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 270 articles )

Showing results 1 to 20 of 270.
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  1. Beauducel, André; Hilger, Norbert: The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables (2017)
  2. 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)
  3. Ching, Boby Ho-Hong; Nunes, Terezinha: Children’s understanding of the commutativity and complement principles: a latent profile analysis (2017) MathEduc
  4. Dudgeon, Paul: Some improvements in confidence intervals for standardized regression coefficients (2017)
  5. Erosheva, Elena A.; Curtis, S.McKay: Dealing with reflection invariance in Bayesian factor analysis (2017)
  6. Finch, Holmes; Bolin, Jocelyn: Multilevel modeling using Mplus (2017)
  7. Grønneberg, Steffen; Foldnes, Njål: Covariance model simulation using regular vines (2017)
  8. Johan Steen and Tom Loeys and Beatrijs Moerkerke and Stijn Vansteelandt: medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models (2017)
  9. Kelava, Augustin; Kohler, Michael; Krzyżak, Adam; Schaffland, Tim Fabian: Nonparametric estimation of a latent variable model (2017)
  10. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  11. Lockl, Kathrin; Ebert, Susanne; Weinert, Sabine: Predicting school achievement from early theory of mind: differential effects on achievement tests and teacher ratings (2017) MathEduc
  12. Nestler, Steffen; Back, Mitja D.: Using cross-classified structural equation models to examine the accuracy of personality judgments (2017)
  13. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017)
  14. Rose, Norman; von Davier, Matthias; Nagengast, Benjamin: Modeling omitted and not-reached items in IRT models (2017)
  15. Xu, Zhenhua; Jang, Eunice Eunhee: The role of math self-efficacy in the structural model of extracurricular technology-related activities and junior elementary school students’ mathematics ability (2017) MathEduc
  16. Aeschlimann, Belinda; Herzog, Walter; Makarova, Elena: How to foster students’ motivation in mathematics and science classes and promote students’ STEM career choice. A study in swiss high schools (2016) MathEduc
  17. Alexandrowicz, Rainer W.; Draxler, Clemens: Testing the Rasch model with the conditional likelihood ratio test: sample size requirements and bootstrap algorithms (2016)
  18. Alivernini, Fabio; Manganelli, Sara; Lucidi, Fabio: The last shall be the first: competencies, equity and the power of resilience in the Italian school system (2016) MathEduc
  19. Arens, A. Katrin; Marsh, Herbert W.; Craven, Rhonda G.; Yeung, Alexander Seeshing; Randhawa, Eva; Hasselhorn, Marcus: Math self-concept in preschool children: structure, achievement relations, and generalizability across gender (2016) MathEduc
  20. Baroody, Alison E.; Rimm-Kaufman, Sara E.; Larsen, Ross A.; Curby, Timothy W.: A multi-method approach for describing the contributions of student engagement on fifth grade students’ social competence and achievement in mathematics (2016) MathEduc

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