Lijin Zhang

All Publications

• A tutorial on Bayesian structural equation modelling: Principles and applications. International journal of psychology : Journal international de psychologie Chen, Q., Su, K., Feng, Y., Zhang, L., Ding, R., Pan, J. 2024

  Abstract

  This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.

  View details for DOI 10.1002/ijop.13258

  View details for PubMedID 39389756

• A technology acceptance model to predict anesthesiologists' clinical adoption of virtual reality. Journal of clinical anesthesia Wang, E. Y., Kennedy, K. M., Zhang, L., Zuniga-Hernandez, M., Titzler, J., Li, B. S., Arshad, F., Khoury, M., Caruso, T. J. 2024; 98: 111595

  Abstract

  Virtual reality (VR) is a novel tool with demonstrated applications within healthcare; however its integration within clinical practice has been slow. Adoption patterns can be evaluated using a technology acceptance model (TAM). The primary study aim was to use VR TAM to assess factors that influence anesthesiologists' acceptance of VR for preoperative anxiolysis. The secondary aim assessed the model's reliability.109 clinical anesthesiologists at Stanford were exposed to a VR application developed as a distraction tool to reduce preoperative patient anxiety. Anesthesiologists were surveyed about their attitudes, beliefs, and behaviors as predictors of their likelihood to clinically use VR. The primary outcome assessed predictive validity using descriptive statistics, construct validity using confirmatory factor analysis, and standardized estimates of model relationships. The secondary outcome assessed reliability with Cronbach's α and composite reliability.Construct validity and reliability was assessed, where all values established acceptable fit and reliability. Hypothesized predictors of consumer use were evaluated with standardized estimates, looking at perceptions of usefulness, ease of use, and enjoyment in predicting attitudes and intentions toward using and purchasing. Past use and price willing to pay did not predict perceived usefulness. Participants in lower age ranges had higher levels of perceived ease of use than those >55 years.All confirmatory factor analysis testing for construct validity had good fit. Perceptions of usefulness and enjoyment predicted an anesthesiologist's attitude toward using and intention to purchase, while perceived ease of use predicted perceived usefulness and enjoyment, attitude toward purchasing and using, and intention to use. Past use and price willing to pay did not influence perceptions of usefulness. Lower age predicted greater perceived ease of use. All scales in the model demonstrated acceptable reliability. With good validity and reliability, the VR-TAM model demonstrated factors predictive of anesthesiologist's intentions to integrate VR into clinical settings.

  View details for DOI 10.1016/j.jclinane.2024.111595

  View details for PubMedID 39213811

• Awe Inducing Elements in Virtual Reality Applications: A Prospective Study of Hospitalized Children and Caregivers. Games for health journal He, E. M., Arshad, F., Li, B. S., Brinda, R., Ganesan, A., Zhang, L., Fehr, S., Renavikar, M., Rodriguez, S. T., Wang, E., Rosales, O., Caruso, T. J. 2024

  Abstract

  Background: Hospitalized pediatric patients and their caregivers often experience anxiety and fear, resulting in withdrawal and aggression. Despite virtual reality (VR) being a safe and effective anxiolytic, it is unknown what software design aspects contribute to its effectiveness. This prospective observational study evaluated which VR application elements increased awe, which is correlated with improved behavior and satisfaction. Methods: Patients aged 6 to 25 years and their caregivers at an academic pediatric hospital interacted with a custom VR application that compared design aspects, including environment, graphics fidelity, and presence of a motivational character. Outcomes investigated self-reported awe, vastness, accommodation, and engagement. Data were analyzed using repeated measure ANOVA tests and correlation analyses. Results: A total of 202 participants were enrolled, and 179 (88 pediatric patients, 91 adult caregivers) were included in the final analysis. A fictional environment was more effective at increasing awe in pediatric patients (P = 0.030) compared with a realistic environment. However, increased graphics fidelity was more effective at increasing awe in caregiver adults (P = 0.023) compared with low resolution graphics. Presence of a motivational character did not influence awe in either patients or caregivers (P = 0.432, P = 0.904, respectively). All measures of awe were positively correlated with application engagement (P < 0.005). Conclusion: In conclusion, when software developers design VR software for pediatric patients and their caregivers, fictional settings and increased graphic fidelity should be considered for pediatric patients and adults, respectively. Future studies will explore other VR elements in gameplay settings.

  View details for DOI 10.1089/g4h.2024.0050

  View details for PubMedID 39109578

• Heterogeneity of Item-Treatment Interactions Masks Complexity and Generalizability in Randomized Controlled Trials JOURNAL OF RESEARCH ON EDUCATIONAL EFFECTIVENESS Ahmed, I., Bertling, M., Zhang, L., Ho, A. D., Loyalka, P., Xue, H., Rozelle, S., Domingue, B. W. 2024

  View details for DOI 10.1080/19345747.2024.2361337

  View details for Web of Science ID 001268996500001

• Testing informative hypotheses in factor analysis models using bayes factors. Psychological methods Gu, X., Zhu, X., Zhang, L., Pan, J. 2023

  Abstract

  This study proposes a Bayesian approach to testing informative hypotheses in confirmatory factor analysis (CFA) models. The informative hypothesis, which is formulated by the constrained loadings, can directly represent researchers' theories or expectations about the tau equivalence in reliability analysis, item-level discriminant validity, and relative importance of indicators. Support for the informative hypothesis is quantified by the Bayes factor. We present the adjusted fractional Bayes factor of which the prior distribution is specified using a part of the data and adjusted according to the hypotheses under evaluation. This Bayes factor is derived and computed using the Markov chain Monte Carlo posterior samples of model parameters. Simulation studies investigate the performance of the proposed Bayes factor. A classic example of CFA models is used to illustrate the construction of the informative hypothesis, the specification of the prior distribution, and the computation and interpretation of the Bayes factor. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

  View details for DOI 10.1037/met0000627

  View details for PubMedID 38095990

• 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

  Abstract

  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

• Variety and Mainstays of the R Developer Community R JOURNAL Zhang, L., Li, X., Zhang, Z. 2023; 15 (3): 5-25

  View details for Web of Science ID 001143302100004

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

  Abstract

  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

  View details for DOI 10.1080/10705511.2022.2136185

  View details for Web of Science ID 000889130300001

• 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

  View details for DOI 10.1080/10705511.2021.2006664

  View details for Web of Science ID 000752395600001

• 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

  View details for DOI 10.1080/10705511.2021.1945456

  View details for Web of Science ID 000691759300001

• 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)

  Abstract

  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

  Abstract

  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

  View details for DOI 10.1080/10705511.2020.1867862

  View details for Web of Science ID 000631937500001

• 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

  Abstract

  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