Lijin Zhang

All Publications

• A Beta Mixture Model for Careless Respondent Detection in Visual Analogue Scale Data PSYCHOMETRIKA Zhang, L., Domingue, B. W., Vogelsmeier, L. E., Ulitzsch, E. 2025: 1-24

  Abstract

  Visual Analogue scales (VASs) are increasingly popular in psychological, social, and medical research. However, VASs can also be more demanding for respondents, potentially leading to quicker disengagement and a higher risk of careless responding. Existing mixture modeling approaches for careless response detection have so far only been available for Likert-type and unbounded continuous data but have not been tailored to VAS data. This study introduces and evaluates a model-based approach specifically designed to detect and account for careless respondents in VAS data. We integrate existing measurement models for VASs with mixture item response theory models for identifying and modeling careless responding. Simulation results show that the proposed model effectively detects careless responding and recovers key parameters. We illustrate the model's potential for identifying and accounting for careless responding using real data from both VASs and Likert scales. First, we show how the model can be used to compare careless responding across different scale types, revealing a higher proportion of careless respondents in VAS compared to Likert scale data. Second, we demonstrate that item parameters from the proposed model exhibit improved psychometric properties compared to those from a model that ignores careless responding. These findings underscore the model's potential to enhance data quality by identifying and addressing careless responding.

  View details for DOI 10.1017/psy.2025.10041

  View details for Web of Science ID 001576315900001

  View details for PubMedID 40985065

• Augmented Reality Medical Simulation: A Multi-Site Study of Factors That Influence Acceptance. Paediatric anaesthesia Wang, E. Y., Castro, S., Zhang, L., Suen, M. Y., Parris, M., Marks, A., Weser, V., Longhini, A. B., Strupp, K. M., Hernandez, M. R., Libaw, J. S., Kupiec-Weglinski, S., Lockhart, T. J., Olbrecht, V. A., Lau, L. L., CHARM Consortium, Caruso, T. J., Consortium, C., Hiefje, K., Auerbach, M., Chung, C. K., Khoury, M., Zuniga-Hernandez, M., Neiman, N., Forbes, T., Qian, D., Li, B. S., Wang, T., Rama, A., Rodriguez, S. 2025

  Abstract

  BACKGROUND: The infrequent occurrence of resuscitating critically ill pediatric patients poses educational challenges for pediatric anesthesiology residents developing competence. Traditional medical simulations, despite their utility, incur significant costs due to the need for monitors, mannequins, and personnel. Augmented reality (AR) medical simulation shows promise as an alternative clinical teaching tool. The Technology Acceptance Model (TAM) assesses usefulness, ease of use, and attitudes toward new technologies, offering insights into their adoption. Following successful application with other healthcare innovations, the TAM can also assess innovations in pediatric anesthesiology resident education, including AR medical simulation.AIMS: The primary aim identified factors that influenced acceptance of AR for medical simulation in pediatric anesthesiology using a TAM. The secondary aims assessed the model's reliability, usability, and ergonomics.METHODS: This prospective, multi-site study was carried out across nine academic children's hospitals around the United States and Hong Kong. We recruited anesthesiology residents with a minimum of two weeks of pediatric anesthesia experience, excluding those with severe motion sickness, seizures, or who wore corrective glasses. Using Magic Leap 1 headsets, participants underwent a simulated AR pediatric resuscitation scenario. Data were collected via electronic surveys, evaluating TAM factors, usability (System Usability Scale), and ergonomics (ISO 9241-400 standard).RESULTS: A total of 101 participants completed the study. The AR TAM model indicated that perceived ease of use and computer self-efficacy predicted perceived usefulness. Behavioral intention to use the AR system was influenced by perceived usefulness and perceived ease of use. System usability scores showed 83% agreement on ease of use. Ergonomic assessments indicated minimal physical discomfort.CONCLUSION: AR simulations are highly acceptable and usable for pediatric resuscitation training, with perceived ease of use and computer self-efficacy influencing AR adoption. These findings align with previous TAM studies, supporting AR's potential to supplement traditional simulations and enhance accessibility.

  View details for DOI 10.1111/pan.70057

  View details for PubMedID 40965037

• Bayesian factor mixture modeling with response time for detecting careless respondents. Behavior research methods Zhang, L., Ulitzsch, E., Domingue, B. W. 2025; 57 (10): 286

  Abstract

  Careless respondents inject noise into data which can distort research findings and compromise model fit. To address this, factor mixture modeling (FMM) has been widely used to identify careless respondents. Traditionally, researchers have relied on reverse-worded questions in FMM to facilitate the detection of careless responding. With the rise of online data collection platforms, response time has appeal as a means for understanding careless behavior. We introduce a Bayesian FMM that leverages this rich source of information to identify careless respondents. By jointly modeling responses and response time, this approach effectively identifies careless individuals rushing through the questionnaire without providing responses that reflect the to-be-measured traits. Our simulation studies demonstrate that this model accurately estimates parameters and classifies respondents as either attentive or careless, while maintaining error rates within acceptable limits. Furthermore, integrating response time enhances model convergence and the precision of classification and estimation. Using mediation models as an example, we illustrate how social science researchers can use this FMM approach to address careless responding in substantive research. An empirical study further tests the applicability of the proposed model in real-world scenarios, comparing its conclusions with traditional methods. To support its use, we provide an R function to streamline implementation.

  View details for DOI 10.3758/s13428-025-02797-x

  View details for PubMedID 40954398

  View details for PubMedCentralID 10013894

• An introduction to the Item Response Warehouse (IRW): A resource for enhancing data usage in psychometrics. Behavior research methods Domingue, B. W., Braginsky, M., Caffrey-Maffei, L., Gilbert, J. B., Kanopka, K., Kapoor, R., Lee, H., Liu, Y., Nadela, S., Pan, G., Zhang, L., Zhang, S., Frank, M. C. 2025; 57 (10): 276

  Abstract

  The Item Response Warehouse (IRW) is a collection and standardization of a large volume of item response datasets in a free and open-source platform for researchers. We describe key elements of the data standardization process and provide a brief description of the over 900 datasets in the current iteration of the IRW (version 28.2). We describe how to access the data through both the website and an API, and offer a brief tutorial with example R code illustrating how to download data from the IRW and use it in standard psychometric analyses. While we are continuing to develop the IRW, this presentation may help researchers utilize data from this resource for work in psychometrics and related fields.

  View details for DOI 10.3758/s13428-025-02796-y

  View details for PubMedID 40913152

  View details for PubMedCentralID 6290086

• Associations between testosterone and future PTSD symptoms among middle age and older UK residents. Translational psychiatry Shen, H., Stafford, C., Meijsen, J., Zhang, L., Reiter, J., Lawn, R. B., Smith, A. K., Vermuri, M., Duncan, L. E. 2025; 15 (1): 268

  Abstract

  Testosterone has been theorized to influence the development of post-traumatic stress disorder (PTSD). However, the relationship between testosterone level and PTSD is still not well understood. We evaluated the potential association between testosterone and subsequent development of PTSD symptoms using a large sample size, in a civilian context, inclusive of both males and females. Out of around 500,000 total UK Biobank participants, our sample had 130,471 participants who: had testosterone measures, completed the mental health questionnaire, and passed outlier exclusion. After adjusting for relevant covariates, we used linear regression to assess the relationship between testosterone level and future development of symptoms, in males and females separately (Nmales = 61,758, Nfemales = 67,053). In both males and females, small but significant nonlinear (and oftentimes U-shaped) relationships were observed between testosterone levels and PTSD symptoms. When grouping participants into deciles of testosterone for both sexes, the strongest associations between testosterone levels and PTSD symptoms were observed in the central deciles. For example, for total testosterone, compared to decile 1: individuals in decile 7 had the lowest PTSD symptom scores in both males (beta = -0.16, p = 1.58 × 10-3) and females (beta = -0.23, p = 3.04 × 10-5). We also found that body mass index (BMI) moderated the relationship between testosterone and PTSD symptoms, such that the relationship was considerably stronger among individuals with higher BMI. Results were similar for depression and anxiety measures. Analyses using calculated free testosterone (cFT) and the free androgen index (FAI) were generally consistent with total testosterone (TT) results. These findings suggest that mid-range testosterone levels are associated with the lowest risk of PTSD symptoms in both sexes, and future work should seek to examine if this relationship is causal.

  View details for DOI 10.1038/s41398-025-03482-5

  View details for PubMedID 40769963

  View details for PubMedCentralID PMC12328684

• Bayesian Growth Curve Modeling with Measurement Error in Time. Multivariate behavioral research Zhang, L., Qu, W., Zhang, Z. 2025: 1-19

  Abstract

  Growth curve modeling has been widely used in many disciplines to understand the trajectories of growth. Two popular forms utilized in the real-world analyses are the linear and quadratic growth curve models. These models operate on the assumption that measurements are conducted exactly at pre-set time or intervals. In essence, the reliability of these models is deeply tied to the punctuality and consistency of the data collection process. However, in real-world data collection, this assumption is often violated. Deviations from the ideal measurement schedule often emerge, resulting in measurement error in time and consequent biased responses. Our simulation findings indicate that such error can skew estimations, especially in quadratic GCM. To account for the measurement error in time, we introduce a Bayesian growth curve model to accommodate the error in the individual time values. We demonstrate the performance of the proposed approach through simulation studies. Furthermore, to illustrate its application in practice, we provide a real-data example, underscoring the practical benefits of the proposed model.

  View details for DOI 10.1080/00273171.2025.2473937

  View details for PubMedID 40103564

• Polytomous explanatory item response models for item discrimination: Assessing negative-framing effects in social-emotional learning surveys. Behavior research methods Gilbert, J. B., Zhang, L., Ulitzsch, E., Domingue, B. W. 2025; 57 (4): 109

  Abstract

  Modeling item parameters as a function of item characteristics has a long history but has generally focused on models for item location. Explanatory item response models for item discrimination are available but rarely used. In this study, we extend existing approaches for modeling item discrimination from dichotomous to polytomous item responses. We illustrate our proposed approach with an application to four social-emotional learning surveys of preschool children to investigate how item discrimination depends on whether an item is positively or negatively framed. Negative framing predicts significantly lower item discrimination on two of the four surveys, and a plausibly causal estimate from a regression discontinuity analysis shows that negative framing reduces discrimination by about 30% on one survey. We conclude with a discussion of potential applications of explanatory models for item discrimination.

  View details for DOI 10.3758/s13428-025-02625-2

  View details for PubMedID 40045064

• Acceptance of Virtual Reality in Trainees Using a Technology Acceptance Model: Survey Study. JMIR medical education Wang, E. Y., Qian, D., Zhang, L., Li, B. S., Ko, B., Khoury, M., Renavikar, M., Ganesan, A., Caruso, T. J. 2024; 10: e60767

  Abstract

  Virtual reality (VR) technologies have demonstrated therapeutic usefulness across a variety of health care settings. However, graduate medical education (GME) trainee perspectives on VR acceptability and usability are limited. The behavioral intentions of GME trainees with regard to VR as an anxiolytic tool have not been characterized through a theoretical framework of technology adoption.The primary aim of this study was to apply a hybrid Technology Acceptance Model (TAM) and a United Theory of Acceptance and Use of Technology (UTAUT) model to evaluate factors that predict the behavioral intentions of GME trainees to use VR for patient anxiolysis. The secondary aim was to assess the reliability of the TAM-UTAUT.Participants were surveyed in June 2023. GME trainees participated in a VR experience used to reduce perioperative anxiety. Participants then completed a survey evaluating demographics, perceptions, attitudes, environmental factors, and behavioral intentions that influence the adoption of new technologies.In total, 202 of 1540 GME trainees participated. Only 198 participants were included in the final analysis (12.9% participation rate). Perceptions of usefulness, ease of use, and enjoyment; social influence; and facilitating conditions predicted intention to use VR. Age, past use, price willing to pay, and curiosity were less strong predictors of intention to use. All confirmatory factor analysis models demonstrated a good fit. All domain measurements demonstrated acceptable reliability.This TAM-UTAUT demonstrated validity and reliability for predicting the behavioral intentions of GME trainees to use VR as a therapeutic anxiolytic in clinical practice. Social influence and facilitating conditions are modifiable factors that present opportunities to advance VR adoption, such as fostering exposure to new technologies and offering relevant training and social encouragement. Future investigations should study the model's reliability within specialties in different geographic locations.

  View details for DOI 10.2196/60767

  View details for PubMedID 39727193

• 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