Skip to main content
. Author manuscript; available in PMC: 2017 Jun 14.
Published in final edited form as: J Pers Soc Psychol. 2016 Jan 28;111(3):430–450. doi: 10.1037/pspp0000090

Table 2.

Regression Models Predicting Differential Stability of Risk-Taking Propensity from Age Across 10 Cohorts

General Driving Financial Recreational Occupational Health Social
R2 .194 .358 .534 .287 .269 .225 .566
b p b p b p b p b p b p b P
Intercept .232
[.096]		 .024 .180
[.089]		 .055 .035
[.087]		 .689 .339
[.065]		 <.001 .139
[.090]		 .135 .265
[.061]		 <.001 .092
[.063]		 .157
Age .009
[.004]		 .027 .015
[.004]		 <.001 .020
[.004]		 <.001 .007
[.003]		 .017 .012
[.004]		 .004 .007
[.003]		 .021 .016
[.003]		 <.001
Age2 −.0001
[.00004]		 .043 −.0002
[.00004]		 <.001 −.0002
[.00004]		 <.001 −.0001
[.00003]		 .008 −.0001
[.00004]		 .004 −.0001
[.0003]		 .014 −.0002
[.00003]		 <.001
Age3

Note. Models contain age2 only if their effect on intercepts and slopes were significant at p < .05. Values in brackets indicate standard errors. The total n for this analysis is 30, which is the number of cohorts by the number of test–retest correlations. Effects of the interval (2004–2009, 2009–2014, 2004–2014) on mean-level trends were controlled for a by a cluster variable within a mixed-effects framework.