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 302 articles )

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  1. Ma, Wenchao: A diagnostic tree model for polytomous responses with multiple strategies (2019)
  2. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  3. Bakk, Zsuzsa; Kuha, Jouni: Two-step estimation of models between latent classes and external variables (2018)
  4. 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)
  5. Desa, Deana: Understanding non-linear modeling of measurement invariance in heterogeneous populations (2018)
  6. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  7. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  8. 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
  9. 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)
  10. Wong, Kin Yau; Zeng, Donglin; Lin, D. Y.: Efficient estimation for semiparametric structural equation models with censored data (2018)
  11. Beauducel, André; Hilger, Norbert: The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables (2017)
  12. Chang, Yu-Wei; Hsu, Nan-Jung; Tsai, Rung-Ching: Unifying differential item functioning in factor analysis for categorical data under a discretization of a normal variant (2017)
  13. Ching, Boby Ho-Hong; Nunes, Terezinha: Children’s understanding of the commutativity and complement principles: a latent profile analysis (2017) MathEduc
  14. Daniel Caro; Przemysław Biecek: intsvy: An R Package for Analyzing International Large-Scale Assessment Data (2017) not zbMATH
  15. Dudgeon, Paul: Some improvements in confidence intervals for standardized regression coefficients (2017)
  16. Erosheva, Elena A.; Curtis, S. McKay: Dealing with reflection invariance in Bayesian factor analysis (2017)
  17. Finch, Holmes; Bolin, Jocelyn: Multilevel modeling using Mplus (2017)
  18. Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne: Growth modeling. Structural equation and multilevel modeling approaches (2017)
  19. Grønneberg, Steffen; Foldnes, Njål: Covariance model simulation using regular vines (2017)
  20. Hayes, Timothy; McArdle, John J.: Should we impute or should we weight? examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables (2017)

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