Journal of the Society for Social Work and Research July 2011

Volume 2, Issue 2, 104-124 ISSN 1948-822X DOI:10.5243/jsswr.2011.6

Journal of the Society for Social Work and Research 104

The Empathy Assessment Index (EAI):

A Confirmatory Factor Analysis of a Multidimensional Model of Empathy

Cynthia A. Lietz

Karen E. Gerdes

Fei Sun

Jennifer Mullins Geiger

M. Alex Wagaman

Elizabeth A. Segal

Arizona State University

Both historically and currently, social work and related fields have expressed considerable interest in the

construct of empathy. However, the ability to define and ultimately measure empathy is limited. This project

validates a revised version of the Empathy Assessment Index (EAI), which is a measure rooted in a social

cognitive neuroscience conceptualization of empathy. To evaluate the instrument‘s psychometric properties, we

administered the 50-item EAI with a five-component model of empathy to a sample of 773 undergraduate

students and community members. We evaluate the EAI psychometrics conducting internal consistency, test-

retest, and confirmatory factor analyses. Findings indicate that a 17-item five-factor model of the EAI offers the

best fit [CFI = .98; RMSEA = .04 (90% CI (.03; .05); WRMR = .80]. The data do not support using empathic

attitudes as a proxy for actions; however, the findings suggest the EAI functions better when measuring a four-

factor model, offering an important implication for future research.

Keywords: empathy, measurement, psychometrics, confirmatory factor analysis

The concept of empathy has a long history within

social work literature. For years, social work scholars

have suggested practitioners need to develop empathy to

understand and to respond appropriately when working

with diverse populations facing a variety of social

problems. Equally important, increased empathy in

populations served by social workers has a number of

positive outcomes, including increased satisfaction with

relationships, improved parenting, and increased social

well-being (Curtner-Smith et al., 2006; Hoffman, 2000;

Hunter, Figueredo, Becker, & Malamuth, 2007).

Although these assumptions regarding empathy remain

ubiquitous across policy and practice texts, the term

empathy lacks clarity, suggesting a need for further

conceptualization (Duan & Hill, 1996; Gerdes, Segal, &

Lietz, 2010; Pithers, 1999).

Recent advances in social cognitive neuroscience

have indicated empathy is an induction process that can

be described and, therefore, can be measured, offering

important implications regarding a conceptualization of

empathy (Decety & Meyer, 2008; Decety & Moriguchi,

2007). Social workers would benefit from having an

empathy measure that can be applied across social work

settings. For example, social work educators seek to

foster empathic responses from students whereas

practitioners might attempt to cultivate empathy within

client populations. Having a robust measure of empathy

would enable educators and practitioners working in

these varied contexts to assess levels of empathy

whatever the population of interest. In addition, because

levels of empathy can inform interpersonal interactions

across settings and purposes, a measure of empathy may

offer important applications beyond social work. This

project used confirmatory factor analysis to test revisions

of the Empathy Assessment Index (EAI), which is an

instrument designed to measure a multidimensional

model of empathy grounded in social cognitive

neuroscience (see Gerdes, Lietz, & Segal, 2011).

Social cognitive neuroscience is a relatively new,

interdisciplinary field, which examines topics that have

traditionally been of interest to social science (e.g.,

information processing, emotional regulation) using

approaches that are more typical of neuroscience such as

brain imaging (e.g., functional magnetic resonance

imaging).

Literature Review

The term empathy can be traced from early literature

informing social work (Richmond, 1917; Rogers, 1975;

Towle, 1945) to current discussions describing social

work practice (Boyle, Hull, Mather, Smith, & Farley,

2006; Hepworth, Rooney, Rooney, Strom-Gottfried, &

Larsen, 2006; Saleebey, 2009; Shulman, 2009). The

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Journal of the Society for Social Work and Research 105

historical development of the empathy concept within

social work suggests that although influential, the

construct lacks clarity and consensus (Gerdes et al.,

2010).

Some theorists have perceived empathy as a capacity

to feel the experiences of another (Kohut, 1959),

suggesting an introspective, cognitive component to

empathy. On the other hand, Rogers (1957) discussed

empathy as a skill that was demonstrated by a therapist‘s

ability to relate with a client without losing his or her

sense of self. Roger‘s work represented some of the

earliest consideration that differentiation between self

and others may be an important part of empathy. Social

developmental psychology contributed to the

conceptualization of empathy by highlighting the

possibility that empathy encompasses multiple

dimensions (Cliffordson, 2002; Davis, 1996), including

components such as awareness of self and others (Batson

et al., 1997) and emotional regulation (Eisenberg at al.,

1994). Hoffman (2000) added to this discussion and

suggested empathy was not simply multidimensional, but

may also represent a developmental process, progressing

from automatic mimicry (Iacoboni, 2009) to cognitive

processing that involves the ability to imagine the

experiences of another.

More recently, Decety and Jackson (2004)

demonstrated that observable brain activity was linked to

four subjectively experienced components of empathy.

The first component is affective sharing, which

comprises automatic reactions based on a person‘s

observation of another. The second, self-awareness, is a

person‘s ability to differentiate the experiences of another

from his or her own. The third component is perspective

taking, which is the cognitive process of imagining the

experiences of another. The fourth component is emotion

regulation that is described as a person‘s ability to sense

another‘s feelings without becoming overwhelmed by the

intensity of this experience. Decety and Moriguchi‘s

(2007) descriptions of these four components increased

the clarity of the overall conceptualization of empathy.

Recent thinking within social work has suggested a

fifth potentially important component to empathy. Gerdes

and Segal (2009) have contended that empathy goes

beyond automatic responses, suggesting the induction

process culminates in a conscious course of action.

Eisenberg (2006, p. 71) referred to this idea as ―empathy-

related responding,‖ asserting the existence of a

relationship between empathy and prosocial behavior. To

measure this fifth component, the EAI includes a proxy

for empathic action defined as empathic attitudes. The

empathic attitudes component is of particular interest to

social work because the component suggests that

empathic attitudes may increase the likelihood for taking

empathic action.

Considering the many variations in how empathy is

conceptualized across disciplines, social work often

serves as a bridging discipline. For example, many

psychologists have described empathy as an automatic

emotional response to others‘ behavior or emotions

(Freedberg, 2007). However, social work incorporates an

interdisciplinary approach to understanding empathy as a

biological, emotional, and cognitive response. In

addition, social work is interested in how empathy can be

developed and cultivated among professionals and client

populations. In this way, as with other concepts within

social work, empathy can be conceptualized by drawing

from the strengths of other disciplines to create a holistic

approach.

Measuring Empathy

Efforts to conceptualize and ultimately measure

empathy have been informed by theoretical developments

and advances in neuroscience. However, no instrument

designed to measure empathy has used a four-factor

model grounded in neuroscience, nor have other

measures incorporated the empathic attitudes component

that is relevant to social work. Moreover, previous

measures fell short regarding their psychometric

properties. For example, one of the earliest and most

widely used multidimensional measures of empathy,

Davis‘ (1980) Interpersonal Reactivity Index (IRI)

includes four factors: perspective taking, fantasy,

empathic concern, and personal distress. Critics of this

instrument have argued that the personal distress and

fantasy factors were inadequate to assess levels of

empathy, and that the instrument measured sympathy

rather than empathy. In addition, the IRI was not

validated by further statistical analysis (Cliffordson,

2002). Similarly, in an evaluation of Hogan‘s (1969)

Empathy Scale, Froman and Peloquin (2001) concluded

that the model suffered from questionable test-retest

reliability, low internal consistency, and poor replication

of factor structure.

Measures of empathy that have been developed more

recently have demonstrated improved psychometric

properties; these measures include the Toronto Empathy

Questionnaire (Spreng, McKinnon, Mar, & Levine,

2009); the Basic Empathy Scale (Joliffe & Farrington,

2006); the Ethnocultural Empathy Scale (Wang et al.,

2003); the E-Scale (Leibetseder, Laireiter, & Köller,

2007); and Hojat et al.‘s (2001) Jefferson Scale of

Physician Empathy. However, some notable limitations

may prevent these measures from fully capturing an

accurate and multidimensional representation of

empathy. For example, the Toronto Empathy

Questionnaire (Spreng et al., 2009) presents empathy

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primarily as an emotional process and does not account

for cognitive components required for perspective taking

and self–other awareness. Conversely, although Hogan‘s

(1969) Empathy Scale has been widely used as a measure

for cognitive empathy, this scale fails to incorporate the

critical component of emotion. Other instruments

measure empathy within a specific group, such as the

Jefferson Scale of Physician Empathy (Hojat et al., 2001)

or individuals‘ empathy toward people of different

racial/ethnic backgrounds (Wang et al., 2003). Although

important contributions, the scales reviewed here do not

offer a measure of empathy with wide-reaching

applicability, nor do they reflect the understanding of

empathy based in neuroscience.

The ability to measure empathy in social work

practice settings is relevant to client assessment and the

evaluation of evidence-based practice. For example, low

levels of empathy have been linked to delinquent and

aggressive behavior in adolescents (de Kemp, Overbeek,

De Wied, Engels, & Scholte, 2007) and sex offenders

(Varker & Devilly, 2007; Whittaker, Brown, Bekett, &

Gerhold, 2006). An empathy measure can help guide

treatment planning and evaluation of treatment goals in

settings within social work and within other disciplines

including education, psychology, and medicine.

Development of the EAI

The EAI was developed to overcome the limitations

of current measures of empathy by incorporating five

components of empathy: affective response, self–other

awareness, perspective taking, emotion regulation, and

empathic attitudes. As described by Gerdes and

colleagues (2011), survey design protocols were followed

in the development of items for each of the five

components (DeVellis, 2003). After an exhaustive

literature review on each component and a review of

current items from existing measures of the five

constructs, the researchers composed their own unique

items. The goal was to follow Sartori and Pasini‘s (2007)

recommendation that item generation should seek to

achieve content validity by creating items that flow

logically and theoretically from the conceptualization of

each component.

Pilot Version of the EAI. The pilot version of the

EAI was a 54-item survey that was administered in

October 2009 to a nonrandom sample of 312 students

(63% response rate; Gerdes et al., 2011). Although this

initial version of the EAI demonstrated some promise, a

confirmatory factor analysis (CFA) could not be

conducted on the five-component pilot data because the

self–other awareness items had unacceptable reliability

analysis (α =.299). Therefore, the researchers performed

an exploratory factor analysis (EFA) using the maximum

likelihood extraction method with oblique rotation.

The EFA results indicated that in addition to

reconceptualizing the self–other awareness construct,

several items on the emotion regulation component

needed revision or elimination, and the affective response

component needed modification. Before the current data

were collected, the researchers improved the EAI by

eliminating items that did not contribute significant

information to the index and, when needed, developing

new items that, in turn, would be further tested. (See the

Method section for a description of how new items were

generated). This article is based on a second round of

data collected in September 2010, to validate the

psychometric properties of a revised version of the EAI.

Method

Revising the EAI Based on EFA Pilot Study, Focus

Groups, and Expert Reviews

Revisions based on the EFA pilot study. Before

collecting data for the CFA of the EAI, the researchers

returned to the social cognitive neuroscience literature to

reconceptualize the items for the self–other awareness

component, to modify the affective response component,

and to add new items to the emotion regulation and

perspective-taking components. In the EAI pilot test, the

self–other awareness component was broadly interpreted

with an overemphasis on boundaries. For example, the

pilot EAI included five self–other awareness items such

as ―Getting ‗over-involved‘ in other people‘s lives

describes me well,‖ and ―When a friend has a problem, I

am supportive, but let him/her handle it.‖ In addition, one

self–other awareness item was unintentionally double-

barreled: ―I am willing to look at my own behavior and

consider how I interact with people.‖ These miscues are

likely responsible for the pilot self–other awareness

component‘s Cronbach alpha of .299.

The researchers reevaluated the self–other awareness

component after carefully considering two important

aspects of the component. First, self–other awareness

allows the empathizer to track the origins of his or her

feelings triggered during the affective arousal phase

(Lamm, Batson, & Decety, 2007). Second, self–other

awareness enables the empathizer ―to suspend one‘s own

experience in order to conjure up the thoughts and

feelings of others‖ (Mitchell, 2009, p. 1314); an ability

which is a prerequisite for perspective taking. With this

context in mind, the researchers generated 10 new self–

other awareness items, including ―I can tell the difference

between someone else‘s feelings and my own,‖ and ―I am

aware of what other people think of me.‖ We also

eliminated reverse-scored items in the self–other

awareness component because negative items tend to

load on a different factor than positive-worded items

(Brown, 2003), and we decided these reverse scored

items were not necessary for this component.

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The emotion regulation component also required

modification because the pilot version items were

insufficient. Although we did not choose reverse-scored

items for self–other awareness, we considered the

approach important for the emotion regulation

component. Regulating emotions is about changing the

way people think, which, in turn, changes the way they

feel; overall, a complex cognitive process (Ochsner,

Bunge, Gross & Gabrieli, 2002). Because cognitive

components of empathy require increased thought,

measurement must reflect that complexity and, therefore,

be more cognitively challenging. Developing these items

required creating a cognitive challenge without creating

undue confusion. Brown (2003) recommended that when

reverse-scored items are considered important,

researchers should use equal numbers of positively and

negatively worded items to better control for method

effects. Following this reasoning, the revised emotion

regulation component included five negatively worded

items and six positively worded.

In the next phase of the revision, we sought to

further clarify and refine the number of items in each

component by seeking feedback from focus groups and

content experts.

Revisions based on the focus groups. We

conducted focus groups with social work students and

community professionals to obtain their feedback

regarding the overall experience of taking the EAI as well

as to gather feedback on specific index items. Six focus

groups were conducted with 49 participants; group size

ranged from six to 15 participants. Four groups included

social work students (n = 24) and two groups were

conducted with social work community professionals (n

= 25) who were recruited from the School of Social

Work‘s field instructor list. Before focus group

discussion started, each participant independently

completed the online version of the EAI. The focus group

participants then provided feedback through a structured,

facilitated dialogue that was documented by a note taker.

Participants identified several challenging aspects of

items, including items deemed difficult to answer using

the given the response options; items for which the

meaning of a word or phrase was unclear or could be

interpreted in multiple ways; and items that were too

broad, which hindered participants‘ ability to provide a

concrete response. Participants also provided feedback

regarding the online process of administration. The

sample was chosen based on convenience and because

social workers receive training in empathy, making the

perspectives of the study sample especially beneficial.

However, that same training is also a possible source of

bias, and must be considered a limitation of this

approach.

Revisions based on the expert reviewers. The

expert reviewers included a sample of three researchers

identified as experts within the area of empathy. The

experts were selected using the following criteria: (a) the

person has published substantial original research in the

area of empathy or self-report instrumentation, and (b)

the person has credentials in developmental psychology,

social cognitive neuroscience, or social work. Each of the

expert reviewers was asked to comment on face and

content validity for the overall measure and identify any

wording and items he or she thought could be

problematic. The feedback from the expert reviewers was

similar to the feedback from focus groups regarding

problems with clarity of specific items.

Two expert reviewers and several focus group

participants recommended the researchers switch from a

5-point Likert scale to a 6-point scale ranging from never

(1) to always (6). The 5-point scale had been chosen for

the EAI based on guidelines for scale development

(DeVellis, 2003), and to provide enough categories to

allow for a meaningful variation in the answers. The

research team debated between 5- and 6-point scales,

opting to go with five response choices in the pilot

administration. The choice for an odd number allowed for

a central or neutral point. However, focus group feedback

indicated the response options did not represent an evenly

divided continuum; specifically, respondents noted that

the responses of frequently and always represented a

greater separation of responses than the other categories

(i.e., these responses were ―further apart‖ than others).

The experts and participants suggested almost always as

a sixth choice. Therefore, the current version of the EAI

offers a range of six responses. An even number of

choices ―forces the respondent to make at least a weak

commitment in the direction of one or the other extreme‖

(DeVellis, 2003, p. 77) and, as such, the variability in

answers may be more meaningful.

Revision of the EAI. Based on the EFA of pilot data

and feedback from the focus groups and expert reviewers,

the revisions of several items and generation of new

items produced a final revision of the EAI that was ready

for testing. To obtain the most valid and useful results

possible, the research team created nine to 11 items

(some potentially redundant) for each component. In a

―survival of the fittest‖ process, researchers understood

that the items that induced the most reliable and valid

scores would emerge from the reliability and factor

analyses. This first iteration of the EAI included 50 items

and five components: (a) affective response comprised 10

items; (b) perspective taking comprised nine items; (c)

self–other awareness comprised 10 items; (d) emotion

regulation comprised 11 items; and (e) empathic attitudes

comprised 10 items. (See Appendix [p. 124] for a copy of

the EAI). The survey also gathered data on eight

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demographic items: sex, age, education level,

race/ethnicity, major (if the respondent was a student) or

occupation (if respondent was not a student), family-of-

origin‘s socioeconomic status, and current household

income.

CFA Phase: Participants, Procedures, and Data

Collection

The Arizona State University Institutional Review

Board granted the research team permission to recruit

students and nonstudents to voluntarily participate in the

testing the revised EAI. Because one objective of

developing the EAI was for the instrument to have

applicability across multiple settings, we used a

convenience sampling approach and recruited undergrad-

uate students from a variety of majors as well as recruited

some participants who were not students but who

represented diverse professional backgrounds.

To recruit student participants representing diverse

fields of interest, the researchers approached under-

graduate students in several general studies courses in

which students with a variety of majors enroll. These

courses included three course sections of Introduction to

Social Work; two course sections of Statistics for Social

Workers; two sections of The Living World, an

introductory-level biology course; and one section of

Macro Economic Principles. When recruiting the

students and explaining the study, the EAI instrument

was referred to as a ―human relations survey‖ to avoid

social desirability or reactivity that may have occurred if

participants were given the names of each measure

included in the research. With the exception of one

instructor, all instructors of the above courses agreed to

offer extra credit to students who participated in the

research survey; the economics instructor chose not to

offer extra credit for participation. The offer of extra

credit was a useful recruitment strategy because it served

as an effective incentive to participate. However, this

inducement to participate may represent some bias in the

reporting.

The researchers sent an e-mail invitation to

participate to the 935 undergraduate students enrolled in

the target courses; those interested in participation could

do so by clicking on a hyperlink to a Qualtrics-based

survey. Qualtrics is an online survey software package

that allows participants to access a website with the

revised EAI at their own convenience. The students were

told the survey was voluntary, and that the survey would

remain active for 72 hours from the time the invitation

was issued. Of the 935 students contacted, 688 students

(74% response rate) completed the first administration of

the index. Four days after the first administration of the

EAI, 695 students (all students in the social work classes)

were asked to take the survey again. For this retest, 454

students (65%) completed the second administration of

the index.

In addition, the research team generated a

convenience snowball sample of 85 nonstudents. The

rationale for including nonstudents held that because the

EAI was intended for use with the general population,

expanding the research to include community

professionals (i.e., nonstudents) had the potential to

increase confidence regarding the robustness of the

index. The nonstudents were also contacted via e-mail,

and invited to participate in the study by clicking on a

link to the Web-based survey. In addition, these potential

participants were invited to forward the invitation and

survey link to colleagues who might be interested in

participating. The sample of nonstudents lived in five

states: Arizona, Illinois, Wisconsin, Colorado, and South

Carolina. Nonstudent participants represented 12

occupations, ranging from construction to finance, with

the majority working in the fields of public health and

social services. The snowball sampling strategy was used

to help increase diversity in the nonstudent sample.

The students and nonstudents were combined

because no important differences were found on the EAI

scores of these subsamples. This finding is explained in

more detail in the Results section. The final analytic

sample included 773 useable surveys for the CFA and

429 surveys for the test-retest analysis. The sample size

(N = 773) met the commonly agreed-on rule that each

item should have 10 or more participants (Nunnally &

Bernstein, 1994). Kim (2005) indicated that the

estimation of sample sizes in structural equation

modeling, given a provided power level, depends on the

number of variables or degrees of freedom, the strength

in relations among variables, and the choices of a

particular fit index (e.g., comparative fit index [CFI], root

mean square error of approximation [RMSEA]). For

example, a five-factor CFA analysis with 15 items needs

417 participants to ensure a power of .80 to detect a CFI

value of .95 or above, whereas the same CFA needs only

185 participants to have the same level of power to

conclude a RMSEA value of .05 or less. The number of

required sample sizes in SEM decreases as the number of

variables and the strength in the relations among these

variables increase (Kim, 2005). For these reasons, the

current sample size (N = 773) appeared to entail suffi-

cient power to detect the differences between the

covariance matrix derived from the data and that from the

hypothesized model.

Additional Measures

Cognitive Emotion Regulation Questionnaire and

the Mindfulness Attention Awareness Scale. The retest

of the EAI (i.e., second round of administration with

n = 429 social work students) included additional items

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from two other instruments. In addition to the EAI items,

the retest round included nine items from the short-form

of the Cognitive Emotion Regulation Questionnaire

(CERQ-short; Garnefski & Kraaji, 2006) and eight items

from the Mindfulness Attention and Awareness Scale

(MAAS; Brown & Ryan, 2003). The intention was to

keep the number of items on the survey to a minimum

while retaining enough data to demonstrate concurrent

validity for the emotion regulation and self–other

awareness components.

The 18-item CERQ-short focuses on measuring

emotion regulation or coping strategies; this scale does

not include items that focus on a person‘s ability to

identify his or her emotions or to distinguish emotions.

The CERQ-short has demonstrated Cronbach's alphas

ranging from .62 to .85 and good factorial validity,

discriminative properties, and construct validity

(Garnefski & Kraaji, 2006).

The MAAS is a 15-item scale designed to measure a

person‘s open or receptive awareness and attention to

what is occurring in the present. Further, the MAAS has

proven to be predictive of self-regulation and self-

awareness (Creswell, Way, Eisenberger, & Lieberman,

2007).The MAAS has been validated with student and

general populations, yielding a Cronbach's alpha of .81

and strong psychometric properties, including convergent

and divergent validity with other measures of

psychological well-being (Brown & Ryan, 2003).

Both CERQ-short and MAAS have been validated in

several studies, with diverse samples (Brown & Ryan,

2003; Carlson & Brown, 2005; Garnefski & Kraaij, 2007;

Jermann, Van, d'Acremont, & Zermatten, 2006; Zhu et

al., 2008). Based on theoretical expectation, we

hypothesized that the emotion regulation component of

the revised EAI would correlate with the CERQ-short

items. In addition, based on previous research and

theoretical expectation, we hypothesized that both the

EAI components of emotion regulation and self–other

awareness would correlate with the MAAS items.

Analytic Procedures

This study involved three types of analyses: missing

value analysis, descriptive analyses, and psychometric

analyses. The Qualtrics-based data were first uploaded to

PASW 18 (formerly SPSS) for analysis. Two items were

eliminated (i.e., Q42, emotion regulation, and Q49,

empathic attitudes) because of problems that occurred

when the items were uploaded to Qualtrics. Participants

were not able to view these items properly and, therefore,

did not provide responses for these items. Twenty-one of

the 48 items had one to three missing cases, which would

result in a loss of 27 cases when using the listwise

deletion method. Following recent recommendations

(Allison, 2003; Peugh & Enders, 2004) that listwise

deletion method is prone to biased estimates, we used the

expectation maximization algorithm (Schafer & Graham,

2002) to impute the missing values. A total of 773 cases

with complete information were read in PASW 18 for

descriptive analyses.

Consistent with our intention to refine the EAI, we

conducted psychometric analyses (reliability and validity

tests) to consider the EAI factor structure and determine

the best model fit. We first performed internal

consistency reliability tests to examine the inter-item

relationship and identify items that would increase the

alpha if deleted.

On the basis of the reliability test, CFAs were

performed using Mplus to identify the best measurement

model of the EAI. Mplus was chosen because this

software provides several estimation methods to deal

with ordinal-level data, and we used the default

estimation method WLSMV (weighted least square with

mean- and variance- adjusted chi-square tests) in our

analysis (Muthén & Muthén, 1998-2010).

The WLSMV method generated a weighted matrix

based on the asymptotic variance and covariance of

polychoric correlations of the observed items (Flora &

Curran, 2004). A two-step CFA approach adopted in

previous scale validation studies (e.g., Brown, 2003) was

used. The two-step process is more feasible than a study

replication in that the two-step process enables

researchers to run CFA independently on both samples to

compare and confirm the results (Schumacker & Lomax,

2004).

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Journal of the Society for Social Work and Research 110

Table 1

Description of Sample (N = 773)

Participants were randomly divided into two

subsamples of comparable size (n = 389 and n = 384).

With the exception of one item, (i.e., Q13), the second

subsample had a slightly higher mean score than the

first subsample; no significant differences were found

in the mean scores of the other 48 items. Model

respecifications were made based on CFA results on the

first half sample before the ideal model fit was

identified. The final version of the EAI scale was tested

on the second half of the sample. There seems to be no

established rules regarding the cutoff values for fit

indices (Marsh, Hau, & Wen, 2004). Given the lack of

Variable f (Valid %) Students Community

Professionals

Sex

Male 196 (25.6%) 176(25.6%) 20(23.5%)

Female

568 (74.4%) 505(74.4%) 63(76.5%)

Race/Ethnicity

African American 47 (6.1%) 41 (6.0%) 6 (7.1%)

American Indian 17 (2.2%) 17 (2.5%) 0 (0%)

Asian American 21 (2.7%) 17 (2.5%) 4 (4.8%)

Caucasian 450 (58.7%) 381 (55.8%) 69 (82.1%)

Latino 130 (16.9%) 128 (18.7%) 2 (2.4%)

Mixed Race 51 (6.6%) 48 (7.0%) 3 (3.6%)

Other

51 (6.6%) 51 (7.5%) 0 (0%)

Major

Social Work 151 (22.3%)

Criminal Justice 114 (16.9%)

Psychology 63 (9.3%)

Nursing 26 (3.8%)

Education 31 (4.6%)

Sociology 18 (2.7%)

Undecided 28 (4.1%)

Other

245 (36.2%)

Employment Field

Education, Health, Social Services 41 (55.4%)

Public Administration or Management 13 (17.6%)

Retail 9 (12.2%)

Arts and Entertainment 3 (4.1%)

Finance, Insurance, Real Estate 1 (1.4%)

Other 7 (9.3%)

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rules, we used the following standards as guidelines for

a good model fit: normed chi-square (NC; i.e., the ratio

of chi-square vs. df ≤ 2.00); CFI ≥ .95; the weighted

root mean residual (WRMR < .95); and RMSEA < .06

with a confidence interval; Hu & Bentler, 1999;

Schreiber, Stage, King, Nora, & Barlow, 2006; Yu,

2002). Finally, the test-retest reliability and convergent

validity analyses were conducted using the whole

sample data (N = 773) to provide additional evidence of

the psychometric strength of the 17-item EAI index

Results

Sample Description

Table 1 describes the gender and race/ethnicity of

the sample (N = 773) as well as the major field of

student participants and occupation of community

professionals. The average age of the sample was 21.37

years. Although students were heavily recruited from

social work courses, the student participants

represented a variety of majors, with only 22% of the

students identifying social work as their major.

Considering the EAI was being developed for use

beyond social work, the research team was pleased that

most participants were involved in occupations or

majors beyond social work. However, a limitation is

that the sample was primarily female (74.4%),

Caucasian (58.7%), and over represents an

undergraduate perspective.

Mean Differences Across Demographic Variables

and the Five EAI Components

The use of t-tests indicated no statistically

significant differences existed between the student and

professionals groups across the means of the five EAI

components. However, t-test findings indicated two

statistically significant findings based on sex

differences. Females scored higher on self–other

awareness (p = .046) and perspective taking (p = .025).

These results are not unusual in that females tend to

score higher than males on self-report empathy

measures (Jolliffe & Farrington, 2006).

One-way ANOVA results identified no statistically

significant differences across racial/ ethnic groups on

four of the five components (i.e., affective response,

self–other awareness, perspective taking, and emotion

regulation). However, African Americans (41.19) and

Latinos (41.06) scored higher than Caucasians (37.74)

on the empathetic attitude component (p = .001).

Students identifying as social work majors also scored

higher on empathic attitudes (F = 7.667, p = .001) than

students who identified other majors. However, no

other statistically significant differences were found

across majors. Finally, participants who grew up in

―poor or working class‖ families scored higher on the

perspective-taking component (F = 6.885, p = .001) and

empathic attitudes (F = 7.667, p = .001) component

than participants who grew up in middle class, upper

class, or wealthy families. The ANOVA results indicate

that among the study sample, people of color, people

who grew up in lower income families, and students

majoring in social work majors had higher levels of

empathic attitudes than their counterparts.

Internal consistency reliability analysis. An in-

ternal consistency analysis was performed on the 50-

item, five-component EAI using Cronbach‘s alpha. The

results of the analysis for each component were as

follows: affective response (α = .84); perspective taking

(α = .82); emotion regulation (α = .72); self–other

awareness (α = .70); and empathic attitudes (α = .81).

These results indicate excellent internal consistency for

the affective response, perspective taking, and empathic

attitudes components (Streiner, 2003). The emotion

regulation and self-other awareness components had

acceptable internal consistency. Parenthetically, the

CERQ-short and MAAS items had Cronbach‘s alpha

scores of .66 and .75, respectively. Emotion regulation

and self–other awareness scales have traditionally had

more difficulty achieving excellent internal consistency

(see Corcoran, 1982). Therefore, the research team

concluded the levels of internal consistency on the

emotion regulation and self-other awareness compo-

nents were reasonable.

The items within components that, if deleted,

would result in higher alphas were flagged and

considered for elimination. In all, eight items were

eliminated based on the reliability analysis, particularly

interitem correlation coefficients and ―alpha if item

deleted‖ results. The eliminated items were Q4, Q10,

Q11, Q13, Q15, Q25, Q28, and Q48.

CFA

After these items were eliminated, the CFA was

conducted on the remaining 40 items using Mplus

software. Consistent with Byrne‘s (1989) suggestion

that model specification should be based on theory as

well as the modification indices, we compared

several alternative models on the first half of the

sample (n = 389). The CFA models and results are

shown in Table 2.

LIETZ et al.

Journal of the Society for Social Work and Research 112

Table 2

Model Fit Indices of the CFA on the First Half of the Sample (n = 389)

Model Details 2 /Df 2 /Df

Ratio

CFI WRMR RMSEA

(90% CI)

40 item five-factor model 2943.00/730** 4.03 .77 2.19 .088(.085;.092)

24-item five-factor model 694.11/242** 2.87 .92 1.33 .069(.063;.075)

24 item five-factor model with eight

error covariance added

400.14/234** 1.71 .96 .94 .043(.035;.050)

17 item five-factor model 213.70/109** 1.96 .97 .88 .05(.040; .060)

17 item five-factor model with

correlated error

185.16/107** 1.73 .98 .80 .04 (.033; .054)

** p < .01

The 40-item five-factor model was not a good fit

(CFI = .77, WRMR = 2.19, RMSEA = .088). An addi-

tional 16 items were eliminated based on the findings for

the 40-item model, including small factor loadings (i.e.,

under .40), cross-loadings (items that could load on more

than one factor), highly correlated items, and items that

could cause the highest expected decrease in chi-square

values according to the modification index (Kline, 2010).

The eliminated items were Q14, Q18, Q32, and Q40 from

the affective response component; items Q6 and Q23

from the self-other awareness component; items Q7, Q31,

Q39, and Q45 from the emotion regulation component;

items Q1, Q34, and Q37 from the perspective-taking

component; and items Q5, Q19, and Q26 from the

empathic attitudes component. The researchers then

tested a 24-item measure using the five-factor model.

However, based on the ―good-fit criterion,‖ the 24-item

five-factor model was still not an ideal fit (CFI = .92;

WRMR = 1.33; and RMSEA = .069). Therefore, in

keeping with the model modification index, several

measurement error covariances were added in the five-

factor model. The 24-item five-factor model with eight

error covariances added improved the model fit (CFI

=.96, WRMR =.94; RMSEA =.043).

The multiple error covariances indicated that the

items could be further refined. Therefore, seven more

items were removed (Q9, Q38, Q41, Q22, Q8, Q12, and

Q33). These items were removed based on the modifica-

tion index and the possibility of cross-loadings. For

example, the measurement error of Q8 is correlated with

the measurement error of Q2 and Q3; indicating Q8

might share some commonality with Q2 and Q3, each of

which represents a different latent construct. To avoid

item redundancy, Q8 was eliminated. As shown in Figure

2, the 17-item five-factor model was a good fit (CFI .97,

WRMR =.88; RMSEA =.05). The 17-item five-factor

model comprised three affective response items, three

self-other awareness items, four perspective-taking items,

four emotion regulation items, and three empathic

attitude items. As expected, all the factor loadings on the

five latent constructs were statistically significant (see

Table 3). Further review of the model fit modification

indices suggested an addition of two error correlations

(i.e., Q24 and Q20, Q43 and Q36). Both item Q20

(―Watching a happy movie makes me feel happy‖) and

item Q24 (―When I am with a happy person, I feel happy

myself‖) assessed affect sharing and loaded on affective

response, suggesting the influence of external forces on

personal affect. Although item Q36 (―Friends view me as

a moody person‖) and item Q43 (―I can imagine what a

character is feeling in a well-written book‖) loaded on

different factors (i.e., emotion regulation and perspective

taking, respectively), both items tap sensitivity to

emotions. For these reasons, the research team considered

the two error correlations to be conceptually sound.

Adding these two error covariances improved the model

fit (CFI =.98; WRMR =.80; and RMSEA=.04).

THE EMPATHY ASSESSMENT INDEX

Journal of the Society for Social Work and Research 113

Table 3

Confirmatory Factor Analysis of the 17-Item Model on the First Half of the Sample

Latent and Observed Variables CFA Model

R2

Affective Response (AR)

Q20 .69 .48

Q24 .80 .64

Q44 .76 .58

Self–Other Awareness (SOA)

Q17 .69 .48

Q21 .69 .48

Q29 .66 .44

Emotion Regulation (ER)

Q27 .63 .40

Q35 .62 .38

Q36 .46 .21

Q47 .61 .37

Perspective Taking (PT)

Q2 .41 .17

Q30 .68 .46

Q43 .66 .44

Q50 .67 .45

Empathic Attitudes (EA)

Q3 .38 .14

Q16 .89 .79

Q46 .77 .59

Note. is the standardized factor loading of the observed variable on the latent construct. The

CFA also yielded results regarding the relationships among the five components (see Table 4).

As expected, the five factors are indeed inter-correlated with each other (p < .01).

LIETZ et al.

Journal of the Society for Social Work and Research 114

Table 4

Standardized Correlation Coefficients Among Latent Factors on the First Sample

Affective

Response

Self–Other

Awareness

Emotion

Regulation

Perspective

Taking

Empathic

Attitudes

Affective

Response 1.00 .58** .32** .54** .18**

Self–Other

Awareness 1.00 .58** .86** .23**

Emotion

Regulation 1.00 .62** .19**

Perspective

Taking 1.00 .20**

Empathic

Attitudes 1.00

**p < .01

Finally, the 24-item and 17-item five-factor models

were applied to the second half of the sample (n = 384).

As shown in Table 5, the model fit was satisfactory for

the 17-item five-factor model with two error covariances

added (CFI = .95, WRMR = .97, RMSEA = .06). Thus,

the 17-item EAI yielded the best model fit for the data in

this study.

Table 5

Model Fit Indices of the CFA on the Second Subsample (n = 384)

Model Details 2 /Df

2 /Df

Ratio

CFI WRMR RMSEA

(90% CI)

24 item five-factor model 694.11/242** 2.87 .87 1.55 .08(.077; .089)

17 item five-factor model 301.68/109** 2.77 .93 1.07 .07(.059; .077)

17 item five-factor model with

error covariance

185.16/107** 1.73 .95 .97 .06(.050; .069)

**p<.01

Comparisons Across Demographic Variables and

Components of the 17-item EAI

The use of t-tests indicated there were no statistically

significant differences between students and nonstudents.

The self–other awareness component approached

statistical significance with females scoring higher

(difference = .493, p = .061). One-way ANOVA results

indicated that the only statistically significant difference

among race or ethnic groups was on the empathic attitude

component (F = 14.01, p = .001) on which African

Americans (m = 12.68) and Latinos (m = 12.55) scored

higher than Caucasians (m = 11.45). Likewise, the only

statistically significant difference between college majors

was found for the empathic attitude component (F =

8.508, p = .001) with social workers having the highest

mean (m = 12.87). Psychology majors had a mean of

12.15, criminal justice 11.59, sociology 11.55, education

11.45 and nursing had a mean of 11.37. Empathic attitude

was the only component for which a statistically

significant difference was found for family-of-origin

socioeconomic status (F = 7.426, p = .001). Participants

who identified as ―poor or working class‖ (m = 12.22)

scored higher on the empathic component than

participants who identified as ―middle class‖ (m = 11.69),

―upper-middle class‖ (m = 11.29), or ―wealthy‖ (m =

10.47).

The mean difference in the total 17-item EAI score

by sex was statistically significant (p = .05), with females

having higher scores. However, when the empathic

attitude component was removed, the statistically

THE EMPATHY ASSESSMENT INDEX

Journal of the Society for Social Work and Research 115

significant difference between the sexes disappeared. No

statistically significant differences in total mean scores

were found between students and nonstudents. In

addition, the mean differences of total scores across

race/ethnicity were not statistically significant with or

without the empathic attitude component. This finding is

important because it provides evidence that the EAI may

be useful with diverse populations. Total mean

differences across majors was statistically significant

(p =. 003) primarily due to social work majors‘ high

mean (m = 75.15). However, when the empathic attitude

component was removed, the statistical significance

disappeared (p = .20).

Finally, the total 17-item EAI mean score difference

across family-of-origins socioeconomic status was

statistically significant with (p = .01) and without (p =

.024) the empathic attitude component. Highest means

were found for participants who identified as poor or

working class, and the mean scores dropped successively

as socioeconomic status increased. This finding is

consistent with recent research that has suggested socio-

economic status influences the ways in which people

experience emotion and shapes empathic accuracy

(Kraus, Cote, & Keltner, 2010; Kraus & Keltner, 2009;

Kraus, Piff, & Keltner, 2009).

Reliability and Test-Retest Analysis

Cronbach‘s alpha was used to perform an internal

consistency analysis on the 17- item EAI (α = .823). In

addition, the alphas for each component are as follows:

the affective response three-item component (α = .751);

the cognitive-based component (11 items; α = .785); and

the three-item empathic attitude component (α = .671).

These results indicate acceptable internal consistency.

Finally, a test-retest reliability study was carried out

using the data from the students who finished both

administrations of the survey within one week of each

other. All of the component or component scores for the

17-item EAI for both administrations were significantly

correlated (n = 429; p = .001). The correlation coeffi-

cients were as follows: affective response (r = .743,

Spearman‘s rho = .739); self–other awareness (r = .686,

Spearman‘s rho = .670); emotion regulation (r = .759,

Spearman‘s rho = .748); perspective taking (r = .771,

Spearman‘s rho = .776); empathic attitude (r = .792

Spearman‘s rho = .759). These are considered to be

strong correlations indicating healthy test-retest reliability

(Cohen, 1988).

Construct Validity Tests

The pilot administration of the EAI (Gerdes et al.,

2011) included items from the empathic concern and

perspective-taking components of the Interpersonal

Reactivity Index (Davis, 1980; 1983). The items were

used to demonstrate concurrent validity for the EAI‘s

affective response, perspective-taking and empathic

attitudes components. In each case, the results indicated

statistically significant correlations between the scales

with Pearson r’s ranging between .48 and .75. For the

current research, the team used correlation coefficients to

analyze the concurrent validity of two components from

the 17-item EAI: ER and self–other awareness.

The first hypothesis predicted a positive relationship

between the emotion regulation component and the

CERQ-short. The emotion regulation component and the

CERQ-short had a statistically significant correlation (n =

429, r = .507, Spearman‘s rho = .493, p = .001). These

findings indicate a moderately strong positive correlation

between the two scales (Cohen, 1988). Keep in mind that

the CERQ-short is designed to measure emotion

regulation coping strategies whereas the emotion

regulation component is designed to measure a person‘s

emotion regulation in the context of empathy. It was also

hypothesized that the four-item emotion regulation and

the three-item self–other awareness components would

be negatively correlated with the MAAS score (a lower

MAAS score indicates more mindfulness). The emotion

regulation and MAAS were modestly correlated (n = 424,

r = -.267, Spearman‘s rho = -.270, p = .001). This result

is acceptable given that the emotion regulation and

MAAS are measuring independent constructs that are

theoretically related but not identical (Creswell et. al,

2007). The self–other awareness and MAAS had a

moderate negative correlation (n = 424, r = -.396,

Spearman‘s rho = -.435, p = .001). Presumably, the

correlation would have been stronger if the researchers

had been measuring self-awareness only rather than self–

other awareness (Decety & Sommerville, 2003).

However, this expected result provides initial evidence

for construct validity.

LIETZ et al.

Journal of the Society for Social Work and Research 116

Figure 1. The 24-item CFA model

Q9

Q20

Q24

Q44

Q17

Q36

Q47

Q2

Q8

Q43

Q21

Q29

Q38

Q41

Q22

Q50

Q3

Q12

Perspective

Taking

Affective

Response

Emotion

Regulation

Self/Other

Awareness

Q30

Q27

Q35

Q33

Q16

Q46

Empathic

Attitudes

.75

.80

.98

.68

.66

.47

.72

.42

.65

.65 .47

.58

.42

.65

.63

.66 .69

.36

.55

.88

.53 .77

.62

.46

.21

.51

.31

.66

.49

.61

.90

.24

.18 .24

THE EMPATHY ASSESSMENT INDEX

Journal of the Society for Social Work and Research 117

Discussion

After examining the internal consistency of the 48-

item EAI, researchers used CFA techniques to test a 40-

item five-factor model of empathy. The 40-item EAI had

insufficient model compatibility; therefore, items were

sequentially eliminated until criteria for a good model fit

were met. Elimination was based on several criteria: (a)

non-significant factor loadings ( < .40), (b) items that

loaded significantly on more than one factor, (c) highly

correlated items (r >. 80), and (d) items that would

cause the highest expected decrease in chi-square values.

The resulting model was a 17-item five-factor model that

achieved good model fit with half the sample and

reasonable model fit with the other half of the sample.

The 17-item EAI version has excellent internal

consistency and strong test-retest reliability. Convergent

validity correlation coefficients for the CERQ-short and

the MAAS were statistically significant.

The CFA highlighted weak to moderate (.32 to .58)

intercorrelations between the affective response

component and the three cognitive components (i.e.,

emotion regulation, self–other awareness, perspective

taking). The intercorrelations between the three cognitive

components ranged from strong to very strong (.58 to

.86). Although the intercorrelations between the empathic

attitude component and the four other components were

statistically significant, the intercorrelations were very

modest (.18 to .23).

It is not surprising that the perspective taking and

self–other awareness components have the highest

intercorrelation (r = .86). The social cognitive

neuroscience literature makes it clear that accurate

perspective taking requires self–other awareness or that

―an essential part of empathy is to recognize the other

person as like the self while maintaining a clear

separation between self and other‖ (Ruby & Decety,

2004, p. 988). It is more difficult to establish

discriminant validity between two constructs that are so

closely intertwined and function simultaneously, though

separately, in the brain. However, the literature supports

the assumption that perspective taking and self–other-

awareness are isolable and observable neural networks

that operate in conjunction with each other (Decety &

Grezes, 2006; Ruby & Decety, 2003, 2004).

Self–Other awareness has a strong correlation with

both perspective taking and affective response. The same

personal boundary must be maintained to feel what the

other person is feeling (affective response) while

maintaining a clear separation between self and other

(Decety & Jackson, 2004; Decety & Meyer, 2008). Given

this reality, it might be assumed that the correlation

between the two components would be even higher. The

moderately strong intercorrelation may be the result of

the lack of content validity in the affective response

component. After elimination procedures, the three-item

affective response component was limited to one aspect

of mirroring (i.e., happiness). In the next round of testing

of the EAI, the researchers will revise the affective

response component by adding items to increase the

content validity. Suggestions for these additional items

include ―I understand other people‘s emotional signals‖

and ―I am good at judging other people‘s emotional

states,‖ which could be added to ―Hearing laughter makes

me smile‖ to bolster the dimensions of measuring

affective response.

It is not unexpected that the empathic attitude

component has the lowest intercorrelations with the other

components, with values ranging from .18 to .23. The

empathic attitude component is the only factor in the

model not grounded in the social cognitive neuroscience

literature. The empathic attitude items are a set of social

justice attitudes that are intended to be reflective of, and a

proxy for, the commitment to change and action that is

integral to the social work profession. However, based on

these results, it can be argued that attitudes are simply not

a good proxy for action. Further research is needed that

can measure action that is implemented (i.e., actually

taken) rather than estimating action through a set of

attitudes.

There are limitations to this study. The sampling

strategy for the focus groups may have overemphasized

the social work perspective, especially considering the

intended purpose was to develop the EAI as an

instrument for application across multiple disciplines and

settings. The use of convenience sampling for the

administration phase was also a limitation in that this

technique over represented the perspectives of

undergraduate students. In addition, the sample was

predominantly female and Caucasian; therefore, the

sample is not representative of the area in which the

study was conducted. Although self-report instruments

are an efficient way to collect data, this mechanism is

prone to social desirability and bias. The use of extra

credit as a participation incentive may have also affected

participant reporting. Finally, the sample is relatively

high functioning considering that all participants were

either students or currently employed. The index should

be tested on clinical samples to see whether the EAI is

applicable for high-risk groups.

LIETZ et al.

Journal of the Society for Social Work and Research 118

Figure 2. The 17-item CFA model

Note. Factor loadings and correlations between factors are reported in Table 3 and Table 4, respectively

Q20

Q24

Q44

Q17

Q36

Q47

Q2

Q43

Q21

Q29

Q50

Q3

Perspective

Taking

Affective

Response

Emotion

Regulation

Self–Other/

Awareness

Q30

Q27

Q35

Q16

Q46

Empathic

Attitudes

THE EMPATHY ASSESSMENT INDEX

Journal of the Society for Social Work and Research 119

Implications

The EAI has many useful applications in practice,

policy, and educational settings in social work and other

disciplines. Students in social work are encouraged to use

empathy in client assessment, interaction, and

interventions. However, the essential components of

empathy are not consistently incorporated into social

work curriculum (Gerdes, Segal, Jackson, & Mullins,

2011). For example, social workers are rarely taught that

the simple act of mirroring clients triggers a biological

response that can enhance the ability to develop rapport

and establish increased level of awareness of the client‘s

feeling state. Understanding the full range of empathic

abilities, including affective response, self–other

awareness, and the ability to perspective take and

regulate emotions may be helpful for practitioners. With

the use of the EAI, students and professionals can

develop better understandings of boundaries and use

these ideas to guide their practice. In addition, by having

a clear understanding of these emotional and cognitive

mechanisms, social workers may be better able to

manage stress, compassion fatigue, and burnout.

In addition, recent social cognitive neuroscience

research has described the potential for increasing brain

elasticity and the brain‘s ability to be retrained, which has

introduced important implications for clinical

intervention with clients dealing with trauma, brain

injuries, and autism. In these cases, the EAI could be

used to identify specific components of treatment and

assess progress towards goals. Similarly, for client

populations thought to have lower levels of empathy,

such as sex and interpersonal violence offenders, the EAI

offers a tool to treatment programs that are considering

how best to incorporate the new science of empathy in

ways that can be evaluated.

Conclusion

In conclusion, the findings indicate the 17-item five-

factor self-report EAI is capable of generating reliable

and sufficiently valid scores. However, the evidence also

indicates that a purely social cognitive neuroscience four-

factor model of empathy (i.e., affective response, self–

other awareness, perspective taking and emotion

regulation) may yield more useful results. The fifth

component, empathic attitudes, was used as a proxy for

action. The use of empathic attitudes, which was a social

justice extension of the social cognitive neuroscience

model of empathy, was not supported by the findings.

Therefore, before the next round of data collection, the

researchers intend to revise the EAI to a 20-item four-

factor model by eliminating the empathic attitude

component. The affective response component will be

modified to include five items that demonstrate improved

content validity over the current three-item component.

The emotion regulation component appears to be

sufficient, whereas the perspective taking and self–other

awareness components also require some revision of

terminology and possible inclusion of a new item to

strengthen the construct.

Author Note

This research was supported by a grant from the

Samuel and Lois Silberman Foundation.

Cynthia A. Lietz is an assistant professor in the

School of Social Work at Arizona State University.

Karen E. Gerdes is an associate professor in the

School of Social Work at Arizona State University;

kegerdes@asu.edu

Fei Sun is an assistant professor in the School of

Social Work at Arizona State University;

fei.sun.1@asu.edu

Jennifer Mullins Geiger is a doctoral student in the

School of Social Work at Arizona State University;

jennifer.mullins@asu.edu

M. Alex Wagaman is a second-year doctoral

student in the School of Social Work at Arizona State

University; mary.wagaman@asu.edu

Elizabeth A. Segal is a professor in the School of

Social Work at Arizona State University;

esegal@asu.edu

Correspondence regarding this article should be

sent to Cynthia Lietz at clietz@asu.edu

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Appendix

The Empathy Assessment Index (EAI) = 50 items (final 17 items are in bold)

Five components: Affective Response (AR), Emotion Regulation (ER), Perspective Taking (PT), Self–Other

Awareness (SOA), and Empathic Attitudes (EA)

____________________________________________________________________________________

Q1 I am open to listening to the points of view of others. (PT)

Q2 I can imagine what it’s like to be in someone else’s shoes. (PT)

Q3 If a person is poor, I believe it is the result of bad personal choices. (EA)

Q4 When I see a stranger crying, I feel like crying. (AR)

Q5 I believe unemployment is brought on by individuals‘ failures. (EA)

Q6 I can tell how I am feeling emotionally by noticing how my body feels. (SOA)

Q7 When something exciting happens, I get so excited I feel out of control. (ER)

Q8 I consider other people‘s point of view in discussions. (PT)

Q9 Seeing someone dance makes me want to move my feet. (AR)

Q10 When someone insults me or verbally attacks me, I don‘t let it bother me. (ER)

Q11 I am not aware of how I feel about a situation until after the situation is over. (SOA)

Q12 I believe poverty is brought on by individuals‘ failures. (EA)

Q13 When a friend is sad and it affects me deeply, it does not interfere with my own quality of life. (ER)

Q14 When I see a friend crying, I feel like crying. (AR)

Q15 I feel what another person is feeling, even when I do not know the person. (AR)

Q16 I believe adults who are poor deserve social assistance. (EA)

Q17 I am aware of my thoughts. (SOA)

Q18 When I am with a sad person, I feel sad myself. (AR)

Q19 I believe government should support our well-being. (EA)

Q20 Watching a happy movie makes me feel happy. (AR)

Q21 I can tell the difference between someone else’s feelings and my own. (SOA)

Q22 I have angry outbursts. (ER)

Q23 I have a physical reaction (such as shaking, crying or going numb) when I am upset. (SOA)

Q24 When I am with a happy person, I feel happy myself. (AR)

Q25 When I care deeply for people, it feels like their emotions are my own. (SOA)

Q26 I think society should help out children in need. (EA)

Q27 When I am upset or unhappy, I get over it quickly. (ER)

Q28 I can imagine what it is like to be poor. (PT)

Q29 I can explain to others how I am feeling. (SOA)

Q30 I can agree to disagree with other people. (PT)

Q31 I get overwhelmed by other people‘s anxiety. (ER)

Q32 When a friend is happy, I become happy. (AR)

Q33 I believe government should be expected to help individuals. (EA)

Q34 I like to view both sides of an issue. (PT)

Q35 Emotional evenness describes me well. (ER)

Q36 Friends view me as a moody person. (ER)

Q37 It is easy for me to see other people‘s point of view. (PT)

Q38 I am aware of how other people think of me. (SOA)

Q39 When I get upset, I need a lot of time to get over it. (ER)

Q40 When a friend is sad, I become sad. (AR)

Q41 I can distinguish my friend‘s feelings from my own. (SOA)

Q42 I have large emotional swings. (ER)

Q43 I can imagine what the character is feeling in a well written book. (PT)

Q44 Hearing laughter makes me smile. (AR)

Q45 I rush into things without thinking. (ER)

Q46 I think society should help out adults in need. (EA)

Q47 I watch other people’s feelings without being overwhelmed by them. (ER)

Q48 I am comfortable helping a person of a different race or ethnicity than my own. (EA)

Q49 I believe the United States economic system allows for anyone to get ahead. (EA)

Q50 I can simultaneously consider my point of view and another person’s point of view. (PT)