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The aim of this study was to determine the dimensionality and task-specificity of balance control by investigating the relationships between different tasks and the degree to which these tasks belong to the same construct in primary school-aged children. Seventy-four South African children were randomly selected from a sample of convenience. They performed 18 different balance tasks that were grouped into four balance scales: the Performance and Fitness (PERF-FIT) static balance score, the PERF-FIT dynamic balance score, the PERF-FIT moving cans balance score and the Balance Sensory score. Spearman rank correlations were calculated between the scores. Principal component analysis (PCA) was used to investigate the number of factors within the construct. Moderate to good correlations were found between: i) PERF-FIT Moving cans balance score and the Balance Sensory score (r = 0.605, p < 0.001); ii) PERF-FIT static balance score and the PERF-FIT Moving cans (r = 0.586, p < 0.001); iii) PERF-FIT static balance score and the Balance Sensory score (r = 0.541, p < 0.001). All other correlations were low to fair. The PCA revealed one component. The three PERF-FIT items (moving cans-, static- and dynamic balance score) and the Balance Sensory score explained 59.4% of the variance of total balance performance.

Objective Concern about getting sick could help prevent disease. The aim of this study was to analyze the psychometric properties of a COVID-19 concern scale (EPCov-19). Materials and methods This is an instrument-based study and information was collected from 224 people of Peruvian nationality in the context of social isolation. The items of the Cancer Concern Scale were adapted for this study. Results A scale with satisfactory psychometric properties was obtained. With a matrix of polychoric correlations, values higher than the standard were obtained in all 6 items (r > 0.3) and the reliability was acceptable (α = 0.866; 95% CI = 0.83–0.89). Parallel analysis suggested unidimensionality of the EPCov-19, the variance explained was 79.7% and saturations were higher than 0.4. Goodness-of-fit indices were satisfactory (CFI = 0.995; GFI = 0.997; TLI = 0.991; and RMSEA = 0.059, 95% CI = 0.012–0.077). Conclusions This is a valid and reliable instrument for measuring concern about the spread of COVID-19 and can be used in future studies.

Many researchers use Cronbach's alpha to demonstrate internal consistency, even though it has been shown numerous times that Cronbach's alpha is not suitable for this. Because the intention of questionnaire and test constructers is to summarize the test by its overall sum score, we advocate summability, which we define as the proportion of total test variation that is explained by the sum score. This measure is closely related to Loevinger's H. The mathematical derivation of summability as a measure of explained variation is given for both scale and dichotomously scored items. Using computer simulations, we show that summability performs adequately and we apply it to an existing productive vocabulary test. An open-source tool to easily calculate summability is provided online (https://sites.google.com/view/summability).

To test the applicability of item response theory (IRT) to the Korean Nurses' Licensing Examination (KNLE), item analysis was performed after testing the unidimensionality and goodness-of-fit. The results were compared with those based on classical test theory. The results of the 330-item KNLE administered to 12,024 examinees in January 2004 were analyzed. Unidimensionality was tested using DETECT and the goodness-of-fit was tested using WINSTEPS for the Rasch model and Bilog-MG for the two-parameter logistic model. Item analysis and ability estimation were done using WINSTEPS. Using DETECT, Dmax ranged from 0.1 to 0.23 for each subject. The mean square value of the infit and outfit values of all items using WINSTEPS ranged from 0.1 to 1.5, except for one item in pediatric nursing, which scored 1.53. Of the 330 items, 218 (42.7%) were misfit using the two-parameter logistic model of Bilog-MG. The correlation coefficients between the difficulty parameter using the Rasch model and the difficulty index from classical test theory ranged from 0.9039 to 0.9699. The correlation between the ability parameter using the Rasch model and the total score from classical test theory ranged from 0.9776 to 0.9984. Therefore, the results of the KNLE fit unidimensionality and goodness-of-fit for the Rasch model. The KNLE should be a good sample for analysis according to the IRT Rasch model, so further research using IRT is possible.

Purpose Many malignancy scores have been developed without comprehensive statistical or measurement validation, and in particular without verification of the necessary property of unidimensionality. Here, we used Rasch analysis to assess unidimensionality and identify measurement biases of malignancy scores. Methods The Weiss histopathological system (WHS), summing nine items of histopathological alteration, was used to evaluate 247 adrenocortical tumors. Rasch model analysis was implemented and compared to classical factor analytic methods to investigate the validity of item-score summation for both the original and modified WHS, to assess differential functioning of the WHS items across various factors related to patient and tumors, and to identify items or subtypes of tumors that could be considered for removal or exclusion from the WHS with the aims of improving measurement and relieving the burden on pathologists. Results The WHS does not meet the necessary property of unidimensionality and is severely affected by differential item functioning in relation to size and weight of the tumor. Moreover, items are not well distributed along the spectrum of malignancy, most being located in the upper part and several at the same place. Conclusion The WHS in its present form should be applied only to small or moderate size tumors, and better scoring systems could be developed by using more appropriately distributed items. Rasch analysis is a powerful method for developing, evaluating, refining and simplifying malignancy scores.

Background and aim: Thyroid diseases are prevalent and chronic. With treatment, quality of life is restored in most, but not all patients. Construct validity of the thyroid-related quality of life questionnaire, ThyPRO, has been established by multi-trait scaling, but not evaluated with more elaborate methods. The purpose of the present study was to evaluate dimensionality of the ThyPRO scales and to attempt to understand possible item misfit through structural equation modeling for categorical data.Methods: The current 85-item version of ThyPRO consists of 13 scales, covering domains of physical (4 scales) and mental (2 scales) symptoms, function and well-being (3 scales) and participation/social function (4 scales). The data were collected from a cross-sectional sample of 907 thyroid patients. One-factor confirmatory models were fitted to each scale, and evaluated by model fit statistics (comparative fit index >0.95, root mean square error of approximation

Relationships between a mathematical measurement model and its real-world applications are discussed. A distinction is made between large data matrices commonly found in educational measurement and smaller matrices found in attitude and personality measurement. Nonparametric methods are evaluated for estimating item response functions and (unconditional and conditional) inter-item covariances.

This paper explores the robustness of conclusions from a statistical model against variations in model choice (rather than variations in random sampling and random assignment to treatments, which are the usual variations covered by inferential statistics). After the problem formulation in section 1, section 2 presents an example from Box and Tiao in which variation in parent distribution is modeled for a one sample location problem. An adaptive Bayesian procedure permits to use a weighted mixture of parent distributions rather than choosing just one, such as a normal or a uniform distribution.In section 3 similar considerations are applied to an event history model for the influence of education and gender on age at first marriage, but here the conclusions are hardly influenced by the choice of the duration distribution. In section 4 a brief discussion of model choice in factor analysis and structural equation models is followed by a more elaborate treatment of the choice of integer valued slopes for item response functions in the OPLM model which is an extension of the Rasch model. A modest simulation study suggests that Adaptive Bayesian Modeling with a mixture of sets of slopes works better than fixing one set of postulated slopes.The paper concludes with some remarks on the roles and sources of prior distributions followed by a short epilogue which argues that simultaneous consideration of a class of models for the same data is sometimes superior to exclusively analyzing the data under one specific model chosen from such a class.

We propose a class of Item Response Theory models for items with ordinal polytomous responses, which extends an existing class of multidimensional models for dichotomously-scored items measuring more than one latent trait. In the proposed approach, the random vector used to represent the latent traits is assumed to have a discrete distribution with support points corresponding to different latent classes in the population. We also allow for different parameterizations for the conditional distribution of the response variables given the latent traits - such as those adopted in the Graded Response model, in the Partial Credit model, and in the Rating Scale model - depending on both the type of link function and the constraints imposed on the item parameters. For the proposed models we outline how to perform maximum likelihood estimation via the Expectation-Maximization algorithm. Moreover, we suggest a strategy for model selection which is based on a series of steps consisting of selecting specific features, such as the number of latent dimensions, the number of latent classes, and the specific parametrization. In order to illustrate the proposed approach, we analyze data deriving from a study on anxiety and depression as perceived by oncological patients., 25 pages; 10 tables

Este estudo avaliou o ajuste do modelo Rasch quanto à unidimensionalidade das Matrizes Progressivas Coloridas de Raven. Foram estudadas 441 crianças de ambos os sexos, de 1ª a 4ª série do ensino fundamental. Para avaliar a possibilidade de violação, optou-se pela análise de componentes principais de resíduos, pois eles podem ser suficientes para violar a unidimensionalidade intrínseca dos itens. Foram selecionados os itens de maior saturação em relação à dimensão positiva e negativa do primeiro fator, em relação ao teste como um todo e a cada série em particular. Esses itens foram novamente analisados pelo modelo de Rasch, separadamente, e correlacionadas as pontuações do fator positivo com o negativo. Os resultados sugeriram que o instrumento como um todo e a série B possibilitam a interpretação da existência de outra dimensão além da extraída pelo modelo de Rasch e nas outras duas séries as correlações estiveram próximas do ponto de rejeição. This study assessed the adjustment of the Rasch model concerning unidimensionality of Raven's Colored Standard Progressive Matrices. Four hundred and forty-one (441) elementary school children, of both genders, attending first to fourth grade were investigated. Considering that residuals may be enough to violate the intrinsic unidimensionality of the items, residual principal component analysis was used to assess the possibility of this violation. Items with higher factorial loading relating to the positive and negative dimensions on the first factor were selected, in respect to the test as a whole and to each subset in particular. These items were re-analyzed with the Rasch model within each data group. Then the positive and negative factor measures were correlated. Results suggested the possibility of other dimensions besides those extracted by Rasch model based on the test and subset B, though the correlation coefficients of the other subsets were close to rejection.

In standard applications of Item Response Theory (IRT), n exchangeable persons have responded to k exchangeable items. Among neither persons nor items subgroups are distinguished This paper reviews methods and results for situations where it is meaningful to consider subgroups (of persons, of items, or both). It does so in the class of nonparametric IRT models, which is briefly explained in the first section. The main reason for such considerations is that IRT, explicitly or implicitly, not only aims to explain why certain person-item combinations have led to a positive or negative answer, but also to predict what a given person would do on other items not actually presented (test equating problems, parallel versions), and how other persons would perform on the given items (optimal test design, inference from sample to population).

A method to compare the use of the attributes between assessors in a conventional profiling panel is proposed. The method is based on Principal Component Analysis (PCA), and for each attribute a matrix containing the scores of the assessors on the product is analysed. When all assessors use an attribute in the same way this will give a large first Eigenvalue. From the Eigenvalues, or equivalently, the percentages of Variance Accounted For, a Unidimensionality or Consonance index is computed. Plots of the loadings enable identification of outlying assessors and possible segmentations of the panel, for particular attributes. The method is illustrated using a number of sensory profiling data sets.

This discussion paper argues that both the use of Cronbach’s alpha as a reliability estimate and as a measure of internal consistency suffer from major problems. First, alpha always has a value, which cannot be equal to the test score’s reliability given the interitem covariance matrix and the usual assumptions about measurement error. Second, in practice, alpha is used more often as a measure of the test’s internal consistency than as an estimate of reliability. However, it can be shown easily that alpha is unrelated to the internal structure of the test. It is further discussed that statistics based on a single test administration do not convey much information about the accuracy of individuals’ test performance. The paper ends with a list of conclusions about the usefulness of alpha.