modification of the edinburgh handedness inventory: a replication study
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Modification of the Edinburgh Handedness Inventory: A replication study
Journal: Laterality: Asymmetries of Body, Brain and Cognition
Manuscript ID: Draft
Manuscript Type: Original Paper
Date Submitted by the Author:
n/a
Complete List of Authors: Dragovic, Milan; Centre for Clinical Research in Neuropsychiatry, Milenkovic, Sanja; Faculty of Medicine, University of Belgrade,
Serbia, Institute of Hygiene and Medical Ecology
Keywords: confirmatory factor analysis, handedness, Edinburgh Handedness Inventory, measurement models, laterality
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Running title: Handedness measurement
Modification of the Edinburgh Handedness Inventory: A replication study
Author:
Sanja Milenkovic1, Milan Dragovic
2,3
1 Institute for Hygiene and Medical Ecology, Faculty of Medicine, University of Belgrade, Serbia,
2 North Metropolitan Area Health Service – Mental Health, Perth, Western Australia
3 Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical
Neurosciences, University of Western Australia, Perth, Australia
Word counts:
Abstract: 79 words
Text: 1,741 words (excluding Abstract and Reference List)
Figures: 1
Tables: 4
Correspondence should be sent to Sanja Milenkovic, Institute for Hygiene and Medical
Ecology, School of Medicine, University of Belgrade, Serbia Email: [email protected]
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ABSTRACT
The Edinburgh Handedness Inventory was administered to 1224 high school students (605
males and 619 females). Confirmatory factor analysis was used to re-examine its psychometric
properties. The results showed that this instrument has poor measurement properties.
Removal of a few problematic items improved internally consistency of the questionnaire and
improved its validity. The results also suggest that individual items provide an uneven
contribution to the measurement construct and that factor scores need to be used for
calculation of the total handedness score.
Key words: confirmatory factor analysis; handedness; Edinburgh Handedness Inventory;
measurement models; laterality
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INTRODUCTION
Measurement of lateralisation of hand preferences (direction and degree) by various
inventories is common practice and employed across various disciplines. Laterality assessment
however is often conducted to obtain secondary measures, e.g., to remove or to control for
the confounding effect of left-handedness from MRI studies. This is perhaps the principal
reason as to why handedness questionnaires are rarely subjected to rigorous investigations of
their psychometric properties. Another rationale may lie in their plain face validity.
Nevertheless, these assumptions do not preclude the question as to whether handedness
questionnaires are really valid and reliable, as is assumed by their designers.
A quick inspection of the literature reveals that two handedness questionnaires, for
better or for worse, have monopolised the laterality arena – the Edinburgh Handedness
Inventory (Oldfield, 1971) and Annett’s handedness questionnaire (Annett, 1970), with the
former being used more frequently (Bishop, 1996). Despite their widespread utilisation, these
questionnaires have only sporadically been submitted to psychometric scrutiny. In addition to
a few factor analytic studies (e.g., McFarland & Anderson, 1980; Williams, 1986) little effort
has been made to truly dissect their validity and reliability. A more recent study on a relatively
small sample (Dragovic, 2004) has convincingly demonstrated that the Edinburgh Handedness
Inventory (EHI) is a poor research instrument that demands modification. It was shown that
the EHI contains items that are almost singular (e.g., writing and drawing), and also items that
are more a benign reflection of the environment than a genuine hand choice (e.g., opening a
box lid). This study has been well received and followed by about twenty subsequent studies
that used the modified version of the EHI recommended by Dragovic. However there were
many more studies that continued with the unmodified versions. For example, Google scholar
lists thousands of articles since 2004 containing the phrase “edinburgh handedness inventory”.
This clearly indicates that the impact of Dragovic’s effort (2004) is somewhat limited. On the
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other hand a small sample (including a mix of mentally well relatives of schizophrenia patients
and healthy controls) used in this study may raise a doubt as to whether the results would
actually survive replication. It is also possible that the composition of that sample has
prevented wider use of the modified EHI. These two limitations have instigated the current
study. Clearly, a successful replication of the original study using a large and more
homogenous sample is warranted.
The principal of this study, therefore, is to corroborate the results of Dragovic’s (2004)
attempt to provide a psychometrically enhanced version of the EHI. We used confirmatory
factor analysis (CFA) to investigate re-investigate latent structure and measurement properties
of the EHI. CFA is often considered as a more rigorous statistical technique as compared to
exploratory factor analysis (Jöreskog, Sörbom, du Toit, & du Toit, 2001).
METHOD
Participants
The complete EHI was administered individually to a large sample of 1224 students
from six government high schools in Belgrade, Serbia. This sample comprised 605 male
students (mean age 15.9, SD=0.42) and 619 female students (mean age 15.8, SD=0.51).
Written informed consent was obtained from all participants.
Instrument
The original Edinburgh Handedness Inventory comprises 10 items indexing hand
preference (writing, drawing, throwing, using scissors, a toothbrush, a knife without a fork,
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using spoon, the upper hand when using a broom, striking a match, and opening the lid of the
box). For each item, participants indicated their hand preference ranging from strong (++), less
strong (+), to indifferent (+/+). As in the original study (Dragovic, 2004), the responses were
recoded into a five-point Likert-type scale. Strong left-handedness was given a score of 1 and
strong right-handedness a score of 5.
Statistical analysis
A confirmatory factor analyses were conducted using LISREL (version 8.52). Prior to
this polychoric coefficients and an asymptotic covariance matrix were generated in PRELIS,
pre-processing software. In addition to the chi-square statistic, the fit of all subsequent scale
modifications is assessed by evaluation a number of other indices (RMSEA - root mean square
error of approximation; SRMR - standardised root mean-square residual; GFI - goodness of fit
index; AGFI - adjusted goodness of fit index, and CFI - comparative fit index). Again, scale
reliability was assessed using Fornell and Larcker’s (1981) approach to calculate the amount of
variance extracted
Three measurement models (parallel, tau-equivalent, and congeneric) were tested
prior to any modification of the EHI and after modification. This was necessary to decide which
way of creating a composite EHI score is justified. In brief, measurement models differ with
regard to the content of both useful and error variance in items that are indicators of the
composite score. First, a parallel model assumes that each item carries an equal prediction of
the composite score. Second, a tau-equivalent model assumes that each item is an equally
accurate indicator of the composite score, but allows error variances to differ. Third, a
congeneric model, in which each item is assumed to indicates the same generic true score, but
with a unique contribution to the composite score and an item-specific error variance. The lack
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of statistically significant differences between the three models, justifies calculation of a total
handedness score by simply summing responses to each items. Otherwise, each item needs to
be weighted separately to reflect their imbalanced contribution to the total score.
RESULTS
First, we compared the fit of three measurement models for the original, 10-items
scale. As in the original study, a congeneric measurement model, i.e., each item contributes
uniquely to the composite score and has an item-specific error variance, was found to be
superior (Table 1) to the other two models. Large absolute values of chi-square statistic
suggest that there might be room for further scale refinement.
Second, we assessed the goodness of fit of the complete 10-items scale. Table 2
contains squared multiple correlations for observed variables, factor scores (i.e., weights), and
several indices of fit for all models (baseline and subsequent modifications). Again, the
complete 10-items scale showed unsatisfactory indicators. Several items had squared multiple
correlations lower than 0.50, suggesting that error variance outweighs variance attributable to
the construct. Factor weights, which are supposed to quantify capacity of each item to indicate
the construct, were condensed within two items only (writing and drawing) indicating a fairly
uneven contribution of individual items to the total score. All other indices of fit failed to reach
conventions thresholds.
The first modification we implemented was to exclude the highly collinear and thus
redundant item – drawing. Elimination of this item produced a slightly better, but still
unacceptable, fit. Next, we removed two items that again had the lowest contribution to the
construct of measurement (opening box lid and holding broom). Elimination of these two
items resulted in improved indices of fit (all above 0.90) and fairly balanced factor scores.
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However, model-based measures of reliability (Fornell & Larcker, 1981), construct reliability
and estimates of variance extracted were not impressive. The second modification led to a
slightly improved construct reliability of 0.74 and slightly larger proportion of the shared
variance in a set of handedness items.
Finally, after the second scale modification, we performed tests of differences
between parallel, tau-equivalent, and congeneric model (Table 3). Differences in chi-square
measures of fit between three measurement models were significant indicating that simple
aggregation of the item scores and its alternative (the laterality quotient) are not acceptable
ways of calculating the total EHI score. In contrast, our results suggest that, due to uneven
input of individual items to the latent construct, each item needs to be weighted using factor
scores. Figure 1 shows a scatter plot of weighted and unweighted scores from the 7-item
version. A jitter function was used to improve the look of scatter plot by avoiding extensive
overlap of data points.
DISCUSSION
Similar to the original study (Dragovic, 2004), confirmatory factor analysis using a large
and homogeneous sample of students clearly demonstrates that the EHI should be used in a
modified form. Removal of a few items generally improved the psychometric properties of this
instrument. Similar to modification proposed by Dragovic (2004), present study demonstrates
that they modified version is statistically superior to the full scale. We suggest that the
modified version should be preferred in research settings.
Both studies, original and present, encountered the same problematic items. One item
(drawing) being problematic due to its excessively high correlation with writing and two items
(holding a broom and opening a box lid) due to an unacceptably low amount of shared
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variance with the handedness construct. These two items have already been described as
items that do not matter (Peters, 1990), and which, due to their marginal contribution to the
measurement construct, should not be used in any handedness questionnaire. Measurement
of these items is also notoriously unreliable (Ransil & Schacter, 1994).
There are two discrepancies with the original study. First, in the second modification
we failed to replicate an important result, namely the lack of significant differences between
the three measurement models (parallel, tau-equivalent and congeneric). Similarity of these
models essentially warrants calculation of simple sum of scores as the EHI total score. Our
study however suggests that this may not be true. The present results demonstrate that
contribution of each item to the total score is slightly different, i.e., some items appear to be
better indicators of the latent handedness construct than others. For this purpose each
response should be weighted using factor scores from Table 3. Although the size of factor
scores appear balanced, weighted responses resulted in a somewhat distinct distribution (e.g.,
Figure 1). Specific weights such as those being produced using our sample can be used to
calculate total score. It would be more accurate however to produce weights, using the same
routine, which are bound to specific sample. Second, significant age differences between the
two samples (original and present) may hint that the younger sample exhibits developmental
differences related to prolonged maturation of hand dominance. Hand preference
development however appears to be strongly established by middle childhood (McManus et
al, 1988; Cavill & Bryden, 2003). Moreover, hand preferences appear to be well established
before birth (e.g., Hepper, McCartney & Shanon 1998; Hepper, Wells & Lynch 2005; McCartney &
Hepper, 1999).
In summary, we conclude that our replication study has additionally strengthened the
need for using the modified EHI version instead of the psychometrically flawed original
version. We have demonstrated that individual items are not equally accurate indicators of the
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handedness construct; therefore we strongly encourage the use of weighted scores since they
provide a more valid indication of the total score.
REFERENCES
Annett, M. (1970). A classification of hand preference by association analysis. British Journal of
Psychology, (61), 303-321.
Bishop, D. V. M. (1996). The measurement of hand preference: a validation study comparing
three groups of right-handers. British Journal of Psychology, (87), 269-285.
Cavill, S., & Bryden, P. (2003). Development of handedness: Comparison of questionnaire and
performance-based measures of preference. Brain and Cognition, 53, 149-151.
Dragovic, M. (2004). Towards an improved measure of the Edinburgh Handedness Inventory: A
one-factor congeneric measurement model using confirmatory factor analysis.
Laterality, 9(4), 411-419.
Fornell, C., & Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable
Variables and Measurement Error. Journal of Marketing Research, 18, 39-50.
Hepper, G. P., McCartney, R.G., & Shanon, E.A. (1998). Lateralised behaviour in first trimester
human foetuses. Neuropsychologia, 36(6), 531-534.
Hepper, P. G., Wells, D.L., & Lynch, C. (2005). Prenatal thumb sucking is related to postnatal
handedness. Neuropsychologia, 43(3), 313-315.
Jöreskog, K., Sörbom, D., du Toit, S., & du Toit, M. (2001). Lisrel 8: New Statistical Features.
Lincolnwood, IL: Scientific Software International, Inc.
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McCartney, G., and Hepper, P. (1999). Development of lateralized behaviour in the human
fetus from 12 to 27 weeks' gestation. Developmental Medicine and Child Neurology, 41,
83-86.
McFarland, K., & Anderson, J. (1980). Factor stability of the Edinburgh Handedness Inventory
as a function of test-retest performance, age, and sex. British Journal of Psychology,
71(1), 135-142.
McManus, I. C., Sik, G., Cole, D. R., Mellon, A. F., Wong, J., & Kloss, J. (1988). The development
of handedness in children. Birtish Journal of Developmental Psychology, 6(257-273).
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh Inventory.
Neuropsychologia, 9, 97-113.
Peters, M. (1990). Phenotype in Normal Left-Handers: An Understanding of Phenotype is the
Basis for Understanding Mechanism and Inheritance of Handedness. In S. Coren (Ed.),
Left-Handedness: Behavioural Implications and Anomalies (pp. 167-192). North-Holland:
Elsevier Science Publishers B.V.
Ransil, B.J., & Schachter, S.C (1994). Test-retest reliability of the Edinburgh Handedness
Inventory and global handedness preference measurement, and their correlation.
Perceptual and Motor Skills, 79, 1355-1372.
Williams, M. S. (1986). Factor analysis of the Edinburgh Handedness Inventory. Cortex, 22(2),
325-326.
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Table 1.
Chi-square measures of fit for three measurement models for
the Edinburgh Handedness Inventory.
Model χ2 df χ
2 diff ∆ df
Parallel 1570.3 53 - -
Tau-equivalent 1743.6 44 173.3* 9
Congeneric# 1501.2 35 69.1* 18
χ2
= Chi-square
* p < .05. # Congeneric versus parallel
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Table 2.
One-factor congeneric measurement models for the EHI and the two modifications, including
squared multiple correlations (proportion of variance explained by the construct), factor score
regressions (specific weight of each item), and goodness-of-fit measures.
Baseline
10-items
1st
modification
9-items
2nd
modification
7-items
Observed
variables
Squared
multiple
correlation
s
Factor
scores
Squared
multiple
correlation
s
Factor
score
Squared
multiple
correlation
s
Factor
scores
Writing .88 .40 .55 .21 .57 .22
Drawing .89 .42 - - - -
Throwing .16 .02 .28 .11 .32 .12
Scissors .31 .04 .41 .13 .41 .14
Toothbrush .36 .05 .53 .19 .53 .19
Knife .22 .03 .36 .11 .37 .14
Spoon .56 .09 .65 .28 .66 .31
Broom .07 .01 .11 .05 - -
Matches .34 .04 .49 .17 .47 .17
Opening box-lid .12 .02 .19 .07 - -
Goodness-of-fit
measures
No of iterations 11 6 6
χ2 1501.1 178.3
§ 128.4
#
Degrees of
freedom (df) 35 27 14
RMSEA .19 .07 .08
Standardised RMR .09 .03 .03
GFI .80 .97 .97
AGFI .69 .95 .94
CFI .91 .98 .98
Construct
reliability .85 .85 .87
Variance extracted
estimate .39 .40 .48
χ2
= chi-square;
RMSEA = Root Mean Square Error of Approximation;
Standardised RMR = Standardised Root Mean-square Residual;
GFI = Goodness-of-Fit Index;
AGFI = Adjusted Goodness-of-Fit Index;
CFI = Comparative fit index; § = difference in chi-square between baseline and 1
st modification (∆χ
2 =
1322.8) is significant (p<.05)
# = difference in chi-square between 1
st and 2
nd modification (∆χ
2 =
49.9) is significant (p<.05)
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Table 3.
Chi-square measures of fit for three measurement models for
the 7-item scale.
Model χ2 df ∆χ
2 ∆df
Parallel 605.1 22 - -
Tau-equivalent 209.7 20 395.4* 6
Congeneric# 128.4 14 81.2* 6
χ2
= Satorra-Bentler Scaled Chi-square
* p > .05 # Congeneric vs Tau-equivalent
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Figure 1.
Scatterplot of weighted and unweighted scores in a modified 7-items version of the Edinburgh
Handedness Inventory.
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Table 4
Comparisons between the full and revised (weighted and non-weighted scores) EHI scale.
EHI 10-items EHI 7-items EHI 7-items / weighted
% left-handed Mean
score*
Laterality
Quotient
Mean
score**
Laterality
Quotient
Mean
score*** Range
Male 9.9 39.3 56.9 35.8 61.2 5.03 1.2-6.2
Female 5.0 40.2 61.3 36.7 67.3 5.17 1.2-6.2
* Scores are in range from 10 (left-handedness) to 50 (right-handedness)
** Scores are in range from 7 (left-handedness) to 35 (right-handedness)
*** Scores are in range from 1.24 (left-handedness) to 6.20 (right-handedness)
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