Mokken Scale Analysis in R

Mokken scale analysis (MSA) is a scaling procedure for both dichotomous and polytomous items. It consists of an item selection algorithm to partition a set of items into Mokken scales and several methods to check the assumptions of two nonparametric item response theory models: the monotone homogeneity model and the double monotonicity model. First, we present an R package mokken for MSA and explain the procedures. Second, we show how to perform MSA in R using test data obtained with the Adjective Checklist.

Advances in nonparametric item response theory for scale construction in quality-of-life research

We introduce the special section on nonparametric item response theory (IRT) in Quality of Life Research. Starting from the well-known Rasch model, we provide a brief overview of nonparametric IRT models and discuss the assumptions, the properties, and the investigation of goodness of fit. We provide references to more detailed texts to help readers getting acquainted with nonparametric IRT models. In addition, we show how the rather diverse papers in the special section fit into the nonparametric IRT framework. Finally, we illustrate the application of nonparametric IRT models using data from a questionnaire measuring activity limitations in walking. The real-data example shows the quality of the scale and its constituent items with respect to dimensionality, local independence, monotonicity, and invariant item ordering

Maximum augmented empirical likelihood estimation of categorical marginal models for large sparse contingency tables

Categorical marginal models (CMMs) are flexible tools for modelling dependent or clustered categorical data, when the dependencies themselves are not of interest. A major limitation of maximum likelihood (ML) estimation of CMMs is that the size of the contingency table increases exponentially with the number of variables, so even for a moderate number of variables, say between 10 and 20, ML estimation can become computationally infeasible. An alternative method, which retains the optimal asymptotic efficiency of ML, is maximum empirical likelihood (MEL) estimation. However, we show that MEL tends to break down for large, sparse contingency tables. As a solution, we propose a new method, which we call maximum augmented empirical likelihood (MAEL) estimation and which involves augmentation of the empirical likelihood support with a number of well-chosen cells. Simulation results show good finite sample performance for very large contingency tables

Development of grading scales of pedal sensory loss using Mokken scale analysis on the Rotterdam Diabetic Foot Study Test Battery data

Introduction: Loss of sensation due to diabetes-related neuropathy often leads to diabetic foot ulceration. Several test instruments are used to assess sensation, such as static and moving 2-point discrimination (S2PD, M2PD), monofilaments, and tuning forks. Methods: Mokken scale analysis was applied to the Rotterdam Diabetic Foot Study data to select hierarchies of tests to construct measurement scales. Results: We developed 39-item and 31-item scales to measure loss of sensation for research purposes and a 13-item scale for clinical practice. All instruments were strongly scalable and reliable. The 39 items can be classified into 5 hierarchically ordered core clusters: S2PD, M2PD, vibration sense, monofilaments, and prior ulcer or amputation. Discussion: Guided by the presented scales, clinicians may better classify the grade of sensory loss in diabetic patients’ feet. Thus, a more personalized approach concerning individual recommendations, intervention strategies, and patient information may be applied

Mokken Scale Analysis in R

Mokken scale analysis (MSA) is a scaling procedure for both dichotomous and polytomous items. It consists of an item selection algorithm to partition a set of items into Mokken scales and several methods to check the assumptions of two nonparametric item response theory models: the monotone homogeneity model and the double monotonicity model. First, we present an R package mokken for MSA and explain the procedures. Second, we show how to perform MSA in R using test data obtained with the Adjective Checklist

Attractiveness of cultural activities in European cities: A latent class approach

Cultural tourism behaviour and destination preference was analysed for 19 European capital cities, utilising the level of participation in cultural activities (participation) and the level of enjoyment of those cultural activities (attractiveness). A latent class model with three classes described the association in the data satisfactorily. Class 1 was labeled “low participation and high attractiveness”, Class 2 was labeled “high participation and high attractiveness”, and Class 3 was labeled “high participation and low attractiveness”. The Class 2 respondents with high participation and attractiveness had the highest cultural capital, and could be considered ‘specific cultural tourists’, whereas the Class 3 respondents could be considered ‘general cultural tourists’. Class 1 respondents, with relatively infrequent participation but high enjoyment, are potentially most interesting in marketing terms