Dr. Carolyn Anderson

Division Affiliation:

• Queries

Software:

• Carolyn J. Anderson (2021).  Pseudo-likelihood Estimation of Log-Multiplicative Assocation Models:  The pleLMA Package in R.  pleLMA: version 0.1.1.   https://cran.r-project.org/web/packages/pleLMA/index.html.
  

Papers:

• Paek, Y. & Anderson, C.J. (2017).  Pseudo-likelihood estimation of multidimensional response models: Polytomous and dichotomous items.  In L.A. van der Ark, M. Wiberg, S.A. Culpepper, H.A., Douglas, & W.C. Wang (eds) Quantitative Psychology – The 81^st Annual Meeting of the Psychometric Society. pp 21--30.  doi:10.1007/978-3-319-56294-0_3;    url: https://experts.illinois.edu/en/publications/pseudo-likelihood-estimation-of-multidimensional-response-models-
  
• Anderson, C.J., & Yu, H.S. (2017).  Properties of second-order exponential models as multidimensional response models.  In L.A. van der Ark, M. Wiberg, S.A. Culpepper, H.A., Douglas, & W.C. Wang (eds) Quantitative Psychology – The 81^st Annual Meeting of the Psychometric Society.  pp 9-19. https://experts.illinois.edu/en/publications/properties-of-second-order-exponential-models-as-multidimensional
  
• Anderson, C.J., Embretson, S., Meulman, J., Moustaki, I., von Davier, A.A., Yan, D. (2020).  Stories in successful careers in psychometrics and what we can learn from them.  In Wiberg, S.A. et al. (Eds) Quantitative Psychology – 83^rd Annual Meeting of the Psychometric Society.  doi: 10.1007/978-3-030-43469-4_1 ,  url: https://experts.illinois.edu/en/publications/stories-of-successful-careers-in-psychometrics-and-what-we-can-le
  
• Timmer, J., & Anderson, C.J., (2020).  Using projects to teach statistics in the social sciences.  In Joseph Rogers (Ed) Teaching Statistics in the 21^st Century.  (pp 266-280). Taylor & Francis.   isbn: 978-1-138-33685-8 (hbk), 978-1-138-33685-5 (pbk), 978-0-429-44281-0 (ebk).
  
• Pulsen, S., Anderson, C.J., & West, M.  (2020).   The relationship between course scheduling and student performance.  Educational Data Mining in Computer Science Education.  Proceedings of 4^th annual CSEDM workshop.  URL: https://experts.illinois.edu/en/publications/the-relationship-between-course-scheduling-and-student-performanc
  
• Huang, M., & Anderson, C.J. (in press).  A Bayesian solution to non-convergence of crossed random effects models. In Wiberg, M., Molenaar, D., Gonzalez, J., Böckenholt, U., Kim, J.-S (Eds) Quantitative Psychology – 84^th Annual Meeting of the Psychometric Society, Springer.
  
• Anderson, C.J., Kateri, M., & Moustaki, I. (forthcoming). Log-linear and Log-multiplicative Association for Categorial Data. In Maria Kateri and Irini Moustaki (Eds) Trends and Challenges in Categorical Data Analysis, Springer.

Dr. Nigel Bosch

Division Affiliation:

• CSTL

• Wammes, J. D., Ralph, B. C. W., Mills, C., Bosch, N., Duncan, T. L., & Smilek, D. (2019). Disengagement during lectures: Media multitasking and mind wandering in university classrooms. Computers & Education, 132, 76–89.

Dr. Kiel Christianson

Division Affiliation:

• CSTL

• Dempsey, J., Liu, Q., & Christianson, K. (2020). Convergent probabilistic cues do not trigger syntactic adaptation: Evidence from self-paced reading. Journal of Experimental Psychology: Learning, Memory, & Cognition, 46(10), 1906–1921.
• Bulkes, N. Z., Tanner, D., Christianson, K. (2020, in press). Semantic constraint, reading control, and the granularity of form-based expectations during semantic processing: Evidence from ERPs. Neuropsychologia, 137.
• Zhou, P., Garnsey, S., & Christianson, K. (2020, in press). ERP data on sentences processing during auditory imagery of native and non-native English speech. Data in Brief.
• Brehm, L., Hussey, E. K., & Christianson, K. (2020). The role of word frequency and morpho-orthography in agreement processing. Language, Cognition and Neuroscience, 35(1), 58-77, DOI: 10.1080/23273798.2019.1631456.

Dr. Jennifer Cromley

Division Affiliations:

• CSTL
• Queries

• Perez, T., Dai, T., Kaplan, A., Cromley, J., Brooks, W., White, A., Mara, K., & Balsai, M. (2019). Interrelations among expectancies, task values, and perceived costs in undergraduate biology achievement. Learning and Individual Differences, 72, 26-38.
• Cromley, J. G. (2018). Introduction to the special issue: Desiderata for a theory of multi-source multi-modal comprehension. Learning and Instruction, 57, 1-4.
• Schunn, C. D., Newcombe, N. S., Alfieri, L., Cromley, J. G., Massey, C., & Merlino, F. J. (2018). Using principles of cognitive science to improve science learning in middle school: What works when and for whom? Applied Cognitive Psychology, 32(2), 225–240.
• Cromley, J. (2019). Analyzing strategic processing: Pros and cons of different methods. In D. Dinsmore, L. Freyer, & M. Parkinson (Eds)., Handbook of strategies and strategic processing: Conceptualization, intervention, measurement, and analysis. Chapter accepted May 24, 2019.
• Cromley, J. G., Dai, T., Fechter, T., Van Boekel, M., Nelson, F. E., & Dane, N. (2019). What cognitive interviewing reveals about a new measure of undergraduate biology reasoning. Online first in the Journal of Experimental Education as of May 22, 2019.

Dr. Ge Gabriella Jiang

Division Affiliation:

• Queries

• Gomer, B., Jiang, G., & Yuan, K. H. (2019). New Effect Size Measures for Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 26(3), 371-389.
  
• Zhang, G., Preacher, K. J., Hattori, M., Jiang, G., & Trichtinger, L. A. (2019). A Sandwich Standard Error Estimator for Exploratory Factor Analysis With Nonnormal Data and Imperfect Models. Applied Psychological Measurement, 43(5), 360-373.
  
• Jiang, G., & Zhang, Z. (2018). Statistical power analysis for repeated-measures ANOVA. In Z. Zhang, & K.-H. Yuan (Eds.), Practical statistical power analysis using WebPower and R (pp. 115-136). Granger, IN: ISDSA Press.

Dr. Justin Kern

Division Affiliation:

• Queries

• Culpepper, S., Aguinis, H., Kern, J., Millsap, R. (2019). High-stakes testing case study: A latent variable approach for assessing measurement and prediction invariance. Psychometrika, 84(1), 285-309.
• Lim, Y-S., Drasgow, F., & Kern, J. (2019). Estimation of the parameters of the reduced RUM model by simulated annealing. Psychological Test and Assessment Modeling, 61(2), 187-205.
• Choe, E., Kern, J., & Chang, H-H. (2018). Optimizing the use of response times for item selection in computerized adaptive testing. Journal of Educational and Behavioral Statistics, 43(2), 135-158.

Dr. H. Chad Lane

Division Affiliation:

• CSTL

• Bell, B. M., Martinez, L., Gotsis, M., Lane, H. C., Davis, J. N., Antunez-Castillo, L., Ragusa, G., & Spruijt-Metz, D. (2018). Virtual Sprouts: A Virtual Gardening Pilot Intervention Increases Self-Efficacy to Cook and Eat Fruits and Vegetables in Minority Youth. Games for Health Journal.
• Lane, C., & D'Mello, S. (2018). Uses of Physiological Monitoring in Intelligent Learning Environments: A Review of Research, Evidence, and Technologies. Mind, Brain and Technology: Learning in the Age of Emerging Technologies ( pp. 67-86). Springer.

Dr. Jose Mestre 

Division Affiliation:

• CSTL (Emeritus)

• Mestre, J.P., Cheville, A., & Herman, G.L. (2018). Promoting DBER-cognitive psychology collaborations in STEM education. Editorial, Journal of Engineering Education, 107 (No. 1), 1-6.
• Herman, G.L., Greene, J.C., Hahn, L.D., Mestre, J.P., West, M., & Tomkin, J. H. (2018). Changing the teaching culture in introductory STEM courses at a large research university. Journal of College Science Teaching, 47 (No. 6), 32-38.
• Ma, S., Herman, G. L., Tomkin, J., Mestre, J., West, M. (2018). Spreading teaching innovations in social networks: The bridging role of mentors. Journal of STEM Education Research.
• Morphew, J.W., Mestre, J.P., Kang, H.A., Chang, H.H., & Fabry, G. (2018). Using computer adaptive testing to assess physics ability and improve exam performance in an introductory physics course. Physical Review-Physics Education Research, 14, 020110.
• Morphew, J.W. & Mestre, J.P. (2018). Exploring the connection between problem solving and conceptual understanding in physics. Revista de Enseñanza de la Física, 30 (#2), 75-85.
• Mestre, J.P., Herman, G.L., Tomkin, J.H., & West, M. (2019). Keep your friends close and your colleagues nearby: The hidden ties that improve STEM education. Change: The Magazine of Higher Learning, 51 (#1), 42-49.
• Shufeng Ma, Geoffrey L. Herman, Matthew West, Jonathan Tomkin & Jose Mestre (2019) Studying STEM Faculty Communities of Practice through Social Network Analysis. The Journal of Higher Education.

Dr. Dan Morrow

Division Affiliations:

• CSTL

• Morrow, D., Azevedo, R. F. L., Garcia-Retamero, R., Hasegawa-Johnson, M., Huang, T., Schuh, W., ... & Zhang, Y. (2019). Contextualizing numeric clinical test results for gist comprehension: Implications for EHR patient portals.. Journal of Experimental Psychology: Applied, 25(1), 41.  

Dr. Chris Napolitano

Division Affiliations:

• Developmental Sciences
• Counseling Psychology

• Napolitano, C. M. & Freund, A. M. (in press). Adding life to one’s added years: Self-regulatory balancing of life domains across old age. Response to Bernardi, Huinink, and Settersten. Advances in Life Course Research.
• Tomasik, M. J., Napolitano, C. M., & Moser, U. (2018). Trajectories of academic performance across compulsory schooling and thriving in young adulthood. Child Development.
• Napolitano, C. M., & Job, V. (2018). Assessing the implicit theories of willpower for strenuous mental activities scale: Multigroup, across-gender, and cross-cultural measurement invariance and convergent and divergent validity. Psychological Assessment.
• Napolitano, C. M. (2018). Serendipity as an example topic for a new four-tiered approach to the study of self-regulation. Research in Human Development, 15, 265-279.

Dr. Michelle Perry

Division Affiliations:

• CSTL
• Developmental Sciences

• Henricks, G., Jay, V., Beilstein, S., Perry, M. Bates, M., Moran, C., & Cimpian, J. (2019). Foundations of community in an online, asynchronous professional development website. In C. Hmelo-Silver, G. Gweon, & M. Baker (Eds.). A wide lens: Combining embodied, enactive, extended, and embedded learning in collaborative settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019 (pp. 577-580). Lyon, France: International Society of the Learning Sciences.
• Sun, J., Anderson, R. C., Perry, M., & Lin, T.-J. (2017). Emergent leadership in children’s cooperative problem-solving groups. Cognition and Instruction, 35, 212-235.
• Bosch, P.N., Huang, E., Angrave, L.C., & Perry, M. (2019). Emergent leadership in children’s cooperative problem-solving groups. In G.A. Papadopoulos, G. Samaras, S. Weibelzahl, D. Jannach, & O. Santos (Eds.), Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (pp. 327-335). ACM: Larnaca, Cyprus.
• Bosch, N., Crues, R. W., *Henricks, G. M., Perry, M., Angrave, L., Shaik, N., Bhat, S., & Anderson, C. J. (2018). Modeling key differences in underrepresented students’ interactions with an online STEM course. In A. L. Story (Ed.), Proceedings of the Technology, Mind, and Society Conference. New York, NY: ACM.

Dr. James Rounds

Division Affiliations:

• Counseling Psychology
• Queries

• Hoff, K. A., Song, Q. C., Einarsdóttir, S., Briley, D. A., & Rounds, J. (2019). Developmental structure of personality and interests: A 4-wave, 8-year longitudinal study. Journal of Personality and Social Psychology.
• Hoff, K. A., Briley, D. A., Wee, C. J. M., & Rounds, J. (2018). Normative changes in interests from adolescence to adulthood: A meta-analysis of longitudinal studies. Psychological Bulletin, 144(4), 426-451.
  
• Su, R., Tay, L., Liao, H-Y, Zhang, Q., & Rounds, J. (2019). Toward a dimensional model of vocational interests. Journal of Applied Psychology, 104, 690-714.

Dr. Michael Tissenbaum

Division Affiliation:

• CSTL

• Tissenbaum, M., & Slotta, J. D. (2019). Developing a smart classroom infrastructure to support real-time student collaboration and inquiry: a 4-year design study. Instructional Science, 1-40.
• Tissenbaum, M., Sheldon, J., & Ableson, H. (2019) From Computational Thinking to Computational Action. Communications of the ACM, 62(3). 34-36.
• Shapiro, B., & Tissenbaum, M. (2019). New programming paradigms. Cambridge Handbook of Computing Education Research (pp. 606-636). Cambridge University Press.
• Patton, E., Tissenbaum, M. & Harunani, F. (2019). MIT App Inventor: Objectives, Design, and Development. Computational Thinking Education. Springer Nature.
• Kumar, V., & Tissenbaum, M. (2019, June). City Settlers: Participatory Games to Build Sustainable Cities. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (pp. 660-663). ACM.
• Tissenbaum, M., Sherman, M., & Sheldon J. (August, 2018) Making Computing Meaningful: Computational Action for Formal and Informal Computing. In Proceedings of the 2018 Connected Learning Summit, Cambridge, MA.
• Tissenbaum, M., Sheldon, J. & Sherman, M. (2018, June). Combining data mining and qualitative analysis to reveal learners' computational practices in open-ended student- driven curricula. In The State of the Field in Computational Thinking Assessment. Symposium conducted in the 13th International Conference of the Learning Sciences (1304-1311), London.

Dr. Yan Xia

Division Affiliation:

• Queries

• Xia, Y. (in press). Determining the number of factors when population models can be closely approximated by parsimonious models. Educational and Psychological Measurement. https://doi.org/10.1177/0013164421992836
  
• Skymba, H. V., Troop-Gordon, W., Modi, H. H., Davis, M. M., Weldon, A. L., Xia, Y., Heller, W., & Rudolph, K. D. (in press). Emotion mindsets and depressive symptoms in adolescence: The role of emotion regulation competence. Emotion. https://doi.org/10.1037/emo0000902
  
• Levy, R., Xia, Y., & Green, S. B. (2021). Incorporating uncertainty into parallel analysis for choosing the number of factors via Bayesian methods. Educational and Psychological Measurement, 81(3), 466-490. https://doi.org/10.1177/0013164420942806

Dr. Jinming Zhang

Division Affiliation:

• Queries

• Lin, C., & Zhang, J. (2018). Detecting Nonadditivity in Single-Facet Generalizability Theory Applications: Tukey’s Test. Journal of Educational Measurement, 55(1), 78–89.
• Choe, E., Zhang, J., & Chang, H. (2018). Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing. Psychometrika, 83(3), 650-673.
• Li, X., Zhang, J., & Chang, H. (in press). Look-ahead Content Balancing Method in Variable Length Computerized Classification Testing. British Journal of Mathematical and Statistical Psychology.