Population
The authors asked patients with hemiplegic upper limb impairment resulting from stroke and other acquired brain injury visiting rehabilitation services in Chiang Mai, completed the ArmA-TH questionnaire.
All patients were between 20-85 years of age with Thai as their mother tongue and graduated from at least elementary school with the ability to understand Thai communication in daily activities. The patients’ demographic characteristics recorded for this study were age, sex, hemiparetic side, diagnosis, education level and ArmA-TH passive and active scores.
Measure
The ArmA-TH is a twenty-item questionnaire for assessing difficulty in functioning of hemiparetic upper limb. There are seven items in the passive function sub-scale and thirteen items in the active function sub-scale. Using a Likert scoring system between 0 (no difficulty) and 4 (unable to do task), the passive function sub-scale scores range from 0 (high function) to 28 and the active function sub-scale scores range from 0 (high function) to 52 [2].
Analysis
Descriptive statistics are used to describe demographic characteristics of the patients, presenting as mean (SD). The ArmA-TH sub-scale scores presented in median (inter-quartile range).
Rasch analysis
To test whether the data fit the Rasch model, the following criteria were investigated [8, 9].
- Unidimensionality. Two methods were evaluated for determining unidimensionality. First, the first principal component of the residuals (first construct) should be no more than 15% or Eigen value less than 2 [10]. Second, that the item fit statistics indicating the extent to which the response to a particular item are consistent with the way the sample respondents have responded to the other items, should be 0.70 and 1.50 [10]. In addition, the correlation of the two sets of person measures and the correlation disattenuated for measurement error, should be greater than 0.7 to indicate unidimenstionality.
- Local independence. To evaluate local independency, a pair of items should not have inter-item residual correlations that higher than 0.2 [11].
- Response category functioning. Ordered categories and thresholds are expected for measurement. Therefore, adjacent categories (thresholds) on the latent scale hold the same position and order on the latent trait measured [12]. Items with a disordered threshold between categories can be evaluated by category probability curves and the item fit of each categorical response is examined (less than 2.0, are acceptable) [8].
- Targeting of persons, items and item hierarchy. Acceptable item-test targeting for compliance with the Rasch model is evaluated through the closeness of the mean of person and the mean of item on the Wright map (no more than 1 logit) [13]. Item hierarchy indicates how the items in difficulty match the intentions of the instrument developer and the expectations of those planning to use the test results [14].
- Reliability. There are two kinds of reliability evaluated by Rasch analysis, the person and the item reliability. The person reliability is interpreted as the ability of the scale to reliably rank the person relative to location within the scale of the measure. Similar to Cronbach’s alpha, the value is often lower because it does not include extreme scores. The item reliability coefficient reflects the extent to which the item hierarchy is replicable with a different set of individuals. A reliability coefficient of > 0.70 is considered acceptable for person, and coefficient of > 0.80 is considered acceptable for item.
Differential item functioning (DIF) for age, sex and education. An ideal item is that it should be invariant across subgroups, meaning that item calibration should be the same in different subgroups of people [8]. Moderate to large DIF was evaluated by significant DIF Contrast’ of < 0.64, thereby indicating an acceptable value [6]. In this study, DIF due to age, sex and education was examined. Both the ArmA-TH passive and active function sub-scales were separately evaluated for fit to the Rasch model.
Winsteps, 4.5.3 (Winsteps® Rasch Measurement, 2017) was used for Rasch analysis.