Mplus is a statistical modeling program that provides researchers with a flexible tool to analyze their data. Mplus offers researchers a wide choice of models, estimators, and algorithms in a program that has an easy-to-use interface and graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program.

References in zbMATH (referenced in 330 articles )

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  1. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  2. Okan Bulut, Christopher David Desjardins: profileR: An R package for profile analysis (2020) not zbMATH
  3. Pan, Junhao; Ip, Edward Haksing; Dubé, Laurette: Multilevel heterogeneous factor analysis and application to ecological momentary assessment (2020)
  4. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  5. Wang, Jichuan; Wang, Xiaoqian: Structural equation modeling. Applications using Mplus (2020)
  6. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  7. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  8. Guerrier, Stéphane; Dupuis-Lozeron, Elise; Ma, Yanyuan; Victoria-Feser, Maria-Pia: Simulation-based bias correction methods for complex models (2019)
  9. Liu, Yang; Yang, Ji Seung; Maydeu-Olivares, Alberto: Restricted recalibration of item response theory models (2019)
  10. Ma, Wenchao: A diagnostic tree model for polytomous responses with multiple strategies (2019)
  11. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  12. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  13. Bakk, Zsuzsa; Kuha, Jouni: Two-step estimation of models between latent classes and external variables (2018)
  14. Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
  15. Desa, Deana: Understanding non-linear modeling of measurement invariance in heterogeneous populations (2018)
  16. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  17. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  18. Jeon, Minjeong; Rijmen, Frank; Rabe-Hesketh, Sophia: CFA models with a general factor and multiple sets of secondary factors (2018)
  19. Joshua M. Rosenberg; Patrick N. Beymer; Daniel J. Anderson; Jennifer A. Schmidt: tidyLPA: An R Package to Easily Carry Out LatentProfile Analysis (LPA) Using Open-Source orCommercial Software (2018) not zbMATH
  20. Kuha, Jouni; Butt, Sarah; Katsikatsou, Myrsini; Skinner, Chris J.: The effect of probing “Don’t know” responses on measurement quality and nonresponse in surveys (2018)

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