Future Time Perspective: An Application of Exploratory Data Mining Techniques

Future time perspective (FTP), or individuals’ estimations of the amount of lifetime they feel they have remaining, is integral to motivation and subjective well-being across adulthood. Despite marked individual differences in FTP within age groups, however, there is a paucity of studies examining contributing factors to these individual differences. Although proponents of the Socioemotional Selectivity Theory (SST) have theorized that age and health are important contributing factors to FTP, scholars have also reported strong associations between FTP and stable personality traits. Utilizing machine-learning techniques, the current study examined the salience of several potential contributors to FTP simultaneously, including Age, Number of Chronic Health Conditions, Frequency of Somatic Symptoms, Self-rated Health, and the Five-factor Personality Domains, in a sample of N = 387 individuals (M_Age = 67.35, SD_Age = 8.89). First, an Elastic Net model indicated that Conscientiousness, Extraversion, and Agreeableness predicted an extensive FTP, whereas Neuroticism and Self-rated Health predicted a limited FTP. Next, a Conditional Inference Tree uncovered several joint and interactive effects among Neuroticism, Extraversion, Conscientiousness, Agreeableness, and Global Self-rated Health; different arrays of these factors were associated with varying degrees of open and limited FTP. Finally, based on a Conditional Inference Forest model, Neuroticism was the most important predictor of FTP, followed by Extraversion, Conscientiousness, Agreeableness, Self-rated Health, and Openness. Contrary to what may be expected based on the SST, these findings imply that stable personality traits more saliently contribute to individual differences in FTP than age and health. Results indicate that future time perspective may be less context-dependent than scholars originally thought.