Dynamic Modeling & Assessment in Psychology (DyMAP)

Research

Recent 5 years

  1. Dong, J., & Liu, H. (2025). When predictors sum to a constant: Trade-off effect analysis using a regression model based on isometric log-ratio transformation. Psychological Methods. https://doi.org/10.1037/met0000668

  2. Luo, X., Liu, H., Hu, Y., & Liu, Y. (2025). Enhancing two-stage estimation in differential equation models: A bias-correction method via stochastic approximation. PsyArXiv. https://doi.org/10.31234/osf.io/g8rjt_v1

  3. Luo, X., Hu, Y., & Liu, H. (2025). Timescale mismatch in intensive longitudinal data: Current issues and possible solutions based on dynamic structural equation models. Psychological Methods. https://doi.org/10.1037/met0000749

  4. Xiao, Y., & Liu, H. (2025). Detrending for Intensive Longitudinal Dyadic Data Analysis Using DSEM. Structural Equation Modeling: A Multidisciplinary Journal, 32(3), 450–459. https://doi.org/10.1080/10705511.2024.2442980

  5. Han, Y., Ji, F., Wang, P., & Liu, H. (2025). Assessing multiple abilities through process data in computer-based assessments: The multidimensional sequential response model (MSRM). Behavior Research Methods, 57(5), 152. https://doi.org/10.3758/s13428-025-02658-7

  6. Li, J., Luo, X., & Liu, H. (2025). Dynamic bidirectional relation between state mindfulness and suicidal ideation among female college students: The moderating effect of trait mindfulness. Death Studies, 49(4), 347–358. https://doi.org/10.1080/07481187.2024.2329180

  7. Liu, H., Yuan, K.-H., & Li, H. (2025). A systematic framework for defining R-squared measures in mediation analysis. Psychological Methods, 30(2), 306–321. https://doi.org/10.1037/met0000571

  8. Xiao, Y., & Liu, H. (2025). A Mixture Modeling Approach to Detect Different Behavioral Patterns for Process Data. Fudan Journal of the Humanities and Social Sciences, 18(1), 79–113. https://doi.org/10.1007/s40647-024-00405-4

  9. Luo, X., Wang, H., Xu, J., Liu, H., Suveg, C., & Han, Z. R. (2025). Dynamic Processes of Parent–Adolescent Conflict and Warmth in Chinese Families: Differences between Mothers and Fathers. Journal of Youth and Adolescence. https://doi.org/10.1007/s10964-025-02160-5

  10. Liu, Y., Fang, F., & Liu, H. (2025). Model Selection for Mixed-Effects Location-Scale Models with Confidence Interval for LOO or WAIC Difference. Multivariate Behavioral Research, 1–17. https://doi.org/10.1080/00273171.2025.2462033

  11. Luo, X., Liu, Y., & Liu, H. (2025). Incorporating Qualitative Distinctions in Within-Person Effect Analyses. Multivariate Behavioral Research, 60(1), 21–22. https://doi.org/10.1080/00273171.2025.2443362

  12. Dong, J., Liu, Y., & Liu, H. (2025). Modeling Disengaged Survey Responding with Response Times as Predictors. Multivariate Behavioral Research, 60(1), 11–12. https://doi.org/10.1080/00273171.2025.2442263

  13. Luo, X., Hu, Y., & Liu, H. (2025). Assessing between- and within-person reliabilities of items and scale for daily procrastination: A multilevel and dynamic approach. Assessment, 32(1), 61–76. https://doi.org/10.1177/10731911241235467

  14. Luo, X., Hu, Y., & Liu, H. (2025). Dynamic prediction of depressive states using stress processes: A multilevel decision tree approach. Acta Psychologica Sinica, 57(8), 1363. https://doi.org/10.3724/SP.J.1041.2025.1363

  15. Wei, D., Zhan, P., & Liu, H. (2024). Can we differentiate a latent growth curve model from competitors? Evidence based on individual case residuals. Structural Equation Modeling: A Multidisciplinary Journal, 31(6), 1005–1026. https://doi.org/10.1080/10705511.2024.2350033

  16. Wang, P., Liu, H., & Xu, M. (2024). An adaptive testing item selection strategy via a deep reinforcement learning approach. Behavior Research Methods, 56(8), 8695–8714. https://doi.org/10.3758/s13428-024-02498-x

  17. Liu, Y., Hau, K.-T., & Liu, H. (2024). Linear mixed-effects models for dependent data: Power and accuracy in parameter estimation. Multivariate Behavioral Research, 59(5), 978–994. https://doi.org/10.1080/00273171.2024.2350236

  18. Wang, P., & Liu, H. (2024). Polytomous effectiveness indicators in complex problem-solving tasks and their applications in developing measurement model. Psychometrika, 89(3), 877–902. https://doi.org/10.1007/s11336-024-09963-8

  19. Liu, Y., Li, Z., Liu, H., & You, X. (2024). The impact of non-effortful responding on item and person parameters in item-pool scaling linking. Applied Measurement in Education, 37(2), 89–108. https://doi.org/10.1080/08957347.2024.2345598

  20. Shao, Y., Liu, H., Zhao, P., Liu, Q., & Liu, J. (2024). Relationship between homework time and academic and non‐academic performance in China: A preliminary test of the nonlinear hypothesis. British Educational Research Journal, 50(1), 218–240. https://doi.org/10.1002/berj.3920

  21. Li, M., Liu, H., Cai, M., & Yuan, J. (2024). Estimation of individuals’ collaborative problem solving ability in computer-based assessment. Education and Information Technologies, 29(1), 483–515. https://doi.org/10.1007/s10639-023-12271-w

  22. He, M., Xu, L.-X., Li, C. R., Liu, Z., Hu, J., Guo, X., Liu, H., & Zhang, J.-T. (2024). Do real-time strategy video gamers have better attentional control? Human Factors: The Journal of the Human Factors and Ergonomics Society, 66(1), 258–270. https://doi.org/10.1177/00187208211064683

  23. Xiao, Y., Liu, H., & Xu, Y. (2024). Model construction for intensive longitudinal dyadic data analysis. Advances in Psychological Science, 32(9), 1450. https://doi.org/10.3724/SP.J.1042.2024.01450

  24. Luo, X., & Liu, H. (2024). Estimating test reliability of intensive longitudinal studies: Perspectives on multilevel structure and dynamic nature. Advances in Psychological Science, 32(4), 700. https://doi.org/10.3724/SP.J.1042.2024.00700

  25. Liu, Y., Xu, L., Liu, H., Han, Y., You, X., & Wan, Z. (2024). Confidence interval width contours: Sample size planning for linear mixed-effects models. Acta Psychologica Sinica, 56(1), 124. https://doi.org/10.3724/SP.J.1041.2024.00124

  26. Luo, X., Liu, H., & Hu, Y. (2023). From cross-lagged effects to feedback effects: Further insights into the estimation and interpretation of bidirectional relations. Behavior Research Methods, 56(4), 3685–3705. https://doi.org/10.3758/s13428-023-02304-0

  27. Zhao, D., Wang, D., He, Z., Yuan, S., Zhu, D., & Liu, H. (2023). Victim profiles and the protective role of school anti-bullying norms: A study of chinese adolescents. Current Psychology, 42(36), 31835–31852. https://doi.org/10.1007/s12144-022-04204-6

  28. Xiao, Y., Wang, P., & Liu, H. (2023). Assessing intra- and inter-individual reliabilities in intensive longitudinal studies: A two-level random dynamic model-based approach. Psychological Methods. https://doi.org/10.1037/met0000608

  29. Wu, X., Wu, R., Hanley, C., Liu, H., & Liu, J. (2023). How to better balance academic achievement and learning anxiety from time on homework? A multilevel and classification and regression tree analyses. Frontiers in Psychology, 14, 1130274. https://doi.org/10.3389/fpsyg.2023.1130274

  30. Xu, X., Chen, C., Wang, L., Zhao, M., Xin, Z., & Liu, H. (2023). Longitudinal relationship between number line estimation and other mathematical abilities in chinese preschool children. Journal of Experimental Child Psychology, 228, 105619. https://doi.org/10.1016/j.jecp.2022.105619

  31. Liu, H., Liu, Q., Du, X., Liu, J., Hoi, C. K. W., & Schumacker, R. E. (2023). Teacher-student relationship as a protective factor for socioeconomic status, students’ self-efficacy and achievement: A multilevel moderated mediation analysis. Current Psychology, 42(4), 3268–3283. https://doi.org/10.1007/s12144-021-01598-7

  32. Li, R., Liu, H., Chen, Z., & Wang, Y. (2023). Dynamic and cyclic relationships between employees’ intrinsic and extrinsic motivation: Evidence from dynamic multilevel modeling analysis. Journal of Vocational Behavior, 140, 103813. https://doi.org/10.1016/j.jvb.2022.103813

  33. Xiao, Y., & Liu, H. (2023). A state response measurement model for problem-solving process data. Behavior Research Methods, 56(1), 258–277. https://doi.org/10.3758/s13428-022-02042-9

  34. Wu, R., Shi, P., Wu, X., Yang, H., Liu, H., & Liu, J. (2023). A multilevel person-centered examination of students’ learning anxiety and its relationship with student background and school factors. Learning and Individual Differences, 101, 102253. https://doi.org/10.1016/j.lindif.2022.102253

  35. You, X., Yang, J., Qin, C., & Liu, H. (2023). Missing data analysis in cognitive diagnostic models: Random forest threshold imputation method. Acta Psychologica Sinica, 55(7), 1192. https://doi.org/10.3724/SP.J.1041.2023.01192

  36. Liu, Y., Fang, F., Liu, H., & Lei, Y. (2023). Model construction and sample size planning for mixed-effects location-scale models. Advances in Psychological Science, 31(6), 958. https://doi.org/10.3724/SP.J.1042.2023.00958

  37. Wang, W., Liu, Y., & Liu, H. (2022). Testing differential item functioning without predefined anchor items using robust regression. Journal of Educational and Behavioral Statistics, 47(6), 666–692. https://doi.org/10.3102/10769986221109208

  38. Han, Y., Liu, H., & Ji, F. (2022). A sequential response model for analyzing process data on technology-based problem-solving tasks. Multivariate Behavioral Research, 57(6), 960–977. https://doi.org/10.1080/00273171.2021.1932403

  39. Xie, Z., Wu, R., Liu, H., & Liu, J. (2022). How does teacher-perceived principal leadership affect teacher self-efficacy between different teaching experiences through collaboration in China? A multilevel structural equation model analysis based on threshold. Frontiers in Psychology, 13, 933838. https://doi.org/10.3389/fpsyg.2022.933838

  40. Li, H., & Liu, H. (2022). A monte carlo confidence interval method for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 29(4), 600–610. https://doi.org/10.1080/10705511.2022.2034114

  41. Han, Y., Xiao, Y., & Liu, H. (2022). Feature extraction and ability estimation of process data in the problem-solving test. Advances in Psychological Science, 30(6), 1393–1409. https://doi.org/10.3724/SP.J.1042.2022.01393

  42. Zhao, D., He, Z., Tian, Y., & Liu, H. (2022). Differences in cognitive and non-cognitive results between only-child and non-only-child children: Analysis of propensity scores based on large-scale assessment. Children, 9(6), 807. https://doi.org/10.3390/children9060807

  43. Zhou, J., Liu, H., Wen, H., Wang, X., Wang, Y., & Yang, T. (2022). The association between physical activity and mathematical achievement among chinese fourth graders: A moderated moderated-mediation model. Frontiers in Psychology, 13, 862666. https://doi.org/10.3389/fpsyg.2022.862666

  44. Wang, D., Liu, H., & Hau, K.-T. (2022). Automated and interactive game-based assessment of critical thinking. Education and Information Technologies, 27(4), 4553–4575. https://doi.org/10.1007/s10639-021-10777-9

  45. Liu, Y., Liu, H., You, X., & Yang, J. (2022). A comparison of standard residual methods and a mixture hierarchical model for detecting non-effortful responses. Acta Psychologica Sinica, 54(4), 411–425. https://doi.org/10.3724/SP.J.1041.2022.00411

  46. Wang, R., Liu, H., & Jiang, J. (2022). Does socioeconomic status matter? Materialism and self-esteem: longitudinal evidence from China. Current Psychology, 41(3), 1559–1568. https://doi.org/10.1007/s12144-020-00695-3

  47. Liu, Y., Wang, C., Liu, J., & Liu, H. (2022). The role of cognitive activation in predicting mathematics self-efficacy and anxiety among internal migrant and local children. Educational Psychology, 42(1), 83–107. https://doi.org/10.1080/01443410.2021.1987388

  48. Liu, Y., & Liu, H. (2021). Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process. Journal of Educational and Behavioral Statistics, 46(6), 717–752. https://doi.org/10.3102/1076998621994366

  49. Liu, H., & Yuan, K.-H. (2021). New measures of effect size in moderation analysis. Psychological Methods, 26(6), 680–700. https://doi.org/10.1037/met0000371

  50. Li, M., Cai, M., Zhong, H., & Liu, H. (2021). Comparisons of academic achievements of one-only children vs. Children with siblings in China. Current Psychology, 40(11), 5658–5671. https://doi.org/10.1007/s12144-020-01263-5

  51. Xiao, Y., He, Q., Veldkamp, B., & Liu, H. (2021). Exploring latent states of problem‐solving competence using hidden markov model on process data. Journal of Computer Assisted Learning, 37(5), 1232–1247. https://doi.org/10.1111/jcal.12559

  52. Liu, Y., & Liu, H. (2021). Mixture model method: A new method to handle aberrant responses in psychological and educational testing. Advances in Psychological Science, 29(9), 1696–1710. https://doi.org/10.3724/SP.J.1042.2021.01696

  53. Liu, H., Yuan, K.-H., & Wen, Z. (2021). Two-level moderated mediation models with single-level data and new measures of effect sizes. Behavior Research Methods, 54(2), 574–596. https://doi.org/10.3758/s13428-021-01578-6

  54. Yuan, K.-H., Liu, H., & Han, Y. (2021). Differential item functioning analysis without a priori information on anchor items: QQ plots and graphical test. Psychometrika, 86(2), 345–377. https://doi.org/10.1007/s11336-021-09746-5

  55. Liu H., Yuan K.-H., & Gan K. (2021). Two-level mediated moderation models with single level data and new measures of effect sizes. Acta Psychologica Sinica, 53(3), 322–336. https://doi.org/10.3724/SP.J.1041.2021.00322

  56. Li, H., Liu, J., Zhang, D., & Liu, H. (2021). Examining the relationships between cognitive activation, self‐efficacy, socioeconomic status, and achievement in mathematics: A multi‐level analysis. British Journal of Educational Psychology, 91(1), 101–126. https://doi.org/10.1111/bjep.12351

  57. Jiang, W., Liu, H., & Jiang, J. (2021). The Development of Materialism in Emerging Adulthood: Stability, Change, and Antecedents. Personality and Social Psychology Bulletin, 47(2), 293–306. https://doi.org/10.1177/0146167220925234

  58. Liu, Y., Cheng, Y., & Liu, H. (2020). Identifying effortful individuals with mixture modeling response accuracy and response time simultaneously to improve item parameter estimation. Educational and Psychological Measurement, 80(4), 775–807. https://doi.org/10.1177/0013164419895068

  59. Zhang, M., Liu, H., & Zhang, Y. (2020). Adolescent social networks and physical, verbal, and indirect aggression in China: The moderating role of gender. Frontiers in Psychology, 11, 658. https://doi.org/10.3389/fpsyg.2020.00658