Farjam, Mike, and Giangiacomo Bravo. 2020. “The Gap Between Self-reported and Actual Contributions to Climate Change Mitigation in US Residents.” SocArXiv. June 11. doi:10.31235/osf.io/rqd4s
Abstract: Surveys measures of environmental concern are know to only weakly predict self-reported environmental behaviour. In addition, self-reported and actual behaviour may not match in empirical settings. To better explore the relation among these variables and the political stance of participants, we ran an online experiment with 805 US residents. Four key variables – environmental concern, self -reported environmental behaviour, observed environmental behaviour (in the form of carbon compensation), and political attitudes – were measured and their interactions in promoting pro-environment behaviour were analysed. We found that self-reported measures hardly held any correlation with real behaviour and that political attitudes mainly predicted self-reported measures, not real environmental behaviour.
Thursday, June 11, 2020
Only 15.33% of manuscripts in Psychological Science 2009-2019 claim to test predictions derived from theories; most psychological research is not driven by theory, nor can it be contributing to theory building
McPhetres, Jonathon, Nihan Albayrak-Aydemir, Ana Barbosa Mendes, Elvina C. Chow, Patricio Gonzalez-Marquez, Erin Loukras, Annika Maus, et al. 2020. “A Decade of Theory as Reflected in Psychological Science (2009-2019).” PsyArXiv. June 11. doi:10.31234/osf.io/hs5nx
Abstract: The dominant belief is that science progresses by testing theories and moving towards theoretical consensus. While it’s implicitly assumed that psychology operates in this manner, critical discussions claim that the field suffers from a lack of cumulative theory. To examine this paradox, we analysed research published in Psychological Science from 2009-2019 (N = 2,225). We found mention of 359 theories in-text, most were referred to only once. Only 53.66% of all manuscripts included the word theory, and only 15.33% explicitly claim to test predictions derived from theories. We interpret this to suggest that most psychological research is not driven by theory, nor can it be contributing to cumulative theory building. These data provide insight into the kinds of research psychologists are conducting and raises questions about the role of theory in the psychological sciences.
Abstract: The dominant belief is that science progresses by testing theories and moving towards theoretical consensus. While it’s implicitly assumed that psychology operates in this manner, critical discussions claim that the field suffers from a lack of cumulative theory. To examine this paradox, we analysed research published in Psychological Science from 2009-2019 (N = 2,225). We found mention of 359 theories in-text, most were referred to only once. Only 53.66% of all manuscripts included the word theory, and only 15.33% explicitly claim to test predictions derived from theories. We interpret this to suggest that most psychological research is not driven by theory, nor can it be contributing to cumulative theory building. These data provide insight into the kinds of research psychologists are conducting and raises questions about the role of theory in the psychological sciences.
Dying to Divulge: The Determinants Of, and Relationship Between, Desired and Actual Disclosure
Carbone, Erin, and George Loewenstein. 2020. “Dying to Divulge: The Determinants Of, and Relationship Between, Desired and Actual Disclosure.” PsyArXiv. May 28. doi:10.31234/osf.io/wfdhx
Abstract: Studies suggest that sharing thoughts and information with others may be inherently pleasurable and confer health, psychological, and social benefits to the discloser. At the same time, self-disclosure exposes individuals to scrutiny and the risk of rejection and reputational damage, particularly with the advent of digital applications and social media outlets that promote public, and often permanent, disclosing. In an effort to understand the tradeoffs that underlie the decision to disclose, we introduce a distinction between the propensity to disclose and the psychological desire to disclose and present a preliminary investigation into when and why these two constructs diverge. Findings from two exploratory studies reveal the types of information that individuals are most eager to share, as well as the contextual factors and individual characteristics that moderate the desire to share and the circumstances under which this desire is most likely to translate into actual sharing. We replicate findings from prior research that the decision to disclose is a function of content emotionality and valence, but find that the propensity to withhold negative information is most pronounced when the information is about oneself than about others, and that gender differences in disclosure are largely driven by the tendency for men to withhold negative, but not positive, information. Additionally, we capture motives and traits, many of them previously unexplored in the disclosure context, to model the underlying decision-making process that leads to information sharing and distinguish between the act of sharing information and the psychological desire that differentially engender disclosing behavior.
Abstract: Studies suggest that sharing thoughts and information with others may be inherently pleasurable and confer health, psychological, and social benefits to the discloser. At the same time, self-disclosure exposes individuals to scrutiny and the risk of rejection and reputational damage, particularly with the advent of digital applications and social media outlets that promote public, and often permanent, disclosing. In an effort to understand the tradeoffs that underlie the decision to disclose, we introduce a distinction between the propensity to disclose and the psychological desire to disclose and present a preliminary investigation into when and why these two constructs diverge. Findings from two exploratory studies reveal the types of information that individuals are most eager to share, as well as the contextual factors and individual characteristics that moderate the desire to share and the circumstances under which this desire is most likely to translate into actual sharing. We replicate findings from prior research that the decision to disclose is a function of content emotionality and valence, but find that the propensity to withhold negative information is most pronounced when the information is about oneself than about others, and that gender differences in disclosure are largely driven by the tendency for men to withhold negative, but not positive, information. Additionally, we capture motives and traits, many of them previously unexplored in the disclosure context, to model the underlying decision-making process that leads to information sharing and distinguish between the act of sharing information and the psychological desire that differentially engender disclosing behavior.
Wednesday, June 10, 2020
25 simple job features explain over half the variance in which jobs are now automated; biggest automation predictor is Pace Determined By Speed Of Equipment; predictors didn't change from 1999 to 2019
Testing the automation revolution hypothesis. Keller Scholl, Robin Hanson. Economics Letters, Volume 193, August 2020, 109287. https://doi.org/10.1016/j.econlet.2020.109287
Highlights
• 25 simple job features explain over half the variance in which jobs are now automated.
• The strongest job automation predictor is: Pace Determined By Speed Of Equipment.
• Which job features predict job automation how did not change from 1999 to 2019.
• Jobs that get more automated do not on average change in pay or employment.
• Labor markets change more often due to changes in demand, relative to supply.
Abstract: Wages and employment predict automation in 832 U.S. jobs, 1999 to 2019, but add little to top 25 O*NET job features, whose best predictive model did not change over this period. Automation changes predict changes in neither wages nor employment.
Keywords: AutomationWagesEmploymentOccupationsArtificial intelligenceTechnology
Highlights
• 25 simple job features explain over half the variance in which jobs are now automated.
• The strongest job automation predictor is: Pace Determined By Speed Of Equipment.
• Which job features predict job automation how did not change from 1999 to 2019.
• Jobs that get more automated do not on average change in pay or employment.
• Labor markets change more often due to changes in demand, relative to supply.
Abstract: Wages and employment predict automation in 832 U.S. jobs, 1999 to 2019, but add little to top 25 O*NET job features, whose best predictive model did not change over this period. Automation changes predict changes in neither wages nor employment.
Keywords: AutomationWagesEmploymentOccupationsArtificial intelligenceTechnology
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