A Multidimensional Rasch Analysis of the Functional Independence Measure Based on the National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems National Database.

A number of studies have evaluated the psychometric properties of the Functional Independence Measure (FIM™) using Rasch analysis, although none has done so using the National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems National Database, a longitudinal database that captures demographic and outcome information on persons with moderate to severe traumatic brain injury across the United States. In the current study, we examine the psychometric properties of the FIM as represented by persons within this database and demonstrate that the FIM comprises three subscales representing cognitive, self-care, and mobility domains. These subscales were analyzed simultaneously using a multivariate Rasch model in combination with a time dependent concurrent calibration scheme with the goal of creating a raw score-to-logit transformation that can be used to improve the accuracy of parametric statistical analyses. The bowel and bladder function items were removed because of misfit with the motor and cognitive items. Some motor items exhibited step disorder, which was addressed by collapsing Categories 1-3 for Toileting, Stairs, Locomotion, Tub/Shower Transfers; Categories 1 and 2 for Toilet and Bed Transfers; and Categories 2 and 3 for Grooming. The strong correlations (r = 0.82-0.96) among the three subscales suggest they should be modeled together. Coefficient alpha of 0.98 indicates high internal consistency. Keyform maps are provided to enhance clinical interpretation and application of study results.

Translation equivalence across PISA countries.

Due to the continuous increase in the number of countries participating in international comparative assessments such as TIMSS and PISA, ensuring linguistic and cultural equivalence across the various national versions of the assessment instruments has become an increasingly crucial challenge. For example, 58 countries participated in the PISA 2006 Main Study. Within each country, the assessment instruments had to be adapted into each language of instruction used in the sampled schools. All national versions in languages used for 5 per cent or more of the target population (that is, a total of 77 versions in 42 different languages) were verified for equivalence against the English and French source versions developed by the PISA consortium. Information gathered both through the verification process and through empirical analyses of the data are used in order to adjudicate whether the level of linguistic equivalence reached an acceptable standard in each participating country. The paper briefly describes the procedures typically used in PISA to ensure high levels of translation/adaptation accuracy, and then focuses on the development of the set of indicators that are used as criteria in the equivalence adjudication exercise. Empirical data from the PISA 2005 Field Trial are used to illustrate both the analyses and the major conclusions reached.

The influence of equating methodology on reported trends in PISA.

In 2005 PISA published trend indicators that compared the results of PISA 2000 and PISA 2003. In this paper we explore the extent to which the outcomes of these trend analyses are sensitive to the choice of test equating methodologies, the choice of regression models and the choice of linking items. To establish trends PISA equated its 2000 and 2003 tests using a methodology based on Rasch Modelling that involved estimating linear transformations that mapped 2003 Rasch-scaled scores to the previously established PISA 2000 Rasch-scaled scores. In this paper we compare the outcomes of this approach with an alternative, which involves the joint Rasch scaling of the PISA 2000 and PISA 2003 data separately for each country. Note that under this approach the item parameters are estimated separately for each country, whereas the linear transformation approach used a common set of item parameter estimates for all countries. Further, as its primary trend indicators, PISA reported changes in mean scores between 2000 and 2003. These means are not adjusted for changes in the background characteristics of the PISA 2000 and PISA 2003 samples - that is, they are marginal rather than conditional means. The use of conditional rather than marginal means results in some differing conclusions regarding trends at both the country and within-country level.