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EMPATHY AND EGOTISM IN NEW YORK TIMES OBITUARIES TODAY’S BREAKING NEWS April 29, 2014 $1.00 By: Sara Konrath The IUPUI Times Acknowledgements – This research.

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Presentation on theme: "EMPATHY AND EGOTISM IN NEW YORK TIMES OBITUARIES TODAY’S BREAKING NEWS April 29, 2014 $1.00 By: Sara Konrath The IUPUI Times Acknowledgements – This research."— Presentation transcript:

1 EMPATHY AND EGOTISM IN NEW YORK TIMES OBITUARIES TODAY’S BREAKING NEWS April 29, 2014 $1.00 By: Sara Konrath The IUPUI Times Acknowledgements – This research was done in collaboration with an undergraduate student, Muneer Khalid. Grants from the John Templeton Foundation, via the Psychology of Character (Wake Forest University) and directly from the sponsor (Grant # 47993).

2 BACKGROUND Empathy, egotism, and health Empathy  no research on longevity of empathic people, but may be linked (e.g. volunteering, giving support) (Konrath & Brown, 2013; Konrath, 2013) Egotism  self-focus linked to higher stress responses, higher risk of cardiovascular disease, and cardiovascular-related death (Konrath & Bonadonna, 2014; Reinhard et al, 2012; Scherwitz et al) Measurement Self-report scales most common  limitations Some content coding (e.g. I, me, mine), but rare

3 BACKGROUND Personality “at a distance” Can measure trait-like tendencies of people who are not otherwise accessible, e.g. politicians, deceased, etc. Human coding: e.g. power, affiliation, achievement motives Computer coding: e.g. count first person singular (I, me) vs first person plural (we, us) pronouns Relevant study: People whose autobiographical texts had more social words lived longer (Pressman & Cohen, 2007)

4 BACKGROUND Obituaries as a data source ‘That special form of life after death’ Summaries of career and character Some prior research examining gender differences, and also in type and age of death based on occupation Yet none that we know of assess traits Obvious sampling problems High profile / famous, more men, etc. Can still be helpful for theory-building / hypothesis generation in relatively undeveloped literatures and difficult to study topics

5 RESEARCH QUESTIONS Are empathy-related and egotism-related words linked to age of death in obituaries? Are empathy-related and egotism-related words linked to cause of death in obituaries? Do people from different occupational categories have different empathy-related and egotism-related words in their obituaries?

6 METHOD Collected full texts of all obituaries in 2012 from the New York Times. Developed list of empathy-related and egotism-related words. Computerized coding via Linguistic Inquiry Word Count Program (LIWC). Also coded by an individual for “subjective kindness.” Recorded date, age, and cause of death, as well as occupation.

7 METHOD: LIWC CODING e.g. empathy wordse.g. egotism words car*justiceaggress*independ* charit*kind*angerinsensitive cooperat*loveangrypride courtesylovingattack*proud diversityoblig*boast*purg* emotionalpatientcallousquarrel* empath*philanthrop*criticalrude endear*protect*criticizesarcas* forgive*provid*desert*scoff friend*rapportego*self genero*reasonableeliteselfish giv*rightsexploit*supremac* gracesensitivefighttake help*serv*forc*unemotional holdsincer*grudgeunkind hugsympath*hate humanitariantrusthit humanityvolunteer*ignor* interdepend*warm*impatient

8 RESULTS: DESCRIPTIVES Total N=484 (85 female, 399 male) Mean age of death = years (+4.17 years than average lifespan)  Range=age 33 to 111 No gender differences in average age of death, F(1,482)=.24, p=.62 This is because women died at about the average age (81 years), but men lived about 5 years longer than usual Gender: LIWC empathy: ns but males > females in egotism words females > males in subjective kindness

9 RESULTS: AGE OF DEATH LIWC empathy: ns LIWC egotism: ns Subjective kindness: associated with longer life, r=.12, p=.01 Conclusion: Only subjective ratings predict longevity But need interrater reliability ratings

10 RESULTS: CAUSE OF DEATH Type of DeathFrequencyAverage Age Cardiovascular16.3%80.15 Cancer23.8%74.97 Infection6.2%85.13 Alzheimer’s / Dementia 3.7%83.67 Injury (intentional or not) 3.7%63.28 “Natural causes”32.0%88.70 Other / unknown14.3%79.06

11 RESULTS: CAUSE OF DEATH Logistic regressions predicting the likelihood of dying from cardiovascular disease or cancer Cardiovascular disease: LIWC Egotism: β=.92, p=.05, OR=2.52 [1.00,6.34] LIWC Empathy: β=.09, p=.77, OR=1.10 [.60, 2.01] Subjective Kindness: β=-.35, p=.03, OR=.71 [.52,.96] Cancer: Egotism: β=-.73, p=.15, OR=.48 [.18, 1.29] Empathy: β=.25, p=.26, OR=1.28 [.77, 2.14] Subjective Kindness: β=-.02, p=.88, OR=.98 [.77, 1.25]

12 RESULTS: OCCUPATION OccupationFrequencyAverage Age Silent Arts (writing, photography, visual art) 24.4%80.60 Entertainment (acting, dancing, theater, TV host, comedy) 24.2%79.75 Public Service (politics, military, law, religion, activism) 17.6%83.95 Scholar (historian, economist, scientist, engineer, educator) 12.8%85.32 Business (CEO, entrepreneur) 9.5%79.46 Sports7.2%77.46 Medicine (doctor, psychologist, dentist) 4.3%82.00

13 RESULTS: OCCUPATION Z-score

14 DISCUSSION Overview of results Age: subjective kindness and longevity Cause: LIWC egotism predicts higher cardiovascular, subjective kindness predicts lower Occupation: Entertainers / sports have most egotistical profile Implications Limited work on empathic / egotistical traits and age or cause of mortality Limited work on trait-like tendencies in obituaries Creating a computerized dictionary for other researchers to use

15 DISCUSSION: LIMITATIONS Obituary descriptions may not represent actual traits of individuals Limitations inherent to acontextual computer coding, e.g. “He was not very caring” Obituaries in a prestigious national newspaper do not represent general population Issues of causality Third variables such as socioeconomic status may help to explain the results

16 DISCUSSION: FUTURE DIRECTIONS Currently: Finishing 2011 obituary data collection Future: Other coding e.g. Implicit motives: power, affiliation, achievement Refine LIWC empathy and egotism dictionaries so that they can be applied to other types of text Could be helpful for measuring personality traits “at a distance” e.g. Validate with high and low empathy groups

17 THANK YOU Questions or comments?


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