Cited by 488Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingStatistical Distribution Estimation and Applications

The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.

Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes

Lee, Sik-Yum, Song, Xin-Yuan·2004·Multivariate Behavioral Research

DOI 10.1207/s15327906mbr3904_4

Open this paper in CoChat — free account

Abstract

The main objective of this article is to investigate the empirical performances of the Bayesian approach in analyzing structural equation models with small sample sizes. The traditional maximum likelihood (ML) is also included for comparison. In the context of a confirmatory factor analysis model and a structural equation model, simulation studies are conducted with the different magnitudes of parameters and sample sizes n = da, where d = 2, 3, 4 and 5, and a is the number of unknown parameters. The performances are evaluated in terms of the goodness-of-fit statistics, and various measures on the accuracy of the estimates. The conclusion is: for data that are normally distributed, the Bayesian approach can be used with small sample sizes, whilst ML cannot.

Integrity signals

Registry

Crossref · 10.1207/s15327906mbr3904_4

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Structural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,944
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2072165 · Cited by 17,475
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
EQS : structural equations program manual
Bentler, P. M. (1989) · Medical Entomology and Zoology · Cited by 9,669
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Untitled work
(1993) · Medical Entomology and Zoology · Cited by 7,690
The Calculation of Posterior Distributions by Data Augmentation
Tanner, M. A., & Wong, W. H. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478458 · Cited by 3,929
Aspects of Multivariate Statistical Theory
Gleser, L. J., & Muirhead, R. J. (1984) · Technometrics · doi:10.2307/1268117 · Cited by 3,369
Bayesian Analysis of Binary and Polychotomous Response Data
Albert, J. H., & Chib, S. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476321 · Cited by 3,160

Flagged first, then by citations

Show all 46 references

1 of the 46 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

Boomsma, A. (1982). The robustness of LISREL against small sample sizes in factor analysis model. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction (Part I, pp. 149-173). Amsterdam: North-Holland.
Gelman A., 1996, Statistica Sinica, 6, 733
10.1037//0033-2909.112.2.351 · DOI
Lindley D. V., 1972, Series B, 34, 1

Sorted by citations

Items 301–400 of 488

Developing a scale measuring perceived knowledge and skills dimensions for mental health promotion: a pilot test using a convenience sample
Lang, G., Stengård, E., & Wynne, R. F. (2016) · The Journal of Mental Health Training Education and Practice · doi:10.1108/jmhtep-02-2015-0005 · Cited by 5
Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data
Cai, J., Ouyang, M., Kang, K., & Song, X. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2017.1407233 · Cited by 5
Self-citation
Lean back and relax: Reclined seating position buffers the effect of frustration on anger and aggression
Krahé, B., Lütz, J., & Sylla, I. (2018) · European Journal of Social Psychology · doi:10.1002/ejsp.2363 · Cited by 5
Oxygenation of the Prefrontal Cortex during Memory Interference
Crawford, L. K., Zou, L., & Loprinzi, P. D. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8122055 · Cited by 5
Standardized estimates for latent interaction effects: Method comparison and selection strategy
Wen, Z., Ouyang, J., & Fang, J. (2021) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2022.00091 · Cited by 6
Measuring Physical Education Teacher Socialization With Respect to Comprehensive School Physical Activity Programming
Merica, C. B., Egan, C. A., Webster, C. A., Mîndrilă, D., Karp, G. G., Paul, D. R., McKown, H. B., & Rose, S. (2023) · Journal of Teaching in Physical Education · doi:10.1123/jtpe.2022-0165 · Cited by 6
The Winner Takes it All: Risk Factors and Bayesian Modelling of the Probability of Success in Escaping from Big Cat Predation
Moya, S. F., Pastrana, C. I., Navas, C. M., Aguilera, M. J. R., Bermejo, J. V. D., & González, F. J. N. (2021) · Animals · doi:10.3390/ani12010051 · Cited by 5
Customer Value Co-creation in a Service-Dominant Logic Perspective: Some Steps Toward the Development of a Measurement Scale
Botti, A., Grimaldi, M., & Vesci, M. (2017) · New economic windows · doi:10.1007/978-3-319-61967-5_8 · Cited by 5
Markov chain monte carlo estimation methods for structural equation modeling: a comparison of subject-level data and moment-level data approaches
Choi, J., & Levy, R. (2017) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2017.06.00182 · Cited by 5
Testing measurement and structural invariance in latent mediation models – A comparison of IPCR and Bayesian MNLFA
Münch, F. F., & Koch, T. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02781-5 · Cited by 5
Finding the paths between job demand–resources and turnover intention of community mental health nurses in Korea
Hwang, E., & Yi, Y. (2025) · International Nursing Review · doi:10.1111/inr.70004 · Cited by 4
Using Item Parameter Predictions for Reducing Calibration Sample Requirements—A Case Study Based on a High‐Stakes Admission Test
Ulitzsch, E., Belov, D. I., Lüdtke, O., & Robitzsch, A. (2025) · Journal of Educational Measurement · doi:10.1111/jedm.12426 · Cited by 4
Chemometric data analysis of gross radioactivity and heavy metal concentrations in soil and sediments of Bendimahi River, Turkey
Şehrïbanoğlu, S., Zorer, H., & Zorer, Ö. S. (2020) · Journal of Radioanalytical and Nuclear Chemistry · doi:10.1007/s10967-020-07361-9 · Cited by 4
Burning questions: investigating officer support for hot spots policing
Tregle, B., & Mourtgos, S. M. (2024) · Police Practice and Research · doi:10.1080/15614263.2024.2435044 · Cited by 4
Muscle-specific neuromuscular responses during running with blood flow restriction
Lubiak, S. M., Proppe, C. E., Rivera, P. M., Howard, M. A., Prajapati, A. J., Shah, N. M., Patel, N. N., Hammer, S. M., Trevino, M. A., Dinyer, T. K., et al. (2025) · International Journal of Sports Medicine · doi:10.1055/a-2493-0617 · Cited by 4
Can Psychometric Measurement Models Inform Behavior Genetic Models? A Bayesian Model Comparison Approach
Wang, T., Wood, P. K., & Heath, A. C. (2015) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-319-20585-4_10 · Cited by 4
The MTMM matrix approach: implications for HRM research
Hamdani, M. R., Vâlcea, S., & Buckley, M. R. (2016) · Personnel Review · doi:10.1108/pr-12-2014-0278 · Cited by 7
Are contextual rather than personal factors at the basis of an anti-school culture? A Bayesian analysis of differences in intelligence, overexcitability, and learning patterns between (former) lower and higher-track students
Bondt, N. D., Donche, V., & Petegem, P. V. (2020) · Social Psychology of Education · doi:10.1007/s11218-020-09597-5 · Cited by 7
Toward a Better Understanding of Gender Differences in Psychopathology in Detained Adolescents: The Role of Maladaptive Personality Traits
Gouwy, M., Verbeke, L., Dierckx, K., Damme, L. V., Colins, O. F., & Clercq, B. J. D. (2022) · Journal of Forensic Psychology Research and Practice · doi:10.1080/24732850.2022.2127345 · Cited by 4
Probabilistic characterisation of transition points in three-stage permanent deformation curves using Bayesian inference approach
Manoj, K. G. (2022) · International Journal of Pavement Engineering · doi:10.1080/10298436.2022.2141742 · Cited by 4
How Vietnamese Export Firms Faced Financial Distress during COVID-19? A Bayesian Small Sample Analysis
Bui, T. D., & Thạch, N. N. (2023) · Economies · doi:10.3390/economies11020041 · Cited by 4
A Model-Based Shrinkage Target to Avoid Non-convergence in Small Sample SEM
Jonckere, J. D., & Rosseel, Y. (2023) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2023.2171420 · Cited by 5
Earnings differentials between H-2A and unauthorized citrus harvesters in Florida
Simnitt, S., & Önel, G. (2023) · Agricultural and Resource Economics Review · doi:10.1017/age.2023.14 · Cited by 4
The effect of vocal tract parameters on aspiration noise discrimination
Labuschagne, I. B., & Ciocca, V. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0000756 · Cited by 4
Analyse factorielle confirmatoire : approche bayésienne
Golay, P., Reverte, I., & Lecerf, T. (2015) · Presses universitaires de Rennes eBooks · doi:10.4000/books.pur.61631 · Cited by 8
Estimation of Technology Acceptance Model (TAM) on the Adoption of Technology in the Learning Process Using Structural Equation Modeling (SEM) with Bayesian Approach
Rafikasari, E. F., & Iriawan, N. (2021) · doi:10.1109/iccsai53272.2021.9609773 · Cited by 4
Bayesian Psychometric Scaling
Fox, J., van den Berg, S. M., & Veldkamp, B. P. (2018) · doi:10.1002/9781118489772.ch17 · Cited by 4
Cholesterol and Phospholipid Distribution Pattern in the Erythrocyte Membrane of Patients with Hepatitis C and Severe Fibrosis, before and after Treatment with Direct Antiviral Agents: A pilot Study
Papadopoulos, C., Panopoulou, M. I., Mylopoulou, T., Mimidis, K., Tentes, I. K., & Anagnostopoulos, K. P. (2020) · MAEDICA – a Journal of Clinical Medicine · doi:10.26574/maedica.2020.15.2.162 · Cited by 4
Prior sensitivity analysis in default Bayesian structural equation modelingPreprint
van Erp, S., Mulder, J., & Oberski, D. L. (2017) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/5j3m9 · Cited by 7
Posterior predictive model checking for multidimensionality in item response theory and Bayesian networks
Levy, R. (2006) · University Libraries (University of Maryland) · Cited by 4
Relationship Between Attitudes Toward and Perceptions of Gender Roles and Equality and the Gender Gap in Bicycle Use Across European Countries
Prati, G. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231179464 · Cited by 3
A novel sex-associated genomic region in Catostomus fish species
Pyne, C. B., Campbell, J. N., McFarlane, S. E., & Mandeville, E. G. (2025) · Journal of Heredity · doi:10.1093/jhered/esaf007 · Cited by 3
Little chance for divergence: The role of interlocutor language constraint in online bilingual accommodation
Vincze, L., Gasiorek, J., & Dragojević, M. (2017) · International Journal of Applied Linguistics · doi:10.1111/ijal.12164 · Cited by 3
Assessing a measure for Quality of Life in patients with severe Alopecia Areata: a multicentric Italian study
Caldarola, G., Raimondi, G., Samela, T., Pinto, L. M., Pampaloni, F., Starace, M. V. R., Diluvio, L., Dall’Oglio, F., Vagnozzi, E., Giudice, M. B. D. F. D., et al. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1415334 · Cited by 3
Assessing the Psychometric Qualities of the Data-Informed School Leadership Survey
Sun, J., Xia, J., Hua, C., Man, K., & Johnson, B. L. (2024) · Educational Administration Quarterly · doi:10.1177/0013161x241271250 · Cited by 5
Dyadic adjustment of persons with albinism and their significant other: A Bayesian mediation model of the Actor-Partner Interdependence (APIMeM)
Fournier, H., Pillaud, N., Morice‐Picard, F., Hadj-Rabia, S., Arveiler, B., & Quintard, B. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.117684 · Cited by 3
A tutorial on the what, why, and how of Bayesian analysis: Estimating mood and anxiety disorder prevalence using a Canadian data linkage study
Rodrigues, M., Edwards, J., Rosic, T., Wang, Y., Talukdar, J. R., Chowdhury, S. R., Parpia, S., Babe, G., de Oliveira, C., Perez, R., et al. (2025) · PLOS mental health. · doi:10.1371/journal.pmen.0000253 · Cited by 5
Bayesian Model Comparison of Structural Equation Models
Lee, S., & Song, X. (2008) · Lecture notes in statistics · doi:10.1007/978-0-387-76721-5_6 · Cited by 3
Self-citation
A Bayesian approach for individual‐level drug benefit‐risk assessment
Li, K., Luo, S., Yuan, S., & Mt‐Isa, S. (2019) · Statistics in Medicine · doi:10.1002/sim.8166 · Cited by 3
Efficacy of Guided Versus Self-Induced Learning of Web-Based Self-Compassionate Journaling by College Students
Williamson, J., & Blackhart, G. C. (2021) · The American Journal of Psychology · doi:10.5406/amerjpsyc.134.1.0045 · Cited by 3
Confirmatory factor analysis with missing data in a small sample: cognitive reserve in people with Down Syndrome
Cañete‐Massé, C., Carbó‐Carreté, M., Figueroa-Jiménez, M. D., Oviedo, G. R., Guerra-Balic, M., Javierre, C. F., Peró, M., & Guàrdia‐Olmos, J. (2021) · Quality & Quantity · doi:10.1007/s11135-021-01264-x · Cited by 3
Investigating the Safety Effectiveness of Wildlife–Vehicle Crash Countermeasures using a Bayesian Approach with a Comparison between Carcass Removal Data and Traditional Crash Data
Ahmed, I., & Ahmed, M. M. (2022) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981221083904 · Cited by 3
An Improved Estimation Using Polya-Gamma Augmentation for Bayesian Structural Equation Models with Dichotomous Variables
Kim, S., Lu, Z. L., & Cohen, A. S. (2018) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366367.2018.1437303 · Cited by 3
Coping in Newly Diagnosed, Spanish-Speaking Men who Have Sex with Men and Live with HIV : A Bayesian Approach
Garrido‐Hernansaiz, H., Alonso‐Tapia, J., & Martín-Fernández, M. (2019) · European Journal of Mental Health · doi:10.5708/ejmh.14.2019.1.3 · Cited by 3
Investigando Evidências de Validade de Conteúdo e Estrutural em Tarefas de um Treino Cognitivo para Idosos
Santos, M. T., Schelini, P. W., Golino, H. F., Pereira, B. L. D. S., & Felix, L. M. (2017) · Revista Avaliação Psicológica · doi:10.15689/ap.2017.1603.12431 · Cited by 5
Bayesian Two-level Model for Repeated Partially Ordered Responses: Application to Adolescent Smoking Behavior Analysis
Wang, X., Wu, H., Feng, X., & Song, X. (2019) · Sociological Methods & Research · doi:10.1177/0049124119826149 · Cited by 3
Self-citation
The Social Relations Model for Count Data
Loncke, J., Cook, W. L., Neiderhiser, J. M., & Loeys, T. (2019) · Methodology · doi:10.1027/1614-2241/a000178 · Cited by 3
Effects of environmental conditions on characteristics of prescribed fires and resprouting of hardwoods in shortleaf pine-oak-hickory ecosystems of the United States
Hmielowski, T. L. (2013) · doi:10.31390/gradschool_dissertations.2762 · Cited by 3
Mixture of longitudinal factor analyzers and their application to the assessment of chronic pain
Ounajim, A., Slaoui, Y., Louis, P., Billot, M., Frasca, D., & Rigoard, P. (2023) · Statistics in Medicine · doi:10.1002/sim.9804 · Cited by 2
Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations Approaches
Shen, Z., Chen, L., Somer, E., Miočević, M., & Falk, C. F. (2025) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2024.2443943 · Cited by 2
Using Testlets in Education: eTIMSS 2019 as an Example
Kabasakal, K. A., & GÖREN, S. (2025) · TED EĞİTİM VE BİLİM · doi:10.15390/eb.2025.14104 · Cited by 2
Historical Measurement Information Can Be Used to Improve Estimation of Structural Parameters in Structural Equation Models With Small Samples
Uanhoro, J., & Soyoye, O. (2025) · Educational and Psychological Measurement · doi:10.1177/00131644251330851 · Cited by 3
Examining independence of facial identity and age processing using the Garner Speeded Classification Paradigm
Attard‐Johnson, J. (2025) · Quarterly Journal of Experimental Psychology · doi:10.1177/17470218251360238 · Cited by 2
GenAI Use Behavior and Post‐Failure Perceptions Among People With Functional Disabilities: A Multimethod Study
Bagnato, G., Moreno, F. R., Campayo-Sánchez, F., & Ali, A. A. (2025) · Psychology and Marketing · doi:10.1002/mar.70028 · Cited by 4
Bayesian algorithm based on auxiliary variables for estimating item response theory models with non-ignorable missing response data
Zhang, J., Zhang, Z., & Tao, J. (2021) · Journal of the Korean Statistical Society · doi:10.1007/s42952-020-00100-6 · Cited by 2
Motor Imagery Combined With Physical Training Improves Response Inhibition in the Stop Signal Task
Son, S. M., Yun, S. H., & Kwon, J. W. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.905579 · Cited by 4
Advanced Considerations in Quantitative Methods for New Directions in Engineering Education Research
Katz, A., Godwin, A., & Brozina, C. (2023) · Routledge eBooks · doi:10.4324/9781003287483-36 · Cited by 3
The Sensitivity of Bayesian Fit Indices to Structural Misspecification in Structural Equation Modeling
Cao, C., Lugu, B. K., & Li, J. (2023) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2023.2253497 · Cited by 3
Proposal of a comprehensive risk assessment model for cut slope considering long-term deterioration characteristics based on the concept of disaster immunity
Zhuang, H., Yasufuku, N., Kasama, K., & Ishikura, R. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104538 · Cited by 2
Variations in stand structure, composition, and fuelbeds drive prescribed fire behavior during mountain longleaf pine restoration
Anderson, C. J., Weand, M. P., Alexander, H. D., Bretfeld, M., & Green, N. S. (2024) · Forest Ecology and Management · doi:10.1016/j.foreco.2024.122372 · Cited by 2
Alpha-Lipoic acid supplementation during in vitro culture of abattoir- and OPU-derived bovine oocytes improves embryonic quality
Fabra, M. C., Campagna, A. A., Farnetano, N. A., Anchordoquy, J. P., Anchordoquy, J. M., Martín, A., Furnus, C. C., & Nikoloff, N. (2024) · Animal Reproduction Science · doi:10.1016/j.anireprosci.2024.107667 · Cited by 2
Positive Affect and Project Team Development and Effectiveness
Knight, A. P. (2009) · ScholarlyCommons (University of Pennsylvania) · Cited by 2
Bayesian Structural Equation Modeling: A Business Culture Application in Kenya
Mwangi, M. E. (2016) · Science Journal of Applied Mathematics and Statistics · doi:10.11648/j.sjams.20160402.13 · Cited by 2
Having the right stuff
McCoy, E. D. (2015) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199672547.003.0003 · Cited by 2
Approaches to statistical inference
Mccarthy, M. A. (2015) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199672547.003.0002 · Cited by 2
BAYESIAN VE NONBAYESIAN KESTİRİM YÖNTEMLERİNE DAYALI OLARAK SINIFLAMA İNDEKSLERİNİN TIMSS-2015 MATEMATİK TESTİ ÜZERİNDE İNCELENMESİ
Çelikten, S., & Çakan, M. (2019) · Necatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi · doi:10.17522/balikesirnef.566446 · Cited by 2
The influence of type of visual image and gender on the perception of horizontality: a subjective visual horizontal (SVH) study
Zakaria, M. N., Tahir, A., Zainun, Z., Salim, R., Sakeri, N. S. M., & Wahat, N. H. A. (2020) · Acta Oto-Laryngologica · doi:10.1080/00016489.2020.1817552 · Cited by 2
A Short Note on Obtaining Item Parameter Estimates of IRT Models with Bayesian Estimation in Mplus
Şen, S., Cohen, A. S., & Kim, S. (2020) · Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi · doi:10.21031/epod.693719 · Cited by 2
Bayesian estimates for genetic and phenotypic parameters of growth traits in Sahiwal cattle
Yadav, N., Illa, S. K., Mukherjee, S., Gujar, G., & Mukherjee, A. (2022) · Tropical Animal Health and Production · doi:10.1007/s11250-022-03446-7 · Cited by 2
A Bayesian region of measurement equivalence (ROME) approach for establishing measurement invariance.
Zhang, Y., Lai, M. H. C., & Palardy, G. J. (2022) · Psychological Methods · doi:10.1037/met0000455 · Cited by 2
Bayesian Estimation of a Continuous-Time Model for Discretely-Observed Panel Data
Boulton, A. J. (2014) · KU ScholarWorks (University of Kansas) · Cited by 2
ACTOR AND PARTNER EFFECTS IN RELATIONSHIPS AMONG MATERNAL/PATERNAL PARENTING BEHAVIORS AND MATERNAL/PATERNAL PERCEPTIONS OF CHILD AGGRESSION IN EARLY CHILDHOOD
Cho, S. I. (2015) · Michigan State University Libraries · doi:10.25335/e2r0-nz12 · Cited by 2
Perceived Fairness of Differential Customer Treatment
Mayser, S. (2011) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 2
Are Dimensions of Gender Inequality Uniformly Associated with Human Values?Preprint
Stefani, S., & Prati, G. (2020) · doi:10.31234/osf.io/jacuw · Cited by 2
Bayesian SEM with Small Samples: Precautions and Guidelines
Smid, S. C. (2023) · Utrecht University Repository (Utrecht University) · doi:10.33540/1755 · Cited by 3
Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations ApproachesPreprint
Shen, Z., Chen, L., Somer, E., Miočević, M., & Falk, C. F. (2024) · doi:10.31219/osf.io/ntzd2 · Cited by 2
Social Inclusivity, Integration and the Fourth Industrial Revolution in South African Universities: Evidence from Vaal University of Technology
Munyanyi, W., & Mothibi, G. (2024) · Southern African Business Review · doi:10.25159/1998-8125/14967 · Cited by 1
Bivariate Latent Change Score Models in Small Sample Contexts: Comparison of Maximum Likelihood and Bayesian Estimation
Ouyang, J., Jiang, Z., Shi, D., Pan, J., & Pan, S. (2025) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2025.2450782 · Cited by 1
A Bayesian Structural Equation Model to Predict Quality of Life in European Older Adults
Tomás, J. M., Sanz, A., & Torres, Z. (2025) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe15070127 · Cited by 1
Research on the Mechanism of the Multimodal Sustained Usage of Sport Drones from the Perspective of the Low-Altitude Economy
Zhang, M., Zhang, A., Tian, J., & Deng, B. (2025) · Applied Sciences · doi:10.3390/app15179348 · Cited by 1
A Bayesian approach to estimate causal peer influence accounting for latent network homophily
Um, S., Sweet, T., & Adhikari, S. (2025) · Journal of the Royal Statistical Society: Series A (Statistics in Society) · doi:10.1093/jrsssa/qnaf131
Physiological and Behavioral Responses of Stabled Horses (Equus caballus) to Three Types of Environmental Enrichment
Brauns, M., Ali, A., Berger, J. M., & McLean, A. (2025) · Animals · doi:10.3390/ani15192779 · Cited by 1
Treatments of Ordinal Scales in Latent Profile Analyses: A Comparison with Monte Carlo Simulations
Yang, C., Olsen, J. A., & Carlyle, C. (2025) · Chinese/English Journal of Educational Measurement and Evaluation · doi:10.59863/civh2487 · Cited by 1
Small Samples, Big Insights: A Methodological Comparison of Estimation Techniques for Latent Divergent Thinking Models
Weiss, S., Elmdust, L. S., & Goecke, B. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13110150 · Cited by 4
A latent class location-scale regression model with an application to calorie intake data
Zhang, X., Siddique, J., Spring, B., & Hedeker, D. (2026) · Journal of Behavioral Medicine · doi:10.1007/s10865-025-00613-7 · Cited by 1
Bayesian Restructuring Technology Acceptance Model (TAM) with Moderating Lecture Self-Managing in E-Learning Adoption
Rafikasari, E. F., Iriawan, N., & Otok, B. W. (2025) · Decision Making Applications in Management and Engineering · doi:10.31181/dmame8120251427 · Cited by 1
Addressing Small Sample Concerns for Multilevel Mediation in MSEM Framework: A Comparison of Alternative Strategies
Li, Y., & Jia, F. (2026) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2026.2679039 · Cited by 1
Psychometric evaluation of the PROMIS® Medication Adherence Scale among patients prescribed oral anticancer medication for multiple myeloma
Belcher, S. M., Scott, P. W., Sereika, S. M., Bender, C. M., Dunbar‐Jacob, J. M., Rosenzweig, M. Q., Muluneh, B., Sabik, L. M., Copeland, V. C., Agha, M., et al. (2026) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-026-01204-z · Cited by 1
Interpretation
Maiväli, Ü. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-418689-7.00006-5 · Cited by 1
Dealing with Sparse Rater Scoring of Constructed Responses within a Framework of a Latent Class Signal Detection Model
Kim, S. (2013) · doi:10.7916/d8t4419t · Cited by 1
A Bayesian analysis of generalized latent curve mixture models
Ip, E. H., Pan, J., & Song, X.-Y. (2013) · Statistics and Its Interface · doi:10.4310/sii.2013.v6.n1.a4 · Cited by 1
Self-citation
Bayes’sche Statistik in der Dienstleistungsforschung
Lange, A.-L., & Otto, P. (2016) · AStA Wirtschafts- und Sozialstatistisches Archiv · doi:10.1007/s11943-016-0189-x · Cited by 1
Methodology Challenges and Cutting Edge Designs for Rural Education Research
Bovaird, J. A., & Bash, K. L. (2016) · Springer eBooks · doi:10.1007/978-3-319-42940-3_6 · Cited by 1
Robust Bayesian Hierarchical Model Using Monte-Carlo Simulation
Chen, G., & Luo, S. (2017) · ICSA book series in statistics · doi:10.1007/978-981-10-3307-0_16 · Cited by 1
Bayesian Empirical Likelihood Estimation for Kink Regression with Unknown Threshold
Yamaka, W., Pastpipatkul, P., & Sriboonchitta, S. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-70942-0_54 · Cited by 1
Estimating structural equation models within a bayesian framework: a concrete example of a markov chain monte carlo method
Choi, J. (2018) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2018.07.00191 · Cited by 1
French validation of the M-Back questionnaire: assessing clinicians' knowledge of metabolic syndrome in psychiatry
Golay, P., Calderoni, M., Eap, C. B., & Conus, P. (2021) · Swiss Archives of Neurology Psychiatry and Psychotherapy · doi:10.4414/sanp.2021.w10011 · Cited by 1
The Comparison of Classical and Bayesian Structural Equation Models Through Ordered Categorical Data: A Case Study of Banking Service Quality
ERKAN, G., Doğan, M., & Tatlıdi̇l, H. (2022) · GAZI UNIVERSITY JOURNAL OF SCIENCE · doi:10.35378/gujs.814405 · Cited by 1
Tekrarlanabilirlik Krizi ve Geçerlilik Krizi Kıskacındaki Psikoloji ve Sosyal Bilimlerde Krizden Çıkış İçin Öne Çıkan İki Trend: “Yeni İstatistik” ve “Bayesyen İstatistik”
Kara, E. (2023) · Anadolu Journal Of Educational Sciences International · doi:10.18039/ajesi.1240655 · Cited by 1
Distinguishing expectation and attention effects in processing temporal patterns of visual input
Tang, J., Solomon, S. S., Kohn, A., & Sussman, E. S. (2024) · Brain and Cognition · doi:10.1016/j.bandc.2024.106228 · Cited by 1

Authors: Lee, S.-Y., & Song, X.-Y.
Venue: Multivariate Behavioral Research
Publisher: Informa UK Limited
Published: 2004-10
Volume: 39
Issue: 4
Pages: 653-686
ISSN: 0027-3171 (ISSN-L)
1532-7906
DOI: 10.1207/s15327906mbr3904_4
OpenAlex: W1999291824
Cited by: 488
References: 46
Citations by year: 2026: 20 · 2025: 33 · 2024: 29 · 2023: 23 · 2022: 35 · 2021: 37 · 2020: 44 · 2019: 38 · 2018: 32 · 2017: 41 · 2016: 24 · 2015: 43 · 2014: 22 · 2013: 21 · 2012: 14
FWCI: 2.71
Type: journal article
Language: en
Open access: closed
Locations: Multivariate Behavioral Research (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Keywords: Bayesian structural equation modeling, maximum likelihood estimation, small sample size, confirmatory factor analysis, goodness-of-fit statistics, estimation accuracy, simulation study, normal data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks