Discussion

These analyses bring to light several critical discoveries about the existing postural-feedback literature. When we included a comprehensive and current set of evidence, comprising 55 studies identified through a systematic review, p-curve analyses revealed (a) clear evidential value for postural feedback on an aggregated set of effects; (b) strong evidential value for a clearly specified and theoretically important single outcome, feelings of power; (c) very strong evidential value for a well-defined and theoretically important category of other feelings effects (i.e., EASE variables, which did not include feelings of power); and (d) an absence of evidential value for the theoretically heterogeneous non-EASE effects that remained after separating out the EASE variables. Our findings also suggest that p-curving is likely to yield more accurate and informative results when researchers address the following practices: (a) faulty sample-selection decisions and (b) undifferentiated aggregation of disparate effects. When these practices are not adequately addressed, p-curve conclusions can lead to misguided dismissals of broad areas of research.

Strong evidential value for postural-feedback effects, particularly for emotions

Our p-curve analyses of emotion- and affect-related outcomes yielded robust evidence that postural feedback influences self-reported affective states. First, we found strong evidential value for a precisely specified outcome, feelings of power. That finding converges with a recent Bayesian meta-analysis of a new set of studies that, as described by Cesario, Jonas, and Carney (2017), “showed a reliable non-zero effect on felt power” (p. 2).5 Presenting their results, Gronau et al. (2017) write that “our meta-analysis yields very strong evidence for an effect of power posing on felt power” (p. 123). In the set of studies presented in our analyses, 11 demonstrated a significant effect of power posing on feelings of power; that does not include studies from 2017, which would increase the total number of replications. Together, the collective evidence provides strong support for the effect of postural feedback on feelings of power. From our theoretical perspective, an expansive posture is a universal expression of power, and adopting such a pose leads people to feel more powerful. The finding of evidential value for self-reported feelings of power directly supports that claim. Moreover, we believe that even transient feelings of power can have long-lasting consequences for people’s lives (e.g., Galinsky et al., 2015).

The robust evidential value for postural-feedback effects on EASE variables—emotions, affect, and self-evaluations—is particularly illuminating. These findings from the present set of studies provide convincing evidence that postural manipulations affected subjects’ specific emotions, affect, mood recovery, retrieval and recall of positive versus negative memories, and self-evaluations, demonstrating that the effects of postural feedback on affective variables clearly extend beyond causing people to feel more powerful. It is worth noting that the direction of most of the EASE effects is consistent with Keltner, Gruenfeld, and Anderson’s (2003) approach-inhibition theory of power: Power activates the behavioral approach system (e.g., recall of more positive than negative words from memory, improved general mood and mood recovery, increased feelings of strength, decreased feelings of fear).

Many studies that are featured in our EASE curve were likely robust to potential demand characteristics, since they used a single- or double-blind study design, deception, or “non-deceptive obfuscation” (Zizzo, 2010, p. 75); tested hypotheses that were simply not intuitive to participants (e.g., mood recovery, changes in various discrete emotion states, changes in negative affect, assignment of valence to a series of thoughts following an open-ended thoughts-listing task); or directly tracked the extent to which participants guessed the hypothesis in exit interviews (which showed that they did not). Some studies were more resilient to demand effects because responses were implicit or otherwise difficult for participants to control (e.g., speed of retrieval of positive and negative personal memories, mood recovery, ability to recall positive and negative words from a list presented earlier in the study, changes to discrete emotion states embedded in a long list of emotions), responses were embedded in a broader survey instrument (e.g., changes in discrete self-reported emotions embedded in a long list of emotions), or, as demonstrated in recent research on demand effects in survey research, participants likely varied in their orientation such that some would have wanted to confirm the hypothesis and some to disconfirm it, and others would have been indifferent (Mummolo & Peterson, 2017). Citations for each of these examples are listed in our supplemental materials at the OSF. Our assessment of the input for our EASE p-curve analysis, the strongest p-curve presented, is that it is unlikely that these postural-feedback effects are demand effects, given the study designs and the latest research on demand characteristics.

In contrast to the EASE p-curve, the non-EASE p-curve comprises a theoretically heterogeneous, noncohesive collection of effects (e.g., number of calories consumed at a meal, pain threshold, vengeful intentions, performance on creativity tasks, hormones, beliefs about religion, performance in a job interview, gambling), making any results, whether they indicate a presence or absence of evidential value, difficult to interpret. Removing the EASE variables flattens the curve for the remaining effects, which could indicate that evidential value for behaviors and hormones is weak. This interpretation is consistent with the mostly null results of the set of studies in a recent special issue of Comprehensive Results in Social Psychology (Jonas et al., 2017) that measured effects of power posing on various behavioral outcomes. However, many of the non-EASE effects include nonbehavioral or hormonal effects, such as cognitive abilities, creativity, and attitudes; the evidence for these effects seems to be stronger. It is also worth noting that the non-EASE effects include measures that are susceptible to demand characteristics, such as gambling, pain tolerance, and action tendencies in hypothetical scenarios. There is also a need for experimental tests of incremental or longitudinal effects of adopting expansive postures over time on various outcomes. Right now, we are not aware of any such research. As more studies are conducted and published, it will become easier for researchers to analyze other theoretically meaningful subsets of effects, such as hormonal effects, performance under stress, risk preferences, and cognitive abilities. Such analyses of these subsets will continue to enhance the definition of this picture.

What do these analyses tell us about the evidence for postural-feedback effects? Given the present p-curve analyses, as strictly interpreted in accordance with the rules of p-curving, one must first conclude that the current literature on postural feedback does possess evidential value. By systematically identifying and analyzing meaningful subsets of effects, p-curving begins to give more definition to our findings and to the overall picture: The existing effects of postural feedback on feelings possess extremely strong evidential value. As the overall body of studies grows, it will become easier to analyze other meaningful subsets, such as cognitive measures, performance behaviors, and psychophysiological outcomes. Combining these more-focused meta-analyses of meaningful categories of effects with new, theory-driven studies that employ improved methods (e.g., preregistration of a priori hypotheses, larger samples, more precise hormone-measurement instruments and methods) and that come from various disciplines will advance and refine our theoretical understanding of postural feedback—and the same will be true for other areas of research. This will lead to the identification of contextual variables that moderate effects and help us to resolve conflicting evidence from studies of some of the specific effects, such as hormones and risk taking, which have produced both significant and null effects. The analyses do not tell us, however, about the extent to which there is evidential value for other meaningful categories of effects, which individual postural-feedback effects are most robust, which of them might be false positives, and how these complex relationships among posture and these many different variables may be affected by various moderators. It should go without saying that these curves are not the final curves. No meta-analysis can be the final meta-analysis, because results hinge entirely on the content of what is included, and that content will continue to grow and change. Science is cumulative by nature.