A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation. The adequacy of this theory is assessed by applying a formalization of the theory, known as the Rasch model (Rasch, 1960; Thissen & Steinberg, 1986), to categorization data for eight natural language categories and subjecting it to a formal test. In validating the model special care is given to its ability to account for inter- and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Rasch model that can be used to uncover the nature of category representations and the sources of categorization differences are discussed

An Estimation and Analysis Framework for the Rasch Model

The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance. While a number of estimators have been proposed for the Rasch model over the last decades, the available analytical performance guarantees are mostly asymptotic. This paper provides a framework that relies on a novel linear minimum mean-squared error (L-MMSE) estimator which enables an exact, nonasymptotic, and closed-form analysis of the parameter estimation error under the Rasch model. The proposed framework provides guidelines on the number of items and responses required to attain low estimation errors in tests or surveys. We furthermore demonstrate its efficacy on a number of real-world collaborative filtering datasets, which reveals that the proposed L-MMSE estimator performs on par with state-of-the-art nonlinear estimators in terms of predictive performance.Comment: To be presented at ICML 201

Psychometric evaluation of the Disabilities of the Arm, Shoulder and Hand (DASH) with Dupuytren's contracture: validity evidence using Rasch modeling

Background Dupuytren’s contracture is a progressive, fibroproliferative disorder that causes fixed finger contractures and can lead to disability. With the advances of new therapeutic interventions, the necessity to assess the functional repercussions of this condition using valid, reliable and sensitive outcome measures is of growing interest. The Disabilities of the Arm, Shoulder and Hand (DASH) is one frequently used patient-reported outcome measure but its reliability and validity have never been demonstrated specifically for a population affected with Dupuytren’s contracture. The objective of this study was to evaluate the psychometric properties of the DASH, with focus on validity evidence using the Rasch measurement model. Methods Secondary analysis was performed on data collected as part of a randomised clinical trial. One hundred fifty-three participants diagnosed with Dupuytren’s contracture completed the DASH at four time points (pre-op, 3, 6 and 12 months post-op). Baseline data were analysed using traditional analysis and to test whether they adhered to the expectations of the Rasch model. Post-intervention data were subsequently included and analyzed to determine the effect of the intervention on the items. Results DASH scores demonstrated large ceiling effects at all time points. Initial fit to the Rasch model revealed that the DASH did not adhere to the expectations of the Rasch partial credit model (χ2 = 119.92; p < 0.05). Multiple items displayed inadequate response categories and two items displayed differential item functioning by gender. Items were transformed and one item deleted leading to an adequate fit. Remaining items fit the Rasch model but still do not target well the population under study. Conclusions The original version of the 30-item DASH did not display adequate validity evidence for use in a population with Dupuytren’s contracture. Further development is required to improve the DASH for this population

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

Rasch Model and Multidimensional Poverty Measurement

The topic of the multidimensionality of poverty is currently at the heart of many theoretical, empirical and institutional debates in the European Union. Despite this increasing interest, there seems to be no consensus on how to define and measure multidimensional poverty. Two aspects may be considered in measuring poverty: the number of dimensions and the nature of the underlying continuum. The question of the dimensionality of poverty, one versus many dimensions, has to be resolved in applying specific multidimensional methods, like factor analysis, where the one-dimensional solution is a special case of the multidimensional procedure. The question of the nature of the continuum concerns the relationship between the items in each dimension. Two kinds of relationship are considered here: homogeneous and hierarchical. In this paper, the interest of the Rasch model for verifying the hierarchical and cumulative nature of the relationship between the items is underlined. After presenting the main characteristics of the model, and its adjustment for testing poverty, an application confirming the multidimensional nature of poverty is performed on a Luxemburgish dataset (PSELL-3).multidimensional poverty ; Rasch model ; accumulation of disadvantages

The Contact Lens Impact on Quality of Life (CLIQ) questionnaire: development and validation

NoPURPOSE. To develop and validate a questionnaire for the measurement of the impact of contact lenses on quality of life (QoL): The Contact Lens Impact on Quality of Life (CLIQ) Questionnaire. METHODS. The questionnaire was developed and validated using conventional methods and Rasch analysis to assure content validity, repeatability, construct validity, and low respondent burden. Item identification and selection (647 items) were performed with an extensive literature review, professional advice, and lay focus groups. Item reduction used focus groups and data obtained from 161 subjects completing a 90-item pilot questionnaire. Validity and reliability, from data of 128 additional subjects, were assessed using Rasch analysis, intraclass correlation coefficient, and Bland-Altman limits of agreement. RESULTS. A 28-item CLIQ Questionnaire was developed and shown to have good validity and reliability by Rasch analysis statistics: real person separation, 2.02; model person separation, 2.17; reliability, 0.80; root mean square measurement error, 2.73; mean square ± SD infit, 1.01 ± 0.18; outfit, 1.01 ± 0.19. The items (mean score, 49.8 ± 4.9) were well targeted to the subjects (mean score, 51.2 ± 6.2) with a mean difference of 1.35 (scale range, 0-100) units. Test-retest intraclass correlation coefficient (0.86) and coefficient of repeatability (±8.00 units) demonstrated good repeatability. CONCLUSIONS. Rasch analysis and standard psychometric analyses demonstrated that the 28-item CLIQ Questionnaire is a valid and reliable measure of QoL in contact lens wearers. A scoring algorithm is provided for CLIQ Questionnaire users to convert raw scores into the Rasch analysis-derived linear person measures