Mokken Scale Analysis in<i>R</i>

Ark, L. Andries van der

doi:10.18637/jss.v020.i11

Cited by 183 publications

(136 citation statements)

References 20 publications

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“…The MSA was conducted using the mokken package in the R statistical environment ([26, 37]). The approach is still relatively unusual in the literature; and a more detailed description than is usual for a Methods section is provided as a guide.…”

Section: Methodsmentioning

confidence: 99%

“…The approach is still relatively unusual in the literature; and a more detailed description than is usual for a Methods section is provided as a guide. Readers interested in even greater detail should refer to [37–39]. …”

Section: Methodsmentioning

confidence: 99%

“…Loevinger’s H value equals [1 – (actual Guttman errors/predicted Guttman errors)] [8]. The scalability coefficient for each item ( H i ), item pair ( H ij ), whole scale ( H ) and transposed Mokken scale ( H T ) may range from 0 to 1 [32, 37]. The H coefficient for each single item as well as for the scale has to be more than 0.30 to satisfy the assumptions of Mokken scale [32].…”

Section: Methodsmentioning

confidence: 99%

“…The H coefficient for each single item as well as for the scale has to be more than 0.30 to satisfy the assumptions of Mokken scale [32]. Widely accepted rules of thumb have developed around the use of the H coefficients, such that 0.30 ≤ H < 0.40 indicates a weak Mokken scale; 0.40 ≤ H < 0.50 a moderate Mokken scale and more than 0.50 a strong Mokken scale [37]. For example, the H coefficient for the 9-item stigma scale is 0.54 – refer to Table 1 – which demonstrates a scale with items with a 46 % rate of Guttman errors, which indicates a strong Mokken scale.…”

Section: Methodsmentioning

confidence: 99%

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A latent trait approach to measuring HIV/AIDS related stigma in healthcare professionals: application of mokken scaling technique

2016

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BackgroundThe attitudes of healthcare professionals towards HIV positive patients and high risk groups are central to the quality of care and therefore to the management of HIV/AIDS related stigma in health settings. Extant HIV/AIDS stigma scales that measure stigmatising attitudes towards people living with HIV/AIDS have been developed using scaling techniques such as principal component analysis. This approach has resulted in instruments that are often long. Mokken scale analysis is a nonparametric hierarchical scaling technique that can be used to develop unidimensional cumulative scales. This technique is advantageous over the other approaches; as the scales are usually shorter, while retaining acceptable psychometric properties. Moreover, Mokken scales also make no distributional assumptions about the underlying data, other than that the data are capable of being ordered by item and by person. In this study we aimed at developing a precise and concise measure of HIV/AIDS related stigma among health care professionals, using Mokken scale analysis.MethodsWe carried out a cross sectional survey of healthcare students at the Monash University campuses in Malaysia and Australia. The survey consisted of demographic questions and an initial item pool of twenty five potential questions for inclusion in an HIV stigma scale.ResultsWe analysed the data using the mokken package in the R statistical environment providing a 9-item scale with high reliability, validity and acceptable psychometric properties, measuring and ranking the HIV/AIDS related stigmatising attitudes.ConclusionMokken scaling procedure not only produced a comprehensive hierarchical scale that could accurately order a person along HIV/AIDS stigmatising attitude, but also demonstrated a unidimensional and reliable measurement tool which could be used in future studies. The principal component analysis confirmed the accuracy of the Mokken scale analysis in correctly detecting the unidimensionality of this scale. We recommend future works to study the generalisability of this scale in a new population.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A latent trait approach to measuring HIV/AIDS related stigma in healthcare professionals: application of mokken scaling technique

2016

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“…There are several tutorials available to aid practitioners interested in fitting these types of models to empirical data; see for example Sijtsma and Molenaar (2002) and Meijer, Tendeiro, and Wanders (2015). The R mokken package ( Van der Ark 2007 can be used to check these assumptions in practice.…”

Section: Person-fit Analysis In Irtmentioning

confidence: 99%

PerFit: An R Package for Person-Fit Analysis in IRT

Tendeiro¹,

Meijer²,

Niessen³

2016

J. Stat. Soft.

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Checking the validity of test scores is important in both educational and psychological measurement. Person-fit analysis provides several statistics that help practitioners assessing whether individual item score vectors conform to a prespecified item response theory model or, alternatively, to a group of test takers. Software enabling easy access to most person-fit statistics was lacking up to now. The PerFit R package was written in order to fill in this void. A theoretical overview of relatively simple person-fit statistics is provided. A practical guide showing how the main functions of PerFit can be used is also given. Both numerical and graphical tools are described and illustrated using examples. The goal is to show how person-fit statistics can be easily applied to testing of questionnaire data.

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Unidimensional Item Response Theory

Meijer¹,

Tendeiro²

2018

The Wiley Handbook of Psychometric Testing

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Mokken Scale Analysis inR

Cited by 183 publications

References 20 publications

A latent trait approach to measuring HIV/AIDS related stigma in healthcare professionals: application of mokken scaling technique

A latent trait approach to measuring HIV/AIDS related stigma in healthcare professionals: application of mokken scaling technique

PerFit: An R Package for Person-Fit Analysis in IRT

Unidimensional Item Response Theory

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