Estimating the Nominal Response Model Under Nonnormal Conditions

Preston, Kathleen S. J.; Reise, Steven P.

doi:10.1177/0013164413507063

Cited by 11 publications

(20 citation statements)

References 31 publications

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“…Furthermore, as Sass, Schmitt, and Walker (2008) argued, while in educational and psychological sciences, the assumption of a normal distribution for a latent trait may be reasonable when respondents are sampled from normally distributed populations at random, when nonrandom sampling techniques are used to obtain samples from normally distributed populations, the potential for nonnormal trait distributions exists. Last, there exist other constructs, such as depression, pain, or gambling, that may not be normally distributed (e.g., Preston & Reise, 2014).…”

Section: Introductionmentioning

confidence: 99%

Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality

Svetina

Valdivia

Underhill

et al. 2017

Applied Psychological Measurement

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Information about the psychometric properties of items can be highly useful in assessment development, for example, in item response theory (IRT) applications and computerized adaptive testing. Although literature on parameter recovery in unidimensional IRT abounds, less is known about parameter recovery in multidimensional IRT (MIRT), notably when tests exhibit complex structures or when latent traits are nonnormal. The current simulation study focuses on investigation of the effects of complex item structures and the shape of examinees' latent trait distributions on item parameter recovery in compensatory MIRT models for dichotomous items. Outcome variables included bias and root mean square error. Results indicated that when latent traits were skewed, item parameter recovery was generally adversely impacted. In addition, the presence of complexity contributed to decreases in the precision of parameter recovery, particularly for discrimination parameters along one dimension when at least one latent trait was generated as skewed.

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Section: Introductionmentioning

confidence: 99%

Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality

Svetina

Valdivia

Underhill

et al. 2017

Applied Psychological Measurement

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“…Figures a and b show the bias values and RMSEs of the substantive parameter estimators (

\overset{δ}{true}

{\overset{false^}{normalσ}}_{θ}^{2}

, and

{\overset{false^}{normalρ}}_{normalθ normalγ}

), respectively, when the data‐generating NESIM was fitted to the simulated incomplete data. It appeared that all of the bias values were rather small (below 0.1 in absolute value) and the RMSEs were all acceptable (below 0.3) (Preston & Reise, ; Wollack et al ., ). Figures c and d show the bias values and RMSEs of non‐substantive parameter estimators (

\overset{λ}{true}

and

\overset{τ}{true}

), respectively, when the data‐generating NESIM was fitted to the simulated incomplete data.…”

Section: Resultsmentioning

confidence: 99%

“…Second, it is assumed that θ and γ follow a bivariate normal distribution in the NESIM. In the face of assumption violations, semi‐parametric methods such as a Ramsay curve can be adopted (Preston & Reise, ), which calls for future studies.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Non‐ignorable missingness item response theory models for choice effects in examinee‐selected items

Liu

Wang

2017

Brit J Math & Statis

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Examinee-selected item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set, always yields incomplete data (i.e., when only the selected items are answered, data are missing for the others) that are likely non-ignorable in likelihood inference. Standard item response theory (IRT) models become infeasible when ESI data are missing not at random (MNAR). To solve this problem, the authors propose a two-dimensional IRT model that posits one unidimensional IRT model for observed data and another for nominal selection patterns. The two latent variables are assumed to follow a bivariate normal distribution. In this study, the mirt freeware package was adopted to estimate parameters. The authors conduct an experiment to demonstrate that ESI data are often non-ignorable and to determine how to apply the new model to the data collected. Two follow-up simulation studies are conducted to assess the parameter recovery of the new model and the consequences for parameter estimation of ignoring MNAR data. The results of the two simulation studies indicate good parameter recovery of the new model and poor parameter recovery when non-ignorable missing data were mistakenly treated as ignorable.

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“…For test items that have polytomous response alternatives (i.e., when there are more than two response options), the different responses may convey unique information. For example, in tests that use a multiple-choice format 1 , each of the different erroneous responses carries distinctive information about the test-taker's ability (Preston, Reise, Cai, & Hays, 2011;Preston & Reise, 2014;Preston & Reise, 2015). Usually some answers are "more wrong" than others, so selecting a poorer response option may signal a lower ability level than selecting an "almost correct" response.…”

Section: Nominal Response Modelmentioning

confidence: 99%

Neuropsychological tests of the future: How do we get there from here?

Bilder

Reise

2018

The Clinical Neuropsychologist

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Objective: This article reviews current approaches to neuropsychological assessment, identifies opportunities for development of new methods using modern psychometric theory and advances in technology, and suggests a transition path that promotes application of novel methods without sacrificing validity. Methods: Theoretical/state-of-the-art review. Conclusions: Clinical neuropsychological assessment today does not reflect advances in neuroscience, modern psychometrics, or technology. Major opportunities for improving practice include both psychometric and technological strategies. Modern psychometric approaches including item response theory (IRT) enable: linking procedures that can place different measures on common scales; adaptive testing algorithms that can dramatically increase efficiency of assessment; examination of differential item functioning (DIF) to detect measures that behave differently in different groups; and person fit statistics to detect aberrant patterns of responding of high value for performance validity testing. Opportunities to introduce novel technologies include computerized adaptive testing, Web-based assessment, healthcare-and bio-informatics strategies, mobile platforms, wearables, and the "internet-of-things." To overcome inertia in current practices, new methods must satisfy requirements for back-compatibility with legacy instrumentation, enabling us to leverage the wealth of validity data already accrued for classic procedures. A path to achieve these goals involves creation of a global network to aggregate item-level data into a shared repository that will enable modern psychometric analyses to refine existing methods, and serve as a platform to evolve novel assessment strategies, which over time can revolutionize neuropsychological assessment practices worldwide .

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Estimating the Nominal Response Model Under Nonnormal Conditions

Cited by 11 publications

References 31 publications

Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality

Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality

Non‐ignorable missingness item response theory models for choice effects in examinee‐selected items

Neuropsychological tests of the future: How do we get there from here?

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