Does Smile Intensity in Photographs Really Predict Longevity? A Replication and Extension of Abel and Kruger (2010).

Abel and Kruger (2010) found that the smile intensity of professional baseball players who were active in 1952, as coded from photographs, predicted these players' longevity. In the current investigation, we sought to replicate this result and to extend the initial analyses. We analyzed (a) a sample that was almost identical to the one from Abel and Kruger's study using the same database and inclusion criteria ( N = 224), (b) a considerably larger nonoverlapping sample consisting of other players from the same cohort ( N = 527), and (c) all players in the database ( N = 13,530 valid cases). Like Abel and Kruger, we relied on categorical smile codings as indicators of positive affectivity, yet we supplemented these codings with subjective ratings of joy intensity and automatic codings of positive affectivity made by computer programs. In both samples and for all three indicators, we found that positive affectivity did not predict mortality once birth year was controlled as a covariate.

Successfully Striving for Happiness: Socially Engaged Pursuits Predict Increases in Life Satisfaction.

Happiness is considered a highly desirable attribute, but whether or not individuals can actively steer their lives toward greater well-being is an open empirical question. In this study, respondents from a representative German sample reported, in text format, ideas for how they could improve their life satisfaction. We investigated which of these ideas predicted changes in life satisfaction 1 year later. Active pursuits per se-as opposed to statements about external circumstances or fortune-were not associated with changes in life satisfaction ( n = 1,178). However, in line with our preregistered hypothesis, among individuals who described active pursuits ( n = 582), those who described social ideas (e.g., spending more time with friends and family) ended up being more satisfied, and this effect was partly mediated by increased socializing. Our results demonstrate that not all pursuits of happiness are equally successful and corroborate the great importance of social relationships for human well-being.

How people know their risk preference.

People differ in their willingness to take risks. Recent work found that revealed preference tasks (e.g., laboratory lotteries)-a dominant class of measures-are outperformed by survey-based stated preferences, which are more stable and predict real-world risk taking across different domains. How can stated preferences, often criticised as inconsequential "cheap talk," be more valid and predictive than controlled, incentivized lotteries? In our multimethod study, over 3,000 respondents from population samples answered a single widely used and predictive risk-preference question. Respondents then explained the reasoning behind their answer. They tended to recount diagnostic behaviours and experiences, focusing on voluntary, consequential acts and experiences from which they seemed to infer their risk preference. We found that third-party readers of respondents' brief memories and explanations reached similar inferences about respondents' preferences, indicating the intersubjective validity of this information. Our results help unpack the self perception behind stated risk preferences that permits people to draw upon their own understanding of what constitutes diagnostic behaviours and experiences, as revealed in high-stakes situations in the real world.

"What else are you worried about?" - Integrating textual responses into quantitative social science research.

Open-ended questions have routinely been included in large-scale survey and panel studies, yet there is some perplexity about how to actually incorporate the answers to such questions into quantitative social science research. Tools developed recently in the domain of natural language processing offer a wide range of options for the automated analysis of such textual data, but their implementation has lagged behind. In this study, we demonstrate straightforward procedures that can be applied to process and analyze textual data for the purposes of quantitative social science research. Using more than 35,000 textual answers to the question "What else are you worried about?" from participants of the German Socio-economic Panel Study (SOEP), we (1) analyzed characteristics of respondents that determined whether they answered the open-ended question, (2) used the textual data to detect relevant topics that were reported by the respondents, and (3) linked the features of the respondents to the worries they reported in their textual data. The potential uses as well as the limitations of the automated analysis of textual data are discussed.