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Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.

Previous research on counterproductive work behavior (CWB) has shown that workplace deviance can be predicted from individual differences and environmental variables, but relatively little is known about CWBs’ relations with counterproductive behaviors in other domains of an individual's life. Data from 500 male twins were used to examine relations among counterproductive behaviors from several life domains, including school, non‐work, substance use, and work. The results supported the hypotheses that counterproductivity in work and a variety of personal life domains, previous and contemporaneous, are strongly and positively related. A general counterproductivity factor, giving rise to rule‐ and norm‐breaking behavioral repertoire of individuals, accounted for approximately half the variance across measures of counterproductivity in specific life domains. To inform theory and research, the etiology of inter‐individual differences in counterproductivity was examined. Biometric analyses revealed that most of the variance in the counterproductivity domains examined, including CWB, is attributable to genetic and unique (nonshared) environmental factors. The general counterproductivity factor spanning different counterproductivity domains was most influenced by genetic factors (75.4%), but was also influenced by unique environmental factors (24.6%). Biometric analyses indicated that 27% of the variance in CWB is attributable to genetic influences arising from the general factor of counterproductivity and 20% from genetic factors specific to CWB. Unique environmental influences associated with the work domain explained 12% of the variance in CWB. For the CWB criterion, regression analyses explored the usefulness of information from other counterproductivity domains for prediction and employee selection. Counterproductivity from academic and non‐work domains are potent predictors of counterproductivity at work (multiple Rs ranging between .50 and .54).

Job satisfaction research has unfolded as an exemplary manifestation of the “person versus environment” debate in applied psychology. With the increasing recognition of the importance of time, it is informative to examine a question critical to the dispositional view of job satisfaction: Are genetic influences on job satisfaction stable across different time points? Drawing upon dispositional and situational perspectives on job satisfaction and recent research in developmental behavioral genetics, we examined whether the relative potency of genetic (i.e., the person) and environmental influences on job satisfaction changed over time in a 3-wave longitudinal twin study. Biometric behavioral genetics analyses showed that genetic influences accounted for 31.2% of the variance in job satisfaction measured at approximately Age 21, which was markedly greater than the 18.7% and 19.8% of variance explained by genetic factors at Age 25 and Age 30. Such genetic influences were mediated via positive affectivity and negative affectivity, but not via general mental ability. After partialing out genetic influences, environmental influences on job satisfaction were related to interpersonal conflict at work and occupational status, and these influences were relatively stable across the 3 time points. These results offer important implications for organizations and employees to better understand and implement practices to enhance job satisfaction.

The current paper examines the assumptions of the MZ co-twin control method that has been applied in attempts to gain more accurate estimates of the returns to education by naturally “controlling” for individual differences on which MZ twins are matched. The current study examined 1738 MZ and 926 DZ twins from Minnesota, including 133 pairs of MZ and 101 pairs of DZ twins discordant for university attendance. They were assessed prospectively on personality, intelligence, GPA, and academic motivation; a subset also has reported income at age 29. MZ twins discordant for university attendance differed significantly and prospectively on verbal IQ, personality traits, and GPA. While MZ co-twin control studies can provide more accurate estimates of the returns to education than analyses of single individuals, these studies do not entirely obviate the need to control for differences between university students and non-students that predate university attendance and might account for income differentials and even non-monetary outcomes.