Benjamin D. Wright

Performance Profiles Of The Functional Independence Measure

The functional independence measure (FIM) is used to determine the degree of disability that patients experience and the progress that they make through programs of medical rehabilitation. Rasch analysis is a statistical technique for constructing interval measures from ordinal data that was applied to derive FIM measures. The major factors that are taken into account to produce FIM measures are the relative difficulty in performance of FIM items and the ability of the persons tested. Our analyses showed the relative difficulties that patients experienced in performing items in the FIM. There were two dominant patterns of difficulty, one for motor FIM items and the other for cognitive FIM items. The patterns were consistent across impairment groups, although not identical. Of the motor items, eating and grooming were easiest whereas stair climbing, tub/shower transfers and locomotion were most difficult. Of the cognitive items, expression and comprehension were easiest and problem solving was the most difficult. The patterns of difficulty in performing FIM items are illustrated by analysis of the following impairment groups: for motor items, orthopedic conditions, stroke with left hemiparesis and spinal cord dysfunction; for cognitive items, orthopedic conditions, brain dysfunction, stroke with right hemiparesis and spinal cord dysfunction. By understanding patterns of difficulty in performing FIM items according to types of impairment and levels of function, clinicians may more precisely design treatment programs, use services and predict outcomes of medical rehabilitation.

Relationships between impairment and physical disability as measured by the functional independence measure.

This study was conducted to scale the Functional Independence Measure (FIM) with Rasch Analysis and to determine the similarity of scaled measures across impairment groups. The results show that the FIM contains two fundamental subsets of items: one measures motor and the second measures cognitive function. Rasch analysis of the Uniform Data System for Medical Rehabilitation patient sample yielded interval measures of motor and cognitive functions. The validity of the FIM was supported by the patterns of item difficulties across impairment groups. Adequate clinical precision of the FIM was demonstrated, though suggestions for improvement emerged. The frequency of misfit between patients and the performance scales varied across impairment groups, but was acceptable. The results of this project will enable clinicians and researchers to plan cost-effective treatment by providing a valid measure of disability.

A Procedure for Sample-Free Item Analysis

Omt purpose is to describe in detail a convenient procedure for performing a new kind of item analysis. This new item analysis is different in a vital way from that described in textbooks like Gulliksen’s Theory of Mental Tests and used in computing programs like TSSA2. The difference is that (a) test calibrations are independent of the sample of persons used to estimate item parameters, and (b) person measurements, the transformation of test scores into estimates of person ability, are independent of the selection of items used to obtain test scores.

The Rasch model as additive conjoint measurement

The object of this paper is to present Raschs psychometric model as a special case of additive conjoint measurement. The connection between these two areas has been discussed before, but largely ignored. Because the theory of conjoint measurement has been formulated determinis tically, there have been some difficulties in its application. It is pointed out in this paper that the Rasch model, which is a stochastic model, does not suffer from this fault. The exposition centers on the analyses of two data sets, each of which was ana lyzed using Rasch scaling methods as well as some of the methods of conjoint measurement. The results, using the different procedures, are com pared.

The Partial Credit Model

The Partial Credit Model (PCM) is a unidimensional model for the analysis of responses recorded in two or more ordered categories. In this sense, the model is designed for the same purpose as several other models in this book, including Samejima’s graded response model (Samejima, 1969). The PCM differs from the graded response model, however, in that it belongs to the Rasch family of models and so shares the distinguishing characteristics of that family: separable person and item parameters, sufficient statistics, and, hence, conjoint additivity. These features enable “specifically objective” comparisons of persons and items (Rasch, 1977) and allow each set of model parameters to be conditioned out of the estimation procedure for the other.

Comparing Rasch measurement and factor analysis

This article illustrates how Rasch measurement is preferable to factor analysis for reducing complex data matrices to unidimensional variables. The two methods: (a) address the same kind of data, but with different interpretations of numerical status; (b) use the same estimation methods, but with different measurement models; and (c) solve the same problems, but with substantially different utility. Factor analysis is faulted for mistaking ordinally labeled stochastic observations for linear measures and for failing to construct linear measurement. The motivation and mathematical basis for Rasch measurement are introduced. How to use Rasch measurement to replace factor analysis is developed for a dichotomy and demonstrated for a rating scale.

The essential process in a family of measurement models

Five members of the Rasch family of latent trait models which have appeared more or less independently in the literature are brought together and identified as one model. In addition to sharing the distinguishing characteristic of the dichotomous Rasch model—separable person and item parameters and hence sufficient statistics—all five models share a common algebraic form and have as their basic element the fundamental process defined by Raschs simple logistic expression. In these models, the sufficient statistics for person and item parameters are counts of events constructed to be indicative of the variable being measured, and the measures they enable are ‘fundamental’.

The Meanings of Color

(1962). The Meanings of Color. The Journal of General Psychology: Vol. 67, No. 1, pp. 89-99.

Validity of the Test of Infant Motor Performance for prediction of 6‐, 9‐ and 12‐month scores on the Alberta Infant Motor Scale

The Test of Infant Motor Performance (TIMP) is a test of functional movement in infants from 32 weeks’post‐conceptional age to 4 months postterm. The purpose of this study was to assess in 96 infants (44 females, 52 males) with varying risk, the relation between measures on the TIMP at 7, 30,60, and 90 days after term age and percentile ranks (PR) on the Alberta Infant Motor Scale (AIMS). Correlation between scores on the TIMP and the AIMS was highest for TIMP tests at 90 days and AIMS testing at 6 months (r=0.67,p=0.0001), but all comparisons were statistically significant except those between the TIMP at 7 days and AIMS PR at 9 months. In a multiple regression analysis combining a perinatal risk score and 7‐day TIMP measures to predict 12‐month AIMS PR, risk, but not TIMP, predicted outcome (21% of variance explained). At older ages TIMP measures made increasing contributions to prediction of 12‐month AIMS PR (30% of variance explained by 90‐day TIMP). The best TIMP score to maximize specificity and correctly identify 84% of the infants above versus below the 10th PR at 6 months was a cut‐off point of 1 SD below the mean. The same cut‐off point correctly identified 88% of the infants at 12 months. A cut‐off of‐0.5 SD, however, maximized sensitivity at 92%. A negative test result, i.e. score above ‐0.5 SD at 3 months, carried only a 2% probability of a poor 12‐month outcome. We conclude that TIMP scores significantly predict AIMS PR 6 to 12 months later, but the TIMP at 3 months of age has the greatest degree of validity for predicting motor performance on the AIMS at 12 months and can be used clinically to identify infants likely to benefit from intervention.

Relationships between disability measures and nursing effort during medical rehabilitation for patients with traumatic brain and spinal cord injury

OBJECTIVE The increasing use of disability measures requires that the validity of these instruments be adequately demonstrated. This study sought to evaluate the concurrent validity of one disability measure, the Functional Independence Measure (FIMSM) using minutes of care reported by nursing staff. STUDY DESIGN Correlational, cohort design. SETTING Eight inpatient medical rehabilitation hospitals that subscribe to the Uniform Data System for Medical Rehabilitation. PATIENTS OR OTHER PARTICIPANTS 129 patients with traumatic brain injury (TBI) and 53 patients with traumatic spinal cord injury (SCI). INTERVENTIONS Routine rehabilitation care. MAIN OUTCOME MEASURES Patient-nurse contact times were recorded with a stop watch for a 24-hour period during the first and last weeks of inpatient rehabilitation. The FIM was also completed during the first and last weeks of rehabilitation. RESULTS Contact times declined from the first to last weeks of rehabilitation, concurrent with improving scores on motor and cognitive measures derived from the FIM. Statistically significant correlations between contact times and FIM measures were observed for medication dispensing, treatment provision, and teaching/activities of daily living at admission and discharge. Smaller and usually nonsignificant correlations were observed in activities that did not involve direct patient contact. Contact times increased exponentially as disability increased. CONCLUSIONS These results support the construct validity of the FIM by demonstrating strong relationships (r values in the range of .40 to .60) between burden of care and a measure of disability.