Link to Google Scholar academic profile (h index = 15)

Publications

Williams, R., Citkowicz, M., Miller, D. I., Lindsay, J., & Walters, K. (2022). Heterogeneity in mathematics intervention effects: Evidence from a meta-analysis of 191 randomized experiments. Journal of Research on Educational Effectiveness, 15(3), 584-634. https://doi.org/10.1080/19345747.2021.2009072 [R code and data]

Miller, D. I., Pinerua, I., Margolin, J., & Gerdeman, D. (2022). Teachers’ pedagogical content knowledge in mathematics and science: A cross-disciplinary synthesis of recent DRK-12 projects. American Institutes for Research.

Witherspoon, E., Miller, D. I., Pinerua, I., & Gerdeman, D. (2022). Mathematical and scientific argumentation in PreK-12: A cross-disciplinary synthesis of recent DRK-12 projects. American Institutes for Research.

Eagly, A. H., Nater, C., Miller, D. I., Kaufmann, M., & Sczesny, S. (2020). Gender stereotypes have changed: A cross-temporal meta-analysis of U.S. public opinion polls from 1946 to 2018. American Psychologist, 75(3), 301–315. https://doi.org/10.1037/amp0000494 [R code and data]

Miller, D. I. (2019). When do growth mindset interventions work? Trends in Cognitive Sciences, 23(11), 910-912. https://doi.org/10.1016/j.tics.2019.08.005 [link to preprint]

Miller, D. I., Nolla, K. M., Eagly, A. H., & Uttal, D. H. (2018). The development of children’s gender-science stereotypes: A meta-analysis of five decades of U.S. Draw-A-Scientist studies. Child Development, 89(6), 1943-1955. https://doi.org/10.1111/cdev.13039 [R code and data]

Atit, K., Miller, D. I., Newcombe, N. S., & Uttal, D. H. (2018). Teachers’ spatial skills across disciplines and education levels: Exploring nationally representative data. Archives of Scientific Psychology, 6(1), 130-137. https://doi.org/10.1037/arc0000041 [Supplemental materials]

Hsu, K. J., Rosenthal, A. M., Miller, D. I., & Bailey, J. M. (2017). Sexual arousal patterns of autogynephilic male cross-dressers. Archives of Sexual Behavior, 46, 247-253. https://doi.org/10.1007/s10508-016-0826-z

  • I was the statistical expert on multilevel modelling for this study.

Eagly, A. H., & Miller, D. I. (2016). Scientific eminence: Where are the women? Perspectives in Psychological Science, 11, 899-904. https://doi.org/10.1177/1745691616663918

Hsu, K. J., Rosenthal, A. M., Miller, D. I., & Bailey, J. M. (2016). Who are gynandromorphophilic men? Characterizing men with sexual interest in transgender women. Psychological Medicine, 46, 819–827. https://doi.org/10.1017/S0033291715002317

  • I was the statistical expert on multilevel modelling for this study.

Miller, D. I., Eagly, A. H., & Linn, M. C. (2015). Women’s representation in science predicts national gender-science stereotypes: Evidence from 66 nations. Journal of Educational Psychology, 107, 631-644. https://doi.org/10.1037/edu0000005 [R code and data]

Miller, D. I., & Wai, J. (2015). The bachelor’s to Ph.D. STEM pipeline no longer leaks more women than men: A 30-year analysis. Frontiers in Psychology, 6, 36. https://doi.org/10.3389/fpsyg.2015.00037

Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex differences. Trends in Cognitive Sciences, 18, 37-45. https://doi.org/10.1016/j.tics.2013.10.011

Miller, D. I., & Halpern, D. F. (2013). Can spatial training improve long-term outcomes for gifted STEM undergraduates? Learning and Individual Differences, 26, 141-152. https://doi.org/10.1016/j.lindif.2012.03.012

Uttal, D. H., Miller, D. I., & Newcombe, N. S. (2013). Exploring and enhancing spatial thinking: Links to STEM achievement? Current Directions in Psychological Science, 22, 367-373. https://doi.org/10.1177/0963721413484756

Peer-Reviewed Conference Proceedings

Lakkaraju, H., Aguiar, E., Shan, C., Miller, D. I., Bhanpuri, N., Ghani, R., & Addison, K. L. (2015). A machine learning framework to identify students at risk of adverse academic outcomes. In Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Sydney, Australia: Association for Computing Machinery.

Aguiar, E., Lakkaraju, H., Bhanpuri, N., Miller, D. I., Yuhas, B., Addison, K., … , Ghani, R. (2015). Who, when, why: A machine learning approach to prioritizing students at risk of not graduating high school on time. In Proceedings of the 5th International Conference on Learning Analytics and Knowledge. Poughkeepsie, NY: Society for Learning Analytics Research.

Matuk, C. F., McElhaney, K. W., Miller, D. I., Chen, J. K., Lim-Breitbart, J., Terashima, H., … , Linn, M. C. (2013). Reflectively prototyping a tool for exchanging ideas. In Proceedings of the 10th International Conference on Computer Supported Collaborative Learning (pp. 101-104). Madison, WI: International Society of the Learning Sciences.

Matuk, C. F., McElhaney, K. W., Chen, J. K., Miller, D. I., Lim-Breitbart, J., & Linn, M. C. (2012). The Idea Manager: A tool to scaffold students in documenting, sorting, and distinguishing ideas during science inquiry. In Proceedings of the 11th International Conference of the Learning Sciences. Sydney, Australia: International Society of the Learning Sciences.

McElhaney, K. W., Matuk, C. F., Miller, D. I., & Linn, M. C. (2012). Using the Idea Manager to promote coherent understanding of inquiry investigations. In Proceedings of the 11th International Conference of the Learning Sciences. Sydney, Australia: International Society of the Learning Sciences.

Miller, D. I., & Halpern, D. F. (2011). Spatial thinking in physics: Longitudinal impacts of 3-D spatial training. In L. Carlson, C. Hoelscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 3465-3470). Austin, TX: Cognitive Science Society. [This research was turned into a full-length journal article]

Miller, D. I. (2018, March 20). Kids draw female scientists more often than they did decades ago. Scientific American.

Miller, D. I. (2017, February 1). Stereotypes can hold boys back in school, too. The Conversation.

Miller, D. I. (2016, June 15). LGBT equality doesn’t exist – but here’s how to fight for it. The Conversation.

Miller, D. I. (2016, February 4). Intersectionality: how gender interacts with other social identities to shape bias. The Conversation.

Wai, J., & Miller, D. I. (2015, December 1). Here’s why academics should write for the public. The Conversation.

Miller, D. I. (2015, October 13). Men and women biased about studies of STEM gender bias – in opposite directions. The Conversation.

  • Discusses a PNAS study showing men, especially those in STEM, are less likely to believe the evidence of gender bias against women in science. Women are also biased against gender bias research, but in the opposite direction.
  • 15,000+ page views across original and republishing outlets including IFL Science.

Miller, D. I. (2015, July 10). Tech companies spend big money on bias training—but it hasn’t improved diversity numbers. The Conversation.

  • Discusses why bias training hasn’t been changing diversity in tech and how to fix that problem using the latest psychological research.
  • Republished by Business Insider and recrafted for U.S. News.

Miller, D. I. (2015, June 9). Beliefs about innate talent may dissuade students from STEM. The Conversation.

Miller, D. I. (2015, May 28). Most people think ‘man’ when they think ‘scientist’ – how can we kill the stereotype? The Conversation.

Miller, D. I. (2015, April 16). Some good news about hiring women in STEM doesn’t erase sex bias issue. The Conversation.

  • Discusses a PNAS study finding that scientists prefer to hire women over men among highly qualified tenure-track applicants.
  • 25,000+ page views across original and republishing outlets including Quartz.

Miller, D. I. (2015, March 3). A metaphor to retire. Inside Higher Ed.