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 227 articles )

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  1. Ching, Boby Ho-Hong; Nunes, Terezinha: Children’s understanding of the commutativity and complement principles: a latent profile analysis (2017)
  2. 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)
  3. Alexandrowicz, Rainer W.; Draxler, Clemens: Testing the Rasch model with the conditional likelihood ratio test: sample size requirements and bootstrap algorithms (2016)
  4. 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)
  5. 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)
  6. 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)
  7. Blair, Clancy; McKinnon, Rachel D.: Moderating effects of executive functions and the teacher-child relationship on the development of mathematics ability in kindergarten (2016)
  8. Brown, Anna: Item response models for forced-choice questionnaires: a common framework (2016)
  9. Bruckmaier, G.; Krauss, S.; Blum, W.; Leiss, D.: Measuring mathematics teachers’ professional competence by using video clips (COACTIV video) (2016)
  10. Calderón-Tena, Carlos O.; Caterino, Linda C.: Mathematics learning development: the role of long-term retrieval (2016)
  11. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  12. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  13. Cirino, Paul T.; Tolar, Tammy D.; Fuchs, Lynn S.; Huston-Warren, Emily: Cognitive and numerosity predictors of mathematical skills in middle school (2016)
  14. Dinkelmann, Iris; Buff, Alex: Children’s and parents’ perceptions of parental support and their effects on children’s achievement motivation and achievement in mathematics. A longitudinal predictive mediation model (2016)
  15. Donker, Monika; Kroesbergen, Evelyn; Slot, Esther; Van Viersen, Sietske; De Bree, Elise: Alphanumeric and non-alphanumeric rapid automatized naming in children with reading and/or spelling difficulties and mathematical difficulties (2016)
  16. Dunekacke, Simone; Jenßen, Lars; Eilerts, Katja; Blömeke, Sigrid: Epistemological beliefs of prospective preschool teachers and their relation to knowledge, perception, and planning abilities in the field of mathematics: a process model (2016)
  17. Finch, Holmes; Bolin, Jocelyn: Multilevel modeling using Mplus (2016)
  18. Ganley, Colleen M.; Theule Lubienski, Sarah: Mathematics confidence, interest, and performance: examining gender patterns and reciprocal relations (2016)
  19. Garrett-Peters, Patricia T.; Mokrova, Irina; Vernon-Feagans, Lynne; Willoughby, Michael; Pan, Yi: The role of household chaos in understanding relations between early poverty and children’s academic achievement (2016)
  20. Goetz, Thomas; Sticca, Fabio; Pekrun, Reinhard; Murayama, Kou; Elliot, Andrew J.: Intraindividual relations between achievement goals and discrete achievement emotions: an experience sampling approach (2016)

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