IBM® SPSS® Amos enables you to specify, estimate, assess and present models to show hypothesized relationships among variables. The software lets you build models more accurately than with standard multivariate statistics techniques. Users can choose either the graphical user interface or non-graphical, programmatic interface. SPSS Amos allows you to build attitudinal and behavioral models that reflect complex relationships. The software: Provides structural equation modeling (SEM)—that is easy to use and lets you easily compare, confirm and refine models. Uses Bayesian analysis—to improve estimates of model parameters. Offers various data imputation methods—to create different data sets.

References in zbMATH (referenced in 58 articles )

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  1. Ma, Wenchao: A diagnostic tree model for polytomous responses with multiple strategies (2019)
  2. Noh, Maengseok; Lee, Youngjo; Oud, Johan H. L.; Toharudin, Toni: Hierarchical likelihood approach to non-Gaussian factor analysis (2019)
  3. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  4. García-Santillán, Arturo: Measuring set latent variables that explain attitude toward statistic through exploratory factor analysis with principal components extraction and confirmatory analysis (2017)
  5. Green, Chloe T.; Bunge, Silvia A.; Briones Chiongbian, Victoria; Barrow, Maia; Ferrer, Emilio: Fluid reasoning predicts future mathematical performance among children and adolescents (2017) MathEduc
  6. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017) not zbMATH
  7. Thanoon, Thanoon Y.; Adnan, Robiah: Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data (2017)
  8. Vaikundamoorthy, K.: Diagnosis of blood cancer using Markov chain Monte Carlo trace model (2017)
  9. Asbeh, Nuaman; Lerner, Boaz: Learning latent variable models by pairwise cluster comparison. II: Algorithm and evaluation (2016)
  10. Bustamante, Juan Carlos; Chacón, Edixon: Estimation and goodness-of-fit in latent trait models: a comparison among theoretical approaches (2016)
  11. Dreher, Anika; Kuntze, Sebastian; Lerman, Stephen: Why use multiple representations in the mathematics classroom? Views of English and German preservice teachers (2016) MathEduc
  12. González, Antonio; Rodríguez, Yolanda; Faílde, José M.; Carrera, María V.: Anxiety in the statistics class: structural relations with self-concept, intrinsic value, and engagement in two samples of undergraduates (2016) MathEduc
  13. Huang, Shi; MacKinnon, David P.; Perrino, Tatiana; Gallo, Carlos; Cruden, Gracelyn; Hendricks Brown, C.: A statistical method for synthesizing mediation analyses using the product of coefficient approach across multiple trials (2016)
  14. Lipnevich, Anastasiya A.; Preckel, Franzis; Krumm, Stefan: Mathematics attitudes and their unique contribution to achievement: going over and above cognitive ability and personality (2016) MathEduc
  15. Siebert, Johannes; Kunz, Reinhard: Developing and validating the multidimensional proactive decision-making scale (2016)
  16. Backhaus, Klaus; Erichson, Bernd; Weiber, Rolf: Advanced multivariate analysis methods. An application oriented introduction (2015)
  17. Costa, Ana; Faria, Luísa: The impact of emotional intelligence on academic achievement: a longitudinal study in Portuguese secondary school (2015) MathEduc
  18. Ledermann, Thomas; Macho, Siegfried: Assessing mediation in simple and complex models (2015)
  19. Pek, Jolynn; Wu, Hao: Profile likelihood-based confidence intervals and regions for structural equation models (2015)
  20. Van Rooijen, M.; Verhoeven, L.; Steenbergen, B.: From numeracy to arithmetic: precursors of arithmetic performance in children with cerebral palsy from 6 till 8 years of age (2015) MathEduc

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