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Neuropsychological Rehabilitation: An International Journal Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/pnrh20
Assessing construct validity of the self-rating version of the European Brain Injury Questionnaire (EBIQ) using Rasch analysis a
b
Andrew Bateman , Tom W. Teasdale & Klaus Willmes
c
a
Oliver Zangwill Centre for Neuropsychological Rehabilitation, Ely, Cambs, UK b
Department of Psychology, University of Copenhagen, Denmark c
Section Neuropsychology, University Hospital, RWTH Aachen University, Aachen, Germany Version of record first published: 29 Oct 2009.
To cite this article: Andrew Bateman , Tom W. Teasdale & Klaus Willmes (2009): Assessing construct validity of the self-rating version of the European Brain Injury Questionnaire (EBIQ) using Rasch analysis, Neuropsychological Rehabilitation: An International Journal, 19:6, 941-954 To link to this article: http://dx.doi.org/10.1080/09602010903021170
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NEUROPSYCHOLOGICAL REHABILITATION 2009, 19 (6), 941– 954
Assessing construct validity of the self-rating version of the European Brain Injury Questionnaire (EBIQ) using Rasch analysis Andrew Bateman1, Tom W. Teasdale2, and Klaus Willmes3 Downloaded by [Andrew Bateman] at 00:07 05 April 2013
1
Oliver Zangwill Centre for Neuropsychological Rehabilitation, Ely, Cambs, UK, 2 Department of Psychology, University of Copenhagen, Denmark, 3 Section Neuropsychology, University Hospital, RWTH Aachen University, Aachen, Germany
Data from the European Brain Injury Questionnaire (EBIQ) responses of 226 consecutive admissions to the Oliver Zangwill Centre were analysed using Rasch analysis (Item Response Theory) techniques with the software package RUMM2020. Some items and overall scales did not meet the expectations of the Rasch model. After removal of items it was possible to validate six subscales from previously published subscales, namely: “cognitive”, “impulsivity”, “somatic”, “depression”, “communication” and “difficulties in social interactions”. A new EBIQ “fatigue” subscale is also proposed. Keywords: EBIQ; Questionnaire.
Symptoms;
Item
response
theory;
Rasch
Scale;
INTRODUCTION The European Brain Injury Questionnaire (EBIQ) was developed from an international project to examine subjective experience in brain injured patients and their close relatives (Teasdale et al., 1997). (The questionnaire Correspondence should be sent to Andrew Bateman, Oliver Zangwill Centre for Neuropsychological Rehabilitation, Ely, Cambs CB6 1DN, UK. E-mail:
[email protected] Funding to support this writing and research was received from PEARL (Norfolk, Suffolk and Cambridgeshire Strategic Health Authority) and the Oliver Zangwill Centre. # 2009 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business http://www.psypress.com/neurorehab DOI:10.1080/09602010903021170
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is available from http://teasdale.psy.ku.dk/EBIQ.pdf. Proposed new scoring key is shown in Table 2.) In this original study, Teasdale et al. (1997) demonstrated reliability and validity of the questionnaire and presented an analysis of data collected from 905 patients using a Facet Theory approach to develop eight subscales (indicative of areas of functioning) [for a helpful introduction to Facet Theory, see Levy’s (2005) article about Louis Guttman]. The EBIQ consists of 63 items requiring a rating of frequency of symptoms (ranging across cognitive, physical and motivational impairments). There are three response categories for each item namely “not at all” (coded as 1), “a little” (2) and “a lot” (3). Traditionally this Likert scaling has been used to create a total score overall (scoring range between 63 and 189) and for each of the eight domains (with total possible raw score ranges depending on number of items in the domain). A subset of 34 “core items” was also described that could yield a global score. For comparison between domains the authors proposed that an average be calculated (theoretical possible range between 1 and 3). There are no standardised guidelines on how missing data should be scored, although typically studies have substituted the integer value closest to the item mean. Since the original publication of the questionnaire, it has since been used to examine the effect of rehabilitation (e.g., Coetzer & Rushe, 2005; Schonberger, Humle, Zeeman, & Teasdale, 2006; Svendsen, Teasdale, & Pinner, 2004). For example, using mean (þ/2SD) scores for each subscale, Svendsen et al. compared patient groups (stroke, TBI and “other” pathology groups) to healthy controls who, as might be expected, reported fewer symptoms. They then transformed the subscales from each of the patient groups to z-scores and used repeated measures analysis of variance, demonstrating lower levels of symptoms after rehabilitation. The paper also includes plots of z-scores pre- and post-rehabilitation for each of the subscales. One problem with the studies to date relates to the use of responses to questionnaire items to generate total scores (or subscale scores) from the EBIQ. This approach to the analysis of questionnaire data is common not only in rehabilitation studies but also in psychometric studies generally. Recently there has been a growing voice challenging the implied assumption that items in a questionnaire can indeed be taken to measure the same latent trait if this has not been tested empirically (see for example, Tesio, 2003). Furthermore, the assumptions about the measurement properties of the scale, such as, for example, whether items contribute equally to the total overall index are also challenged. Item Response Theory (IRT), particularly using Rasch models, provides a robust test for such assumptions and an alternative method for analysis of such data. Rasch analysis techniques have also been shown to be an informative method for examining rating
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scale validity for use within a given population (e.g., Kutlay, Kucukdeveci, Elhan, Yavuzer, & Tennant, 2007). The aim of the study reported here was to establish baseline item response characteristics for the EBIQ questionnaire in our client group. Once this is established we aim to use data collected to evaluate the impact of rehabilitation at the Oliver Zangwill Centre for Neuropsychological Rehabilitation. We also plan to use Rasch techniques in the comparison of patients between our centre and other rehabilitation centres (e.g., Tennant et al., 2004). There has been an increasing use of the Rasch approach in rehabilitation research, but to date, since an early paper by Willmes (1981) there have been relatively few published examples in neuropsychology assessment and rehabilitation literature. Therefore, a secondary aim is to illustrate the utility of the Rasch analysis approach in this context.
METHODS The English language version of the questionnaire was used. There are parallel versions of the questionnaire to be completed by patients and their relative or carer. This paper presents analysis of self-report item response characteristics.
Rasch analysis background information Rasch analysis refers to a specific construct modelling approach within item response theory. The Rasch Model (Rasch, 1960/1980), when applied to achievement and ability applications, provides a way of relating item difficulty and person ability. When applied to attitude applications, terms such as item scale value and person attitude towards something can be represented in item and respondent locations. The model asserts, first of all, that the easier the item the more likely it will be passed or endorsed (denoted by item location value in logits). Secondly, the more able the person, the more likely they will pass an item compared with a less able person (denoted by respondent location, also in logits, i.e., on the same scale as item locations) (Wilson, 2005). In the context of the EBIQ, item difficulty refers to the likelihood of the symptom being endorsed (symptom severity), and person ability refers to the number of symptoms endorsed (overall disorder severity). The Rasch model places both item difficulty and person ability on the same latent continuum. When it is demonstrated that the model holds, the person locations and item locations on this continuum have interval scale properties. This logic leads to the potential to contribute to parametric measurement of change.
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Rasch analysis procedure The Rasch analysis approach reported in this study follows a sequence of analyses that has been explained and illustrated in detail elsewhere (for other recent examples in psychology and rehabilitation literature, see Eyres, Carey, Gilworth, Neumann, & Tennant 2005; Pallant & Tennant, 2007; Tennant et al., 2004). We have followed the guidelines for reporting Rasch analyses described by Lamprianou (2006) and Smith, Linacre, and Smith (2006). All participants’ responses were entered even when the questionnaire was incomplete, based on the assumption that non-endorsement of an item or items may indicate information about the item. Using the software package RUMM2020 (version 4 for Windows) (Andrich, Sheridan, & Lou, 2005, upgrade number 4600.0164) www.rummlab. com, data were fitted to the Rasch model (Figure 1) to determine if the items from the scale performed in the way expected by the model. P is the probability of a person n affirming item i; u represents the person’s level of distress caused by brain injury, d is the level of difficulty (likelihood of symptom being endorsed) expressed by the item at each item threshold j. A likelihood ratio analysis was undertaken to test the assumption that the partial-credit model (as shown in the equation below) could be used. Overall fit to the model was examined by an item-trait interaction statistic denoted by a chi-squared statistic with a non-significant chi-squared value indicating adequate fit to the model. In contrast to many published questionnaire analyses, no items required re-scoring in this study. Therefore, at this stage, items were examined individually for fit to the model. Item fit is denoted by two fit statistics, residuals and chi-squared probability values. The chi-squared statistic was calculated by grouping data into four class intervals based on number of symptoms endorsed and then comparing observed and expected response means for each interval. For each item, a non-significant chi-squared value and residuals within the range 22.5 to þ 2.5 were required. A Bonferroni adjustment to the item fit chi-squared probability values was applied. In RUMM2020 an estimate of the internal consistency reliability of a scale is available as a “person separation index”. This is equivalent to Cronbach’s
Figure 1. The Rasch model (logit form). For a person (n) responding to item (i) with a polytomous response ( j), the log of the odds for affirming a response category ( j) as opposed to an adjacent response category j 2 1 is the difference between that person’s ability u and the difficulty of the item d.
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alpha, only using the logit value (linear person estimate) as opposed to the raw score in the same formulae. It is interpreted in a similar manner, that is, a minimum value of 0.7 is required for group use and 0.85 for individual use (Tennant & Conaghan, 2007, p. 1361). Items were further examined for consistent performance across gender. At any given level of overall symptom frequency, the probability of endorsing an item should be the same irrespective of gender. Therefore item bias or differential item functioning with respect to gender subgroups was examined using two-way ANOVA (gender class interval/severity level) of the personresponse residuals for each item, which mark the extent to which each person diverges from the expected response for their particular total. Unidimensionality, defined as a single latent trait being able to account for the performance on items, has itself been placed by Smith (2002) on a continuum that can be evaluated using principal components analysis (PCA). In this study two subsets of items were defined by positive and negative loadings on the first residual component after the first “Rasch” factor. These were separately fitted to the Rasch model and the person estimates obtained. A paired t-test was used to compare person estimates, with a non-significant difference taken to support the claim for unidimensionality of the scale. The analyses were completed in the following stages: (1) examination of the EBIQ scale as a whole, (2) examination of previously published EBIQ sub-scales, (3) removal of items until whole-scale and sub-scale fit were achieved, and (4) assessment of unidimensionality of the sub-scales.
Study participants The data reported in this paper comprise responses to the questionnaire by 226 patients (72% male; median age ¼ 34, range ¼ 16 –65) with acquired brain injury (1– 10 years prior to assessment; 77% traumatic brain injury, other pathologies including stroke 8%, anoxia 6%, open head injury 3%, and other conditions representing 6% of the sample). This sample represented consecutive referrals to Oliver Zangwill Centre between November 1996 and November 2005. The questionnaire was completed at the first appointment for assessment in the centre.
RESULTS Missing data Responses to the EBIQ from 226 people were entered into the analysis. In all 163 patients submitted completed questionnaires. Of the remaining responses, 53 participants submitted questionnaires with only 1– 5 items
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not endorsed. Eight participants left larger numbers of items unmarked (range 15– 48 items). Inspection of missing data by items revealed that of the unmarked items, two questions were most frequently left: 15 participants (6.6% of sample) left item 56 (“loss of sexual interest or pleasure”) unmarked suggesting that this item, requiring disclosure of personal information, was especially difficult to respond to. Sixteen people (7.1% of sample) left item 52 (“acting inappropriately in dangerous situations”) unmarked. The remainder of missing data were evenly spread throughout the questionnaire.
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Construct validity EBIQ overall scale. A person separation index of 0.94 was found, indicating good separation of items along the construct. This suggests that there is sufficient power to discriminate between four class interval groups of respondents (Fisher, 1992). The EBIQ showed good overall fit to the Rasch model in terms of item – person interaction statistics (mean ¼ 0.048, SD ¼ 0.977). However, itemtrait interaction was significant (x2 ¼ 322.0, p , .0001) suggesting that the scale as a whole is deviating significantly from the model’s expectations and lacks invariance across the construct of “total distress” caused by brain injury. Only one item displayed disordered thresholds (item 56, loss of sexual interest), and, given the large number of other items in the questionnaire, a rescoring procedure was not undertaken. The overall misfit was demonstrated at the individual item level. Fourteen of the 63 items showed significant item-trait fit statistics ( p , .05). (1, headaches; 11, being confused; 20, needing reminding of personal hygiene; 22, trouble concentrating; 25, having their feelings easily hurt; 36, being unsure in dangerous situation; 43, inclined to eat too much; 45, lacking energy or being slowed down; 47, feeling of worthlessness; 49, needing help with personal hygiene; 51, feeling tense; 55, leaving others to take the initiative in conversations; 56, having loss of sexual interest or pleasure; 63, having problems in general). Three additional items (27, feeling irritated; 41, crying easily; 42, finding way) displayed significant differential item functioning by gender (see for examples, Figure 2a and b). After removal of these 17 items, the overall fit to the Rasch model was recorded (item fit mean ¼ 0.00, SD ¼ 0.63; x2 ¼ 159.7, p ¼ .10). Unidimensionality of the reduced scale was assessed using the principal components analysis (PCA) of the person residuals. Once the “Rasch” factor of brain injury symptom severity was extracted, the first residual factor accounted for only 13.14% of the variance. Significant differences at the 5% level were found on 93/226 (41%) of the person estimates given by the two subsets when compared to person estimates given by the full
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Figure 2a. Significant uniform differential item function by gender for EBIQ item 42 (“finding your way”), F(1) ¼ 12.4, p , .001.
Figure 2b. Significant uniform differential item functioning by gender for EBIQ item 27 (irritability), F(1) ¼ 19.7, p , .001.
46-item scale. The criteria for percentage of t-tests outside of the range – 1.96 to þ1.96 should not exceed 5% (Tennant & Conaghan, 2007). This means the shortened 46-item scale does not demonstrate local independence within the scale. Given the overall scale is made up of several subscales this is not surprising and further analyses were conducted examining the individual subsets of items as proposed by previous researchers (see results for EBIQ sub-scales below). Visual inspection of a plot of targeting revealed a good spread of items across the range of respondents’ scores indicating that the items provide a good operational measurement range (from – 3.2 to þ1.6 logits) for difficulties experienced by people with brain injury accounting for all but 6/226
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(2.6%) of the participants in this sample (e.g., Figure 3 shows the full-scale person–item threshold distribution). A plot of person location and total scores revealed an ogive attesting to approximately linear performance of the total score in the range between 80 and 160 points (Figure 4).
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EBIQ sub-scales. Some of the papers reporting the use of the EBIQ (e.g., Sopena, Dewar, Nannery, Teasdale, & Wilson, 2007) have used the nine sub-scales originally described by Teasdale et al. (1997), the labels listed in Table 1, Analyses numbers 1 – 9.
Figure 3. Person item threshold distribution showing a good operational match between item thresholds and the distribution of abilities of the patient group.
Figure 4. Plot of EBIQ total scores (SUM) against individual person locations (abscissa unit ¼ Logit).
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Each subscale was, where necessary, modified to achieve fit by removal of the misfitting items listed above. At this stage the PCA and paired t-test stage of analysis for unidimensionality was repeated. The “Core” subscale has many attributes comparable to the multidimensional Overall Scale described above and was not examined further. Table 1 Analyses 2– 7 shows that five of the subscales achieved satisfactory fit to the model and had a person separation index that makes the scale useful at least for comparing groups of patients (i.e., . 0.7). Each of these five subscales went on to pass the test for unidimensionality (Table 2, Analyses 2 – 6). Martin, Viguier, Deloche, and Dellatolas (2001) identified a simpler structure of three subscales labelled “depressive”, “cognitive” and “difficulties with social interactions” (Table 1, Analyses numbers 10 –12). The depression subscale from this study was not better than the Teasdale et al. scale and was not examined further. There was also no difference between the two cognitive subscales of Martin et al. or Teasdale et al. The third subscale concerning difficulties in social interactions appears to be a useful additional domain. Finally, examination of the discarded items and using clinical judgement, a new subscale was constructed to examine the topic of fatigue after brain injury (Tables 1 and 2, Analysis 13). This scale, which also includes items that are found in other subscales, was found to fit the model, pass the tests for unidimensionality, and have a Person Separation Index score sufficient for use at the group level.
DISCUSSION Discussion of missing data Most participants were able to complete this questionnaire, and only very occasionally did they require support. The main reasons for support concerned comprehension difficulties or other acquired reading difficulties, in which case an assistant read the questionnaire aloud. A significant proportion left one or more item unendorsed. This may reflect the length of the questionnaire. However, it appears that the most frequently unmarked items relate to information that participants, perhaps understandably, felt that they did not wish to disclose (e.g., regarding sexual activity or admitting to being unsafe). There were no participants at the floor or ceiling of the possible response range (63– 189).
Implications of results In recent years, the relevance of Item Response Theory approaches in rehabilitation has been promoted (for example, see Tesio, 2003). It has also become increasingly possible to implement these admittedly somewhat complex
Analysis ID 1 2 3 4 5 6 7 8 9 10 11 12 13
Subscale name
No. of items in original scale
No. of items assessed
x2
df
Core Cognitive scale (1) Impulsivity scale (1) Somatic scale (1) Depression scale (1) Physical (1) Communication (1) Motivation (1) Isolation (1) Depressive symptoms (2) Cognitive difficulties (2) Difficulties in social interactions (2) Fatigue scale (§)
34 13 13 8 9 6 4 5 4 9 12 8 (n/a)
34 12 10 7 5 4 4 5 3 9 12 5 8
199.29 43.18 37.72 17.79 14.05 21.26 7.997 45.78 16.45 42.76 41.93 21.55 36.2
102 36 30 24 15 18 12 15 9 21 36 15 24
p
PSI
.000 .191 .160 .660 .520 .267 .785 .000 .058 .003 .229 .120 .053
0.93 0.84 0.89 0.75 0.83 †0.56 0.68 0.77 †0.38 0.83 0.85 0.74 0.74
Significant p values (, .05) indicate deviation from expectations of the model suggesting that the subscale is not valid. †The physical and isolation scales are noted to have very low PSI and were also rejected. (1) Teasdale et al., 1997 subscales; (2) Martin et al., 2001 subscales; (§) New proposed fatigue subscale.
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TABLE 1 Chi-squared value, degrees of freedom (df), probability ( p) and Person Separation Index (PSI) for each of the subtests.
Analysis ID 1 2 3 4 5 6 7 8 9 10 11 12 13
Scale
No. of items assessed
Core Cognitive scale (1) Impulsivity scale (1) Somatic scale (1) Depression scale (1) Communication (1) Physical (1) Motivation (1) Isolation (1) Depressive symptoms (2) Cognitive difficulties (2) Difficulties in social interactions (2) Fatigue scale (§)
34 12 10 7 5 4 6 5 3 9 12 5 8
PC1%
%t-tests , 5%
Items as numbered in original questionnaire
222 215 216 185 193
4.95 1.4 2.78 0 0
2,4,8,11,15,21,22,23,36,46,54,59 3,10,13,14,19,24,37,44,57,62 1,7,16,32,45,50,51 9,12,30,31,53 5,35,55,60
220 206 218
5.45 1.94 2.75
2,4,7,8,15,22,26,42,45,46,54,59 3,14,19,51,57 2,7,15,26,29,32,45,55
n
14.93 17.5 25.52 35.7 36.67
14.58 29.91 19.4
PCA and t-tests not undertaken as scale did not achieve fit or there was insufficient internal reliability (PSI) to justify this. (1) Teasdale et al., 1997 subscales, (2) Martin et al., 2001 subscales, (§) New proposed fatigue subscale.
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TABLE 2 Assessing for unidimensionality in the subscales: Percentage of variance of the first residual factor from the Principal Components Analysis of Person Residuals, the percentage number of paired t-tests that are significant, and a list of the items contributing to each subscale.
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analyses through availability of new user-friendly software such as RUMM2020 (used for the present study). The importance of applying appropriate psychometric methods in evaluating rehabilitation has been stressed (Hobart et al., 2001). Classical test theory has been the underpinning for most test construction and evaluation for the past century. Criticism of classical test theory has primarily been directed against the fundamental assumption of this approach, namely the independence of a “true score” and an error component (Streiner & Norman, 2003; Wilson, 2005). Constructively, the approach pioneered by Rasch (1960/ 1980) enables the development of specifically objective measurement. Item difficulty, discriminability and reliability are transparent in this alternative approach which has clear utility in a clinical setting. Since this analysis, in our day-to-day clinical activity, we have opted not to remove the misfitting items from the questionnaire that is routinely administered to patients as responses to these questions may still be considered clinically informative. In future analyses, the responses to these items will, however, not be included. Rather, the total score computed from items showing fit to the model will be used in planned studies investigating analyses of impact of treatment and comparison between centres.
CONCLUSIONS Studies of effectiveness of rehabilitation require measurement tools that have established measurement properties that are appropriate for the patient group that is being studied. This analysis demonstrated the original scale does not work as a 63-item scale. However, we propose that the present analysis supports the use of modified sub-scale total scores. It is argued that these revised sub-scales meet the expectations of the Rasch model and can be considered valid uni-dimensional scales for assessing personal experience of brain injury. A modified 46-item EBIQ total score is not supported due to lack of local independence. Items were removed from 7/8 of the original Teasdale sub-scales due to mis-fit or differential item functioning by gender. The removal of three items from the motivation subscale reduced it to the extent that it can no longer operate. It might well be that aetiology is another important aspect for differential item functioning. We have not completed an analysis of these dimensions for this paper due to insufficient numbers in each subgroup, but suggest that future studies may examine, for example, the symptom profiles of patients with diffuse and focal (e.g., post-stroke) brain injury. Another limitation of this study is that the data were not collected prospectively for validation purposes. Rather this paper represents part of a series of
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planned secondary analyses of routinely collected data that form part of our clinic service evaluation. Careful evaluation of item response data can build confidence in measurement properties of a questionnaire. That this scale is validated for our client group means that further planned analyses of impact of rehabilitation are now possible. We will use the 46-item scale that indicates an outcome space of a proposed construct of total distress caused by brain injury. Analyses using this score will acknowledge that this is not a uni-dimensional scale. We will also be able to use the modified sub-scales. The present analyses provide a platform for further parametric analysis of impact of rehabilitation (e.g., at an item level, changes in item location using analysis of variance for before and after rehabilitation measures, and at a sub-scale level using logit transformed scores to quantify percentage change). Further analysis of self-rater and carer responses are also planned to investigate indicators of insight and awareness as indicated by difference in perspectives between carer and self-rating. Finally, we propose that a multicentre comparison using this differential item functioning approach is appropriate.
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