From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM).

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.

Enemies and friends in high-tech places: the development and validation of the Online Social Experiences Measure.

BACKGROUND: Interpersonal positivity and negativity are separable dimensions both recognized as having important effects on health. Though online social interactions and research examining them are increasingly common, no validated instrument has heretofore been developed to examine social support and social negativity specifically in online contexts. The present studies describe the development and validation of the Online Social Experiences Measure (OSEM) to allow for assessment of online social positivity and negativity simultaneously. METHODS AND RESULTS: In Study I, responses to a potential item pool were collected from participants (N = 557), who were then subdivided into two groups for the purposes of cross-validation via exploratory and confirmatory factor analyses. Results suggested the emergence of two factors: online social support (OSEM+ subscale) and online social negativity (OSEM- subscale). Across Studies I and II (N = 139), the OSEM showed good convergent and discriminant validity, and both subscales had high internal consistency. Study II included an ambulatory cardiovascular assessment and found the OSEM- subscale to be predictive of elevated diastolic blood pressure and heart rate. The OSEM+ subscale was predictive of more favorable cardiovascular readings for some types of participants (e.g. those with a greater number of online-only social ties). CONCLUSIONS: Overall, results suggest online social positivity and negativity may have implications for physical health, and that the OSEM may be a useful tool in understanding online social processes.

Societal Conditions and the Gender Difference in Well-Being: Testing a Three-Stage Model.

Findings from a meta-analysis on gender differences in self-esteem (Zuckerman et al., 2016) suggest that the relation between the degree to which societal conditions are favorable to women and gender difference in self-esteem might be quadratic; when conditions improve, women's self-esteem (relative to that of men) trends downward but when conditions continue to improve, women's self-esteem begins to trend upward. Testing whether these relations generalize to subjective well-being, the present study found a quadratic relation between improving societal conditions and the gender difference in life satisfaction and positive affect (women are lower than men when societal conditions are moderately favorable compared to when they are at their worst and at their best); the relation was linear for negative emotion (women report more negative emotions than men when societal conditions are better). Directions for future research that will address potential explanations for these results are proposed.