklofske et al. (2003) attempted to generate multifactor representations for the SEIS, but were unable to validate the initial four-factor

structure nor converge on similar solutions. Gignac, Palmer, Manocha,

& Stough (2005) tested both of these solutions — as well as an alternative model with 6 factors. Once again, they were not able to conclude a

clear multifactor solution. Gignac et al. (2005) did identify that a nested

model may serve as a viable representation for the SEIS. However,

subfactors in that model often included insufficient numbers of items

to be durable. Given the lack of convergence in identifying a factorial solution to the SEIS, we only used the overall total EI score in our analyses,

which has been shown to be durable and reliable across validation


2.2.3. COPE inventory

The COPE Inventory is a 60-item measure designed to assess the use

of functional and dysfunctional coping strategies. Participants indicated

how often they utilized each presented coping strategy on a 4-point

Likert-type scale (1 = I usually don’t do this at all,4= I usually do this

a lot). COPE items can be used to create 15, 4-item subscales: (Positive

Reinterpretation and Growth, Mental Disengagement, Focus On and

Venting of Emotions, Use of Instrumental Social Support, Active Coping,

Denial, Religious Coping, Humor, Behavioral Disengagement, Restraint,

Use of Emotional Social Support, Substance Use, Acceptance, Suppression of Competing Activities, Planning). All subscales are scored such

that higher values indicate increased use of particular coping strategy.

The 15 subscales demonstrated acceptable internal consistency in the

current examination (see Table 1).

Conceptually, COPE subscales have been articulated to represent

broad domains of coping that include problem-focused, emotion-focused, avoidant, and socially-supported strategies (Carver, Scheier, &

Weintraub, 1989; Folkman & Moskowitz, 2004; Litman, 2006). It is important to note that the creators of the COPE inventory intentionally

provide no guidance regarding how to combine COPE subscale scores

to create indices of overarching coping constructs (e.g., problem-focused coping, emotion-focused coping). However, Carver et al. (1989)

suggest factor analytic techniques can be used to generate secondorder factors that can be then used as variables in an examination.

Therefore, it was our decision to generate second-order factors corresponding to broader domains of coping from COPE subscales using exploratory factor analysis (EFA). The validity of the factor structure

obtained via EFA was tested using confirmatory factor analysis (CFA).

To generate second-order factors representing broad coping domains, we employed a two-step sequential factor analysis process by

randomly splitting our complete sample of subjects completing the

COPE (n = 534) into two groups (EFA Sample N = 210, CFA Sample

N = 324). First, we conducted an exploratory factor analysis on the 15

COPE subscales using Principle Axis Factoring with Promax rotation.

To determine the number of factors to be retained the Scree plot,

Kaiser’s greater than one criterion (Kaiser, 1970), and parallel analysis

(Horn, 1965) were used. Collectively, the identified methods suggested

a four-factor solution was optimal, accounting for 65% of the total variance (see Table 1 for factor loadings). The first factor (Problem-focused

coping) contained the Planning, Active coping, Suppression of competing activities, and Religious coping subscales. The second factor (Avoidance coping) contained the Denial, Substance use, Behavioral

disengagement, and Mental disengagement subscales. The third factor

(Social-focused coping) consisted of the Venting emotions, Use of instrumental social support, and Use of emotional social support subscales. The final factor (emotion-focused coping) contained the

positive reinterpretation and growth, acceptance, and humor subscales.

To evaluate the generalizability of the identified four-factor solution,

we conducted a CFA on the second sample using the diagonally weighted least squares estimation method. CFA analyses were conducted using

R 3.3.1 (R Core Team 2016) and the lavaan package (Rosseel, 2012).

Model fit was assessed using Root Mean Square Error of Approximation

(RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and

Standardized Root Mean Square Residual (SRMR). CFA results demonstrated the four-factor solution provided an adequate fit to the data,

CFI = 0.91, TLI = 0.89, RMSEA = 0.09, SRMR = 0.08.

3. Results

3.1. Descriptive and correlational analyses

A series of correlational analyses were conducted to explore the relationships among the variables of interest. As expected, 1st year cumulative GPA, emotional intelligence, cognitive test anxiety and use of

avoidance coping strategies, were significantly correlated with students’

graduating cumulative GPA (see Table 2). However, contrary to our prediction, correlational analyses revealed cognitive test anxiety was not

meaningfully associated with either emotional intelligence or emotion-focused coping strategies. This finding was surprising given previous research noting emotional intelligence provides students with the

ability to successfully manage the emotional challenges associated

with cognitive test anxiety (e.g., Abdollahi & Talib, 2015).

Additional examination of the correlations point to predictable relationships among cognitive test anxiety and coping strategies — with

avoidance coping sharing the strongest relationship with CTA.

Table 1

Factor loadings for exploratory factor analysis with Promax rotation of COPE inventory scales.

Scales M(SD) ? Problem focused Avoidance Social-focused Emotion focused

Planning 11.17(2.52) 0.78 0.827 ?0.167 0.022 0.083

Active coping 10.56(2.19) 0.64 0.842 ?0.057 0.039 ?0.029

Suppression of competing activities 9.16(2.17) 0.59 0.672 0.270 ?0.005 ?0.014

Restraint 9.20(2.25) 0.64 0.445 0.195 ?0.135 0.246

Religious coping 10.61(4.16) 0.94 0.323 ?0.011 0.039 0.007

Denial 6.20(2.41) 0.79 0.070 0.817 0.033 ?0.086

Behavioral disengagement 6.34(2.28) 0.76 ?0.023 0.764 0.007 0.010

Substance use 5.56(2.65) 0.92 0.023 0.509 ?0.017 0.090

Mental disengagement 10.08(2.38) 0.54 ?0.120 0.475 0.154 0.354

Use of emotional social support 11.24(3.24) 0.88 ?0.119 ?0.058 0.996 0.094

Use of instrumental social support 11.27(2.73) 0.81 0.195 ?0.054 0.646 0.117

Focus on venting emotions 10.00(3.11) 0.82 0.088 0.203 0.757 ?0.292

Positive reinterpretation and growth 12.00(2.63) 0.83 0.195 ?0.144 0.104 0.670

Humor 9.40(3.13) 0.88 ?0.060 0.303 ?0.119 0.616

Acceptance 10.81(2.26) 0.63 0.156 ?0.026 ?0.059 0.513

Note: Factor loadings were considered meaningful if they fell above the criterion value of 0.30 (Tabachnick & Fidell, 2013).

C.L. Thomas et al. / Learning and Individual Differences 55 (2017) 40–48 43

Emotional intelligence was positively related to social-, emotion-, and

problem-focused coping strategies and negatively related to avoidance

strategies. Worthy of discussion, the first-year GPA values were unrelated to EI, CTA, and coping strategies, which led to attention to the value of

first year GPA as a predictor in our primary analysis.

3.2. Hierarchical regression analysis

To more directly explore the iterative as well as the collective influence of the independent variables on four-year GPA, two theoreticallydriven hierarchical multiple regression analyses were conducted and

examined. Hierarchical regression analysis was chosen as the primary

analysis technique in the current examination because the method

afforded two primary benefits. First, the procedure allowed us to evaluate the unique influence of each of the independent variables on the

outcome of interest. Second, the use of hierarchical regression analysis

allowed for the simultaneous investigation of the incremental validity

of the predictor variables. That is, the use of hierarchical regression permitted us to examine the extent to which a

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