Wagerman & Funder (2007) showed that conscientiousness predicted college GPA independent of SAT and high school GPA. Also see Noftle & Robins (2007, http://psychology.okstate.edu/faculty/jgrice/psyc4333/FiveFactor_GPA_JPSP.pdf) who found conscientiousness as the best personality predictor of GPA. This could be the "character" component of GPA that also predicts debtor responsibility. Perhaps banks should give five-factor tests rather than/along with looking at transcripts.
Wagerman, S. A., & Funder, D. C. (2007). Acquaintance reports of personality and academic achievement: A case for conscientiousness. Journal of Research in Personality, 41, 221–229 ________________________________________ From: Mike Palij <m...@nyu.edu> Sent: Monday, July 27, 2015 11:41 AM To: Teaching in the Psychological Sciences (TIPS) Cc: Michael Palij Subject: [tips] Fw:CORRECTED If Your Students Want To Get A Mortgage When They Graduate, Tell Them "Be Good Students" Okay, pressed the send accidentally. I've completed the post below. Still trying to get this internet thing down. ;-) -MP ----- Original Message ----- Robyn Dawes has argued that a simple linear regression-type equation is better at predicting a person's behavior or achievement in some process than subjective judgments (e.g., impressions from an interview) in the following wonderfully titled article: Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American psychologist, 34(7), 571. Dawes' claims that such linear decision models were more fair that subjective judgments (along with their implicit biases) were but this was not accepted by many because people, especially clinical psychologists (or, if you're a sports fan, sports management; read the book or see the movie "Moneyball" for a comparable example) who felt their intuitions and experience were better guides to judging a person than some equation, even if the equation gave more successful predictions. Dawes has said such experts may know what variables are important but they can't "add them together" (i.e., integrate the information into an appropriate predication without bias and error). People in economics and finance, however, took Dawes advice to heart and realized that if they were going to lend people money, they'd better be able to predict whether they pay back that money along with interest. Computer algorithms now go over a person's credit history, assess the risk of lending money to such a person, and related information. But what about a person who went to college right from high school, graduates from college, has a job, and now wants a loan. Such a person has a limited credit history so the traditional algorithms really don't apply or, if used, will excessively deny good risks. But what makes a person a good risk for a loan if they have a very limited credit history? Some people have suggested using indicators of a person's "character" to assess their riskiness. The NY Times has a article in its "Bits" section on a couple of companies that have developed such algorithmic systems, systems that indicate "good" or "bad" or neutral character. See: http://bits.blogs.nytimes.com/2015/07/26/using-algorithms-to-determine-character/ Quoting from the article:|"If you take two people with the same job and circumstances,|like whether they have kids, five years later the one who had|the higher G.P.A. is more likely to pay a debt," said Paul Gu,|Upstart's co-founder and head of product. "It's not whether you|can pay. It's a question of how important you see your obligation."||The idea, validated by data, is that people who did things like|double-checking the homework or studying extra in case there|was a pop quiz are thorough and likely to honor their debts.||Analytics, meet judgment of people. "I guess you could call it|character, though we haven't used that label," said Mr. Gu,|who is 24.So, students who are conscientious in their school work and infulfilling course requirements -- fullfillng their educationalobligations --may be better credit risks, even if they are a "subprime"candidate given traditional algorithmic evaluation.This seems to make sense and I think Robyn Dawes would bepleased with this but it will be interesting to see what getspublished on this. I think, however, I'll bet my money oncharacter. ;-)-Mike PalijNew York universitym...@nyu.edu --- You are currently subscribed to tips as: wsc...@wooster.edu. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13058.902daf6855267276c83a639cbb25165c&n=T&l=tips&o=46161 or send a blank email to leave-46161-13058.902daf6855267276c83a639cbb251...@fsulist.frostburg.edu --- You are currently subscribed to tips as: arch...@mail-archive.com. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13090.68da6e6e5325aa33287ff385b70df5d5&n=T&l=tips&o=46184 or send a blank email to leave-46184-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
