Background: While often confused, fatigue (as opposed to sleepiness) mostly requires rest, not sleep, to recover from. Clinical evaluations of fatigue mainly rely on assessments of symptom intensity, however without taking into account the need to engage in behavioral countermeasures. We therefore developed an 8-item 4-point Likert scale (the Brugmann Fatigue Scale; BFS) sharing a similar conceptual background with the Epworth Sleepiness Scale (ESS), assessing mental and physical fatigue and focusing specifically on rest propensity. Methods: Out of 496 consecutive patients addressed to the sleep unit of an academic hospital, we selected a sample of 295 hypnotic-free subjects (122 females). The present study examines (a) the psychometric properties of the BFS and (b) measurement invariance regarding perceived sleep quality, in parallel with common sleepiness and fatigue scales (ESS and Fatigue Severity Scale; FSS). In addition, (c) correlations of the BFS with clinical scales and polysomnographic v...

The Brugmann Fatigue Scale: An Analogue to the Epworth Sleepiness Scale to Measure Behavioral Rest Propensity

In correspondence with pertinent statistical tests, it is of practical importance to design datasampling when the Rasch model is used for calibrating an achievement test That is, determining the sample size according to a given type-I- and type-II-risk, and according to a certain effect of model misfit which is of practical relevance is of interest However, pertinent Rasch model tests use chisquared distributed test-statistics, whose degrees of freedom do not depend on the sample size or the number of testees, but only on the number of estimated parameters We therefore suggest a new approach using an F-distributed statistic as applied within analysis of variance, where the sample size directly affects the degrees of freedom The Rasch model’s quality of specific objective measurement is in accordance with no interaction effect in a specific analysis of variance design In analogy to Andersen’s approach in his Likelihood-Ratio test, the testees must be divided into at least two groups according to some criterion suspected of causing differential item functioning (DIF) Then a three-way analysis of variance design () AB C × � with mixed classification is the result: There is a (fixed) group factor A, a (random) factor B of testees within A, and a (fixed) factor C of items cross-classified with AB � ; obviously the factor B is nested within A Yet the data are dichotomous (a testee either solves an item or fails to solve it) and only one observation per cell exists The latter is not assumed to do harm, though the design is a mixed classification But the former suggests the need to perform a simulation study in order to test whether the type-I-risk holds for the A×C interaction F-test – this interaction effect corresponds to Rasch model’s specific objectivity If so, the critical number of testees is of interest for fulfilling the pertinent precision parameters The simulation study (100 000 runs for each of several special cases) proved that the nominal type-I-risk holds as long as there is no significant group effect Analysing a certain DIF, this F-test has fair power, consistently higher than Andersen’s test Hence, we advise researchers to apply our approach as long as there is no significant group effect, and only to use other Rasch model tests if it is significant Keep in mind that this is true only for some special cases and needs to be generalized in further research Then a formula needs to be provided which will allow explicit calculation of the number of testees, given a type-I-, a type-II-risk, and a relevant effect as concerns Rasch model misfit

/pdf/on-designing-data-sampling-for-rasch-model-calibrating-an-4sf84qtm99.pdf

On designing data-sampling for Rasch model calibrating an achievement test

Rasch scalability of the somatosensory amplification scale: a mixture distribution approach

Item response theory is a widely used framework for the design, scoring, and scaling of measurement instruments. Item response models are typically used for dichotomously scored questions that have only two score points (e.g., multiple-choice items). However, given the increasing use of instruments that include questions with multiple response categories, such as surveys, questionnaires, and psychological scales, polytomous item response models are becoming more utilized in education and psychology. This study aims to demonstrate the application of explanatory item response models to polytomous item responses in order to explain common variability in item clusters, person groups, and interactions between item clusters and person groups. Explanatory forms of several polytomous item response models – such as Partial Credit Model and Rating Scale Model – are demonstrated and the estimation procedures of these models are explained. Findings of this study suggest that explanatory item response models can be more robust and parsimonious than traditional item response models for polytomous data where items and persons share common characteristics. Explanatory polytomous item response models can provide more information about response patterns in item responses by estimating fewer item parameters.

/pdf/explanatory-item-response-models-for-polytomous-item-i7kh3g9om9.pdf

Explanatory Item Response Models for Polytomous Item Responses

Formative tests and assessments have high potential in supporting learning, especially for students with special educational needs. One approach to gain assessment information on student learning is to monitor learning progress. For that, multiple repeated tests are often performed by practitioners. In order to be useful in practice, tests must meet various interdependent quality criteria. A property of tests that touches various criteria as the utility and economy is the length. A test has to be long enough to give a meaningful, reliable and comparable measure but short enough to be usable in classroom situations. An approach to evaluate and minimize the length of a computer-based test on sentence comprehension is introduced. It is shown that the test can be shortened from eight to five minutes while the estimation of the student´s abilities remains relatively stable for a random item order and a fixed item order variant. The consequences of test development of progress monitoring and the procedure for test time reduction for the different quality criteria are outlined. An approach to evaluate and minimize the length of a computer-based test by using a one parameter logistic model on a test of sentence comprehension (N=761) is introduced. The data and the syntax is published in the OSF project https://osf.io/hnbs8/.

/pdf/minimization-of-a-short-computer-based-test-in-reading-4h7ujpup14.pdf

Minimization of a Short Computer-Based Test in Reading

The Rasch model has been suggested for psychological test data (subjects × items) for various scales of measurement. It is defined to be specifically objective. If the data are dichotomous, the use of the dichotomous model of Rasch for psychological test construction is almost inevitable. The two- and three-parameter logistic models of Birnbaum and further models with additional parameters are not always identifiable. The linear logistic model is useful for the construction of item pools. For polytomous graded response data, there are useful models (Samejima, 1969; Tutz, 1990; and again by Rasch, cf. Fischer, 1974, or Kubinger, 1989) which, however, are not specifically objective. The partial credit model (Masters, 1982) is not meaningful in a measurement theory sense. For polytomous nominal data, the multicategorical Rasch model is much too rarely applied. There are limited possibilities for locally dependent data. The mixed Rasch model is not a true Rasch model, but useful for model controls and heuristic purposes. The models for frequency data and continuous data are not discussed here. The nonparametric ISOP-models are "sample independent" (ordinally specifically objective) models for (up to 3 dependent) graded responses providing ordinal scales or interval scales for subject-, item- and response-scale-parameters. The true achievement of sample-independent Rasch models is an extraordinary generalizeability of psychological assessment procedures. Key words: specific objectivity; measurement structures; graded responses; local dependence; generalized assessment procedures 1. Basic concepts Any kind of measurement requires comparison. There are no directly available observable outcomes of the contact of the 'intelligences' of two subjects (or of the 'difficulties' of two items). We can only observe the reactions of subjects to items which do depend on both simultaneously. We need instruments to compare latent dimensions. The items are the instruments used for the comparison of subjects and vice versa. The difficulty in (psychological) measurement is to achieve comparisons on a set of subjects (or a set of items) in regard to a specific latent dimension which do not depend on the choice of instruments used for the comparison. Definition: Let be a 'frame of reference' (Rasch, 1961) or a '(probabilistic) instrumental paired comparison system' (Irtel, 1987) where A = a,b,c,...} is a set of subjects and Q = x, y,z,. . .} is a set of items and P[t;a,x) = PIT Definition: An instrumental paired comparison system is a Rasch model if it is specifically objective, i.e. if there exist comparison functions on the sets A×A and Q×Q which do not depend on the parameters of the respective other set, i.e. v(a,b) independent of jc,v,z,...} and w(x,y) independent of {a,b, c,... .} Pseudo-Rasch models are models which call themselves Rasch models without being specifically objective. The present section defines the basic problem. The subsections of Section 2 are organized along the lines of Fischer's (1974) proof of the uniqueness of the specific objectivity of the dichotomous Rasch model among dichotomous models. Each assumption will be examined with respect to usefulness and necessity. Fischer's list of necessary and sufficient conditions ensures that no important aspect is overlooked. Section 3 illustrates the true advantages of Rasch models for theory and practice. Section 4 presents nonparametric Rasch models together with an empirical application. The discussion in Section 5 summarizes the results. 2. Dichotomous indicators If & is a third item i ≠ j≠k then the probability that two of the discriminations are equal or that a parameter is equal to the sum of the two others is zero; and so on for finite numbers k of items. …

/pdf/rasch-and-pseudo-rasch-models-suitableness-for-practical-37rp189ex5.pdf

Rasch and Pseudo-Rasch Models: Suitableness for Practical Test Applications1

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Rasch and Pseudo-Rasch Models: Suitableness for Practical Test Applications1