WEB APPENDIX: STIMULI & FINDINGS. Feeling Love and Doing More for Distant Others:

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1 1 WEB APPENDIX: STIMULI & FINDINGS Feeling Love and Doing More for Distant Others: Specific Positive Emotions Differentially Affect Prosocial Consumption LISA A. CAVANAUGH JAMES R. BETTMAN MARY FRANCES LUCE Stimuli STUDY 1 Dispositional Emotion Measures Items from Shiota, Keltner, and John 2006; Shiota 2004 Dispositional Love 1. Other people are generally trustworthy. 2. I develop strong feelings of closeness to people easily. 3. I find it easy to trust others. 4. I can depend on people when I need help. 5. People are usually considerate of my needs and feelings. 6. I love many people. Dispositional Hope 1. In uncertain times, I usually expect the best. 2. I am a very optimistic person. 3. I ve found that things usually turn out for the best in the long run. 4. When I think about the future, I have a strong feeling of hope. 5. I often feel hopeful. 6. I tend to plan in terms of best-case scenarios. 7. I am not a quitter. Prosocial Consumption DV Close others 1. Buy locally grown produce. 2. Donate used items/ clothing to a charitable organization to help local families in need. 3. Buy products made from recycled materials, helping to preserve local forest lands.

2 2 4. Volunteer your time to a charitable organization benefiting local youth. Distant others 1. Refuse to buy a product if it is made using child or sweat shop labor in foreign countries. 2. Buy a product that donates part of its profits to a charitable organization helping refugee families in a foreign country. 3. Donate money to a charitable organization / cause benefiting rainforest conservation in foreign countries. 4. Refuse to buy a product because it was tested on animals abroad. Filler items 1. See a foreign film. 2. Attend a local wine or food tasting event. 3. Take a trip out of the country. 4. Eat at a Japanese restaurant. 5. Attend a live music concert. 6. Purchase a movie DVD. 7. Have a professional massage. STUDY 2 PILOT Emotion Check Instructions Now, we would like to ask you some more specific feelings questions about the autobiographical event you wrote about earlier. We're interested in your feelings while writing about this event. Below is a range of possible feelings you may or may not have experienced in response to the event. Please think of your feelings, and indicate how much, if any, you were experiencing the following feelings. Please use the following 0=None to 8=More Than Ever to make your ratings. If you did not experience a particular feeling, you should indicate that with a 0. STUDY 2 Emotion Induction Prosocial Consumption DV Broadening Tendencies See pilot description in paper and writing samples below See study 1 for items INSTRUCTIONS: Now, we would like to ask you some additional questions about the story that you wrote earlier in this study session. We're interested in how you were FEELING when you were WRITING this story. Specifically, we would like you to READ the story that you wrote and recall specifically how you were feeling. After you have finished reading your story, we will ask you some questions about it. Here is the story you wrote: Social connection 1. To what extent did you feel close or closer to another individual or group 2. To what extent did you feel more connected to another individual or group 3. To what extent did it affect the way you thought about your relationship with some individual or group

3 3 STUDY 3 Emotion Induction Fundraising Appeal Instructions See pilot description Imagine yourself receiving the following appeal from the American Red Cross. After you have read the appeal, you will be asked to make donation decisions. Please make these decisions as you really would at this moment. We will select 1 out of every 20 participants decisions and actually donate to the American Red Cross as they have specified. Donation Letter The American Red Cross provides disaster assistance to people in need. Every dollar fuels our mission of doing whatever it takes to relieve and prevent people from suffering through lifesaving programs and services in your local community, across the country, and around the world. The American Red Cross is where people mobilize to help their neighbors down the street, across the country and around the world in emergencies. Together, we provide relief to victims of disasters, collect and supply blood for those in need and help people prevent, prepare for, and respond to emergencies. You can help ensure these lifesaving services are always available, and the Red Cross continues to have the resources, talent and ability to deliver them. Domestic vs. International Relief DV Broadening Tendencies Social connection Perceived control (Items from Smith & Ellsworth 1985) The American Red Cross has relief funds that differ in terms of the people who are helped and the services provided. How would you allocate your $50 donation? Please enter a dollar amount ($0 - $50) in each of the spaces provided. $ DOMESTIC RELIEF FUND Your gift to the American Red Cross Domestic Relief Fund helps provide people in your community and across the country immediate relief from suffering and needed long-term support. $ INTERNATIONAL RELIEF FUND Your gift to the American Red Cross International Relief Fund helps provide people around the world immediate relief from suffering and needed long-term support. See study 2 for items 1. To what extent did you feel that circumstances beyond anyone's control were controlling what was happening in this situation? 2. To what extent did you feel that you had the ability to influence what was happening in this situation? 3. To what extent did you feel that someone other than yourself was controlling what was happening in this situation? STUDY 4 PILOT Instructions MAGAZINE ADVERTISEMENT FROM CANON

4 4 We are interested in your impressions of and reactions to an advertisement for Canon, the camera company. You will be shown an advertisement that Canon might use to promote its brand. Please take your time in viewing the advertisement. Please read the advertising copy (i.e., the text) carefully and take a few moments to reflect on how the ad makes you feel. Emotion Induction Emotion Check Instructions On the next screen you will be shown the advertisement. Magazine Advertisements (see five full page ads below) MAGAZINE ADVERTISEMENT FEELINGS Please indicate the extent to which you experienced the following emotions or specific feelings while viewing the advertisement. If you did not experience a particular feeling, you should indicate that with a 1. Keeping the advertisment in mind To what extent would you rate your emotional response as POSITIVE? 2. To what extent would you rate your emotional response as NEGATIVE? 3. To what extent did you FEEL EMOTIONAL when viewing that advertisement? STUDY 4 Emotion Induction Domestic vs. International Environmental Fund Magazine Advertisements (see five full page ads below) How do you choose to allocate the 10 dollar bonus payment? Please enter amounts ($0-10) in the spaces below. Environmental Defense Local Fund Natural Resource Federation International Fund Keep for Self

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10 10 Example Stories Written by Participants Love L1 I feel the most love when I receive a phone call out of the blue from and old friend i haven't talked to in a while. It feels great because I know that my friendship meant something to them so they are going out of their way to catch up with me. I feel very special and feel like I share an intimate relation with the person that I share with no one else. L2 L3 When I experience affection for no particular reason. Whether it's my sister asking me to spend time with her and go see a movie, or my mom bringing home food from my favorite restaurant, or my dad invading my room in order to discuss a humorous situation for the day I feel awesome for no other reason than the fact that I love to do the same to them. Last year during finals week I was up days on end. My girlfriend was done, and had nothing to do - or so I am lead to believe. Anyway, I was working all day, studying for my exams. I had not seen her for a day or so, and out of no where she shows up with a whole plate of peanut butter cookies (my favorite) and a bottle of ice cold cherry coke (also a favorite). She did this without even asking, or even letting me know. It was such a surprise! It felt like someone actually cared about me - for real. That someone would go out of their way to do something for me without even asking. It felt great to have a friend (in this instance a girlfriend) that would think about and spend time on me even when if she didn't, I wouldn't know otherwise. Hope H1 I hope that I can travel with my friends and enjoy this last opportunity before real life starts. I will have to put a lot of effort into the medical school admissions process. However, I am happy to do so because I am excited about continuing on the path to becoming a doctor. There is a lot of stress and nervousness involved in the application - interview process, but at the same time it is exciting to see your future unfolding in front of you. I'm hopeful that I can get into a good medical school, and I feel that my credentials give me a good chance to be accepted by one. H2 I am most hopeful that a graduate degree will allow me to have a better quality of life than the present. I currently do not have a graduate degree and the jobs that I am able to apply for are still quite limited. A graduate degree would definitely allow me to apply for much more interesting positions that have more interesting work in general. Also, I would be compensated better with a graduate degree in theory with higher salary and more benefits as well.

11 11 Pride P1 The one situation that I felt most proud was when I landed an amazing internship last summer. The process was extremely complicated and out of 8000 applicants only 81 were chosen. I think that my persistance in school my preparation and dedication to knowing what I wanted helped me succeed. It helped that I had the support of my friends and family. I prepared a great deal and after different rounds and interviews I got the experience I wanted and the job I had dreamt about for a long time.:) P2 I feel the most pride when thinking of my academic achievements throughout my whole education thus far. Academics have always been important to me and my family but it is very rewarding to know that all the hard work I put into my school work is recognized and has allowed me to come out on top. Another source of pride from my academic achievements comes from the fact that most of my achievements and good grades were due to my hard work alone so I know as an individual I was able to complete something great. Being recognized for my hard work also gives me a confidence boost that I can do anything I set my mind to. Neutral N1 First I check my planner to see what homework I have to do when I get home from class. I go to eat dinner at 5:45 unless I have a meeting and can"t make it to the sorority house by then. After dinner I go back to my room and plan out which assignments I will complete first based on their due dates and level of difficulty. If a TV show I like is coming on later I will try to get work done so I can watch it. But if I don t get the work done in time I will record the TV show and watch it later. I will usually take a break towards 9pm or 10pm and get a snack and water. Then I finish up the last part of my homework check my and get ready for bed. N2 In the evenings depending on the intensity of the day I either start my homework right away or I make music for about an hour to 2 hours. After that little 2 hour buffer zone I usually go with my suitemates to dinner at rams head and from there I come back to my room to either watch tv/do homework or go play basketball at the gym. It really does depend on how much work I have to do for the next day somedays if I have a lot of work to do I just do homework in my room till its time for me to go to bed. If however I don t have that much to do for the next day I usually make music or do some sort of physical activity such as play basketball or go running

12 12 LIWC Story Analyses LIWC Metrics Content Analysis Study 2 (Love/ Hope/ Neutral) Study 3 (Love/ Pride/ Neutral) Cognitive processes No difference was found in cognitive processes for the love (18.8) and hope (17.7; F=1.18, NS) conditions, but both were greater than neutral (13.4), F=39.69, p< Cognitive processes for love (22.1) were greater than pride (17.8; F=18.37, p<.0001) and neutral (17.1; F=25.21, p<.0001). Affective processes/ Emotionality Positive emotion Love (7.7) and hope (6.8) were equivalent, F=1.89, NS, but greater than neutral (1.8), F=115.94, p< Negative emotion Love (0.76) and hope (0.67) were equivalent, F=0.19, NS, but greater than neutral (0.36), F=4.49, p<.05. Love (7.4) and pride (6.7) were equivalent, F=1.48, p<.23, but greater than neutral (2.7), F=74.92, p< Love (0.85) and pride (0.81) were equivalent, F<1, NS, but greater than neutral (0.21), F=13.87, p< Perceptual processes Social processes Punctuation Love (4.2) was greater than both hope (2.6), F=10.70, p<.002, and neutral (1.8), F=26.19, p< Love (14.8) was significantly greater than hope (3.9), F=164.20, p<.0001, and neutral (4.1), F=171.19, p<.0001, which were not different from each other, F=0.04, NS. Love (9.8) and hope (10.2) were equivalent, F=0.19, NS, but less than neutral (12.9), F=13.42, p<.001. No difference was found for the love (3.2) and pride (3.3) conditions, F<1, NS), but both were greater than neutral (1.7; F=27.86, p<.0001). Love (12.1) was significantly greater than pride (5.8), F=61.72, p<.0001, and neutral (4.0), F=97.90, p< Pride was greater than neutral (F=5.27, p<.02). Love (5.2) and pride (5.0) were equivalent, F<1, NS, but less than neutral (8.3), F<48.28, p<.001. Notes from LIWC: Emotional Writing studies require participants to write about their emotions and thoughts about personally relevant topics. Control Writing involves writing about non-emotional topics, such as plans for the day or descriptions of ordinary objects or events (Pennebaker et al 2007). LIWC analyses of Psychological Processes involve the following predefined variables: cognitive processes, affective processes, perceptual processes, and social processes. Cognitive processes: word groupings that gauge insight, causation, discrepancy, tentativeness, certainty, inhibition, inclusivity, and exclusivity Affective processes/emotionality: word groupings that gauge the presence of positive emotion and negative emotion Perceptual processes: word groupings that gauge multiple sensory and perceptual dimensions Social processes: word groupings that denote social processes, including all non-first-person-singular personal pronouns and verbs that suggest human interaction (e.g., talking, sharing, connecting)

13 13 Study 2: Content analyses of emotion stories LIWC Story Analyses (Continued) In Study 2, content analyses were used to test whether the difference found between emotion conditions could be attributed to potential alternative explanations, such as differences in emotional intensity, cognitive processing style, or semantic priming causing the increase in behaviors benefiting distant others. Participants stories were analyzed using a textual analysis program with predefined word categories and designated word groupings to gauge emotional intensity (i.e., presence of positive emotion and negative emotion), cognitive processes (i.e., denoting insight, causation, discrepancy, tentativeness, certainty, inhibition, inclusivity, and exclusivity), and social processes (i.e., pronouns, such as we, us, our and verbs suggesting human interaction, such as talking, sharing, and connecting; LIWC; Pennebaker, Booth, and Francis 2007; see Web Appendix for details on analyses). If emotional intensity were driving the results (e.g., if love generated greater emotional intensity), we would expect to find a higher magnitude of emotion words used in love stories. If it were differences in cognitive processing style (e.g., if love generated more heuristic processing), we would expect to find a difference in words related to cognitive processes in the stories. If semantic priming were responsible for our findings, we would expect to find social processes mediate the relationship between love and behavior benefiting distant others owing to the mere presence or repeated use of social words which could arguably serve as primes. Love and hope were found to be equivalent in cognitive processes (F(1, 71) = 1.18, NS) and emotionality (positive emotion (F(1, 71) = 1.89, NS) and negative emotion (F(1, 71) = 0.19, NS)). Social processes were higher for love than hope, F(1, 71) = , p<.0001, but social processes did not mediate the relationship between love and behaviors. The indirect effect of neutral vs. love was B = (SE =.3466) with a 95% CI that included zero [-.7345,.6219] for distant behaviors as well as close (B =.2596, SE =.3855, 95% CI [-.4711, ]). The indirect effect of hope vs. love was B = (SE =.3535) with a 95% CI that included zero [-.7966,.5834] for distant as well as close (B =.2640, SE =.3796, 95% CI [-.4799, ]), showing that social words are neither sufficient nor predictive. In sum, the data do not provide support for emotional intensity, cognitive processing, or semantic priming as alternative explanations for our results. Study 3: Content analyses of emotion stories In study 3, the emotion stories were analyzed using LIWC to provide additional evidence of the proposed mechanism and check for evidence of alternative mechanisms. The analyses revealed no systematic differences in presence or intensity of emotion between the love and pride conditions (positive emotion, F(1, 173) = 1.48, NS; negative emotion, F<1, NS). In contrast to Study 2, cognitive processes were actually greater for love than pride (F(1, 173) = 18.37, p<.0001), providing further evidence that love is not simply producing more heuristic processing. Similarly while social processes were greater for love than pride (F(1, 173) = 61.72, p<.0001), social processes did not mediate the relationship between emotion and behavior at any level. The indirect effect of neutral vs. love with 95% bias-corrected bootstrapped confidence interval was not significant at any level of positivity: one SD below (B = , SE = ; 95% CI [ , ]), at the mean (B = , SE = ; 95% CI [ , ]), or one SD above the mean level of positivity (B = , SE = ; 95% CI [ , ]). Similarly, the indirect effect of pride vs. love was not significant at any level of positivity: one SD below (B = , SE = ; 95% CI [ , ]), at the mean (B = , SE = ; 95% CI [ , ]), or one SD above the mean level of positivity (B = , SE = ; 95% CI [ , ]). The lack of mediation by social processes does not support a mere priming account. See Web Appendix for more details.

14 14 Study 2 Means for Prosocial Consumption Behaviors Benefiting Distant Others and Close Others Emotion Distant Others Close Others Hope 3.11 a 5.48 c Neutral 3.50 a 4.86 d Love 4.04 b 5.54 c Note: Different superscript letters indicate significant differences at the level of at least p <.05.

15 15 Study 4 Love Differentially Predicts Giving to International (vs. Local) Charities 0.70 Proportion Donated to International Charity Compassion Hope Love Neutral Pride Emotion Ad Condition

16 Results 16 STUDY 1: DISPOSITIONAL LOVE AND HOPE DIFFERENTIALLY PREDICT PROSOCIAL CONSUMPTION BEHAVIOR FOR CLOSE AND DISTANT OTHERS proc mixed data=long; class subj; model scbrating= dlove dhope dist dist*dlove dist*dhope / DDFM=SATTERTH; random int dist / SUBJECT=subj TYPE=UN(1); run; PROC MIXED The Mixed Procedure Model Information Data Set Dependent Variable Covariance Structure Subject Effect Estimation Method Residual Variance Method Fixed Effects SE Method Degrees of Freedom Method WORK.MERGED2 scbrating Banded subj REML Profile Model-Based Satterthwaite Class Level Information Class Levels Values subj Dimensions Covariance Parameters 4 Columns in X 6 Columns in Z Per Subject 2 Subjects 82 Max Obs Per Subject 2 Number of Observations Number of Observations Read 164 Number of Observations Used 164 Number of Observations Not Used 0

17 17 Iteration History Iteration Evaluations -2 Res Log Like Criterion Convergence criteria met. Covariance Parameter Estimates Cov Parm Subject Estimate UN(1,1) subj UN(2,1) subj 0 UN(2,2) subj Residual Fit Statistics -2 Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Null Model Likelihood Ratio Test DF Chi-Square Pr > ChiSq Type 3 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F dlove dhope dist <.0001 dlove*dist dhope*dist STUDY 1 FOOTNOTE proc reg; model distoth closoth= dlove dhope; run; The REG Procedure Model: MODEL1 Dependent Variable: distoth Number of Observations Read 82 Number of Observations Used 82

18 18 Analysis of Variance Source DF Sum of Mean Squares Square F Value Pr > F Model Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Variable DF Parameter Standard Estimate Error t Value Pr > t Intercept <.0001 dlove dhope The SAS System The REG Procedure Model: MODEL1 Dependent Variable: closoth Number of Observations Read 82 Number of Observations Used 82 Analysis of Variance Source DF Sum of Mean Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Variable DF Parameter Standard Estimate Error t Value Pr > t Intercept <.0001 dlove dhope

19 STUDY 2: LOVE AND HOPE LEAD TO DIFFERENT PATTERNS OF PROSOCIAL CONSUMPTION BEHAVIOR FOR CLOSE AND DISTANT OTHERS 19 EMOTION INDUCTION PILOT STUDY Analysis for Valence ( Happiness ) predicted by emotion condition The GLM Procedure Class Level Information Class Levels Values gender english econd 4 Hope Love Neut Pride The GLM Procedure Dependent Variable: mchappy3 Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE mchappy3 Mean Source DF Type I SS Mean Square F Value Pr > F econd Source DF Type III SS Mean Square F Value Pr > F econd

20 20 Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neut Hope v. Pride Love v. Neut Love v. Pride Pride v. Neut Pos v. Neut <.0001 Analyses for All Emotion Check Measures predicted by emotion condition controlling for positivity; see corresponding means in Table 1 in the paper The GLM Procedure Dependent Variable: mchappy3 Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE mchappy3 Mean Source DF Type I SS Mean Square F Value Pr > F eventpos <.0001 econd Source DF Type III SS Mean Square F Value Pr > F eventpos <.0001

21 econd Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Pride Love v. Pride Pos v. Neut The GLM Procedure Dependent Variable: mcarousal2 Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE mcarousal2 Mean Source DF Type I SS Mean Square F Value Pr > F eventpos <.0001 econd Source DF Type III SS Mean Square F Value Pr > F eventpos <.0001 econd Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neut

22 Hope v. Pride Love v. Neut Love v. Pride Pride v. Neut Pos v. Neut The GLM Procedure Dependent Variable: mchope2 Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE mchope2 Mean Source DF Type I SS Mean Square F Value Pr > F eventpos <.0001 econd Source DF Type III SS Mean Square F Value Pr > F eventpos <.0001 econd Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neut Hope v. Pride Love v. Neut Love v. Pride Pride v. Neut

23 Pos v. Neut The GLM Procedure Dependent Variable: mclove2 Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE mclove2 Mean Source DF Type I SS Mean Square F Value Pr > F eventpos <.0001 econd Source DF Type III SS Mean Square F Value Pr > F eventpos econd Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neut Hope v. Pride Love v. Neut Love v. Pride Pride v. Neut Pos v. Neut The GLM Procedure

24 Dependent Variable: mcpride2 24 Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE mcpride2 Mean Source DF Type I SS Mean Square F Value Pr > F eventpos <.0001 econd <.0001 Source DF Type III SS Mean Square F Value Pr > F eventpos <.0001 econd <.0001 Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neut Hope v. Pride Love v. Neut Love v. Pride Pride v. Neut <.0001 Pos v. Neut <.0001

25 25 The GLM Procedure Least Squares Means mcarousal2 mchappy3 mchope2 mclove2 mcpride2 econd LSMEAN LSMEAN LSMEAN LSMEAN LSMEAN Hope Love Neut Pride STUDY 2 Social Connection Scores Dependent Variable: mcsc The GLM Procedure Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE mcsc Mean Source DF Type I SS Mean Square F Value Pr > F econd <.0001 Source DF Type III SS Mean Square F Value Pr > F econd <.0001 Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neu Love v. Neu <.0001 Pos v. Neu Level of econd N Mean mcsc Std Dev Hope Love Neu

26 26 Prosocial Consumption Behaviors proc mixed; class subj econd dist; model scbrating= econd dist econd*dist / DDFM=SATTERTH; random int dist / SUBJECT=subj TYPE=UN(1); run; The Mixed Procedure Model Information Data Set WORK.STUDY2FINAL Dependent Variable scbrating Covariance Structure Banded Subject Effect subj Estimation Method REML Residual Variance Method Profile Fixed Effects SE Method Model-Based Degrees of Freedom Method Satterthwaite Class Lev els subj 74 econd 3 Hope Love Neu dist Class Level Information Values Dimensions Covariance Parameters 7 Columns in X 12 Columns in Z Per Subject 3 Subjects 74 Max Obs Per Subject 2 Number of Observations Number of Observations Read 148 Number of Observations Used 148 Iteration History Iteration Evaluations -2 Res Log Like Criterion Convergence criteria met. Covariance Parameter Estimates Cov Parm Subject Estimate UN(1,1) subj UN(2,1) subj 0 UN(2,2) subj 0

27 UN(3,1) subj 0 UN(3,2) subj 0 UN(3,3) subj Residual Fit Statistics -2 Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Null Model Likelihood Ratio Test DF Chi-Square Pr > ChiSq Type 3 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F econd dist <.0001 econd*dist proc glm; class subj econd dist; model scbrating= econd subj(econd) dist econd*dist dist*subj(econd); test h=econd e=subj(econd)/ htype=1 etype=1; test h=dist econd*dist e=dist*subj(econd)/htype=1 etype=1; means econd dist econd*dist; run; Study 2 The GLM Procedure Class Level Information Class econd Levels Values 3 Hope Love Neu dist Number of Observations Used 148 The GLM Procedure Dependent Variable: scbrating scbrating Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total

28 R-Square Coeff Var Root MSE scbrating Mean Source DF Type I SS Mean Square F Value Pr > F econd subj(econd) dist econd*dist subj*dist(econd) Source DF Type III SS Mean Square F Value Pr > F econd subj(econd) dist econd*dist subj*dist(econd) Tests of Hypotheses Using the Type I MS for subj(econd) as an Error Term Source DF Type I SS Mean Square F Value Pr > F econd Tests of Hypotheses Using the Type I MS for subj*dist(econd) as an Error Term Source DF Type I SS Mean Square F Value Pr > F dist <.0001 econd*dist The GLM Procedure Level of econd N scbrating Mean Std Dev Hope Love Neu Level of dist N scbrating Mean Std Dev

29 29 The GLM Procedure dist=0 Class Level Information Class econd Levels Values 3 Hope Love Neu ethnic dist 1 0 subj 74 Number of Observations Read 74 Number of Observations Used 74 The GLM Procedure Dependent Variable: scbrating scbrating dist=0 Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE scbrating Mean Source DF Type I SS Mean Square F Value Pr > F econd Source DF Type III SS Mean Square F Value Pr > F econd Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neu Love v. Neu Pos v. Neu The GLM Procedure dist=0 Level of econd N scbrating Mean Std Dev

30 Level of econd N scbrating Mean Std Dev 30 Hope Love Neu The GLM Procedure dist=1 Number of Observations Read 74 Number of Observations Used 74 The GLM Procedure Dependent Variable: scbrating scbrating dist=1 Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE scbrating Mean Source DF Type I SS Mean Square F Value Pr > F econd Source DF Type III SS Mean Square F Value Pr > F econd Contrast DF Contrast SS Mean Square F Value Pr > F Hope v. Love Hope v. Neu Love v. Neu Pos v. Neu The GLM Procedure dist=1 Level of econd N scbrating Mean Std Dev Hope

31 Level of econd N scbrating Mean Std Dev 31 Love Neu Social Connection Mediates Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 14 Y = scbratin X = emot2 (Neutral) M = mcsc V = dist Statistical Controls: CONTROL= emot1 (Hope) Sample size 148 ************************************************************************** Outcome: mcsc Model Summary R R-sq F df1 df2 p Model coeff se t p constant emot emot ************************************************************************** Outcome: scbratin Model Summary R R-sq F df1 df2 p Model coeff se t p constant mcsc emot dist int_

32 emot Interactions: int_1 mcsc X dist ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator dist Effect Boot SE BootLLCI BootULCI mcsc mcsc ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 14 Y = scbratin X = emot1 (Hope) M = mcsc V = dist Statistical Controls: CONTROL= emot2 (Neutral) Sample size 148 ************************************************************************** Outcome: mcsc Model Summary R R-sq F df1 df2 p Model coeff se t p constant emot

33 emot ************************************************************************** Outcome: scbratin Model Summary R R-sq F df1 df2 p Model coeff se t p constant mcsc emot dist int_ emot Interactions: int_1 mcsc X dist ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator dist Effect Boot SE BootLLCI BootULCI mcsc mcsc ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX Content Analyses of Emotion Stories/ Types of Words Used in Stories Does Not Mediate See LIWC Story Analyses summarized in Table above as well as supplemental PROCESS analyses below. Note: frequency of social words in LIWC analyses does not mediate Run MATRIX procedure:

34 ************* PROCESS Procedure for SPSS Beta Release ************* 34 Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 14 Y = scbratin X = emot2 (Neut) M = social V = dist Statistical Controls: CONTROL= emot1 (Hope) Sample size 144 ************************************************************************** Outcome: social Model Summary R R-sq F df1 df2 p Model coeff se t p constant emot emot ************************************************************************** Outcome: scbratin Model Summary R R-sq F df1 df2 p Model coeff se t p constant social emot dist int_ emot Interactions: int_1 social X dist ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p

35 35 Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator dist Effect Boot SE BootLLCI BootULCI social social ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 14 Y = scbratin X = emot1 (Hope) M = social V = dist Statistical Controls: CONTROL= emot2 (Neut) Sample size 144 ************************************************************************** Outcome: social Model Summary R R-sq F df1 df2 p Model coeff se t p constant emot emot ************************************************************************** Outcome: scbratin Model Summary R R-sq F df1 df2 p

36 Model coeff se t p constant social emot dist int_ emot Interactions: int_1 social X dist ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator dist Effect Boot SE BootLLCI BootULCI social social ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX STUDY 3: LOVE (NOT PRIDE) INCREASES DONATIONS TO DISTANT OTHERS Social Connection The GLM Procedure Dependent Variable: sc Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE sc Mean

37 37 Source DF Type I SS Mean Square F Value Pr > F mcpos <.0001 emot <.0001 Source DF Type III SS Mean Square F Value Pr > F mcpos <.0001 emot <.0001 Contrast DF Contrast SS Mean Square F Value Pr > F Love v. Neu <.0001 Love v. Pride <.0001 Neut v. Pride Pos v. Neu The GLM Procedure Least Squares Means emot sc LSMEAN love neut prid Dollars Donated to International Relief The GLM Procedure Dependent Variable: intl Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE intl Mean Source DF Type I SS Mean Square F Value Pr > F mcpos emot Source DF Type III SS Mean Square F Value Pr > F mcpos emot

38 Contrast DF Contrast SS Mean Square F Value Pr > F Love v. Neu Love v. Pride Neut v. Pride Pos v. Neu The GLM Procedure Least Squares Means emot intl LSMEAN love neut prid Social Connection Mediates Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 7 Y = intl X = emotx2 (Neut) M = sc W = cmcpos Statistical Controls: CONTROL= emotx1 (Pride) Sample size 177 ************************************************************************** Outcome: sc Model Summary R R-sq F df1 df2 p Model coeff se t p constant emotx cmcpos int_ emotx Interactions: int_1 emotx2 X cmcpos

39 39 ************************************************************************** Outcome: intl Model Summary R R-sq F df1 df2 p Model coeff se t p constant sc emotx emotx ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator cmcpos Effect Boot SE BootLLCI BootULCI sc sc sc Values for quantitative moderators are the mean and plus/minus one SD from mean ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 7 Y = intl X = emotx1 (Pride) M = sc

40 W = cmcpos 40 Statistical Controls: CONTROL= emotx2 (Neut) Sample size 177 ************************************************************************** Outcome: sc Model Summary R R-sq F df1 df2 p Model coeff se t p constant emotx cmcpos int_ emotx Interactions: int_1 emotx1 X cmcpos ************************************************************************** Outcome: intl Model Summary R R-sq F df1 df2 p Model coeff se t p constant sc emotx emotx ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator cmcpos Effect Boot SE BootLLCI BootULCI sc sc sc

41 Values for quantitative moderators are the mean and plus/minus one SD from mean 41 ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX Content Analyses of Emotion Stories/ Types of Words Used Does Not Mediate See LIWC Story Analyses summarized in Table above as well as supplemental PROCESS analyses below. Note: Frequency of social words in LIWC analyses does not mediate Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 7 Y = intl X = emotx2 (Neut) M = social W = cmcpos1 Statistical Controls: CONTROL= emotx1 (Pride) Sample size 176 ************************************************************************** Outcome: social Model Summary R R-sq F df1 df2 p Model coeff se t p constant emotx cmcpos int_ emotx

42 42 Interactions: int_1 emotx2 X cmcpos1 ************************************************************************** Outcome: intl Model Summary R R-sq F df1 df2 p Model coeff se t p constant social emotx emotx ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator cmcpos1 Effect Boot SE BootLLCI BootULCI social social social Values for quantitative moderators are the mean and plus/minus one SD from mean ******************** ANALYSIS NOTES AND WARNINGS ************************* Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000 Level of confidence for all confidence intervals in output: END MATRIX Run MATRIX procedure: ************* PROCESS Procedure for SPSS Beta Release ************* Written by Andrew F. Hayes, Ph.D. ************************************************************************** Model = 7 Y = intl

43 X = emotx1 (Pride) M = social W = cmcpos1 43 Statistical Controls: CONTROL= emotx2 (Neut) Sample size 176 ************************************************************************** Outcome: social Model Summary R R-sq F df1 df2 p Model coeff se t p constant emotx cmcpos int_ emotx Interactions: int_1 emotx1 X cmcpos1 ************************************************************************** Outcome: intl Model Summary R R-sq F df1 df2 p Model coeff se t p constant social emotx emotx ******************** DIRECT AND INDIRECT EFFECTS ************************* Direct effect of X on Y Effect SE t p Conditional indirect effect(s) of X on Y at values of the moderator(s) Mediator cmcpos1 Effect Boot SE BootLLCI BootULCI social social