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 20 articles )

Showing results 1 to 20 of 20.
Sorted by year (citations)

  1. Callingham, Rosemary; Carmichael, Colin; Watson, Jane M.: Explaining student achievement: the influence of teachers’ pedagogical content knowledge in statistics (2016)
  2. 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)
  3. O’Shea, Ann; Breen, Sinéad; Jaworski, Barbara: The development of a function concept inventory (2016)
  4. 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)
  5. Golia, Silvia: Assessing the impact of uniform and nonuniform differential item functioning items on Rasch measure: the polytomous case (2015)
  6. Jong, Cindy; Hodges, Thomas E.: Assessing attitudes toward mathematics across teacher education contexts (2015)
  7. San Martín, Ernesto; González, Jorge; Tuerlinckx, Francis: On the unidentifiability of the fixed-effects 3PL model (2015)
  8. Christensen, Karl Bang: Conditional maximum likelihood estimation in polytomous Rasch models using SAS (2013)
  9. Christensen, Karl Bang (ed.); Kreiner, Sven (ed.); Mesbah, Mounir (ed.): Rasch related models and methods for health science (2013)
  10. Hsieh, Feng-Jui: Strengthening the conceptualization of mathematics pedagogical content knowledge for international studies: a Taiwanese perspective (2013)
  11. Beswick, Kim; Callingham, Rosemary; Watson, Jane: The nature and development of middle school mathematics teachers’ knowledge (2012)
  12. 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)
  13. Montanari, Giorgio E.; Ranalli, M.Giovanna; Eusebi, Paolo: Latent variable modeling of disability in people aged 65 or more (2011)
  14. Carmichael, Colin; Callingham, Rosemary; Hay, Ian; Watson, Jane: Measuring middle school students’ interest in statistical literacy (2010)
  15. Bradley, Kelly; Sampson, Shannon; Royal, Kenneth: Applying the Rasch rating scale model to gain insights into students’ conceptualisation of quality mathematics instruction (2006)
  16. Grimbeek, Peter; Nisbet, Steven: Surveying primary teachers about compulsory numeracy testing: combining factor analysis with Rasch analysis (2006)
  17. Wu, Margaret; Adams, Raymond: Modelling mathematics problem solving item responses using a multidimensional IRT model (2006)
  18. Bertoli-Barsotti, Lucio: On the existence and uniqueness of JML estimates for the partial credit model (2005)
  19. Irwin, Kathryn C.; Britt, Murray S.: The Algebraic Nature of Students’ Numerical Manipulation in the New Zealand Numeracy Project (2005)
  20. Ram, Nilam; Chow, Sy-Miin; Bowles, Ryan P.; Wang, Lijuan; Grimm, Kevin; Fujita, Frank; Nesselroade, John R.: Examining interindividual differences in cyclicity of pleasant and unpleasant affects using spectral analysis and item response modeling (2005)