Laterality, 2014 Vol. 19, No. 6, 718–744, http://dx.doi.org/10.1080/1357650X.2014.911747
Patterns of hand preference in Italian adolescent high-school students Alessandra Lai1, Marianna Serra1, Donatella Rita Petretto1,2, Carmelo Masala1,2, and Antonio Preti1,3 1
Department of Education, Psychology, Philosophy, Section on Clinical Psychology, University of Cagliari, Cagliari, Italy 2 Associazione Centro Studi Ricerche ed Intervento “Neuropsicopedagogia” Onlus, Cagliari, Italy 3 Centro Medico Genneruxi, Cagliari, Italy The Annett Hand Preference Questionnaire (AHPQ) is amongst the most widely used self-report measures of handedness. The psychometric properties of the AHPQ have been rarely evaluated outside the Anglo-Saxon culture where the majority of the studies on the AHPQ were done. In this study, 1,023 students (males = 49.5%) from four large high schools operating in the district of Cagliari (Italy) were invited to fill in the Italian version of the AHPQ. The AHPQ was proved to measure a unidimensional latent trait, and the questionnaire was good at assessing deviation from right-handedness with high discrimination between subjects. Some items were more informative than others, and in particular the non-equivalence between the primary and the non-primary actions was confirmed by both the confirmatory factor and the item response theory analysis. The use of the rule of thumb that classifies subjects on the basis of the primary actions was supported for the distinction between consistent right- and left-handed. However, the mixed-handed group identified on the basis of the rule of thumb was not entirely consistent with the mixed-handed class predicted by the latent class analysis. Males were about twice as likely as females to be in the mixed-handed class.
Keywords: Handedness; Annett Hand Preference Questionnaire; Confirmatory factor analysis; Item response theory; Latent class analysis; Sex differences; Italy.
Address correspondence to: Alessandra Lai, Department of Education, Psychology, Philosophy, Section on Clinical Psychology, University of Cagliari, Cagliari 09123, Italy. Email: alessandralai1@ gmail.com Dr A. Lai and Dr M. Serra have received financial support from the government of the Regione Autonoma della Sardegna [grant number CRP2_356, PO Sardegna FSE 2007–2013, in accordance with the regional law L.R. 7/2007 “Promozione della ricerca scientifica e dell’innovazione tecnologica in Sardegna”]. The Regione Sardegna had no further role in the design of the study; in the collection, analysis and interpretation of data; in the writing of the report; nor in the decision to submit the paper for publication. No other forms of financial support were received for this study. © 2014 Taylor & Francis
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Self-reported hand preference is often used as a measure of handedness, which, in turn, serves as a proxy for hemispheric lateralization (Annett, 1970; Oldfield, 1971). Preference for the right hand is thought to reflect left hemisphere specialization in motor skills and is considered an indirect marker of lateralization of language functions (Basic et al., 2004; Isaacs, Barr, Nelson, & Devinsky, 2006; Knecht et al., 2000 There is evidence that hand preference in single-hand tasks is related to hand skill, when measured with tasks like the peg-moving task or similar (Bryden, Roy, & Spence, 2007; Corey, Hurley, & Foundas, 2001). Hand preference in single-hand tasks might be considered a reasonably approximation of handedness, although it does not overlap completely with hand dominance (Brown, Roy, Rohr, & Bryden, 2006).
Annett Hand Preference Questionnaire The Annett Hand Preference Questionnaire (AHPQ) is amongst the most widely used self-report measures of handedness. The AHPQ distinguishes between primary and non-primary manual actions, on the basis of the inter-correlation and the load of each item score on the total score (Annett, 1970). Primary actions are writing, throwing a ball, holding a tennis racquet, striking a match, hammering and using a toothbrush. Most of these actions can be performed with a single hand, and usually they are. Non-primary actions are managing scissors, managing a needle, sweeping, shovelling, dealing cards and unscrewing the lid of a jar. These actions generally require motor coordination of both hands and can be performed primarily with one hand or the other according to the circumstances. Three main categories of hand preference can be derived from the AHPQ on the basis of a rule of thumb: those who are consistently right-handed on the six primary actions are assigned to the right-hand category; those who are consistently lefthanded on the six primary actions are assigned to the left-hand category; and those who use the right hand on some primary actions and the left-hand on the other primary actions are classified in the mixed-handed category. However, the questionnaire allows three replies to each item: right, left and either. The “either” replies are sometimes assigned to the hand used to write, after checking the hand actually used to fill in the questionnaire. With this method, the main AHPQ factor in deciding someone’s handedness is the writing hand preference. A latent class analysis (LCA) retrieved the three expected handedness categories of the AHPQ, with good fit of the model (Dragovic & Hammond, 2007). Nevertheless, across the 12 investigated actions, respondents usually show different patterns of replies, with hand preference varying continuously from the right to the left. Using the peg-moving task, Annett (1970) mapped the continuum of preference onto the continuum of relative hand skill and defined eight classes, on the basis of the left to right time of peg-moving: two classes of strongly right- and left-handed; three additional classes of right-handed for
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writing who used the left hand for some actions; and three classes of left-handed for writing who used the right hand for some actions. Further refinement of this classification produced a deletion of one intermediate class (Annett, 2004). Independent replies of this classification are lacking. More recently, Annett (2009) reanalyzed the patterns of hand preference by observation of hand use for specific actions. When items specifying actions other than writing were paired with the writing hand, a variable pattern of association was found, and in particular the right-hand per left-hand pairings varied across actions, while the left-hand per right-hand pairings were fairly constant. This has implications in the classification of handedness based on hand preference, particularly when exploring neuropsychological and clinical correlates (e.g., Annett & Moran, 2006; Basso, 2007). The psychometric properties of the AHPQ have been rarely evaluated, apart from the studies of the questionnaire’s creator (Annett, 1970, 1985, 2002, 2004). Reliability and test–retest stability were found good in some studies with students (McMeekan & Lishman, 1975; Williams, 1991), but only one study, carried out in a metropolitan Australian sample, detailed the general psychometric characteristics of the AHPQ (Dragovic & Hammond, 2007). The Australian study, like the other validation studies, used the original questionnaire with no need of translation, since the AHPQ was applied to a native English-speaking population. Less is known about the psychometric characteristics of the AHPQ when the questionnaire is translated and applied to populations with a different background than the Anglo-Saxon culture where the majority of the studies on the AHPQ were done. Some evidence from Japan shows that cultural factors can impact on the pattern of replies on the AHPQ (Asai & Tanno, 2009; Gregory, Claridge, Clark, & Taylor, 2003).
Aims This study sets out to explore the psychometric properties of the Italian version of the AHPQ in a sample including over 1,000 high-school students. Since the whole student population was invited to take part in the study and the participation rate was very high (1,023 out of 1,032), bias in enrollment is expected to be low and results can be considered representative of the highschool population in the area. We aimed at establishing the reliability, test–retest stability, factorial unidimensionality and accuracy of the AHPQ. We also aimed at determining the number of handedness categories with a statistically grounded procedure rather than on an arbitrary cut-off and examining the level of agreement between a LCA model of hand preference and the range of arbitrary criteria for categorization of individuals into handedness classes. Gender and age differences were reported in handedness. In some studies, males resulted left-handed more often than females (Martin, Papadatou-Pastou, Jones, & Munafò, 2010;
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Papadatou-Pastou, Martin, Munafò, & Jones, 2008), and an increase of left-hand preference in the younger generations was described in the latest decades (McManus, Moore, Freegard, & Rawles, 2010; Preti et al., 2011). Therefore, we tested differences by gender and age in handedness categories as predicted by the LCA.
METHODS Overall 1,032 students were invited to take part in the study, among all those attending the three final classes (out of five) of four large high schools (two focusing on scientific studies and two on technology) operating in the district of Cagliari, the main town of Sardinia (Italy). Thousand twenty-three participants provided full information, more specifically 506 males (49.5%) and 517 females (50.5%). Mean age in the sample was 17.3 years (SD 1.3 years; range 15–24 years), with no difference by gender. At the time of the study, the area and its surroundings counted 16,660 males and 15,558 females in the age interval of the participants. In Sardinia, about 85% of all adolescents attend high school after compulsory school. The participant sample therefore included about 4% of all adolescents attending a high school in the area. The students were invited to take part in a study concerning the knowledge of mental disorders and their treatment. The major goal of the study was to evaluate the effectiveness of an intervention aimed at reducing stigma against people with mental disorders (Serra et al., 2013). The assessment of hand preference was not a major aim of the study, so it should have not been influenced by the study goals. The Regional Authority of Sardinia financed and authorized the study (FUMO: Fight for the future: Understanding and MOdifying stigma of mental illness). The appropriate institutional review boards (of the local University Department performing the study and the involved high schools) approved the protocol of the study; this conforms to the provisions of the Declaration of Helsinki in 1995 (as revised in Tokyo 2004). Teachers and the parents of the students were specifically asked to give their consent to invite the students in the study; all the involved parts gave their consent to the study and all the students gave their informed consent, too.
Study procedure The AHPQ was used to assess hand preference on 12 tasks, as described above. The AHPQ was translated from the original English version, as in Annett (2004), into Italian with the standard procedure (Guillemin, Bombardier, & Beaton, 1993). The Italian version was then back-translated into English and translation
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accuracy was confirmed by an independent English-speaking translator, then optimized with the help of a third English-speaking editor. Additionally, students were asked to report which hand they used to fill in the questionnaire. Following a suggestion by the author (Annett, unreferenced personal communication), the “either” (E) reply was assigned to the hand used to fill in the questionnaire. This is just a way of dealing with the problem of considering the “either” reply in calculating a laterality (right versus left) score (see discussion in Annett, 2004). The rationale for assigning the “either” reply to the hand used for writing is that people sometimes mistakenly mark a “either” reply for actions that require coordination of two hands even when the main part of those actions is performed with the preferred hand, which is likely to be the dominant hand. The Results section reports both the original replies (original score: right = 1, either = 2 and left = 3) and the corrected replies to each item. The final summary score for the scale is the sum of the dichotomized items, with one point assigned to each “left-hand” reply on the item. The total score on the AHPQ ranges from 0 (no “left-hand” replies) to 12 (all replies were “left-hand”) and it has to be considered a measure of departure from right-handedness. Six months later, the participants were invited to do the AHPQ again to assess test–retest stability. All students agreed to participate in this part of the study and the test–retest stability was done on 887 participants out of 1,032 (145 students were absent from school).
Statistics Data were analyzed with the Statistical Package for Social Science (SPSS) for Windows (Chicago, IL 60606, USA), version 20 and some statistical packages running in R (R Core Team, 2013). All tests were two-tailed. According to Bayesian interpretations, the chance of replication in future studies is low for p values between .05 and .01, moderate for p values between .01 and .001 and high for p < .001 (Katki, 2008). The differential item functioning (DIF) according to participants’ gender was analyzed on the dichotomized version of the AHPQ by Mantel–Haenszel χ2 statistics. The presence of DIF presumes that the probability of a person’s obtaining a correct response does not depend solely on that person’s level in the object of measurement but, rather, is also conditioned by whether the person belongs to a certain group (social, cultural and other), generating a lack of metric equivalence among scores.
Internal consistency and test–retest reliability Scales reliability was measured by Cronbach’s alpha. For group comparisons, reliability values of .70 are considered satisfactory (Nunnally, 1978). However, if
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individual and important decisions have to be made on the basis of reliability estimates, as is the case with screening or classificatory tools, values should be at least .90 (Kottner et al., 2011). Test–retest stability was assessed with the intraclass correlation coefficient (ICC), with 95% confidence of interval (CI). The dimensionless statistical tool “ICC” describes the reproducibility of repeated measurements in the same population: ICC values .60 are considered acceptable for clinical use (Brennan & Silman, 1992). The Bland and Altman (1986) method was used, too, to assess agreement at retest. The Bland–Altman plot displays the agreement between the scores of a test measured at two different assessment points by plotting the difference between test and retestscores against the mean of test and retest scores for each participant. CIs for the mean difference are calculated to determine if the mean difference deviates significantly from zero, which should not. Graphically, the upper and lower limits of agreement are drawn, indicating the range within which 95% of the test scores of two assessments can be expected to vary. The Bland–Altman plot was drawn according to an ad hoc code (Mateos, 2013) running in R (R Core Team, 2013).
Confirmatory factor analysis Confirmatory factor analysis (CFA) was used to test the unidimensional model expected on the basis of previous studies (Dragovic & Hammond, 2007). CFA was carried out with the lavaan package (Rosseel, 2012) running in R (R Core Team, 2013). The lavaan package was shown to generate the same results as other software packages (Narayanan, 2012). The items were treated as categorical variables and analyzed by polychoric correlations (Jöreskog, 1994). The mean- and variance-adjusted (diagonally) weighted least squares (WLSMV) estimator was used (Flora & Curran, 2004). The ratio of χ2 to the degrees of freedom (df) was calculated in addition to χ2 to evaluate model fitting, with ratios larger than 3 indicating poor fit (Byrne, 1989; Hu & Bentler, 1999, p. 2). Additional parameters for fit estimation were the comparative fit index (CFI) and the root mean square error of approximation (RMSEA). RMSEA values of .08 or lower and CFI values of .90 or higher are considered acceptable (Browne & Cudeck, 1993; Marsh, Hau, & Grayson, 2005). It should be considered that the χ2 test is very sensitive to sample size (Tanaka, 1993), and this has influence on the fit measures based on it (e.g., RMSEA). Factor loading in excess of .71 (accounting for 50% variance) is considered excellent, .63 (40%) very good, .55 (30%) good and .45 (20%) fair; factor loading .32 (10% of variance) was considered the minimum requirement for an item to be included in the final global score (Comrey & Lee, 1992).
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Since gender differences were reported in handedness (Martin et al., 2010; Papadatou-Pastou et al., 2008), measurement invariance by gender was tested. The assessment of measurement invariance by CFA serves the purpose of demonstrating that across groups of interest (e.g., gender) the participants interpret the single items, as well the underlying latent factor, in the same way. Conversely, failure to prove measurement invariance indicates that groups or individuals interpret the items differently, and as a consequence factor means cannot be compared (Jöreskog, 1971; Vandenberg & Lance, 2000). Measurement invariance by gender was calculated on the best model according to Byrne and van de Vijver (2010) by using the lavaan package (Rosseel, 2012) running in R (R Core Team, 2013). The mean- and variance-adjusted (diagonally) weighted least squares (WLSMV) estimator was used to test CFA models. Typically a hierarchical set of steps is followed to test invariance, starting from the determination of a well-fitting baseline model and continuing with the establishment of successive equivalence constraints in the model parameters across groups. Configural, metric and scalar invariance was tested. Configural invariance refers to whether the same CFA is valid in each group. Metric invariance concerns the equivalence of the factorial loadings across groups. Scalar invariance is assumed when the item intercepts and the factor loadings are equally constrained across groups. The confirmation of intercepts invariance allows the comparison of the latent means in both groups. Models were compared on the basis of changes in CFI (delta-CFI). When delta-CFI is >.01 between two nested models, it is assumed that the additional constraints have led to a poorer fit and the more constrained model has to be rejected (Cheung & Rensvold, 2002)
Item response theory analysis When unidimensionality is proved, the Item Response Theory (IRT) can provide information on the parameters that influence the way an item is answered to. According to the IRT, the probability of endorsing an item increases as a function of the latent trait of factor. Different items have different functions, typically represented by logistic regressions with different intercepts and slopes. In the Rasch model, or the one-parameter logistic model, the difficulty of the items (i.e., the probability that an item is positively replied) is in cause in explaining the score. Other models take into account the discrimination of the item (the capacity of the item to discriminate between people with different levels of the trait) and the guessing (the possibility that the respondent may produce a positive reply by guessing). IRT can serve the purpose of demonstrating the measurement accuracy of the AHPQ, which is a requisite to differentiate individuals by their degree of handedness. An advantage of IRT is that the latent trait predicts not only the sample-relative performance but also the absolute performance. As a
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consequence, the estimated measurement properties of the tool can be generalized across populations. IRT analysis was carried out with the ltm package (Rizopoulos, 2006) running in R (R Core Team, 2013). For dichotomous items, the IRT is implemented as a unidimensional one-parameter (estimating the difficulty of the item), twoparameter (estimating the discrimination besides item difficulty) and threeparameter logistic models (estimating the guessing besides item discrimination and difficulty; Baker & Kim, 2004). The one-parameter logistic model poses that there is no guessing and that the discrimination parameter equals one (Rasch, 1960). The two-parameter logistic model allows for different discrimination parameters per item, always maintaining guessing = 0. The three-parameter logistic model estimates all three parameters per item (Birnbaum, 1968). The three-parameter logistic model was not considered in this study. The package ltm fits the models by using marginal maximum likelihood estimation (MMLE). Under MMLE, parameter estimation is done by assuming that the respondents represent a random sample from a population and their ability is distributed as a function of the ability. The model parameters are estimated by maximizing the observed data log-likelihood obtained by integrating out the latent variables; the required integrals are approximate using the Gauss-Hermite quadrature rule: 21 quadrature points are used [further details in Rizopoulos (2006)]. Each model tests first the unidimensionality of the tool, then estimates the parameters and finally produces a curve of the ability (i.e., handedness) versus the probability of positive reply. Unidimensionality was checked using modified parallel analysis on the basis of a matrix of tetrachoric correlations. When the observed second eigenvalue is statistically higher than the second eigenvalue of a Monte Carlo resampling (n = 100), unidimensionality is considered as having been proved. To compare models, the likelihood ratio test and the Akaike Information Criterion (AIC; Akaike, 1987) were used. When the likelihood ratio test shows no differences, the model with the lowest AIC should be preferred. Suggested conventional thresholds to describe levels of item difficulty were: “very easy” (+2.0) (Baker, 2001). Suggested conventional thresholds to describe levels of item discrimination were: “none” (0), “very low” (0.01–0.34), “low” (0.35–0.64), “moderate” (0.65– 1.34), “high” (1.35–1.69) and “very high” (>1.70) (Baker, 2001). Information can be defined as the reciprocal of the precision with which a parameter can be estimated. Item information function is an indicator of item quality and can be calculated as the reciprocal of the variance: information = 1/ variance. For dichotomous items, variance is equal to p (probability of positive answering) multiplied by 1–p. The amount of information conveyed by the test at
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any ability level was calculated as the sum of the item information at a given ability level. Plotting the amount of test information against ability yields a graph of the test information function, which is helpful to understand how well the test is doing in estimating ability over the whole range of ability scores (see Figure 4). The item information function is symmetrical about the value of the item’s difficulty parameter under the one- and two-parameter models.
Latent class analysis To the purpose of differentiating individuals by their degree of handedness, we applied LCA to the AHPQ scores. LCA posits that a heterogeneous group can be reduced to several homogeneous subgroups by evaluating and then minimizing the associations among responses across multiple variables and tests for the existence of discrete groups with a similar symptom or item endorsement profile (Lazarsfeld & Henry, 1968; McCutcheon, 1987). LCA was used to explore the three main expected handedness categories of the AHPQ (Dragovic & Hammond, 2007). LCA was carried out with the poLCA package (Linzer & Lewis, 2011) running in R (R Core Team, 2013). PoLCA estimates the latent class model by maximizing the log-likelihood function (Linzer & Lewis, 2011). Parsimony criteria are applied to strike a balance between over- and under-fitting the model to the data by penalizing the log-likelihood by a function of the number of parameters being estimated (Linzer & Lewis, 2011). The preferred models are those that minimize the values of the Bayesian information criteria (BIC; Schwarz, 1978) and the AIC (Akaike, 1987). The likelihood ratio χ2 test was also used to determine how well a particular model fits the data with reference to the ratio of the observed cell counts versus the predicted cell counts (Goodman, 1970). Preferred models are those that minimize the likelihood ratio without excessively increasing the number of parameters. Standardized entropy measure was used to assess accuracy of participants’ classification (0–1), with higher values indicating better classification. To minimize problems of non-convergence and local solutions, several starting points (n = 10) and repeated iteration (n = 5,000) were specified to replicate the best log-likelihood values. Participants were assigned to the latent class to which they had the highest probability of belonging (average probabilities per class 85%). Multinomial logistic regression was used to assess the association between class membership and demographic variables (i.e., gender and age). In the model, those in the right-handed class were used as the baseline and compared to those in the mixed- and left-handed class. Differences between classes were expressed with odds ratio (95% CI).
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RESULTS Cronbach’s alpha was .931 in females and .885 in males for the original format of the AHPQ; it was .964 in females and .929 in males after correction (i.e., by assigning the “either” reply to the hand used to fill in the questionnaire). In the original, non-corrected format of the AHPQ, 55 (10.9%) male students reported to use habitually the left hand to write a text legibly, while 6 (1.2%) reported to use either hand to write; the corresponding figures among female students were 50 (9.7%) and 7 (1.4%), respectively. However, in the sample 57 males (11.3%) and 52 females (10.1%) reported to have used the left hand to fill in the questionnaire. Overall, 914 subjects (89.3%) reported to have used the right hand to fill in the AHPQ and 109 (10.7%) reported they used the left hand. Among males, one student used the left hand to write the questionnaire but declared himself right-handed when filling in the AHPQ; among females, again, one student filled in the questionnaire with her left hand but declared herself right-handed when filling in the AHPQ. No other inconsistency was found. When handedness was established on the basis of the primary actions according to the enunciated rule of thumb, 869 students (84.9%) were classified as right-handed, 92 (9.0%) as mixed-handed and 62 (6.1%) as left-handed. Table 1 reports the distribution of left-hand replies in the sample by gender, in the original format and after correction (as above). Gender differences were found for the hand used to unscrew the lid of a jar (χ2 = 21.12, df = 2, p < .0001 TABLE 1 Distribution of left-hand replies on the AHPQ in the original format and after correction Original, tripartite scores Males (N = 506) Left
Either
1. Writing 55 (10.9) 6 (1.2) 2. Throw a ball 36 (7.1) 52 (10.3) 3. Hold a racquet 40 (7.9) 38 (7.5) 4. Striking a match 44 (8.7) 78 (15.4) 5. Scissors 33 (6.5) 65 (12.8) 6. Guide a needle 64 (12.6) 72 (14.2) 7. Broom 100 (19.8) 139 (27.5) 8. Shovel 73 (14.4) 117 (23.1) 9. Cards 52 (10.3) 65 (12.8) 10. Hammer 45 (8.9) 15 (3.0) 11. Toothbrush 51 (10.1) 62 (12.3) 12. Unscrew a jar 98 (19.4) 104 (20.6) Hand used to fill in the questionnaire All data: n (%).
Dichotomic scores
Females (N = 517) Left
Either
50 (9.7) 35 (6.8) 41 (7.9) 38 (7.4) 36 (7.0) 63 (12.2) 79 (15.3) 62 (12.0) 44 (8.5) 36 (7.0) 48 (9.3) 62 (12.0)
7 (1.4) 77 (14.9) 43 (8.3) 59 (11.4) 22 (4.3) 61 (11.8) 135 (26.1) 103 (19.9) 49 (9.5) 14 (2.7) 55 (10.6) 75 (14.5)
Males (N = 506) Left
Females (N = 517) Left
57 (11.3) 42 (8.3) 43 (8.5) 52 (10.3) 41 (8.1) 73 (14.4) 115 (22.7) 83 (16.4) 60 (11.9) 48 (9.5) 57 (11.3) 108 (21.3) 57 (11.3)
52 (10.1) 44 (8.5) 46 (8.9) 48 (9.3) 40 (7.7) 70 (13.5) 93 (18.0) 71 (13.7) 49 (9.5) 40 (7.7) 53 (10.3) 72 (13.9) 52 (10.1)
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in the original format; χ2 = 9.19, df = 1, p = .002, after correction; Mantel– Haenszel χ2 = 9.19; df = 1; p = .002; OR = 1.67; 95%CI = 1.21–2.33), and for the hand used to manage scissors (χ2 = 24.27, df = 2, p < .0001 in the original format; but χ2 = 0.01, df = 1, p = .92, after correction). There were no other differences by gender, age or type of school (data not shown).
Test–retest reliability The test–retest reliability of the original AHPQ, as measured by ICC, was .940 (95%CI = 0.932–0.948) in males and 0.961 (0.955–0.966) in females. Test–retest reliability of the dichotomized AHPQ, as measured by ICC, was .964 (0.959– 0.969) in males and .979 (0.976–0.982) in females. The mean difference between the first and the second assessment with dichotomized AHPQ in the 887 participants was 0.02 (SD = 1.21). The 95% CI for the mean difference was −0.06 to 0.10 (i.e., 0 is within the CI, therefore the mean difference did not differ statistically from 0). By plotting the differences and the means of the two assessments in the Bland–Altman plot, 43 cases only out of 887 (4.8%) were outside the upper and lower limits of agreement (Figure 1).
Figure 1. Bland–Altman scatterplot of the AHPQ test–retest assessment (n = 887). Graphical jittering was used to deal with overlapping data, which occur when many data points having identical variable combinations on the X and Y axes of the plot are superimposed on top of each other, i.e., are not individually visible.
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The Bland–Altman plot was not calculated for the original AHPQ, since the scores of the tool are calculated on the dichotomized items.
CFA CFA yielded a solution that was compatible with a single factor in both the original and the corrected form of the AHPQ (Table 2). The dichotomized model had a better fit than the original scoring model, particularly as far as the RMSEA was concerned (but still below optimality). As shown in Table 2, loading of the primary actions was higher than the loading of the non-primary actions, meaning that the primary actions contributed the most to the fitting. Overall, loading was excellent to very good for all items in both the original and the corrected form of the AHPQ. However, in both models items 7 (broom) and 8 (shovel), which were those with a greater chance of an “either” reply, also had the lower loading on the unidimensional factor. Excluding these items from the analysis improved the fit for both the original scoring model (S–B χ2 = 99.78, df = 35, p < .0001; χ2/df = 2.8; CFI = .998; RMSEA = .043; 95% CI: 0.028–0.058) and the dichotomized model (S–B χ2 = 74.11, df = 35, p < .0001; χ2/df = 2.1; CFI = .990; RMSEA = .033; 95%CI: 0.000–0.071). TABLE 2 Confirmatory factor solution of the AHPQ Model: Mean- and variance-adjusted (diagonally) weighted least squares estimates 1. Writing Primary action 2. Throw a ball Primary action 3. Hold a racquet Primary action 4. Striking a match Primary action 5. Scissors Non-primary action 6. Guide a needle Non-primary action 7. Broom Non-primary action 8. Shovel Non-primary action 9. Cards Non-primary action 10. Hammer Primary action 11. Toothbrush Primary action 12. Unscrew a jar Non-primary action Model fit summary χ2 [with Satorra–Bentler (S–B) corrected robust estimation] χ2/df RMSEA (90% CI) Comparative fit index (CFI) Estimated Cronbach’s alpha
Original scoring
Corrected scoring
.981 .831 .878 .860 .904 .763 .645 .712 .765 .965 .890 .634
.903 .866 .866 .875 .899 .731 .604 .685 .738 .861 .879 .606
S–B χ2 = 610.7, df = 54, p < .0001 11.3 .100 (0.090–0.111) .985 .957
S–B χ2 = 360.7, df = 54, p < .0001 6.6 .075 (0.053–0.098) .949 .947
Original scoring: Right = 1, either = 2, left = 3; corrected scoring: right = 0, left = 1.
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TABLE 3 Fit indices [with Satorra–Bentler (S–B) corrected robust estimation] for invariance tests of the unidimensional model (sample: n = 1,023) Model
S–Bχ2
df
p
χ2/df
CFI
RMSEA (95% CI)
Delta-CFI
Configural invariance Metric invariance Scalar invariance
459.34 364.04 394.67
108 119 130
.0001 .0001 .0001
4.2 3.0 3.0
.941 .959 .955
.080 (0.057–0.104) .064 (0.047–0.081) .063 (0.047–0.079)
+.018 −.004
Gender: Male (n = 506) versus female (n = 517) participants.
Configural, metric and strong invariance across gender was supported by the results of the measurement invariance CFA. There was an overall improvement of fit with increasingly constrained models. The difference in the CFI between one model and the more constrained one was positive or the decrement did not exceed .01 (Table 3).
IRT analysis IRT was applied to the dichotomized form of the AHPQ, since it provided the best fit in the CFA. Unidimensionality was confirmed in the IRT analysis, but the Hessian matrix was stable at convergence for the two-parameter model only (Table 4). In the two-parameter model, item 2 (hand used to throw a ball), 5 (hand used to manage scissors) and 10 (hand used to manage a hammer) had the greatest difficulty, while item 7 (hand at the top of a broom while sweeping), 8 (hand at the top of a shovel when moving sand) and 12 (hand used to unscrew the lid of a jar) provided the lowest difficulty. Greatest discrimination was provided by items 1 (hand used to write), 2, 5 and 11 (hand used to hold a toothbrush); the lowest discrimination was provided by items 7, 8, 9 (hand used to deal cards) and 12. Items 2, 3 and 5 combined the highest difficulty with higher discrimination (Figure 2). Overall, in the two-parameter model all items were difficult according to the pre-defined thresholds, and all had very high discrimination. Not all items contributed to define handedness: some were flattered, providing scarce information on the increasing deviation from right-handedness. The hand used to write, to manage scissors and hold a toothbrush provided the greatest information; the hand for sweeping, shovelling and unscrewing had the lowest information (Figure 3). For the whole test, information in the area of difficulty (from 0 to +4) was 98.7% (Figure 4).
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TABLE 4 Results of the IRT analysis of the dichotomized AHPQ (n = 1,023) 2-PL Item Writing Throw a ball Hold a racquet Striking a match Scissors Guide a needle Broom Shovel Cards Hammer Toothbrush Unscrew a jar Second eigenvalues of the observed data Average of second eigenvalues in Monte Carlo samples (n = 100) Comparison of observed vs. resampled eigenvalue Hessian matrix at convergence Log-likelihood value at convergence AIC Information based on all the items
Difficulty
Discrimination
1.25 (0.05) 1.52 (0.06) 1.49 (0.06) 1.38 (0.06) 1.54 (0.06) 1.23 (0.06) 0.99 (0.06) 1.22 (0.07) 1.47 (0.07) 1.52 (0.06) 1.27 (0.05) 1.18 (0.07) .499 .407
6.68 5.16 4.92 4.81 6.65 2.95 2.32 2.58 2.89 4.54 5.65 2.08
(0.91) (0.61) (0.58) (0.54) (1.00) (0.28) (0.22) (0.23) (0.26) (0.47) (0.67) (0.19)
p = .019 Stable solution −2327.03 4702.06 99.9%
All data: Difficult or discrimination value (SE).
Latent class analysis The three-class solution was the best compromise between all the considered indexes (Table 5). The AIC progressively declined across the different models. However, the BIC values were higher in the five- and the six-class solutions than in the preceding three- and four-class solutions, with a progressive decline of entropy, and therefore the three-class solution should be preferred on the basis of parsimony. In the three-class solution entropy was .82, which indicated a good classification of participants in the model. This solution yields to a right-handed class (LC1) with no or very low endorsement of most AHPQ “left-hand” items, including 805 (78.6%) participants; a mixed-handed class (LC2), including 137 (13.4%) participants; and a third class of left-handed (LC3), with high endorsement on pretty all AHPQ “left-hand” items, and including 81 (7.9%) participants (Figure 5). In the still plausible four-class solution, there was a right-handed class including 74.1% participants; a left-handed class including 7.2% participants; a
732 LAI ET AL. Figure 2. Distribution of item difficulty by item discrimination for the IRT two-parameter model as applied to the dichotomized AHPQ. The Y-axis represents difficulty by item, with greater difficulty on the top of the figure. The X-axis represents discrimination by item, with greater discrimination on the right side of the figure. The items are described by a number (the numerical order they have in the AHPQ) and by a short description of the investigated action.
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Figure 3. Item information curves for each item of the AHPQ. The amount of information conveyed by the item at any ability level was plotted against the ability at a given level.
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Figure 4. Test information function of the AHPQ. The amount of information conveyed by the test at any ability level was calculated as the sum of the item information at a given ability level.
mixed-handed class including 5% participants and another mixed-handed class including 13.7% participants and resembling the “right-handed weakly lefthanded” class described by Annett (2004), with “left-hand” replies on the needle, broom, shovel and unscrew the lid of a jar items (but not on the racket item, as expected by Annett’s classification). We checked the correspondence between the classification produced by the LCA and the self-reported handedness on writing. The right-handed class included only those students who reported to have used the right hand to fill in the AHPQ; the left-handed class was formed only by those students who reported to have used the left hand to fill in the AHPQ. The mixed-handed class counted 100 students (10.9% of all right-handed) who reported a right-hand preference in writing and 28 students (25.7% of all left-handed) who reported a left-hand preference in writing. Regarding the three handedness groups identified according to the rule of thumb, the LCA (which was applied to both primary and non-primary actions) assigned all the students defined as left-handed by the rule of thumb to the leftTABLE 5 Fit indices for the LCA of the AHPQ items
Model Two classes Three classes Four classes Five classes Six classes
Maximum log-likelihood
AIC
BIC
LRT
No. of parameters
Entropy
–2477.17 –2317.04 –2303.88 –2244.42 –2245.13
5004.68 4725.06 4604.31 4588.35 4581.20
5127.94 4912.42 4855.76 4903.90 4960.85
933.57 627.95 481.20 439.24 406.09
25 38 51 64 77
.88 .82 .79 .78 .77
LRT, likelihood ratio test.
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Figure 5. Profile plot for the LCA of the AHPQ (12 items). The Y-axis represents the class-specific mean scores as proportions of the maximum score for the indicator concerned. The X-axis contains the 12-item profile of the AHPQ.
handed class, and up to 92.4% of the right-handed according to the rule of thumb were in the right-handed LCA class; the remaining 7.6% were assigned to the mixed-handed class and none to the left-handed class. Among those classified as mixed-handed according to the rule of thumb, 67.4% remained in the mixedhanded class according to the LCA, while 20.7% were assigned to the lefthanded class and 12.0% to the right-handed class.
Multinomial logistic regression As shown in Table 6, the model found a statistically significant impact of gender on handedness class (−2 log-likelihood = 50.23; likelihood ratio χ2 = 11.28, df = 2, TABLE 6 Association between latent classes and demographics
Gender (n = 1,023) Females Males Age (n = 1,023) 17 years old and older 15–16 years old
LC1 Right-handed n = 814 (10.3%)
LC2 Mixed-handed n = 128 (18.6%)
LC3 Left-handed n = 81 (71.0%)
428 (52.6%) 386 (47.4%) 1
47 (36.7%) 81 (63.3%) OR = 1.91 (1.30–2.86)
42 (51.9%) 39 (48.1%) OR = 1.03 (0.65–1.62)
557 (68.4%) 257 (31.6%) 1
89 (69.5%) 39 (30.5%) OR = 0.96 (0.64–1.44)
59 (72.8%) 22 (27.2%) OR = 0.81 (0.48–1.34)
All data: n (%). Latent Class I, corresponding to the right-handed class, was used as a reference term. CIs not including unity indicate statistical significance (p < .05). Significant results in bold.
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p = .004). There was a slight, non-statistically significant excess of female participants in the right- and left-handed classes; males were about twice as likely as females to be in the mixed-handed class (Wald = 10.85, df = 1, p = .001). There were no statistically significant differences by age (−2 loglikelihood = 39.64; likelihood ratio χ2 = 0.69, df = 2, p = .70).
DISCUSSION The study confirmed the cross-cultural reliability of the AHPQ: both the internal consistency and the six-month tes–-retest stability of the scores on the Italian version of the AHPQ were excellent according to conventional thresholds. The AHPQ was proved to measure a unidimensional latent trait, and the questionnaire was good at assessing deviation from right-handedness with high discrimination between subjects. Some items were more informative than others, and in particular the non-equivalence between the primary and the non-primary actions was confirmed by both the CFA and the IRT analysis. The use of the rule of thumb that classifies subjects on the basis of the primary actions was supported for the distinction between consistent right- and left-handed. However, the mixed-handed group identified according to the rule of thumb was not entirely consistent with the mixed-handed class predicted by the LCA. Misclassification of cases by sub-categorization of mixed-handedness using cut-off points was reported for the Edinburgh Handedness Inventory, too (Dragovic, 2004).
Age and gender differences by handedness In a past investigation based on a single query on hand preference for writing, Preti et al. (2011) found in a large sample of Sardinian inhabitants (4,239 participants; males = 37.4%) that left-hand preference in writing was negatively related to age, with increasing left-hand preference in the younger generations. In that study, males were not more likely to report left-hand preference in writing (n = 161; 10.1%) than females (n = 270; 10.2%). A similar finding was observed when the analysis was limited to participants aged 14–18 years old, with 40 males (10%) and 74 (9.9%) females reporting left-hand preference for writing. In this study, which was carried out on a completely different and representative sample of adolescent students in Sardinia, males were found about twice as likely as females to be mixed-handed. No age difference was found in the sample by handedness. In this study, the age range was too short to allow detection of differences by age. Instead, the detection of the gender difference in handedness is attributable to the detailed investigation of hand preference by the AHPQ. In a past meta-analysis including 144 studies and
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1,787,629 participants, gender differences by handedness were greater for mixedhandedness than for left-handedness, with males being more likely to be classified as mixed-handed than female participants (Papadatou-Pastou et al., 2008). Albeit there is evidence that the hand preference in writing is a good approximation of handedness, the definition of hand preference solely in terms of the writing hand preference dichotomy is poorly informative because of the drastic effects of cultural pressure on the writing hand in the older populations in both Western (Coude, Mignot, Lyonnet, & Munnich, 2006; Galobardes, Bernstein, & Morabia, 1999) and Eastern cultures (Gregory et al., 2003; Salmaso & Longoni, 1985). Moreover, there is evidence that the pattern of hand preference in other actions than writing is informative to detect variations along the continuum of handedness.
Pattern of hand preference The CFA gave support to the distinction between primary and non-primary actions in estimating the pattern of hand preference. However, the results of the IRT showed that a non-primary action such as managing scissors is as much informative as the writing hand preference in detecting handedness. As a matter of fact, managing scissors does not count as a primary action for left-handers because they often use scissors with the right hand, but it is a primary action for right-handers (Annett, 2004, 2009). A differentiated pattern of responding emerged when considering the entire set of actions. Students were less consistent in reporting the hand they use to hold the top of a broom or a shovel, probably a reflection of less experience with these actions (regarding shovels, snow is very rare in Sardinia and the young have no experience of farming; young people generally do not use the broom at home in Italy). However, these tools were also frequently reported be used with either hand, which is less likely with other tools (such as the hammer or the toothbrush). The results of CFA suggested that the fit of the unidimensional model would improve by removing the items on broom and shovel. The results of IRT showed that the items on broom and shovel had the lowest information. In a past analysis of the AHPQ carried out on data from a metropolitan Australian sample, the replacing of items on sweeping and shovelling with more “modern” bimanual activities (such as “typing SMS messages”) was suggested to improve the ability of the questionnaire in detecting true variation in handedness (Dragovic & Hammond, 2007). The prevalence of writing hand preference in this sample (around 10%) was strictly comparable to the rate (9.9%) observed in the normative sample described by Annett (2004), and it can be taken as the current prevalence of left-handedness in the population.
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It is worth noticing that the prevalence of writing hand preference was quite coincident with the prevalence of hand preference for toothbrush. A high concordance between writing and brushing one’s teeth was also recently reported in a large (n = 2,236) national sample of Italian adolescents (males 68%; age range 9–15.7) who were investigated with direct observation of actual preference (Merni, Di Michele, & Soffritti, 2014). Inconsistency in the preference for writing and toothbrush might be an indicator of forced writing correction as a result of the cultural pressure to use the right hand in writing.
Limitations and strengths of the study The study did not include a direct evaluation of manual dexterity, such as the peg-moving task. Participants were not enquired on whether they were “corrected” in hand writing, although this is now likely a much rarer experience than in the past (Coude et al., 2006; Galobardes et al., 1999; Salmaso & Longoni, 1985). However, the sample was large enough to have a reasonably low estimated error in responding for the expected percentage of left-hand preference on each item. The use of state-of-the-art statistics in evaluating the psychometric properties of the AHPQ is the major contribution of the study.
Implications for research The AHPQ was developed to detect deviation from right-handedness as a tool to test the author’s right shift (RS) theory. According to Annett, a genetic-balanced polymorphism with a heterozygote advantage would rule the distribution of hand preference, influenced by a gene for left hemisphere advantage (Annett, 1998). The RS theory distinguishes between an accidental, non-genetic, Gaussian distribution of asymmetry for hand skill and a factor for left hemisphere advantage, hence right-hand preference that displaces handedness distribution in the dextral direction. This factor for left hemisphere advantage is supposedly a single RS+ gene, which is relevant for cerebral dominance, not for handedness. An unstable area in the Xq21.3/Yp regions of homology, coding for the so-called protocadherins, was suggested as the potential candidate for the still hypothetical RS+ gene (Crow, 2013; Williams, Close, Giouzeli, & Crow, 2006). According to the RS theory, random asymmetry affects every individual, whether twin or single-born. This explains the observation of monozygotic twins discordant for handedness (Medland, Duffy, Wright, Geffen, & Martin, 2006; Sommer, Ramsey, Mandl, & Kahn, 2002). Additional genes for left-handedness would impact on the accidental, Gaussian distribution of asymmetry, and they could be under selective pressure particularly in males. Some advantage for left-handers was suggested in studies investigating one-on-one fighting situations (Brooks, Bussière, Jennions, & Hunt, 2004; Faurie, Schiefenhövel, le
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Bomin, Billiard, & Raymond, 2005; Hagemann, 2009; Holtzen, 2000; Loffing, Hagemann, & Strauss, 2010; Wood & Aggleton, 1989). The advantage of lefthanders against right-handers is expected when the frequency of left-handers is the smallest, since right-handers are expected to be accustomed to encountering other right-handers (Raymond, Pontier, Dufour, & Moller, 1996). Additional benefits from right cerebral dominance resulting in left-handedness would depend on higher intermanual coordination and greater interhemispheric transfer in non-right-handers (Laurens, Faurie, & Raymond 2009). This would result in further greater advantage in one-on-one fighting situations and enhanced creativity (Laurens et al., 2009). These benefits would compensate for the health-detrimental effects that were described in non-right-handers. Indeed, a greater prevalence of non-right-handers was reported in those who suffer from depression (Elias, Saucier, & Guylee, 2001), schizophrenia (Dragovic & Hammond, 2005), epilepsy and Down’s syndrome (Lewin, Kohen, & Mathew, 1993), developmental coordination disorder (Cairney et al., 2008), autoimmune disorders (Morfit & Weekes, 2001), breast cancer (Fritschi, Divitini, TalbotSmith, & Knuiman, 2007), asthma (Kaynar & Dane, 2003; Preti, Lai, Serra, & Zurrida, 2008) and diabetes (Searleman & Fugagli, 1987). This study showed that the AHPQ is a reliable and valid tool to study the RS theory even when translated and applied to populations with different backgrounds than the Anglo-Saxon culture, where the majority of the studies on the AHPQ were done.
CONCLUSION This study provided further evidence on the existence of at least three major handedness classes. Convergent evidence shows that hand preference is a good approximation of hand functionality; individuals with a strong right-hand preference are proportionally less able to use the left hand in single-hand tasks requiring manual dexterity (Brown et al., 2006; Corey et al., 2001). Exercise has an impact on manual dexterity, too (Koeneke, Lutz, Esslen, & Jäncke, 2006). Nevertheless, mixed-hand preference can result from both real ambidexterity (with the subject’s being able to use both hands in single-hand tasks requiring manual dexterity), and poor dexterity, with one hand used at preference according to the circumstances. Information on these different groups of mixed-hand preference can have implications for the investigation of the correlates of neurodevelopmental disorders (e.g., Annett, & Moran, 2006; Shaw, Claridge, & Clark, 2001; Sommer, Aleman, Ramsey, Bouma, & Kahn, 2001; Stefanis et al., 2006; Preti, Sardu, & Piga, 2007; Tsuang, Chen, Kuo, & Hsiao, 2013). It is therefore important to use tools and statistical procedures that may distinguish between different types of mixed-hand preference. Sample size
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is a factor influencing the power to detect such differences and it should be taken into account when designing the studies. Manuscript received 2 February Revised manuscript received 25 March Revised manuscript accepted 31 March First published online 30 April
2014 2014 2014 2014
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