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Trace Measures Learning about people from the things left in their wake Sam Gosling Department of Psychology University of Texas, Austin, USA.

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Presentation on theme: "Trace Measures Learning about people from the things left in their wake Sam Gosling Department of Psychology University of Texas, Austin, USA."— Presentation transcript:

1 Trace Measures Learning about people from the things left in their wake Sam Gosling Department of Psychology University of Texas, Austin, USA

2 Goals Challenges of collecting trace measures Ubiquity of traces Viability of approach

3 Lindsay Graham Univ. of Texas Laura Naumann Sonoma State Univ. Jason Rentfrow Cambridge Univ. Jamie Pennebaker Univ. of Texas Simine Vazire Washington Univ. Matthias Mehl Univ. of Arizona

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7 Domains in which personality could be expressed LOW CONTROL HIGH CONTROL PUBLIC PRIVATE Websites Attractiveness Dorm rooms Office space Nationality Language style Injuries Dreams Passwords Underwear Journal Wallet Books Thoughts Writing Movie prefs Sleep Social life Food prefs Music prefs Residence Profession Handshake Sports prefs Language content Appearance Friends Car Bumper sticker Email signature Ans. Mach. message Geographic location

8 Physical environments: Offices and bedrooms Virtual environments: websites, Facebook, & Foursquare Aural environments: Music

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13 Trace Measures Pros Nonreactive New populations Often aggregates behavior Can be hard to fake (because it’s real) Cons Not all contexts contain traces Not all traits leave traces (A vs. O) It’s hard!

14 It’s Hard… 1.Identify likely traces to match construct 2.Must measure those traces reliably 3.Must combine them into an index

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16 Personal Living Space Cue Inventory (PLSCI) 725 Individual Cues

17 WALLS, FLOORS, ETC. FURNITURE. POSTERS/ART.

18 OTHER THIINGS ON WALLS. BOOKS MAGAZINES

19 COOKING STUFF BEAUTY PRODUCTS CLOTHING.

20 MUSIC STATIONERY ELECTRONICS OTHER…

21 Act Frequency Approach (AFA) (Buss & Craik, 1983) GREGARIOUSNESS He (she) took the initiative when meeting neighbors for the first time. He (she) introduced him/herself to new co-workers without hesitation. He (she) threw a surprise party for a friend. He (she) made people in a crowded elevator laugh and smile. ALOOFNESS He (she) displayed no emotion when meeting the long lost friend at the airport He (she) hid in the bedroom when the others came over to visit. When spoken to, he (she) seemed to feign not hearing and walked quickly away in the other direction. He (she) avoided her (his) love-struck gaze and walked with his (her) nose in the air.

22 Riverside Behavioral Q-sort (RBQ) (Funder, Furr, & Colvin, 2000) Behavior at “Psychologically Meaningful” level Tries to control the interaction Dominates the interaction Is reserved and unexpressive Laughs frequently Shows high enthusiasm and a high energy level Behaves in a fearful or time manner

23 PLSCI 725 Individual Cues 42 Global attributes Undecorated Vs. Decorated Gloomy Vs. Cheerful Drab Vs. Colorful Dirty Vs. Clean Messy Vs. Neat Uncluttered Vs. Cluttered Empty Vs. Full Cramped Vs. Roomy Cheap Vs. Expensive Repelling Vs. Inviting Ordinary Vs. Distinctive Unstylish Vs. Stylish Stuffy Vs. Drafty

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26 Varied books Distinctive Varied magazines Many CDs Multi-purpose Decorated Many books Varied CDs.36.44.48.27 Openness Openness to Experience Judge-Reported Openness.35.33.23.34.39.31.50 r =.79

27 Uncluttered Well-organized Neat Organized clothing Relatively empty Organized CDs Organized books Organized stationery Clean Inviting Comfortable Good condition Cheerful Colorful Not much stationery Conscientiousness r =.81 Judge-Reported Conscientiousness.30.46.42.57.64.62.61.50.58.47.29.36.75.56.70 Conscientiousness.27.29.36.32.27

28 But not all traits are expressed in spaces

29 Cheerful Colorful Inviting Neat Clean Good condition Well-organized Organized CDs Organized stationery Few clothes Agreeableness r = -.22 Judge-Reported Agreeableness.39.43.26.36.33.37.52.66.51


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