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2. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.

Author

Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L

Subjects
conceptual replications, crowdsourcing, forecasting, research robustness, scientific transparency, Crowdsourcing Hypothesis Tests Collaboration, Crowdsourcing, stimulus sampling, replication, Psychology, Cognitive Sciences, Marketing, Social Psychology
Abstract

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = -0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Published
2020

6. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M. Brent, Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, Uhlmann, Eric L., Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M. Brent, Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L.

Published
2020

7. Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

Author

Landy, J, Jia, M, Ding, I, Viganola, D, Tierney, W, Dreber, A, Johannesson, M, Pfeiffer, T, Ebersole, C, Gronau, Q, Ly, A, van den Bergh, D, Marsman, M, Derks, K, Wagenmakers, E, Proctor, A, Bartels, D, Bauman, C, Brady, W, Cheung, F, Cimpian, A, Dohle, S, Donnellan, M, Hahn, A, Hall, M, Jiménez-Leal, W, Johnson, D, Lucas, R, Monin, B, Montealegre, A, Mullen, E, Pang, J, Ray, J, Reinero, D, Reynolds, J, Sowden, W, Storage, D, Su, R, Tworek, C, Van Bavel, J, Walco, D, Wills, J, Xu, X, Yam, K, Yang, X, Cunningham, W, Schweinsberg, M, Urwitz, M, The Crowdsourcing Hypothesis Tests, C, Uhlmann, E, Mari, S, Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, Uhlmann, Eric L, Landy, J, Jia, M, Ding, I, Viganola, D, Tierney, W, Dreber, A, Johannesson, M, Pfeiffer, T, Ebersole, C, Gronau, Q, Ly, A, van den Bergh, D, Marsman, M, Derks, K, Wagenmakers, E, Proctor, A, Bartels, D, Bauman, C, Brady, W, Cheung, F, Cimpian, A, Dohle, S, Donnellan, M, Hahn, A, Hall, M, Jiménez-Leal, W, Johnson, D, Lucas, R, Monin, B, Montealegre, A, Mullen, E, Pang, J, Ray, J, Reinero, D, Reynolds, J, Sowden, W, Storage, D, Su, R, Tworek, C, Van Bavel, J, Walco, D, Wills, J, Xu, X, Yam, K, Yang, X, Cunningham, W, Schweinsberg, M, Urwitz, M, The Crowdsourcing Hypothesis Tests, C, Uhlmann, E, Mari, S, Landy, Justin F, Jia, Miaolei Liam, Ding, Isabel L, Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R, Gronau, Quentin F, Ly, Alexander, van den Bergh, Don, Marsman, Maarten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M, Bauman, Christopher W, Brady, William J, Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, M Brent, Hahn, Adam, Hall, Michael P, Jiménez-Leal, William, Johnson, David J, Lucas, Richard E, Monin, Benoît, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A, Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M, Van Bavel, Jay J, Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Kai Chi, Yang, Xiaoyu, Cunningham, William A, Schweinsberg, Martin, Urwitz, Molly, The Crowdsourcing Hypothesis Tests Collaboration, and Uhlmann, Eric L

Abstract

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = -0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

Published
2020

9. Crowd-sourcing Hypothesis Tests: Making Transparent How Design Choices Shape Research Results

Author

Landy, Justin, primary, Jia, Miaolei, additional, Ding, Isabel, additional, Viganola, Domenico, additional, Tierney, Warren, additional, Dreber, Anna, additional, Johanneson, Magnus, additional, Pfeiffer, Thomas, additional, Ebersole, Charles, additional, Gronau, Quentin, additional, Ly, Alexander, additional, van den Bergh, Don, additional, Marsman, Maarten, additional, Derks, Koen, additional, Wagenmakers, Eric-Jan, additional, Proctor, Andrew, additional, Bartels, Daniel M., additional, Bauman, Christopher W., additional, Brady, William J., additional, Cheung, Felix, additional, Cimpian, Andrei, additional, Dohle, Simone, additional, Donnellan, M. Brent, additional, Hahn, Adam, additional, Hall, Michael P., additional, Jiménez-Leal, William, additional, Johnson, David J., additional, Lucas, Richard E., additional, Monin, Benoit, additional, Montealegre, Andres, additional, Mullen, Elizabeth, additional, Pang, Jun, additional, Ray, Jennifer, additional, Reinero, Diego A., additional, Reynolds, Jesse, additional, Sowden, Walter, additional, Storage, Daniel, additional, Su, Runkun, additional, Tworek, Christina M., additional, Van Bavel, Jay, additional, Walco, Daniel, additional, Wills, Julian, additional, XU, Xiaobing, additional, Yam, Kai Chi, additional, Yang, Xiaoyu, additional, Cunningham, William A., additional, Schweinsberg, Martin, additional, Urwitz, Molly, additional, and Uhlmann, Eric Luis, additional

Published
2020
Full Text
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11. Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, Uhlmann, Eric Luis, Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, and Uhlmann, Eric Luis

Abstract

peer-reviewed, To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

12. Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, Uhlmann, Eric Luis, Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, and Uhlmann, Eric Luis

Abstract

peer-reviewed, To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

13. Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Author

Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, Uhlmann, Eric Luis, Landy, Justin F., Jia, Miaolei (Liam), Ding, Isabel L., Viganola, Domenico, Tierney, Warren, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Ebersole, Charles R., Gronau, Quentin F., Ly, Alexander, van den Bergh, Don, Marsman, Maaten, Derks, Koen, Wagenmakers, Eric-Jan, Proctor, Andrew, Bartels, Daniel M., Bauman, Christopher W., Brady, William J., Cheung, Felix, Cimpian, Andrei, Dohle, Simone, Donnellan, Brent M., Hahn, Adam, Hall, Michael P., Jiménez-Leal, William, Johnson, David J., Lucas, Richard E., Monin, Benoit, Montealegre, Andres, Mullen, Elizabeth, Pang, Jun, Ray, Jennifer, Reinero, Diego A., Reynolds, Jesse, Sowden, Walter, Storage, Daniel, Su, Runkun, Tworek, Christina M., Van Bavel, Jay J., Walco, Daniel, Wills, Julian, Xu, Xiaobing, Yam, Chi Kai, Yang, Xiaoyu, Cunningham, William A., Schweinsberg, Martin, Urwitz, Molly, and Uhlmann, Eric Luis

Abstract

peer-reviewed, To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.

14. The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence.

Author

Yam KC, Tang PM, Jackson JC, Su R, and Gray K

Subjects
Humans, Workplace psychology, Unemployment psychology, Singapore, Job Satisfaction, Surveys and Questionnaires, Robotics, Burnout, Professional
Abstract

Robots are transforming the nature of human work. Although human-robot collaborations can create new jobs and increase productivity, pundits often warn about how robots might replace humans at work and create mass unemployment. Despite these warnings, relatively little research has directly assessed how laypeople react to robots in the workplace. Drawing from cognitive appraisal theory of stress, we suggest that employees exposed to robots (either physically or psychologically) would report greater job insecurity. Six studies-including two pilot studies, an archival study across 185 U.S. metropolitan areas (Study 1), a preregistered experiment conducted in Singapore (Study 2), an experience-sampling study among engineers conducted in India (Study 3), and an online experiment (Study 4)-find that increased exposure to robots leads to increased job insecurity. Study 3 also reveals that this robot-related job insecurity is in turn positively associated with burnout and workplace incivility. Study 4 reveals that self-affirmation is a psychological intervention that might buffer the negative effects of robot-related job insecurity. Our findings hold across different cultures and industries, including industries not threatened by robots. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Published
2023
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