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Part IV    Dispositional Theories428
ratings at(r  = 0.56). Another outcome measure washowwellthese human and
computer-generated personality predicted realworld outcomes, such assubstance 
use,physical health, depression, impulsivity ,andlife-satisfaction. Youyou andcol-
leagues found thatFacebook “likes” predicted these outcomes better thanallbut
self-ratings ofpersonality. 
Lastly, howweusesocial media ispartly impacted byourpersonalities. For
instance, people highinagreeableness receive more “likes” and“comments” on
their Facebook profile pictures thanlessagreeable people; people highinextraver -
sionaremore active onsocial media, upload more photos, update their status more 
frequently, andhave more posted friends thanthose lowinextraversion (Eftekhar, 
Fullwood, &Morris, 2014; Lee,Ahn, &Kim, 2014; Michikyan, Subrahmanyam, 
&Dennis, 2014; Ongetal.,2011). People highinanxiety andneuroticism use
more negative words intheir posts andaremore likely todisclose private aspects 
ofthemselves compared topeople lowinanxiety andneuroticism (Schwartz etal.,
2013; Seidman, 2013). 
Critique of Trait and Factor Theories
Trait andfactor methods—especially those ofEysenck andadvocates oftheBigFive
model—provide important taxonomies thatorganize personality intomeaningful clas-
sifications. Aspointed outinChapter 1,however, taxonomies alone donotexplain or
predict behavior, twoimportant functions ofuseful theories. .00
Openness Conscientiousness Extraversion Agreeableness Neuroticism
Big Five T raitAccuracy
.05.10.15.20.25.30.35.40.45 Human
Computer
FIGURE 13.5  Accuracy of human- and computer-based predictions of personanlity traits obtained from meta-analyses 
by Tskhay and Rule (2014; gray bars) and Azucar, Marengo, and Settanni (2018; black bars). Accuracy was measured as the 
correlation between observers ’ and targets ’ personality scores (0 = no relationship; 1 = perfect relationship). 
Source: Adapted from Hinds andJoinson (2019)Final PDF to printer