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Virum Praenobilissimum, Amplissimum, Consultissimum Doctissimumque Dominum Dominum Rudolphum Josephum Leonhardt Raschium, Reverendissimi Ac Serenissimi Ducis Saxo-Merseburgensis a Consiliis Et Secretis Feudalibus, De Morte Filii Natu Maximi, Domini Jo. Henr. Rudolphi Raschii, Juris Et Philosophiae Cultoris Strenui, Optimae Spei Juvenis, Consolantur Patrueles Devinctissimi Erdmann Henricus Augustus Rasch, Mauritius Lebrecht Rasch : Merseburgi, VI. Jd. Decembr. MDCCXXXV.
No full textNach anderen Drucken ist Rasch am 8.12.1735 verstorbe
Combining Rasch and cluster analysis: a novel method for developing rheumatoid arthritis states for use in valuation studies
No full textPurpose: Health states that describe an investigated condition are a crucial component of valuation studies. The health states need to be distinct, comprehensible, and data-driven. The objective of this study was to describe a novel application of Rasch and cluster analyses in the development of three rheumatoid arthritis health states. Methods: The Stanford Health Assessment Questionnaire (HAQ) was subjected to Rasch analysis to select the items that best represent disability. K-means cluster analysis produced health states with the levels of the selected items. The pain and discomfort domain from the EuroQol-5D was incorporated at the final stage. Results: The results demonstrate a methodology for reducing a dataset containing individual disease-specific scores to generate health states. The four selected HAQ items were bending down, climbing steps, lifting a cup to your mouth, and standing up from a chair. Conclusions: Overall, the combined use of Rasch and cluster analysis has proved to be an effective technique for identifying the most important items and levels for the construction of health states.health state; Rasch analysis; cluster analysis; quality of life; rheumatoid arthritis
Making Rasch decisions: the use of Rasch analysis in the construction of preference based health related quality of life instruments
No full textObjective: To set out the methodological process for using Rasch analysis alongside traditional psychometric methods in the development of a health state classification that is amenable to valuation. Methods: The overactive bladder questionnaire is used to illustrate a four step process for deriving a reduced health state classification from an existing nonpreference based health related quality of life instrument. Step I excludes items that do not meet the initial validation process and step II uses criteria based on Rasch analysis and psychometric testing to select the final items for the health state classification. In step III, item levels are examined and Rasch analysis is used to explore the possibility of reducing the number of item levels. Step IV repeats steps I to III on alternative data sets in order to validate the selection of items for the health state classification. Conclusions: The techniques described enable the construction of a health state classification amenable for valuation exercises that will allow the derivation of preference weights. Thus, the health related quality of life of patients with conditions, like overactive bladder, can be valued and quality adjustment weights such as quality adjusted life years derived.Rasch analysis; health related quality of life; condition specific measure; preference-based measures; overactive bladder syndrome
Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
No full textItem response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch modeling) for computing Rasch models and several extensions. A main characteristic of some IRT models, the Rasch model being the most prominent, concerns the separation of two kinds of parameters, one that describes qualities of the subject under investigation, and the other relates to qualities of the situation under which the response of a subject is observed. Using conditional maximum likelihood (CML) estimation both types of parameters may be estimated independently from each other. IRT models are well suited to cope with dichotomous and polytomous responses, where the response categories may be unordered as well as ordered. The incorporation of linear structures allows for modeling the effects of covariates and enables the analysis of repeated categorical measurements. The eRm package fits the following models: the Rasch model, the rating scale model (RSM), and the partial credit model (PCM) as well as linear reparameterizations through covariate structures like the linear logistic test model (LLTM), the linear rating scale model (LRSM), and the linear partial credit model (LPCM). We use an unitary, efficient CML approach to estimate the item parameters and their standard errors. Graphical and numeric tools for assessing goodness-of-fit are provided.
Flexible Rasch Mixture Models with Package psychomix
No full textMeasurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estimation of the item parameters (given the raw scores) along with flexible specification of two model building blocks: (1) Mixture weights for the unobserved classes can be treated as model parameters or based on covariates in a concomitant variable model. (2) The distribution of raw score probabilities can be parametrized in two possible ways, either using a saturated model or a specification through mean and variance. The function raschmix() in the R package "psychomix" provides these models, leveraging the general infrastructure for fitting mixture models in the "flexmix" package. Usage of the function and its associated methods is illustrated on artificial data as well as empirical data from a study of verbally aggressive behavior.mixed Rasch model, Rost model, mixture model, flexmix, R
Deconstructing therapy outcome measurement with Rasch analysis of a measure of general clinical distress: the Symptom Checklist-90-Revised
No full textRasch analysis was used to illustrate the usefulness of item-level analyses for evaluating a common therapy outcome measure of general clinical distress, the Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1994). Using complementary therapy research samples, the instrument's 5-point rating scale was found to exceed clients' ability to make reliable discriminations and could be improved by collapsing it into a 3-point version (combining scale points 1 with 2 and 3 with 4). This revision, in addition to removing 3 misfitting items, increased person separation from 4.90 to 5.07 and item separation from 7.76 to 8.52 (resulting in alphas of .96 and .99, respectively). Some SCL-90-R subscales had low internal consistency reliabilities; SCL-90-R items can be used to define one factor of general clinical distress that is generally stable across both samples, with two small residual factors
Developing a health state classification system from NEWQOL for epilepsy using classical psychometric techniques and Rasch analysis: a technical report
No full textAims: Resource allocation amongst competing health care interventions is informed by evidence of both clinical- and cost-effectiveness. Cost-utility analysis is increasingly used to assess cost effectiveness through the use of Quality Adjusted Life Years (QALYs). This requires health state values. Generic measures of health related quality of life (HRQL) are usually used to produce these values, but there are concerns about their relevance and sensitivity in epilepsy. This study develops a health state classification system for epilepsy from the NEWQOL battery, a validated questionnaire measuring QoL in epilepsy. The classification system will be amenable to valuation for calculating QALYs. Methods: Factor and other psychometric analyses were undertaken to investigate the factor structure of the battery, and assess the validity and responsiveness of the items. These analyses were used alongside Rasch analysis to select the dimensions included in the classification system, and the items used to represent each domain. Analysis was carried out on a trial dataset of patients with epilepsy (n=1611). Rasch and factor analysis were performed on one half of the sample and validated on the remaining half. Dimensions and items were selected that performed well across all analyses. Results: The battery was found to demonstrate reliability and validity but responsiveness across time periods for many of the items was low. A six dimension classification system was developed: worry about seizures, depression, memory, cognition, stigmatism and control, each with four response levels. Conclusions: It is feasible to develop a health state classification system from a battery of instruments using a combination of classical psychometric, factor and Rasch analysis. This is the first condition-specific health state classification developed for epilepsy and the next stage will produce preference weights to enable the measure to be used in cost-utility analysis.quality adjusted life years; health related quality of life; Rasch analysis; preference-based measures of health; health states; epilepsy
Estimation of Models in a Rasch Family for Polytomous Items and Multiple Latent Variables
No full textThe Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of log-linear-by-linear association (LLLA) models, which are derived from a Rasch family of models. The LLLA models are special cases of log-linear models with bivariate interactions. Maximum likelihood estimation of LLLA models in this form is limited to relatively small problems; however, pseudo-likelihood estimation overcomes this limitation. Maximizing the pseudo-likelihood function is achieved by maximizing the likelihood of a single conditional multinomial logistic regression model. The parameter estimates are asymptotically normal and consistent. Based on our simulation studies, the pseudo-likelihood and maximum likelihood estimates of the parameters of LLLA models are nearly identical and the loss of efficiency is negligible. Recovery of parameters of Rasch models fit to simulated data is excellent.
A new method for detecting differential item functioning in the Rasch model
No full textDifferential item functioning (DIF) can lead to an unfair advantage or disadvantage for certain subgroups in educational and psychological testing. Therefore, a variety of statistical methods has been suggested for detecting DIF in the Rasch model. Most of these methods are designed for the comparison of pre-specified focal and reference groups, such as males and females. Latent class approaches, on the other hand, allow to detect previously unknown groups exhibiting DIF. However, this approach provides no straightforward interpretation of the groups with respect to person characteristics. Here we propose a new method for DIF detection based on model-based recursive partitioning that can be considered as a compromise between those two extremes. With this approach it is possible to detect groups of subjects exhibiting DIF, which are not prespecified, but result from combinations of observed ovariates. These groups are directly interpretable and can thus help understand the psychological sources of DIF. The statistical background and construction of the new method is first introduced by means of an instructive example, and then applied to data from a general knowledge quiz and a teaching evaluation.item response theory, IRT, Rasch model, di erential item functioning, DIF, structural change, multidimensionality.
The Rasch Sampler
No full textThe Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It is a Markov chain Monte Carlo (MCMC) algorithm. The program can handle matrices of up to 1024 rows and 64 columns. A special option allows to sample square matrices with given marginals and fixed main diagonal, a problem prominent in social network analysis. In all cases the stationary distribution is uniform. The user has control on the serial dependency.
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