All Publications

  • Predicting pediatric healthcare provider use of virtual reality using a technology acceptance model. JAMIA open Wang, E. Y., Kennedy, K. M., Zhang, L., Qian, D., Forbes, T., Zuniga-Hernandez, M., Li, B. S., Domingue, B., Caruso, T. J. 2023; 6 (3): ooad076


    The primary aim of this study was to apply a novel technology acceptance model (TAM) for virtual reality (VR) in healthcare. The secondary aim was to assess reliability of this model to evaluate factors that predict the intentions of pediatric health providers' use of VR as an anxiolytic for hospitalized pediatric patients.Healthcare providers that interacted with pediatric patients participated in a VR experience available as anxiolysis for minor procedures and then completed a survey evaluating attitudes, behaviors, and technology factors that influence adoption of new technologies.Reliability for all domain measurements were good, and all confirmatory factor analysis models demonstrated good fit. Usefulness, ease of use, curiosity, and enjoyment of the VR experience all strongly predict intention to use and purchase VR technologies. Age of providers, past use, and cost of technology did not influence future purchase or use, suggesting that VR technologies may be broadly adopted in the pediatric healthcare setting.Previous VR-TAM models in non-healthcare consumers formulated that age, past use, price willing to pay, and curiosity impacted perceived ease of use. This study established that age, past use, and cost may not influence use in healthcare. Future studies should be directed at evaluating the social influences and facilitating conditions within healthcare that play a larger influence on technology adoption.The VR-TAM model demonstrated validity and reliability for predicting intent to use VR in a pediatric hospital.

    View details for DOI 10.1093/jamiaopen/ooad076

    View details for PubMedID 37693368

    View details for PubMedCentralID PMC10483581

  • Bayesian regularization in multiple-indicators multiple-causes models. Psychological methods Zhang, L., Liang, X. 2023


    Integrating regularization methods into structural equation modeling is gaining increasing popularity. The purpose of regularization is to improve variable selection, model estimation, and prediction accuracy. In this study, we aim to: (a) compare Bayesian regularization methods for exploring covariate effects in multiple-indicators multiple-causes models, (b) examine the sensitivity of results to hyperparameter settings of penalty priors, and (c) investigate prediction accuracy through cross-validation. The Bayesian regularization methods examined included: ridge, lasso, adaptive lasso, spike-and-slab prior (SSP) and its variants, and horseshoe and its variants. Sparse solutions were developed for the structural coefficient matrix that contained only a small portion of nonzero path coefficients characterizing the effects of selected covariates on the latent variable. Results from the simulation study showed that compared to diffuse priors, penalty priors were advantageous in handling small sample sizes and collinearity among covariates. Priors with only the global penalty (ridge and lasso) yielded higher model convergence rates and power, whereas priors with both the global and local penalties (horseshoe and SSP) provided more accurate parameter estimates for medium and large covariate effects. The horseshoe and SSP improved accuracy in predicting factor scores, while achieving more parsimonious models. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

    View details for DOI 10.1037/met0000594

    View details for PubMedID 37498692

  • The Influence of Using Inaccurate Priors on Bayesian Multilevel Estimation STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL Zheng, S., Zhang, L., Jiang, Z., Pan, J. 2022
  • Evaluation and Comparison of SEM, ESEM, and BSEM in Estimating Structural Models with Potentially Unknown Cross-loadings STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL Wei, X., Huang, J., Zhang, L., Pan, D., Pan, J. 2022; 29 (3): 327-338
  • Criteria for Parameter Identification in Bayesian Lasso Methods for Covariance Analysis: Comparing Rules for Thresholding, p-value, and Credible Interval STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL Zhang, L., Pan, J., Ip, E. 2021; 28 (6): 941-950
  • Problematic Internet Usage and Self-Esteem in Chinese Undergraduate Students: The Mediation Effects of Individual Affect and Relationship Satisfaction INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH Zeng, G., Zhang, L., Fung, S., Li, J., Liu, Y., Xiong, Z., Jiang, Z., Zhu, F., Chen, Z., Luo, S., Yu, P., Huang, Q. 2021; 18 (13)


    The aim of this cross-sectional study was to examine the mediating effects of individual affect and relationship satisfaction on the relationship between self-esteem and Problematic Internet Use (PIU). Affect was measured using the Positive and Negative Affect Schedule (PANAS), relationship satisfaction was assessed using a positive and negative semantic dimension scale, self-esteem was measured using the Rosenberg Self-Esteem Scale, and PIU was measured using the Problematic Internet Use scale with a sample of 507 Chinese university students (Mage = 20.41 years, SD = 2.49). The relationships between the variables were tested using structural equation modelling with a multiple mediation model. The results revealed that negative affect and the negative semantic dimensions of relationship satisfaction mediated the relationship between self-esteem and PIU. The implications of the results and the study's theoretical contributions are discussed.

    View details for DOI 10.3390/ijerph18136949

    View details for Web of Science ID 000671104800001

    View details for PubMedID 34209642

    View details for PubMedCentralID PMC8296993

  • A Partially Confirmatory Approach to Scale Development With the Bayesian Lasso PSYCHOLOGICAL METHODS Chen, J., Guo, Z., Zhang, L., Pan, J. 2021; 26 (2): 210-235


    The exploratory and confirmatory approaches of factor analysis lie on two ends of a continuum of substantive input for scale development. Recent advancements in Bayesian regularization methods enable more flexibility in covering a wide range of the substantive continuum. Based on the Bayesian Lasso (least absolute shrinkage and selection operator) methods for the regression model and covariance matrix, this research proposes a partially confirmatory approach to address the loading and residual structures at the same time. With at least one specified loading per item, a one-step procedure can be applied to figure out both structures simultaneously. With a few specified loadings per factor, a two-step procedure is preferred to capture the model configuration correctly. In both cases, the Bayesian hierarchical formulation is implemented using Markov Chain Monte Carlo estimation with different Lasso or regular priors. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed approach across different situations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

    View details for DOI 10.1037/met0000293

    View details for Web of Science ID 000655413000005

    View details for PubMedID 32658502

  • blcfa: An R Package for Bayesian Model Modification in Confirmatory Factor Analysis STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL Zhang, L., Pan, J., Dube, L., Ip, E. 2021; 28 (4): 649-658
  • Integration of Moderation and Mediation in a Latent Variable Framework: A Comparison of Estimation Approaches for the Second-Stage Moderated Mediation Model FRONTIERS IN PSYCHOLOGY Feng, Q., Song, Q., Zhang, L., Zheng, S., Pan, J. 2020; 11: 2167


    An increasing number of studies have focused on models that integrate moderation and mediation. Four approaches can be used to test integrated mediation and moderation models: path analysis (PA), product indicator analysis (PI, constrained approach and unconstrained approach), and latent moderated structural equations (LMS). To the best of our knowledge, few studies have compared the performances of PA, PI, and LMS in evaluating integrated mediation and moderation models. As a result, it is difficult for applied researchers to choose an appropriate method in their data analysis. This study investigates the performance of different approaches in analyzing the models, using the second-stage moderated mediation model as a representative model to be evaluated. Four approaches with bootstrapped standard errors are compared under different conditions. Moreover, LMS with robust standard errors and Bayesian estimation of LMS and PA were also considered. Results indicated that LMS with robust standard errors is the superior evaluation method in all study settings. And PA estimates could be severely underestimated as they ignore measurement errors. Furthermore, it is found that the constrained PI and unconstrained PI only provide acceptable estimates when the multivariate normal distribution assumption is satisfied. The practical guidelines were also provided to illustrate the implementation of LMS. This study could help to extend the application of LMS in psychology and social science research.

    View details for DOI 10.3389/fpsyg.2020.02167

    View details for Web of Science ID 000575360600001

    View details for PubMedID 33013556

    View details for PubMedCentralID PMC7511593