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 249 articles )

Showing results 1 to 20 of 249.
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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. Ching, Boby Ho-Hong; Nunes, Terezinha: Children’s understanding of the commutativity and complement principles: a latent profile analysis (2017) MathEduc
  3. Finch, Holmes; Bolin, Jocelyn: Multilevel modeling using Mplus (2017)
  4. Kelava, Augustin; Kohler, Michael; Krzyżak, Adam; Schaffland, Tim Fabian: Nonparametric estimation of a latent variable model (2017)
  5. Lockl, Kathrin; Ebert, Susanne; Weinert, Sabine: Predicting school achievement from early theory of mind: differential effects on achievement tests and teacher ratings (2017) MathEduc
  6. Rose, Norman; von Davier, Matthias; Nagengast, Benjamin: Modeling omitted and not-reached items in IRT models (2017)
  7. 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
  8. 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
  9. Alexandrowicz, Rainer W.; Draxler, Clemens: Testing the Rasch model with the conditional likelihood ratio test: sample size requirements and bootstrap algorithms (2016)
  10. 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
  11. 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
  12. 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
  13. Blair, Clancy; McKinnon, Rachel D.: Moderating effects of executive functions and the teacher-child relationship on the development of mathematics ability in kindergarten (2016) MathEduc
  14. Brown, Anna: Item response models for forced-choice questionnaires: a common framework (2016)
  15. Bruckmaier, G.; Krauss, S.; Blum, W.; Leiss, D.: Measuring mathematics teachers’ professional competence by using video clips (COACTIV video) (2016) MathEduc
  16. Calderón-Tena, Carlos O.; Caterino, Linda C.: Mathematics learning development: the role of long-term retrieval (2016) MathEduc
  17. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  18. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  19. Chiu, Chia-Yi; Köhn, Hans-Friedrich; Zheng, Yi; Henson, Robert: Joint maximum likelihood estimation for diagnostic classification models (2016)
  20. Cirino, Paul T.; Tolar, Tammy D.; Fuchs, Lynn S.; Huston-Warren, Emily: Cognitive and numerosity predictors of mathematical skills in middle school (2016) MathEduc

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Further publications can be found at: http://www.statmodel.com/references.shtml