Mplus

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 289 articles )

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  1. 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)
  2. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018)
  3. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  4. 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)
  5. Wong, Kin Yau; Zeng, Donglin; Lin, D. Y.: Efficient estimation for semiparametric structural equation models with censored data (2018)
  6. Beauducel, André; Hilger, Norbert: The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables (2017)
  7. 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)
  8. Ching, Boby Ho-Hong; Nunes, Terezinha: Children’s understanding of the commutativity and complement principles: a latent profile analysis (2017) MathEduc
  9. Daniel Caro; Przemysław Biecek: intsvy: An R Package for Analyzing International Large-Scale Assessment Data (2017)
  10. Dudgeon, Paul: Some improvements in confidence intervals for standardized regression coefficients (2017)
  11. Erosheva, Elena A.; Curtis, S. McKay: Dealing with reflection invariance in Bayesian factor analysis (2017)
  12. Finch, Holmes; Bolin, Jocelyn: Multilevel modeling using Mplus (2017)
  13. Grønneberg, Steffen; Foldnes, Njål: Covariance model simulation using regular vines (2017)
  14. 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)
  15. Johan Steen and Tom Loeys and Beatrijs Moerkerke and Stijn Vansteelandt: medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models (2017)
  16. Kelava, Augustin; Kohler, Michael; Krzyżak, Adam; Schaffland, Tim Fabian: Nonparametric estimation of a latent variable model (2017)
  17. Ken Beath: randomLCA: An R Package for Latent Class with Random Effects Analysis (2017)
  18. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  19. Lockl, Kathrin; Ebert, Susanne; Weinert, Sabine: Predicting school achievement from early theory of mind: differential effects on achievement tests and teacher ratings (2017) MathEduc
  20. Nestler, Steffen; Back, Mitja D.: Using cross-classified structural equation models to examine the accuracy of personality judgments (2017)

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