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 38 articles )

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  1. Asbeh, Nuaman; Lerner, Boaz: Learning latent variable models by pairwise cluster comparison. II: Algorithm and evaluation (2016)
  2. Dreher, Anika; Kuntze, Sebastian; Lerman, Stephen: Why use multiple representations in the mathematics classroom? Views of English and German preservice teachers (2016)
  3. 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)
  4. Lipnevich, Anastasiya A.; Preckel, Franzis; Krumm, Stefan: Mathematics attitudes and their unique contribution to achievement: going over and above cognitive ability and personality (2016)
  5. Costa, Ana; Faria, Luísa: The impact of emotional intelligence on academic achievement: a longitudinal study in Portuguese secondary school (2015)
  6. Pek, Jolynn; Wu, Hao: Profile likelihood-based confidence intervals and regions for structural equation models (2015)
  7. 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)
  8. Byrne, Barbara M.: Structural equation modeling with Lisrel, Prelis, and Simplis. Basic concepts, applications, and programming (2014)
  9. Gunzler, D.; Tang, W.; Lu, N.; Wu, P.; Tu, X.M.: A class of distribution-free models for longitudinal mediation analysis (2014)
  10. Steinmayr, Ricarda; Wirthwein, Linda; Schöne, Claudia: Gender and numerical intelligence: does motivation matter? (2014)
  11. Furnham, Adrian; Nuygards, Sarah; Chamorro-Premuzic, Tomas: Personality, assessment methods and academic performance (2013)
  12. Henseler, Jörg; Sarstedt, Marko: Goodness-of-fit indices for partial least squares path modeling (2013)
  13. Jones, Brett D.; Wilkins, Jesse L. M.; Long, Margaret H.; Wang, Feihong: Testing a motivational model of achievement: how students’ mathematical beliefs and interests are related to their achievement (2012)
  14. Schwinger, Malte; Wild, Elke: Prevalence, stability, and functionality of achievement goal profiles in mathematics from third to seventh grade (2012)
  15. Yang, Kai-Lin: Structures of cognitive and metacognitive reading strategy use for reading comprehension of geometry proof (2012)
  16. Boker, Steven; Neale, Michael; Maes, Hermine; Wilde, Michael; Spiegel, Michael; Brick, Timothy; Spies, Jeffrey; Estabrook, Ryne; Kenny, Sarah; Bates, Timothy; Mehta, Paras; Fox, John: OpenMx: an open source extended structural equation modeling framework (2011)
  17. Kline, Rex B.: Principles and practice of structural equation modeling (2011)
  18. Kraijewski, Kristin; Schneider, Wolfgang: Early development of quantity to number-word linkage as a precursor of mathematical school achievement and mathematical difficulties: findings from a four-year longitudinal study (2009)
  19. Lawson, Benn; Cousins, Paul D.; Handfield, Robert B.; Petersen, Kenneth J.: Strategic purchasing, supply management practices and buyer performance improvement: An empirical study of UK manufacturing organisations (2009)
  20. Mulaik, Stanley A.: Linear causal modeling with structural equations. (2009)

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