Reliability and validity of the estimates of the Rasch latent trait model

Kwan, Patrick Y. K.; Shannon, A. G.

doi:10.1080/0020739940250605

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From a quantitative perspective, when using a Likert-type rating scale item, a practitioner might make the assumption that as an examinee’s level of θ increases, each consecutively higher ordered response option on the rating scale has a higher probability of selection (Andrich, 2013). This assumption can be evaluated by assessing the ordering of the step parameters from fitting the NRM to the data (Preston & Reise, 2015) or by fitting ordered IRT and Rasch models to the scale data (Jeon & De Boeck, 2016; Kwan & Shannon, 1994; Linacre, 1999; Yang, 2014), with ordered step parameters indicating that the respondent data may be aligned with the theoretical assumption that higher levels of θ are associated with higher probabilities of making a “step” to a higher rating category. While we focus this study on that traditional assumption, we do note to the reader that this assumption is not always held in rating scale data or the models that one may use to calibrate such data (e.g., see literature on unfolding IRT models—e.g., Andrich, 1996; Roberts, Donoghue, & Laughlin, 2000).…”

Section: Introductionmentioning

confidence: 99%

Applying Unidimensional Models for Semiordered Data to Scale Data With Neutral Responses

Cohn

Huggins-Manley

2019

Educational and Psychological Measurement

View full text Add to dashboard Cite

The purpose of this study is to evaluate whether a recently developed semiordered model can be used to explore the functioning of neutral response options in rating scale data. Huggins-Manley, Algina, and Zhou developed a class of unidimensional models for semiordered data within scale items (i.e., items with both ordered response categories and an additional nominal response category) and found promising results when applying them to scale data with Not Applicable response categories. In this study, we extended the application of the semi–partial credit model (PCM) to evaluate whether the semi-PCM can be used to calibrate potentially unordered neutral responses in rating scale data, and if so, how the approach compares with alternate methods of dealing with the neutral response option. Findings indicate that the semi-PCM can (a) assist practitioners in evaluating the ordered or unordered nature of neutral responses and (b) provide a viable alternative for θ estimation in the presence of an unordered neutral category. The process used in this study also provides a methodological framework for researchers and practitioners to use when dealing with neutral responses in their own data.

show abstract