We investigate the effect of personality on prosocial behavior in a Bayesian multilevel meta-analysis (MLMA) of 15 published, interdisciplinary experimental studies. With data from the 15 studies constituting nearly 2500 individual observations, we find that the Big Five traits of Agreeableness and Openness are significantly and positively associated with prosocial behavior, while none of the other three traits are. These results are robust to a number of different model specifications and operationalizations of prosociality, and they greatly clarify the contradictory findings in the literature on the relationship between personality and prosocial behavior. Though previous research has indicated that incentivized experiments result in reduced prosocial behavior, we find no evidence that monetary incentivization of participants affects prosocial tendencies. By leveraging individual observations from multiple studies and explicitly modeling the multilevel structure of the data, MLMA permits the simultaneous estimation of study- and individual-level effects. The Bayesian approach allows us to estimate study-level effects in an unbiased and efficient manner, even with a relatively small number of studies. We conclude by discussing the limitations of our study and the advantages and disadvantages of the MLMA method.

In this commentary, we embed the volume's contributions on public beliefs about science in a broader theoretical discussion of motivated political reasoning. The studies presented in the preceding section of the volume consistently find evidence for hyperskepticism toward scientific evidence among ideologues, no matter the domain or context-and this skepticism seems to be stronger among conservatives than liberals. here, we show that these patterns can be understood as part of a general tendency among individuals to defend their prior attitudes and actively challenge attitudinally incongruent arguments, a tendency that appears to be evident among liberals and conservatives alike. We integrate the empirical results reported in this volume into a broader theoretical discussion of the John Q. Public model of information processing and motivated reasoning, which posits that both affective and cognitive reactions to events are triggered unconsciously. We find that the work in this volume is largely consistent with our theories of affect-driven motivated reasoning and biased attitude formation.

Enns et al. respond to recent work by Grant and Lebo and Lebo and Grant that raises a number of concerns with political scientists' use of the general error correction model (GECM). While agreeing with the particular rules one should apply when using unit root data in the GECM, Enns et al. still advocate procedures that will lead researchers astray. Most especially, they fail to recognize the difficulty in interpreting the GECM's "error correction coefficient." Without being certain of the univariate properties of one's data it is extremely difficult (or perhaps impossible) to know whether or not cointegration exists and error correction is occurring. We demonstrate the crucial differences for the GECM between having evidence of a unit root (from Dickey-Fuller tests) versus actually having a unit root. Looking at simulations and two applied examples we show how overblown findings of error correction await the uncareful researcher.

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There is reason to believe that an increasing proportion of the news consumers receive is not from news producers directly but is recirculated through social network sites and email by ordinary citizens. This may produce some fundamental changes in the information environment, but the data to examine this possibility have thus far been relatively limited. In the current paper, we examine the changing information environment by leveraging a body of data on the frequency of (a) views, and recirculations through (b) Twitter, (c) Facebook, and (d) email of New York Times stories. We expect that the distribution of sentiment (positive-negative) in news stories will shift in a positive direction as we move from (a) to (d), based in large part on the literatures on self-presentation and imagined audiences. Our findings support this expectation and have important implications for the information contexts increasingly shaping public opinion.