The Theory and Practice of Item Response Theory

By De Ayala. 2008

1,670 citations recorded by the source corpus.

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Citations in the corpus

Introduction to Latent Class Analysis With Applications (2017, methodology): “IRT models use the observed responses (to a number of items) to measure a continuous LV and the strength of the relationship between item scores, that is, the probability of responding into a particular category and LVs (De Ayala, 2009; Hambleton & Swaminathan, 1985; Sijtsma & Molenaar, 2002).”
Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules (2017, background): “…our development and is justified in our application where X does not contain information thought to be informative about patient preferences beyond W ; however, this assumption is not necessary as one can include baseline covariates into the latent patient preference model (De Ayala, 2013).”
Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It (2014, methodology): “For general references about various IRT models, see De Ayala (2009) and Embretson and Reise (2000).”
Practical Guide to Conducting an Item Response Theory Analysis (2014, methodology): “If this latter step is done, it would be prudent to collect data from a new sample (De Ayala, 2009) with newly applied labels to reassess the validity of the model and item parameters; otherwise, the item calibrations could be sample specific.”
Development and validation of attitudes measurement scales: fundamental and practical aspects (2019, methodology): “To perform the CFA and CCA, the following sources are recommended: Hair et al. (2019b) and Brown (2015); for IRT, Alaya (2009) and Baker and Kim (2017) are useful; and for CCA, Hair et al. (2019a).”
Measuring mindfulness? An Item Response Theory analysis of the Mindful Attention Awareness Scale (2010, methodology): “A theoretically appropriate IRT model to explore this item response format is the Graded Response Model (GRM; see de Ayala (2009), Embretson and Reise (2000), Ostini and Nering (2006), Samejima (1969) and Samejima (1996)).”
On a signal detection approach to m-alternative forced choice with bias, with maximum likelihood and Bayesian approaches to estimation (2012, methodology): “, de Ayala, 2009) because, as shown in Eq. (8), the random effect (εj in this case) is integrated out of the conditional response probability (and so the ‘marginal’ likelihood is maximized). Some useful references that discuss relevant details of NLMIXED (and other programs) in this context are Rijm”
Analysis of Multivariate Social Science Data (2nd ed.) (2011, background): “…also seems legitimate to ask whether the didactic presentation in this book is superior to other specialized books that are targeted at the same readership such as Brown (2006) and Kline (2010) for factor-analytic and structural equation methods or de Ayala (2009) for item response theory methods.”