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Online Personality Trends in Love and Friends
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Online Personality Trends in Love and Friends






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Online Personality Trends in Love and Friends Online Personality Trends in Love and Friends Presentation Transcript

  • About me• Koen De Couck• Master student Experimental Psychology (Universiteit Gent)• Belgian entrepreneur (geolocation dating), interested in personality-basedrecommendation systems, human computer interaction, online crowd systems• Currently: Research intern at The Psychometrics Centre, University of Cambridge What role does personality play in our lives? What makes two people come together… and stay together? How similar are you to your friends, your partner? (+ Can we do something with that?)Koen De Couck 2Psychometrics Centre, Cambridge
  • Assortative mating (Similarity hypothesis)Koen De Couck 3Psychometrics Centre, Cambridge View slide
  • Literature review: Explained Variance Personality Physical attractiveness Decuyper, Debolle & De Fruyt, 2012 Intelligence Braun et al., 2001 Escorial & Martin-Buro, 2012 Higher scores are more preferred, however… A matching score is a superior predictor on whether the couple will stay together.Koen De Couck 4Psychometrics Centre, Cambridge View slide
  • Application (Google Maps)Koen De Couck 5Psychometrics Centre, Cambridge
  • Problems with previous studies• Small sample size (~100 couples)• Method of recruiting, paid participants• Focus on newly weds, heterosexual couples• Cross-sectional designs: Only short term !Koen De Couck 6Psychometrics Centre, Cambridge
  • Conceptual distinctions Gender preferences Age Preferences Short term (dating) ≠ long term (marriage) Mate preferences ≠ actual mate choices Actual personality ≠ Perceived personality Relationship status ≠ relationship satisfactionKoen De Couck 7Psychometrics Centre, Cambridge
  • MethodologyKoen De Couck 8Psychometrics Centre, Cambridge
  • myPersonality App• Running since June 2007• Allows Facebook users to take real psychologicalquestionnaires and receive feedback on their scores• n = 6.500 000 users• 25+ reliable and valid questionnaires available!• 100-item IPIP NEO-PI-R Measure• Users can opt in to sharing their FB profile datahttps:// apps.facebook.com/mypersonalityKoen De Couck 9Psychometrics Centre, Cambridge
  • myPersonality AppKoen De Couck 10Psychometrics Centre, Cambridge
  • myPersonality AppRich and robust research tradition !NEO-PI-R (Costa & McCrae, 1992), IPIP (Goldberg, 2006), …Factor Analysis:Orthogonal 5-Dimensional Structure of Personality - Openness - Conscientiousness - Extraversion - Agreeableness - Neuroticismhttps:// apps.facebook.com/mypersonalityKoen De Couck 11Psychometrics Centre, Cambridge
  • myPersonality App Similarity score: ‘76.76%’https:// apps.facebook.com/mypersonalityKoen De Couck 12Psychometrics Centre, Cambridge
  • Why use Facebook data for dating research?• Advantage: • Popularity • Less faking than dating sites • Sample of volunteers• Disadvantage: • Set size: 7,000,000 > 144,707 > 8,500 > 6,467• However, keep in mind: • Male preferences ≠ female preferences • Short term (dating) ≠ long term (marriage) • Mate preferences ≠ actual mate choices • Actual personality ≠ Perceived personality • Relationship status ≠ relationship satisfactionKoen De Couck 13Psychometrics Centre, Cambridge
  • Sample• n = 6467 heterosexual couples• Age: 16 – 65 (Mean: 26, SD: 7.16)• In a relationship (60%), Engaged (11%), Married (29%)• n = 636 lesbian couples (9% of original sample)• Age: 16- 50 (Mean: 24, SD: 7.28)• In a relationship (24%), Engaged (11%), Married (52%)• n = 188 gay couples (3% of original sample)• Age: 17 – 49 (Mean: 27, SD: 10.11)•In a relationship (53%), Engaged (11%), Married (26%)Age & gender representative of Facebook population.Koen De Couck 14Psychometrics Centre, Cambridge
  • Gender differences?• O: 49% • O: 52%• C: 49% • C: 48%• E: 52% • E: 52%• A: 51% • A: 47%• N: 58% • N: 43%• Small effects of • Higher focus onpartner choice based physicalon personality attractiveness, personality of partner of little significance Average personality gender difference: 7% (n= 144,707) Percentiles, ANOVAKoen De Couck 15Psychometrics Centre, Cambridge
  • Sexuality differences?• O: High • O: High• C: Low • C: Low• E: / • E: /• A: / • A: Low (~ straight• N: In between straight men)men and women • N: / ANOVA, t-tests As compared to straight men / straight womenKoen De Couck 16Psychometrics Centre, Cambridge
  • Similarity measures Mathematical problem: Given two sets of 5D-coordinates x1, x2, find an expression for proximity / similarity for both points.Koen De Couck 17Psychometrics Centre, Cambridge
  • Similarity measures Characteristics of a set xKoen De Couck 18Psychometrics Centre, Cambridge
  • Similarity measures• Rescaled D-score (D) • Elevation + Scatter of profiles • Cf. Euclidean distance •• Pearson correlation (r) • Shape of profiles •• Intraclass correlation (ICCDE) • Elevation + Scatter + Shape • Furr, 2009Koen De Couck 19Psychometrics Centre, Cambridge
  • ResultsKoen De Couck 20Psychometrics Centre, Cambridge
  • Personality similarity in couplesKoen De Couck 21Psychometrics Centre, Cambridge
  • Personality similarity in couplesKoen De Couck 22Psychometrics Centre, Cambridge
  • Personality similarity in couplesKoen De Couck 23Psychometrics Centre, Cambridge
  • Personality similarity in couples• n = 6467• ‘In a relationship’, ‘Engaged’, ‘Married’•Similarity to partner: • D = 76 (76/76/75) NS • r = .30 (.32/.26/.25) • ICC = .18 (.20/.15/.14)• Similarity to different sex friend: • D = 76 • r = .26 • ICC = .16Koen De Couck 24Psychometrics Centre, Cambridge
  • Personality trends in couplesKoen De Couck 25Psychometrics Centre, Cambridge
  • Personality trends in couples• n= 144,707•In a relationship: • Logistic regression: sig. effect of individual’s O+, E+, A+, N+, p<.001. Sig effect C+, p<0.05•Married: • Logistic regression: sig. effect of individual’s O-, C+, N+, p<.001• Point-biserial correlations: • All traits: ~ .03 (relationship) • C: r = 0.12 (married)Koen De Couck 26Psychometrics Centre, Cambridge
  • Are gay couples more similar in personality?• n = 188• In gay couples, ICCDE similarity scores (MICC = 0.29, SDICC = 0.42) are significantlyhigher than for straight couples (MICC = 0.18, SDICC = 0.45): tICC (199.188) = 3.369, p < .001• Not merely (lack of) gender difference !• Difference to Partner – Stranger is also higher for gay couples than for straight couples• Shape of personality profile• Not found for lesbian couplesKoen De Couck 27Psychometrics Centre, Cambridge
  • Hypotheses• Hypothesis: “The degree of personality similaritypredicts the quality of the relationship” • Similarity reduces in marriages • Arranged marriages, other research• Hypothesis: “Selection for personality occurs infunction of potential mate presence (i.e. can youafford to be choosy?)” • No relation #friends – ICC • However… • ICC in gay couples = .30 ≠ heterosexual couples (ICC = .18) • Population density (city vs rural areas)Koen De Couck 28Psychometrics Centre, Cambridge
  • How about IQ ?• Correlation IQ: r = 0.30, p = 0.003 (Mean = 113, n = 93 couples) • = Correlation personality !*• IQ difference: 13 points, sd: 7 (n = 23 couples)Koen De Couck 29Psychometrics Centre, Cambridge
  • How about friends ?• Friends are similar to you! ( .16 > .13)• You’re just as similar to male friends as female friends (excl gender differences)• However, Similarity amongst men (.21) > similarity amongst women (.12)•Correlation Big Five Traits: r ~ 0.06, p = 0.001• Correlation similarity for your ‘best friend’ (ratio shared friends): r = 0.04• Complication: correlations trait – trait similarity: .19, .21, .27, .43, .11Koen De Couck 30Psychometrics Centre, Cambridge
  • ApplicationKoen De Couck 31Psychometrics Centre, Cambridge
  • Recommendation systems• Usefulness of ‘Couple entities’ in personality systems (e.g. recommendationsystems) • E.g. preference tool (‘single’ vs ‘In a relationship’) OCEAN OCEAN OCEAN 12345 33333 54321Koen De Couck 32Psychometrics Centre, Cambridge
  • Dating applicationsKoen De Couck 33Psychometrics Centre, Cambridge
  • Join us @myPersonality Project• Running since June 2007• n = 6.500 000 users• 50+ data sets publicly available, reliable and valid questionnaires!• Couples, social network analysis, user likes, happiness, IQ, … http://mypersonality.org/wikiKoen De Couck 34Psychometrics Centre, Cambridge
  • Questions?Koen De Couck 35Psychometrics Centre, Cambridge
  • Why relationships fail: Individual predictors for successful * poor communication relationships: *commitment *criticism *flexibility *defensiveness *loyalty *respect for differences *trust *contempt *absence of substance abuse *stonewalling *sense of humor *communication skills *self-discovery *self-awareness *self change Couples predictors for failure: Couples predictors for success: *substance abuse *similar backgrounds, goals and lifestyles *mental health issues *support from extended family and *lack of sexual intimacy friendships *trust and commitment issues *sexual intimacy *divorce record in a family *common interest *age — the younger the couple the tougher *humor and creativity the challenge *balancing power *emotional connection Gottman, 1993Koen De Couck 36Psychometrics Centre, Cambridge