Rasch Analysis and Rasch Measurement Software. Rasch measurement converts dichotomous and rating scale observations into linear measures. It links qualitative analysis to quantitative methods. Rasch scaling is often classified under item response theory, IRT, or logit-linear models. Rasch specifies how persons, probes, prompts, raters, test items, tasks, etc. must interact statistically through probabilistic measurement models for linear measures to be constructed from ordinal observations. Rasch analysis requires the investigation and quantification of accuracy, precision, reliability, construct validity, quality-control fit statistics, statistical information, linearity, local dependency and unidimensionality. Rasch implements stochastic Guttman ordering, conjoint additivity, Campbell concatenation, sufficiency and infinite divisibility.

References in zbMATH (referenced in 27 articles )

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  1. Müller, Marianne: Item fit statistics for Rasch analysis: can we trust them? (2020)
  2. Callingham, Rosemary; Carmichael, Colin; Watson, Jane M.: Explaining student achievement: the influence of teachers’ pedagogical content knowledge in statistics (2016) MathEduc
  3. Carney, Michele B.; Smith, Everett; Hughes, Gwyneth R.; Brendefur, Jonathan L.; Crawford, Angela: Influence of proportional number relationships on item accessibility and students’ strategies (2016) MathEduc
  4. Golia, Silvia: A proposal for categorizing the severity of non uniform differential item functioning: the polytomous case (2016)
  5. O’Shea, Ann; Breen, Sinéad; Jaworski, Barbara: The development of a function concept inventory (2016) MathEduc
  6. Pampaka, Maria; Pepin, Birgit; Sikko, Svein Arne: Supporting or alienating students during their transition to higher education: mathematically relevant trajectories in the contexts of England and Norway (2016) MathEduc
  7. Ciavolino, Enrico; Carpita, Maurizio; Al-Nasser, Amjad: Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches (2015)
  8. Golia, Silvia: Assessing the impact of uniform and nonuniform differential item functioning items on Rasch measure: the polytomous case (2015)
  9. Jong, Cindy; Hodges, Thomas E.: Assessing attitudes toward mathematics across teacher education contexts (2015) MathEduc
  10. Maja Olsbjerg, Karl Bang Christensen: lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models (2015) not zbMATH
  11. San Martín, Ernesto; González, Jorge; Tuerlinckx, Francis: On the unidentifiability of the fixed-effects 3PL model (2015)
  12. Annoni, Paola; Weziak-Bialowolska, Dorota; Farhan, Hania: Measuring the impact of the Web: Rasch modelling for survey evaluation (2013)
  13. Christensen, Karl Bang: Conditional maximum likelihood estimation in polytomous Rasch models using SAS (2013)
  14. Christensen, Karl Bang (ed.); Kreiner, Sven (ed.); Mesbah, Mounir (ed.): Rasch related models and methods for health science (2013)
  15. Hsieh, Feng-Jui: Strengthening the conceptualization of mathematics pedagogical content knowledge for international studies: a Taiwanese perspective (2013) MathEduc
  16. Beswick, Kim; Callingham, Rosemary; Watson, Jane: The nature and development of middle school mathematics teachers’ knowledge (2012) MathEduc
  17. Boone, William J.; Abell, Sandra K.; Volkmann, Mark J.; Arbaugh, Fran; Lannin, John K.: Evaluating selected perceptions of science and mathematics teachers in an alternative certification program (2011) MathEduc
  18. Montanari, Giorgio E.; Ranalli, M. Giovanna; Eusebi, Paolo: Latent variable modeling of disability in people aged 65 or more (2011)
  19. Carmichael, Colin; Callingham, Rosemary; Hay, Ian; Watson, Jane: Measuring middle school students’ interest in statistical literacy (2010) MathEduc
  20. Rabe-Hesketh, Sophia; Skrondal, Anders: Classical latent variable models for medical research (2008)

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