Guessing and the Rasch Model

Holster, Trevor A.; Lake, J.

doi:10.1080/15434303.2016.1160096

Cited by 24 publications

(12 citation statements)

References 14 publications

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“…From Rasch's point of view this separability is a sine qua non for objective measurement" (Lord & Wright, 2010, p. 1289. Similarly, the differences between Rasch measurement and IRT have piqued the interest of language assessment researchers (see Holster &Lake, 2016, andStewart, McLean, &Kramer, 2017).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of Rasch measurement in language assessment: Recommendations and guidelines for research

Aryadoust

Sayama

2020

Language Testing

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Over the past decades, the application of Rasch measurement in language assessment has gradually increased. In the present study, we reviewed and coded 215 papers using Rasch measurement published in 21 applied linguistics journals for multiple features. We found that seven Rasch models and 23 software packages were adopted in these papers, with many-facet Rasch measurement ( n = 100) and Facets ( n = 113) being the most frequently used Rasch model and software, respectively. Significant differences were detected between the number of papers that applied Rasch measurement to different language skills and components, with writing ( n = 63) and grammar ( n = 12) being the most and least frequently investigated, respectively. In addition, significant differences were found between the number of papers reporting person separation ( n = 73, not reported: n = 142) and item separation ( n = 59, not reported: n = 156) and those that did not. An alarming finding was how few papers reported unidimensionality check ( n = 57 vs 158) and local independence ( n = 19 vs 196). Finally, a multilayer network analysis revealed that research involving Rasch measurement has created two major discrete communities of practice (clusters), which can be characterized by features such as language skills, the Rasch models used, and the reporting of item reliability/separation vs person reliability/separation. Cluster 1 was accordingly labelled the production and performance cluster, whereas cluster 2 was labelled the perception and language elements cluster. Guidelines and recommendations for analyzing unidimensionality, local independence, data-to-model fit, and reliability in Rasch model analysis are proposed.

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Section: Introductionmentioning

confidence: 99%

A comprehensive review of Rasch measurement in language assessment: Recommendations and guidelines for research

Aryadoust

Sayama

2020

Language Testing

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“…The PCAR analysis results on the FHBQ instrument resulted in a raw variance explained by measure of 12.8 in eigenvalues units and 47.7% in percentage size. With the criteria that a size of > 40% (Holster & Lake, 2016) is sufficient evidence of unidimensionality factor structure, it can be said that the assumption of the unidimensionality of FHBQ in this study is fulfilled.…”

Section: Unidimensionalitymentioning

confidence: 92%

Female Hedonistic Behavior Questionnaire (FHBQ): Psychometric Properties Based on the Rasch Model

Deviana

Hayat

Tresniasari

2020

JP3I

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This study aims to develop standard measurement tools from hedonistic behavior using the perspective of the Rasch measurement model. The evaluation of psychometric characteristics conducted in this study (1) to check unidimensionality, (2) testing local independence assumptions, (3) testing scale rating diagnostics, (4) testing item fit, (5) reliability for person and items, and (6) displays the wright map and test information function to compare people and items on the same scale and find out the functioning of the test when given to individuals with the level of trait obtained. The sample in this study were 335 hijab-wearing Muslim women. Rasch analysis results show that the psychometric characteristics obtained are very good and precise, as well as the suitability of the items to the model. With this, the measuring instrument developed by researchers was named the Female Hedonistic Behavior Scale (FHBQ) with 14 valid items from the Rasch rating scale model from the measurement model perspective.

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“…In this research, unidimensionality was tested using the principal component analysis of a standardized residual (PCAR;Smith, 2002) to ensure that the scale met this requirement of the model. Based on the PCAR, a large Rasch dimension, with approximately 40% of the variance explained, provided sufficient unidimensionality (Holster & Lake, 2016); the eigenvalue of the first contrast was not greater than 1.5 as the critical value of the unidimensionality violations (Smith, 2002).…”

Section: Unidimensionality and Local Independencementioning

confidence: 99%

The Indonesian version of the Life Orientation Test-Revised (LOT-R): Psychometric properties based on the Rasch model

2021

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Guessing and the Rasch Model

Cited by 24 publications

References 14 publications

A comprehensive review of Rasch measurement in language assessment: Recommendations and guidelines for research

A comprehensive review of Rasch measurement in language assessment: Recommendations and guidelines for research

Female Hedonistic Behavior Questionnaire (FHBQ): Psychometric Properties Based on the Rasch Model

The Indonesian version of the Life Orientation Test-Revised (LOT-R): Psychometric properties based on the Rasch model

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