Research

Book

Culpepper, S. A. (2022). Latent Variable Models (Draft)

Articles/Preprints/Book Chapters

  1. Liu, Y. & Culpepper, S. A. (In Press). Restricted latent class models for nominal response data: Identifiability and estimation, Psychometrika. https://doi.org/10.1007/s11336-023-09940-7
  2. Li, A. & Culpepper, S. A. (In Press). A hierarchical prior for Bayesian variable selection with interactions. Proceedings of the 2023 International Meeting of the Psychometric Society.
  3. Aguinis, H., & Culpepper, S. A. (2024). Improving our understanding of predictive bias in testing, Journal of Applied Psychology, 109(3), 402–414. https:/doi.org/10.1037/apl0001152
  4. Drocco, J., Halliday, K. Stewart, B., Sandholtz, S., Morrison, M., Thissen, J., Be, N., Zwilling, C., Wilcox, R., Culpepper, S. A., Barbey, A., & Jiang, C. (2023). Efforts to enhance reproducibility in a human performance research project, F1000Research, 12:1430. https://doi.org/10.12688/f1000research.140735.1
  5. Bogdan, P. C., Dolcos, F., Moore, M., Kuznetsov, I. Culpepper, S. A., Dolcos, S. (2023). Social expectations are primarily rooted in reciprocity: An investigation of fairness, cooperation, and trustworthiness, Cognitive Science, 47(8), e13326. https://doi.org/10.1111/cogs.13326
  6. Jimenez, A. Balamuta, J. J., & Culpepper, S. A. (2023). A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy, British Journal of Mathematical and Statistical Psychology, 76, 513-538. https://doi.org/10.1111/bmsp.12307
  7. Liu, Y., Culpepper, S. A., & Chen, Y. (2023). Identifiability of hidden Markov models for learning trajectories in cognitive diagnosis, Psychometrika, 88, 361-386. 10.1007/s11336-023-09904-x
  8. Chen, Y., Culpepper, S. A., & Chen, Y. (2023). Bayesian Inference for an unknown number of attributes in restricted latent class models, Psychometrika, 88, 613-635.
  9. Culpepper, S. A. & Xu, G. (2023). Introduction to JEBS special issue on diagnostic statistical models, Journal of Educational and Behavioral Statistics, 48(6), 1-3. https://doi.org/10.3102/10769986231210002
  10. Culpepper, S. A. & Balamuta, J. J. (2023). Inferring latent structure in polytomous data with a higher-order diagnostic model, Multivariate Behavioral Research, 58, 368-386. https://doi.org/10.1080/00273171.2021.1985949
  11. Yigit, H. D. & Culpepper, S. A. (2023). Extending exploratory diagnostic classification models: Inferring the effect of covariates, British Journal of Mathematical and Statistical Psychology, 79, 372-401. https://doi.org/10.1111/bmsp.12298
  12. Culpepper, S. A. (2023). A note on weaker conditions for identifying restricted latent class models for binary responses, Psychometrika, 88, 158–174. https://doi.org/10.1007/s11336-022-09875-5
  13. He, S., Douglas, J. A., & Culpepper, S. A. (2023). A sparse latent class model for polytomous attributes in cognitive diagnostic assessments. In: van der Ark, L.A., Emons, W.H.M., Meijer, R.R. (Eds) Essays on Contemporary Psychometrics (pp. 413-442). Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-031-10370-4_21.
  14. Bowers, J. & Culpepper, S. A. (2022). Dependent latent class models.
  15. Balamuta, J. J. & Culpepper, S. A. (2022). Exploratory restricted latent class models with monotonicity requirements under Pólya-gamma data augmentation, Psychometrika, 87, 903–945. https://doi.org/10.1007/s11336-021-09815-9
  16. Man, A. & Culpepper, S. A. (2022). A mode-jumping algorithm for Bayesian factor analysis. Journal of the American Statistical Association, 117, 277-290. doi:10.1080/01621459.2020.1773833.
  17. Chen, Y., Liu, Y., Culpepper, S. A., & Chen, Y. (2021). Inferring the number of attributes for the exploratory DINA model, Psychometrika, 86, 30-64 10.1007/s11336-021-09750-9
  18. McCaffrey, D. F., & Culpepper, S. A. (2021). Introduction to JEBS special issue on NAEP linked aggregate scores. Journal of Educational and Behavioral Statistics, 46(2), 135-137.
  19. Kern, J. & Culpepper, S. A. (2020). A restricted four-parameter IRT model: The dyad four-parameter normal ogive (Dyad-4PNO) model, Psychometrika, 85, 575-599.
  20. Culpepper, S. A. (2020). Editorial statement, Journal of Educational and Behavioral Statistics, 45, 1, 3-4.
  21. Chen, Y. & Culpepper, S. A. (2020). A multivariate probit model for learning trajectories: A fine-grained evaluation of an educational intervention, Applied Psychological Measurement, 44, 515-530.
  22. Chen, Y., Culpepper, S. A., and Liang, F. (2020). A sparse latent class model for cognitive diagnosis, Psychometrika, 85, 121-153.
  23. Culpepper, S. A. (2020). Advances in psychometric methods for uncovering latent structure and cognitive processes. In J. Hong & R. W. Lissitz (Eds.), Innovative Psychometric Modeling and Methods (pp. 1-16). The MARCES Book Series. Information Age Publishing, Charlotte, N.C. (early version)
  24. Culpepper, S. A. (2019). An exploratory diagnostic model for ordinal responses with binary attributes: Identifiability and estimation, Psychometrika, 84, 921-940. https://doi.org/10.1007/s11336-019-09683-4
  25. Zhang, S., Douglas, J., Wang, S., & Culpepper, S. A. (2019). Reduced reparameterized unified model applied to learning spatial reasoning skills. In M. von Davier & Y. Lee, Handbook of diagnostic classification models (pp.503-524). New York, Springer.
  26. Culpepper, S A. (2019). Estimating the cognitive diagnosis Q matrix with expert knowledge: Application to the fraction-subtraction dataset, Psychometrika, 84, 333-357. doi: 10.1007/s11336-018-9643-8
  27. Culpepper, S. A., Aguinis, H., Kern, J., & Millsap, R. (2019). High-stakes testing case study: A latent variable approach for assessing measurement and prediction invariance, Psychometrika, 84, 285-309.
  28. Culpepper, S A. & Chen, Y. (2018). Development and application of an exploratory reduced reparameterized unified model, Journal of Educational and Behavioral Statistics, 44, 3-24. (supplemental appendices)
  29. Moore, M., Culpepper, S. A., Phan, K. L., Strauman, T. J., Dolcos, F., & Dolcos, S.(2018). Neuro-behavioral mechanisms of resilience against emotional distress: An integrative brain-personality-symptom approach using structural equation modeling, Personality Neuroscience, 1, 1-10.
  30. Wang, S., Zhang, S., Douglas, J., & Culpepper, S. A. (2018). Using response times to assess learning progress: A joint model for responses and response times. Measurement: Interdisciplinary Research and Perspectives, 16, 45-58.
  31. Chen, Y., Culpepper, S. A., Chen, Y., & Douglas, J. (2018). Bayesian estimation of the DINA Q matrix. Psychometrika, 83, 89–108. doi: 10.1007/s11336-017-9579-4.
  32. Chen, Y., Culpepper, S. A., Wang, S., & Douglas, J. (2018). A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis with Application to Spatial Rotation Skills. Applied Psychological Measurement, 42, 1, 5-23.
  33. Culpepper, S. A. & Hudson, A. (2018). An improved strategy for Bayesian estimation of the reduced reparameterized unified model. Applied Psychological Measurement, 42, 99-115.
  34. Wang, S, Yang, Y., Culpepper, S. A., & Douglas, J. (2018). Tracking skill acquisition with cognitive diagnosis models: Application to spatial rotation skills. Journal of Educational and Behavioral Statistics, 43, 57-87.
  35. Culpepper, S. A. (2017). The prevalence and implications of slipping on low-stakes, large-scale assessments. Journal of Educational and Behavioral Statistics, 42, 706 – 725. Supplemental appendix of item parameters
  36. Culpepper, S. A. & Park, T. (2017). Bayesian estimation of multivariate latent regression models in large-scale educational assessments: Gauss versus Laplace. Journal of Educational and Behavioral Statistics, 42, 591-616.
  37. Kern, J. & Culpepper, S. A. (2017). A review of “Analyzing Spatial Models of Choice and Judgment with R”. Journal of Educational and Behavioral Statistics, 42, 243-247.
  38. Williams, I. & Culpepper, S.A. (2018). Gain scores, analysis of. In Frey, B.B. (ed). Encyclopedia of educational research, measurement and evaluation (pp. 715-716). Thousand Oaks, CA: Sage.
  39. Culpepper, S. A. & Balamuta, J. J. (2017). A hierarchical model for accuracy and choice on standardized tests, Psychometrika, 82, 820-845. DOI: 10.1007/s11336-015-9484-7. cIRT R package
  40. Aguinis, H., Culpepper, S. A., & Pierce, C. A. (2016). Differential prediction generalization in college admissions testing. Journal of Educational Psychology, 108, 1045-1059. DOI: 10.1037/edu0000104.
  41. Culpepper, S. A. (2016). Revisiting the 4-parameter item response model: Bayesian estimation and application, Psychometrika, 81, 1142-1163. DOI: 10.1007/s11336-015-9477-6. fourPNO R package
  42. Culpepper, S. A. (2016). An improved correction for range restricted correlations under extreme, monotonic quadratic nonlinearity and heteroscedasticity, Psychometrika, 81, 550-564. DOI: 10.1007/s11336-015-9466-9
  43. Ye, S., Fellouris, G., Culpepper, S. A., & Douglas, J. (2016). Sequential detection of learning in cognitive diagnosis. British Journal of Mathematical and Statistical Psychology, 69, 139-58.
  44. Culpepper, S. A. (2015). Bayesian estimation of the DINA model with Gibbs sampling. Journal of Educational and Behavioral Statistics, 40, 454-476. DOI: 10.3102/1076998615595403. dina R package
  45. Aguinis, H. & Culpepper, S. A. (2015). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods, 18, 155-176. iccbeta R package
  46. Culpepper, S. A. (2014). If at first you don’t succeed, try, try again: A repeated attempts item response model. Applied Psychological Measurement, 38, 632-644. (Example R Code, Supplemental Appendix)
  47. Culpepper, S. A. (2014). The reliability of linear gain scores at the classroom level in the presence of measurement bias and student tracking. Applied Psychological Measurement, 38, 503-517.
  48. Aragon, A., Culpepper, S. A., McKee, M. W., & Perkins, M. (2014). Understanding profiles of preservice teachers with different levels of commitment to teaching in urban schools. Urban Education, 9, 543-573.
  49. Aguinis, H., Gottfredson, R. K., Culpepper, S. A., Dalton, D. R., & De Bruin, G. (2013). Doing good and doing well: On the multiple contributions of journal editors. Academy of Management Learning & Education, 12, 564-578.
  50. Yeatman, S., Sennott, C., & Culpepper, S. A. (2013). Young women’s dynamic fertility preferences in the context of transitioning fertility. Demography, 50, 1715-1737.
  51. Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013). Best-practice recommendations for estimating interaction effects using multilevel modeling. Journal of Management, 39, 1490-1528.
  52. Culpepper, S. A. (2013). The reliability and precision of total scores and IRT estimates as a function of polytomous IRT parameters and latent trait distribution. Applied Psychological Measurement. 37, 201-225. (R code for comparing the reliability of total scores and and IRT estimates)
  53. Mathieu, J., Aguinis, H., Culpepper, S. A., & Chen, G. (2012). Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. Journal of Applied Psychology, 97, 951-966. (Authors contributed equally and order was determined randomly)
  54. Culpepper, S. A. (2012). Evaluating EIV, OLS, and SEM estimators of group slope differences in the presence of measurement error: The single indicator case. Applied Psychological Measurement, 36, 349-374.
  55. Culpepper, S. A. (2012). Using the criterion-predictor factor model to compute the probability of detecting prediction bias with ordinary least squares regression. Psychometrika, 77, 561-580.
  56. Culpepper, S. A. & Aguinis, H. (2011). R is for revolution: A review of a cutting-edge, free, open source statistical package. Organizational Research Methods, 14, 735-740.
  57. Culpepper, S. A. & Aguinis, H. (2011). Using analysis of covariance (ANCOVA) with fallible covariates. Psychological Methods, 16, 166-178.
  58. Culpepper, S. A., Basile, C., Ferguson, C. A., Lanning, J. A., & Perkins, M. A. (2010).  Understanding the transition between high school and college mathematics and science. The Journal of Mathematics and Science: Collaborative Explorations, 12, 157-167.
  59. Aguinis, H., De Bruin, G., Cunningham, D., Hall, N. L., Culpepper, S. A., & Gottfredson, R. K. (2010). What does not kill you (sometimes) makes you stronger: Productivity fluctuations of journal editors. Academy of Management Learning & Education, 9, 683-695.
  60. Aguinis, H., Culpepper, S. A., & Pierce, C. A. (2010). Revival of test bias research in preemployment testing. Journal of Applied Psychology, 95, 648–680.
  61. Culpepper, S. A. (2010). Studying individual differences in predictability with gamma regression and nonlinear multilevel models. Multivariate Behavioral Research, 45, 153-185.
  62. Culpepper, S. A. (2009). A multilevel nonlinear profile analysis model for dichotomous data. Multivariate Behavioral Research, 44, 646-667.
  63. Aguinis, H., Pierce, C., & Culpepper, S. A. (2009). Scale coarseness as a methodological artifact: Correcting correlation coefficients attenuated from using coarse scales. Organizational Research Methods, 12, 623-652.
  64. Culpepper, S. A. & Davenport, E. C. (2009). Assessing differential prediction of college grades by race/ethnicity with a multilevel model. Journal of Educational Measurement, 46, 220-242.
  65. Culpepper, S. A. & Davenport, E. C., Jr. (2009). Identifying common high school coursework profiles with multidimensional scaling. IR Applications, 20, 1-18.
  66. Culpepper, S. A., Davenport, E. C., Jr., & Davison, M. L. (2008). A method for choosing weights to predict college grades for admission decisions and to assess their fairness by race/ethnicity. Multiple Linear Regression Viewpoints, 34(2), 4-14.
  67. Culpepper, S. A. (2008). Conducting external profile analysis with multiple regression. Practical Assessment, Research & Evaluation, 13(1). Available online: http://pareonline.net/getvn.asp?v=13&n=1
  68. Rapp, D. N, Culpepper, S. A., Kirkby, K., & Morin, P. (2007). Fostering students’ comprehension of topographic maps. Journal of Geoscience Education, 55(1), 5-16.

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