How do behavioral scientists collect good data online? This question drives most of my research. At CloudResearch, I study the platforms researchers use to collect data, the challenges they face in getting quality responses, and recently, how AI is changing the research process.
A lot of my work focuses on data quality. Some of this is practical: how do you screen out participants who aren't paying attention or aren't who they say they are? For many years, my colleagues and I have shown that the "bots" many researchers blame for bad data are actually people committing survey fraud. Now that the bots are actually coming for online surveys, we are studying how to detect AI agents.
I've recently written a research methods textbook with Cambridge University Press that pulls much of my research together into a practical guide for researchers. Part I of the book focuses on experiential learning of basic research methods concepts; Part II is a guide to conducting successful online research.
Jaffe, S. N., Moss, A. J., Rosenzweig, C., Gautam, R., Robinson, J., & Litman, L. (2026). The bots ruining social science are not bots at all. Perspectives on Psychological Science. https://doi.org/10.1177/17456916251404872
Moss, A. J., Hauser, D. J., Rosenzweig, C., Robinson, J., & Litman, L. (2024). Mechanical Turk: A versatile tool in the behavioral scientist’s toolkit. In J. E. Edlund & A. L. Nichols (Eds.), The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences, Vol. 2. Cambridge University Press.
Moss, A. J., Hauser, D. J., Rosenzweig, C., Jaffe, S., Robinson, J., & Litman, L. (2023). Using market research panels for behavioral science: An overview and tutorial. Advances in Methods and Practices in Psychological Science, 6(2). https://doi.org/10.1177/25152459221140388
Litman, L., Rosen, Z., Rosenzweig, C., Weinberger-Litman, S. L., & Moss, A. J. (2023). Did people really drink bleach to prevent COVID-19? A guide for protecting survey data against problematic respondents. PLOS ONE, 18(7), e0287837.
Moss, A. J., Rosenzweig, C., Robinson, J., & Litman, L. (2023). Is it ethical to use Mechanical Turk for behavioral research? Relevant data from a representative survey of MTurk participants and wages. Behavior Research Methods. https://doi.org/10.3758/s13428-022-02005-0
Hauser, D., Moss, A. J., Rosenzweig, C., Jaffe, S. N., Robinson, J., & Litman, L. (2022). Evaluating CloudResearch’s Approved Group as a solution for problematic data quality on MTurk. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01999-x
Hartman, R., Moss, A. J., Rabinowitz, I., Bahn, N., Rosenzweig, C., Robinson, J., & Litman, L. (2022). Do you know the Wooly Bully? Testing era-based knowledge to verify participant age online. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01944-y
Moss, A. J., Rosenzweig, C., Robinson, J., & Litman, L. (2020). Demographic stability on Mechanical Turk despite COVID-19. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2020.05.014
Robinson, J., Rosenzweig, C., Moss, A. J., & Litman, L. (2019). Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the Mechanical Turk participant pool. PLOS ONE, 14(12), e0226394. https://doi.org/10.1371/journal.pone.0226394
Chandler, J., Rosenzweig, C., Moss, A. J., Robinson, J., & Litman, L. (2019). Online panels in social science research: Expanding sampling methods beyond Mechanical Turk. Behavior Research Methods. https://doi.org/10.3758/s13428-019-01273-7
Rivera, E. D., Wilkowski, B. M., Moss, A. J., Rosenzweig, C., & Litman, L. (2022). Assessing the efficacy of a participant-vetting procedure to improve data quality on Amazon’s Mechanical Turk. Methodology, 18(2), 126–143. https://doi.org/10.5964/meth.8331
I also study how people perceive and respond to prejudice — both in others and in themselves.
Some of this work examines how people decide whether something counts as discrimination. People's judgments about intent and harm often shape their perceptions of discrimination in ways that don't always align with how the law or policy defines discrimination. I've also studied how group membership affects these judgments — people often see the same behavior differently depending on who's involved.
I've also studied what happens when people confront evidence of their own bias. Most of us are motivated to see ourselves as good people, which can make acknowledging prejudice difficult. My dissertation research explored how social norms might make it easier for people to admit bias rather than deny it.
Moss, A. J., Budd, R., Blanchard, M. A., & O’Brien, L. T. (2022). The upside of acknowledging prejudiced behavior. Journal of Experimental Social Psychology. https://doi.org/10.1016/j.jesp.2022.104401
Kulibert, D., Moss, A.J., Appleby, J. and O'Brien, L.T. (2025), Perceptions of Political Deviants in the US Democrat and Republican Parties. Journal of Applied Social Psychology, 55: 87-102. https://doi.org/10.1111/jasp.13079
Simon, S., Moss, A. J., & O’Brien, L. T. (2019). Pick your perspective: Racial-group membership and judgments of intent, harm, and discrimination. Group Processes & Intergroup Relations. https://doi.org/10.1177/1368430217735576
Moss, A. J., Blodorn, A., Van Camp, A. R., & O’Brien, L. T. (2018). Gender equality, value violations, and prejudice toward Muslims. Group Processes & Intergroup Relations. https://doi.org/10.1177/1368430217716751
You can find all my publications on Google Scholar.