Measuring empathy in the 21st century: development of an empathy index rooted in social cognitive neuroscience and social justice.
KEY WORDS: affective response; emotion regulation; empathic attitudes; empathy; self-awareness
Recent breakthroughs in neuroscience, particularly social cognitive neuroscience, have given social scientists unprecedented opportunities to observe and attempt to measure various aspects of human thought, feeling, and behavior (Doidge, 2007). Among these psychological phenomena is empathy. The importance of empathy for effective social work practice is commonly accepted within the profession (Hepworth, Rooney, Rooney, Strom-Gottfried, & Larsen, 2006; Shulman, 2009) .Twenty-first century technology has allowed neuroscientists to demonstrate that empathy can be empirically observed and quantified (Jackson, Brunet, Meltzoff, & Decety, 2006; Lamm, Batson, & Decety, 2007; Schulte-Ruther, Markowitsch, Shah, Fink, & Piefke, 2008).
Zaki, Weber, Bolger, and Ochsner (2009) conducted one of the first studies to cross-check and validate the correlation between three different empathy-related phenomena: (1) the subjective experience of people telling an emotional autobiographical story, (2) the empathic response of people watching the storytellers, and (3) the brain activity of the story viewers. This study suggests that brain activity can be used as an empirical measure of affective sharing and cognitive processing and that "accurate" empathy may be physically differentiated from projection or other emotional reactions.
Such research can enrich our ability to cultivate greater empathy in social workers and their clients. As all the social sciences reorient themselves to accommodate the mounting empirical evidence of the brain's neuroplasticity (Rakio, 2002) and the interpersonal neurobiology of empathy, we would like to help open social work research to a richer, more multifaceted examination of empathy. Most social workers do not have access to technologies like functional magnetic resonance imaging (fMRI) machines, nor do they have sufficient funds for expensive and labor-intensive neurological workups of themselves or their clients. In an ideal world, social workers would have a reasonably accurate, affordable, and user-friendly way of quantifying empathy that incorporates new findings from social cognitive neuroscience.
We believe the best initial approach is the development of a self-report index that can be tested and validated by ongoing correlation of phenomenological observation, self-report responses, and specific brain activity. The findings reported in this article represent a first step toward that end. We describe a theoretical framework for the initial development and pilot application of an empathy self-report instrument, the Empathy Assessment Index (EAI). The EAI is based on a comprehensive definition of empathy that is rooted in social cognitive neuroscience, developmental psychology, and social work's commitment to social justice. Finally, we present reliability, concurrent validity, and data reduction and refinement results from the first administration of the index.
DEFINING EMPATHY: PAST, PRESENT, AND FUTURE
Before we discuss the theoretical framework for and development of our assessment index, we briefly review the history of how empathy has been conceptualized and measured in the literature. Surprisingly, there is a relative paucity of empirical research on empathy in the social work literature. Perhaps the scarcity of research can be explained, in part, by the lack of a clear and comprehensive definition of empathy. Even a cursory review of recent empathy research in social work and related fields--such as Berg, Raminani, Greer, Harwood, and Safren (2008); Busby and Gardner (2008); Forrester, Kershaw, Moss, and Hughes (2007); Green and Christensen (2006); Sale, Bellamy, Springer, and Wang (2008); and Waldinger, Schultz, Hauser, Allen, and Cromwell (2004)--reveals that definitions of empathy are not always consistent across studies. As a result of this semantic fuzziness, conceptualizations and measurement techniques for empathy vary--so much so that it has been difficult to engage in meaningful comparison or to reach significant conclusions about how we define and measure empathy and how to effectively cultivate it in social workers and clients (Pedersen, 2009; Preston & de Waal, 2002).
The Past: 20th Century Conceptualizations of Empathy
The earliest conceptualization of empathy dates back to German and American psychologists Theodor Lipps (1903/1979) and Edward Tichener (1909). Intrigued by the psychological phenomenon of imitation, they coined the word Einfuhlung, literally "in-feeling," which is Latinized as "empathy." Their articulation of empathy was indicative of people's tendency to imitate when observing another person, commonly referred to as the "other" (Davis, 1996). Empathy was conceived as both a passive reflection of the other and as an active effort to get inside the other.
The early 20th century view of empathy eventually led to the identification of two different components of empathy: (1) affect(ive) sharing or emotional empathy (Batson, 1987, 1991) and (2) perspective taking or cognitive empathy (Hoffman, 1981, 2000). Twentieth century researchers alternately used one or the other or both elements of empathy to develop instruments (Hojat et al., 2002). For example, the Hogan Empathy Scale (Hogan, 1969) focuses on cognitive empathy, the Questionnaire Measure of Emotional Empathy (Mehrabian & Epstein, 1972) emphasizes the affective component of empathy; and the Interpersonal Reactivity Index (IRI) (Davis, 1980, 1983) attempts to measure both cognitive and emotional elements of empathy. All of these measures are self-report instruments using Likert-type scales and have demonstrated some ability to generate reliable and valid data. However, over the past 10 years, a new conceptualization of empathy has emerged from the field of social cognitive neuroscience (Decety & Jackson, 2004), one that is richer and more actionable. In spite of the innovative 21st century conceptualization of empathy, more recent self-report measures of empathy, including the Toronto Empathy Scale (Spreng, McKinnon, Mar, & Levine, 2009) and the Basic Empathy Scale (Jolliffe & Farringon, 2006), have not incorporated recent findings. Our goal was to pilot an instrument that quantified and operationalized empathy in the context of the latest scientific findings.
The Present: Mirror Neurons and 21st Century Definition of Empathy
Recent research combining neuroscientific brain imagining (Gazzola, Aziz-Zadeh, & Keysers, 2006) with social cognitive neuroscience (Kaplan & Iacoboni, 2006; Lamm et al., 2007) has led to the identification of additional components of empathy and a clearer articulation of the affective and cognitive elements of empathy (Decety &Jackson, 2004; Decety & Lamm, 2006; Decety & Moriguchi, 2007). Using fMRI equipment, neuroscientists confirmed that when we see another person's actions, our bodies unconsciously and automatically respond as if we were the "actor" and not just an observer (Jackson et al., 2006). This phenomenon is called mirroring, and the circuitry of the brain responsible for this is referred to as the mirror neuron system (MNS) (Iacoboni, 2008).
When we hear people speak or watch their posture, gestures, and facial expressions, the neural networks in our brains are stimulated by a "shared representation." The result is an inner reflection or simulation of the experiences of those whom we are observing. Mirroring appears to be innate and part of human hardwiring (Iacoboni, 2008). The affective automatic and involuntary responses triggered by the MNS are only one part of empathy. Neuroscientists recognize that there are cognitive components as well.
Combining social science research on empathy with the new findings in social cognitive neuroscience, Decety and Jackson (2004), Decety and Lamm (2006), and Decety and Moriguchi (2007) developed a conceptualization of empathy based on the interaction of four "neural networks": affective sharing, self-awareness, perspective taking, and emotion regulation. These networks are empirically observable brain phenomena (Decety & Moriguchi, 2007). They are defined later, in the Conceptualizations of the Five EAI Constructs section.
These networks include both involuntary affective responses to another and voluntary cognitive aspects of processing the experience. The inclusion of self-awareness and emotion regulation as necessary components of empathy is critical. For example, observation of another person's emotional state can lead to distress rather than empathic concern if the observer has not developed the capacities for self-other differentiation and emotion regulation (Lamm et al., 2007).
Whether 20th- or 21st-century, the conceptualizations of empathy we have discussed typically end after describing what an individual needs to "feel" and "think" to experience empathy for another person. External actions by the observer or social changes as a result of people's empathy are not examined. For example, Davis (1996) regarded empathy as an experience limited to the individual. Davis (1996) himself considered this narrow focus on the individual as a limitation of his empathy model, noting that "what is lacking is a consideration of the emergent processes or outcomes resulting from the interaction of observer and target" (p. 220). For Hoffman (2000), empathy might lead to action, but not necessarily. He hoped that empathy could be linked to caring and justice principles. Iacoboni (2008) accepted that although we have neuroscientific evidence of empathy, that does not guarantee action. He hoped that "a more explicit level of understanding of our empathetic nature will at some point be a factor in the deliberate, reflective discourse that shapes society" (Iacoboni, 2008, p. 271). As social workers, we believe that empathy includes an element of what a person needs to "do." Empathic action needs to be taken to experience the full extent of empathy. In addition, we consider social empathy (Segal, 2007)--or the ability to understand the circumstances of other people's living conditions in the context of broader educational, health, and socioeconomic structures and institutions--to be an essential part of a holistic and comprehensive conceptualization of empathy.
The Future: A Comprehensive Social Work Conceptualization of Empathy
We argue that the full extent of empathy is not simply a feeling. Empathy culminates with a decision about what to do with one's aroused empathic feelings and thoughts. On the basis of the disciplinary approaches of neuroscience, social science, and social work practice, we have developed a comprehensive framework of empathy (Gerdes & Segal, 2009, 2011). The framework consists of three components: (1) the affective response to another's emotions and actions, (2) the cognitive processing of one's affective response and the other person's perspective, and (3) the conscious decision making to take empathic action. Though this is a convenient way to conceptualize empathy as a phenomenon, we understand and emphasize that empathy as experienced by humans contains intermingled affective, cognitive, and decision-making elements. Separating them is a heuristic device, like describing the wave frequency of different colors in a rainbow--just as each color of the spectrum is always present (but identifiably separate), each aspect of empathy is present but independently discernible (Gerdes, Segal, & Lietz, 2010).
The first component is affective response. This part of the model identifies the involuntary physical reactions we have that are triggered by our exposure to external events. This is the MNS. The second component is the cognitive processing of unconscious mirrored feelings and intentions. This process is voluntary and requires mental skills to interpret the physiological sensations as well as the thoughts that are triggered by mirrored experiences. The processes of self-awareness, perspective taking, and emotion regulation are all part of cognitive processing. Through cognitive processing, we can understand the experiences of others. The third component is conscious decision making. This component draws from social work and stresses empathic action. To empathically understand people means to enter into their situations in ways that reveal their feelings and can raise awareness of situations of inequality and disparity. Such awareness can lead to action that is empowering and promotes fairness.
Why Is Empathic Action a Component of Empathy? Empathic action is the result of the third component of the model, conscious decision making. Social workers need to develop empathy, but empathy alone will not allow them to generate interventions to help clients obtain a better quality of life. Empathic action requires that we move beyond affective responses and cognitive processing toward use of behavioral health values and knowledge to inform our actions and choices (Segal, 2007, in press). Having empathy includes voluntarily taking action in response to cognitive processing (perspective taking, self-awareness, emotion regulation) and affective (emotional) reaction. This imperative to act differentiates a social work model of empathy from those of other disciplines. Our practice wisdom tells us that empathy-driven actions are empowering, whereas sympathy or pity-driven actions can be at best enabling and at worst destructive to our clients.
Thus, to be empathic is to experience an affect, process it, and then take action. It is our belief that social work education can help students to develop empathy and then enable social workers, through knowledge and skills, to take appropriate, effective and empowering, empathy-driven action. The action can be at the micro or macro level. For example, personal empathy may involve efforts to help an individual, and social empathy may lead to actions such as advocacy designed to affect or generate public economic and health policies that are more fair and just.
Do We Need All Three Components to Have Empathy? Our social work framework of empathy includes the three components of affective response, cognitive processing, and conscious decision making. The extent to which people can experience all three components in any given situation varies greatly. There is a dynamic nature to empathy. Some aspects are experienced more than others. Individuals have varying levels of the skills required for effective cognitive processing and may have different strengths in their neural pathways.
Although our model is described progressively, empathy does not flow in a perfectly linear way. However, if we try to define empathy without including all three components, we dilute the meaning and power of the concept. Empathy is not only a condition, it is an action motivated by affect and cognition. With this three-part framework as the conceptual basis, we developed a self-report instrument to assess these components.
DEVELOPMENT OF THE EAI
Our three-component empathy framework encompasses five discrete constructs: affective response (AR), perspective taking (PT), self-awareness (SA), emotion regulation (ER), and empathic attitudes (EAs). A self-report questionnaire or survey format is the most feasible and appropriate approach for measuring our multiconstruct model. Multiple constructs or abilities cannot be effectively observed without constant monitoring of an individual over long periods of time (DeVellis, 2003). Therefore, we developed the EAI, a self-report measure with five subscales that uses a five-point Likert-type scale.
Conceptualizations of the Five EAI Constructs
The research team followed the protocols of survey design in the development of the EAI (Converse & Presser, 1986; DeVellis, 2003; Henerson, Morris, & Fitz-Gibbons, 1987; Patten, 1998). Specifically, we began by conducting an exhaustive review of the literature on the conceptualization and measurement of empathy (Gerdes et al., 2010). The conceptualization literature helped us compose concise conceptualizations for each of the EAI'S five constructs.
Decety and others have confirmed that AR is an innate neurobiological capacity that can be cultivated and increased (Decety & Lamm, 2006; Decety & Moriguchi, 2007). PT, SA, and ER are cognitive abilities that emerge developmentally and can be strengthened or improved (Preston & deWaal, 2002). EAs constitute a set of socially responsible empathic beliefs. People who identify with these beliefs are more supportive of intervention to address poverty and perhaps more likely to take individual action as well. Given this context for the EAI'S five constructs, we define each one here.
AR is the capacity to mirror and accurately identify the feelings of others. AR is one's involuntary physiological reactions and feelings when observing or listening to another person, watching a movie, or reading a stow, for example. AR is not sympathy, concern, or personal distress, but it may lead to these emotional states (Decety & Meyer, 2008).
PT is the ability to be mentally flexible (that is, imagine another's situation "from the inside," step into another's shoes) and to be open to different points of view. ER is the internal ability to change or control one's own emotional experience of mirrored feelings. ER implies that the observer is less likely to be overwhelmed by the emotional contagion aspect of affective sharing and thereby avoid personal distress. SA is the ability to temporarily identify with someone else without confusion between self and other.This is the ability to maintain appropriate boundaries between one's own emotions and thoughts and another person's emotions and thoughts (see Decety & Meyer, 2008; Decety & Moriguchi, 2007; Preston & de Waal, 2002).
The conscious decision-making or "taking action" component of our empathy framework is difficult to measure using a self-report instrument. A factorial survey vignette design might be better suited to effectively measure this component, but it was not feasible given our limited resources at this time (Taylor, 2006). Therefore, we created the EA subscale as a proxy for the conscious decision-making component of our framework. The EA items are intended to assess how much a respondent subscribes to or identifies with a particular set of socially responsible, empathic attitudes and beliefs. Our underlying assumption is that a person who identifies more closely with these attitudes would be more inclined to take or support empathic action (Segal, 2007, in press).
Item Generation for Each of the Five Subscales
Item generation for each subscale was guided by the articulated conceptualization of each construct.We developed items by reviewing current items from existing measures of affect sharing and PT (for example, Davis, 1980; Jolliffe & Farrington, 2006; Mehrabian & Epstein, 1972), discussing constructs and potential items that are not as well represented in the empathy literature (that is, SA and ER), composing our own unique items, and seeking feedback from experts and lay people. Our primary goal in generating items was to ensure content validity by constructing items that included the important substance, abilities, and experiences that are logically or theoretically connected to our conceptualizations (Sartori & Pasini, 2007). For example, one of our AR items is "Watching a happy movie makes me feel happy." This item reflects the mirroring and emotional congruence elements of affect sharing. For the AR and ER subscales, we followed the advice of Jolliffe and Farrington (2006) by focusing on basic emotions such as happiness, sadness, and anger while avoiding more complex emotional states like anxiety.
The EA subscale included items about respondents' attitudes toward individual, societal, and governmental action to help others and to what extent they were committed to understanding social conditions (for example, "Government should be expected to help individuals").
Once a pool of preliminary items was developed, they were piloted by administering them to three experts in measurement and to three lay people. These respondents provided extensive feedback about theoretical relevance, face validity, wording, and comprehension. On the basis of this feedback, we made several edits throughout the five subscale items. The edited draft was once again shared with the experts and lay people, who reported satisfaction with the updated version.
Pilot Version of the EAI
On the basis of the articulated criteria, we generated 54 items across five subscales: AR (16 items), PT (six items), EP, (six items), SA (five items), and EA (21 items). We used a Likert-type response scale on which 1 = never, 2 = rarely, 3 = sometimes, 4 = frequently, and 5 = always. The items were randomized and included 12 reverse-score items to slow respondents' recognition or awareness of the concepts being measured (Kline, 1986). It should take 10 to 15 minutes to complete the assessment.
One of the practical goals of our initial application of the EAI was to refine and reduce the number of items. Our early thinking was that we wanted to end up with eight to 10 items for each of the three components in our framework: AR, cognitive processing, and conscious decision making. We elaborate in the Results section, but we quickly realized that our initial logic was flawed. We should have generated enough items for each subscale to end up with eight to 10 items per subscale. This oversight will be addressed in future revisions of the instrument.
Participants, Procedures, and Data Collection
Our institution's internal review board gave us permission to approach students in five sections of pre-BSW and BSW-only courses and two sections of foundational MSW courses. Four hundred and ninety-five undergraduate and graduate students were sent an e-mail inviting them to participate in the study by clicking on a link to a Qualtrics-based survey. Qualtrics is an online survey software package that allowed for participants to access the index through a Web site at their own convenience. The students were told that the survey was voluntary, and they were given 48 hours to complete it. Three hundred and twelve students (63% response rate) completed the first administration of the index. In addition, 232 (74%) of the 312 students also responded to a second e-mail, and one week later they completed a retest or second administration of the index. The sample is described in Table 1. Participants ranged in age from 18 to 60 years. The sample included students with six different ethnicities and 23 different academic majors.
In addition to the EAI items, students were asked to respond to 21 items from the IRI (Davis, 1980, 1983) to test for concurrent validity. When used with college students, the IRI has consistently demonstrated validity and reliability (Davis, 1983), and it has been used extensively in numerous fields (for example, Johnson, Cheek, & Smither, 1983; Wise & Cramer; 1988; Yarnold, Bryant, Nightingale, & Martin, 1996). However, some critics have argued that the Empathic Concern subscale of the IRI measures sympathy and not empathy (Cliffordson, 2001).
Davis's (1980, 1983) IRI is based on the foundation that there are only two aspects of empathy, affective and cognitive. The IRI consists of four subscales: (1) Perspective Taking (PT), (2) Fantasy (F), (3) Empathic Concern (EC), and (4) Personal Distress (PD). PT and F are designed to measure the cognitive aspects of empathy, and EC and PD are designed to measure the reactive affective aspects of empathy. The 21 items included in our survey were from the PT, F, and EC scales.
The purpose of including the IRI items from the PT, F, and EC subscales was to assess evidence of concurrent validity for the EAI subscales AR, PT, and EA. We did not ask the students in our sample to complete additional self-awareness and emotion regulation measures due to the length of the survey.
However, we will add that comparison component in our next round of research.
The Qualtrics-based data were uploaded to SPSS. Cronbach's alpha was used for reliability or internal consistency analysis on the items in each of the five subscales: AR, PT, SA, ER, and EA. After completing the reliability analysis, we performed an exploratory factor analysis with oblique rotation. We also used correlation coefficients to analyze the relationship between the EAI and IRI subscales. Finally, we carried out a test-retest reliability study using the data from the 232 students who finished both administrations of the survey within one week of each other.
We carefully considered which type of preliminary analysis was appropriate to use in the refinement of the EAI. Two options were worth considering: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) (Osborne, Costello, & Kellow, 2008). CFA is most often used to confirm a priori hypotheses and to demonstrate construct
validation (Smith & McCarthy, 1995). In contrast, EFA is mainly applied for the orderly simplification of interrelated items (Floyd & Widaman, 1995) or to find underlying patterns in data. Although we have a well-developed framework that includes five constructs (which seemingly lends itself well to CFA), the items for one of our construct subscales, SA, did not hold up well in the reliability analysis. Therefore, we could not include SA as a construct in a CFA model. Our empathy framework is based on the assumption that AR, PT, SA, and ER must all be present. Our inability to include one of the key constructs of our framework in a CFA hypothesis resulted in our use of EFA.
Indeed, our primary concern for the initial administration of the EAI was careful item selection and preliminary item analysis. We are attempting to measure, through self-report, four concepts (AR, PT, SA, and ER) that are closely related and difficult to tease apart--an extremely challenging task. In particular, the literature has provided very sparse theoretical guidance on SA and ER items in the context of empathy. Therefore, our most important goal for the preliminary analysis of the EAI was not the increased unidimensionality available through CFA, but, rather, exploratory data reduction and item refinement through the use of EFA (Kachigan, 1986). In future research, using a different sample, we intend to perform a CFA on the second administration of the refined EAI and provide evidence to support construct validity.
Reliability Analysis of the Empathy Assessment Index Subscales
The reliability analysis on the EAI indicated excellent internal consistency (Streiner, 2003) for the
AR scale (.831), the EA scale (.810), and the PT subscale (.809) (see Table 2). These findings are based on the deletion of one item from each scale. The ER subscale (.701) had acceptable consistency; however, it could be improved. Parenthetically, the EC subscale of the IRI had an internal consistency of .795, and the alpha for the PT subscale of the EAI was .809--both are considered excellent.
Finally, the SA subscale (.299) did not even approach acceptable levels of reliability. We believe this was due, in part, to a lack of content validity in the items that we developed for this subscale. The literature on SA in the context of empathy is sparse, which made it difficult to identify a group of items that were logically and theoretically connected to the construct. Therefore, the SA subscale was not used in the EFA.
The reliability analysis included 54 items. As a result of the reliability analysis findings, we eliminated eight items before completing the EFA. The five SA items were dropped, as were one item each from the AR and EA scales and the PT subscale. The remaining 46 items were used in the analysis. The analysis was completed using best practices guidelines (Costello & Osborne, 2005; Osborne et al., 2008). Our sample size was 312, resulting in a subject-to-item ratio of 6:1. This is below the general rule of thumb for a desirable ratio (10:1). However, our data proved moderately strong, with high communalities without cross-loadings and a few variables loading strongly on each factor. The data were fairly normally distributed; therefore, we used the maximum-likelihood extraction method with oblique rotation. Using the pattern matrix, we identified six out of 14 factors within the appropriate range (that is, visual inspection of the screeplot indicated that the component was located before the curve flattened) (Osborne et al., 2008; Peres-Neto, Jackson, & Somers, 2005).
The six factors had eigenvalues ranging from 1.652 to 8.261, which accounted for 43.192% of the explained variance. Results for these factors are presented in Table 3. Twenty-six items, more than half of the 46 items in the EAI, were located in the first six factors. We assigned a label to each factor on the basis of the items in the factor and which subscale an item originated in. Not surprisingly, there were two factors labeled AR (happy and sad) and an additional factor that was labeled both PT and AR because it included items from both.This was because of the number of items in the AR category (16). Only EA had more items (21). The three AR factors each had a distinctive focus: (1) "happy" feeling items, (2) "sad" feeling items, and (3) "feeling" items that result in action (for example, crying, smiling).
The patterns discovered in the EFA allow us to eliminate some unnecessary items. For example, we will delete six items from the next version of the EAI because the loading scores were below .350. We think these items are dispensable because of the relative strength of similar items. In addition, we will be deleting six items from Table 3 that have factor loadings below .475. The EFA used 46 items, and as a result of the findings, we are deleting at least 12, leaving us with 34 items.
Initial Evidence of Concurrent Validity: Correlations between Subscales of the EAI and the IRI
We used Pearson's r and Spearman's rho correlation coefficients to analyze the concurrent validity of three EAI subscales. Concurrent validity is a form of criterion-related validity that is generally analyzed by using correlation coefficients to determine whether two measures for the same construct or theoretically related constructs are highly correlated (Sartori & Pasini, 2007).We collected data on the EAI subscales at the same time that we collected data on the IRI subscales. We used both parametric and nonparametric coefficients to analyze the data because of the disagreement among statisticians regarding the appropriate use of parametric statistics, such as the Pearson's r, on ordinal level data.
Hypothesis 1 was that the AR scale of the EAI would be positively correlated with the EC subscale of the IRI (N = 312) (r = .476, Spearman's p = .475, p < .001).The results indicated a moderate positive correlation between the two scales (see Cohen, 1988, for strength of relationship guidelines). AN and EC measure two different but related constructs. AR is intended to measure affect sharing or emotional congruence, whereas EC is designed to measure sympathy or concern.
Hypothesis 2 was that the EA scale of the EAI would be positively correlated with the EC subscale of the IRI (N = 312) (r = .565, Spearman's [rho] = .577, p < .001). The results indicated a strong positive correlation between the two scales. Once again, EA and EC are related constructs but not the same construct.
Hypothesis 3 was that the PT subscale of the EAI would be positively correlated with the PT subscale of the IRI (N = 312) (r = .745, Spearman's [rho] = .762, p < .001). The results indicated a strong positive correlation between the two subscales. This result is evidence of the strongest finding of concurrent validity, because both scales are intended to measure the same construct.
In the next stage of the EAI development process, we will administer existing emotion regulation and self-awareness measures that have demonstrated the ability to produce reliable and valid data to determine how the EAI ER and SA subscales correlate with them.We did not include them in the current administration because of the length of the survey.
Test-Retest Analysis of the EAI Subscales
A test-retest reliability analysis was completed on the EAI scales using the scores that were generated from two administrations of the index, with at least five days between them. Pearson's r and Spearman's rho correlation coefficients that resulted from the test-retest data for each subscale of the EAI are presented in Table 4. Before performing the test-retest analysis, we dropped one item from all but one of the subscales, as indicated in the reliability analysis (seeTable 2). The Pearson's r correlation coefficients for each of the scales ranged from .802 to .854, not including the SA subscale (.592), and these were all statistically significant (p < .001).
Development of a comprehensive social work measure of empathy grounded in both the social and neurological sciences has important implications for social work education and practice. With an empathy index that can produce valid and reliable data, we can assess clients' strengths and develop empathy interventions to improve behavioral health outcomes. Our findings indicate that the initial version of the EAI subscales has acceptable to excellent internal consistency, with the exception of the SA subscale. There is evidence of concurrent validity for the following subscales:AR, EA, and PT. The test-retest reliability analysis of the EAI scales, not including the SA subscale, indicated very strong correlations, all of which were statistically significant (p < .001).
The purpose of the EFA was to find patterns in the data for the purpose of compression. One of our practical goals was to shorten the length of the EAI to make it more efficient and easier to administer and complete. In fact, the initial version of the EAI had 54 items, which was reduced to 46 after the reliability analysis and 34 after the EFA. The EFA identified six components that accounted for 43.192% of the explained variance. We think we can improve on the percentage of explained variance by including a new SA subscale and an improved ER subscale. In the next phase of EAI development we will add new items for SA and additional items for ER. We also plan to perform a CFA on the next round of data. Construct validity is established over a period of time through accumulated evidence; therefore, a CFA is the next logical step (Sartori & Pasini, 2007).
Instruments currently being used to measure empathy do not reflect the recent neuroscientific scholarship on mirror neurons or the importance of SA and ER in experiencing the fullest extent of empathy. In addition, the empathic action perspective of social work is not reflected in current measures. We have developed the foundation for a comprehensive measure of empathy that is rooted in social cognitive neuroscience and social justice, and further work will help to refine and ultimately validate a final version of the instrument.
Original manuscript received January 11, 2010
Final revision received July 16, 2010
Accepted July 21, 2010
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Karen E. Gerdes, PhD, is associate professor, Cynthia A. Lietz, PhD, LCSW, is assistant professor, and Elizabeth A. Segal, PhD, is professor, School of Social Work, College of Public Programs, Arizona State University, Phoenix. The authors acknowledge the Silberman Fund Faculty grant program for ongoing support of the research reported in this article. Address correspondence to Karen E. Gerdes, Arizona State University, College of Public Programs, School of Social Work, 411 North Central Avenue, Suite 800, Phoenix, AZ 85004; e-mail: kegerdes@.asu.edu.
Table 1: Description of Sample (N = 312) Variable n % Sex (a) Male 50 16.0 Female 260 83.0 Other 1 0.003 Race (b) African American 19 6.0 American Indian 10 3.0 Asian American 8 3.0 Caucasian 175 56.0 Latino 61 20.0 Mixed race 21 7.0 Other 13 4.0 Age (years) 18-20 177 57.0 21-25 65 21.0 26-30 23 7.0 31-35 12 4.0 36-40 11 3.5 41-45 10 3.0 Over 45 14 4.5 Major Social work 136 44.0 Nursing 35 11.0 Criminal justice 17 5.0 Psychology 12 4.0 Sociology 8 3.0 Other 87 28.0 Undecided 17 5.0 (a) There was one missing value for this variable. (b) There were five missing values for this variable. Table 2: Reliability Analysis Results for the Empathy Assessment Index (EAI) (N = 312) EAI Scale Item Removed from Final Scale Affective Response (a) Listening to loud music makes my pulse beat faster. Cognitive Processing Perspective Taking subscale (b) When I am sure of something, I do Emotion Regulation subscale (c) not need other viewpoints Self-Awareness subscale (d) Empathic Attitudes The U.S. economic system allows for everyone to get ahead. Cronbach's [alpha] With Item EAI Scale With Item Removed Affective Response (a) .822 .831 Cognitive Processing Perspective Taking subscale (b) .391 .809 Emotion Regulation subscale (c) .701 Self-Awareness subscale (d) .299 Empathic Attitudes .788 .810 (a) Sixteen items. (b) Six items (n = 311). (c) Six items. (d) Five items. (e) Twenty-one items (n = 311). Table 3: Exploratory Factor Analysis: Six Factors Component and Item Eigenvalue Fmpathctic Attitudes 8.261 Government should be expected to help individuals. Government should support our well-being. People who are poor deserve social assistance. People are worthy of being helped by government. I think society should help out people in need. We should be collectively responsible for everyone's well-being. Affective Response (happy) 3.834 When a friend is happy, I become happy. When I am with a happy person, I feel happy myself. Watching a happy movie makes me happy. Perspective Taking 2.922 I am open to listen to the points of view of others. I consider other people's point of view in discussions. I like to view both sides of an issue. It is easy for me to see other people's point of view. Affective Response (sad) 2.178 When a friend is sad, I become sad. When I am with a sad person, I feel sad myself. When I see a friend crying, I feel like crying. Watching a sad movie makes me feel sad. Perspective Taking and Affective Response 1.885 I feel what another person is feeling even when I don't know the person. I can feel the characters in a well-written book. Hearing laughter makes me smile. When I see someone getting picked on, it upsets me. I can imagine what it is like to be poor. When I see a stranger crying, I feel like crying. Seeing someone dance makes me want to move my feet. Emotion Regulation 1.652 Friends view me as a moody person. (reverse scored) When I get upset, I need a lot of time to get over it. (reverse scored) Factor Component and Item Loading Fmpathctic Attitudes Government should be expected to help individuals. .890 Government should support our well-being. .672 People who are poor deserve social assistance. .662 People are worthy of being helped by government. .651 I think society should help out people in need. .464 We should be collectively responsible for .400 everyone's well-being. Affective Response (happy) When a friend is happy, I become happy. .964 When I am with a happy person, I feel .931 happy myself. Watching a happy movie makes me happy. .531 Perspective Taking I am open to listen to the points of view of others. .993 I consider other people's point of view in discussions. .940 I like to view both sides of an issue. .583 It is easy for me to see other people's point of view. .519 Affective Response (sad) When a friend is sad, I become sad. .937 When I am with a sad person, I feel sad myself. .642 When I see a friend crying, I feel like crying. .578 Watching a sad movie makes me feel sad. .418 Perspective Taking and Affective Response I feel what another person is feeling even .686 when I don't know the person. I can feel the characters in a well-written book. .608 Hearing laughter makes me smile. .519 When I see someone getting picked on, it upsets me. .474 I can imagine what it is like to be poor. .472 When I see a stranger crying, I feel like crying. .445 Seeing someone dance makes me want to move my feet. .417 Emotion Regulation Friends view me as a moody person. (reverse scored) .925 When I get upset, I need a lot of time to get over it. (reverse scored) .412 Explained Component and Item Variance (%) Fmpathctic Attitudes 17,211 Government should be expected to help individuals. Government should support our well-being. People who are poor deserve social assistance. People are worthy of being helped by government. I think society should help out people in need. We should be collectively responsible for everyone's well-being. Affective Response (happy) 7.988 When a friend is happy, I become happy. When I am with a happy person, I feel happy myself. Watching a happy movie makes me happy. Perspective Taking 6.087 I am open to listen to the points of view of others. I consider other people's point of view in discussions. I like to view both sides of an issue. It is easy for me to see other people's point of view. Affective Response (sad) 4.537 When a friend is sad, I become sad. When I am with a sad person, I feel sad myself. When I see a friend crying, I feel like crying. Watching a sad movie makes me feel sad. Perspective Taking and Affective Response 3.928 I feel what another person is feeling even when I don't know the person. I can feel the characters in a well-written book. Hearing laughter makes me smile. When I see someone getting picked on, it upsets me. I can imagine what it is like to be poor. When I see a stranger crying, I feel like crying. Seeing someone dance makes me want to move my feet. Emotion Regulation 3.442 Friends view me as a moody person. (reverse scored) When I get upset, I need a lot of time to get over it. (reverse scored) Table 4: Test-Retest Analysis of the Empathy Assessment Index Scales/Subscales (n = 232) Number Pearson's Spearmans's Scale/Subscale of Items r [rho] Affective Response .854 .848 Perspective Taking 5 .803 .803 Emotion Regulation 5 .802 .744 Self-Awareness 5 .592 .573 Empathic Attitudes 20 .850 .840 Note: For all values, p < .001.
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|Author:||Gerdes, Karen E.; Lietz, Cynthia A.; Segal, Elizabeth A.|
|Publication:||Social Work Research|
|Date:||Jun 1, 2011|
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