A Meta-Analysis of School-Age Children's Attitudes Towards Persons with Physical or Intellectual Disabilities

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A Meta-Analysis of School-AgeChildren's Attitudes TowardsPersons with Physical orIntellectual DisabilitiesElizabeth A. Nowicki & Robert SandiesonPublished online: 21 Jul 2010.

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International Journa l of D isability, D evelopment and EducationV ol. 49, N o. 3, 2002

A Meta-Analysis of School-Age Children’sAttitudes Towards Persons with Physicalor Intellectual DisabilitiesELIZABETH A. NOWICKI*Department of Psychology, Social Sciences Center, The University of Western Ontario,London, Ont., Canada N6A 5C2

ROBERT SANDIESONFaculty of Education, The University of Western Ontario

ABSTRACT Factors associated with children’s attitudes towards persons with physical andintellectual disabilities were examined in a meta-analysis spanning the years 1990 to 2000.A total of 20 studies met the inclusion criteria allowing for 65 comparisons across 2,240participants. Factors of interest were attitudinal components, type of disability, age andgender of respondents, and role of inclusion. The majority of research � ndings revealed thatchildren preferred target children without disabilities compared to targets with physical orintellectual disabilities. Three methods for calculating average effect sizes were used: (a)unweighted means, (b) weighted means, and (c) vote counting. It was concluded thatbiases in attitudes do exist but that summary results need to be interpreted with regard toindividual study differences and the methods used to calculate mean effect sizes.

Introduction

Over two decades have passed since inclusive education became a reality in anumber of school districts. During that time a considerable body of research hasaddressed the associated social bene� ts and dif� culties. Proponents of inclusionargue that placing students with disabilities into regular schools and classrooms willbreak down negative stereotypes and that personal contact may result in thedevelopment of positive attitudes (Hastings & Graham, 1995). This perspective hasbeen questioned. Gresham and MacMillan (1997) commented on the lack of solidempirical evidence supporting the anticipated bene� ts of inclusion on the socialacceptance and self-perceptions of students with special needs.

In spite of the bene� ts anticipated by inclusive education proponents, one of themajor problems is the acceptance of children with intellectual or physical disabilities

*[email protected]

ISSN 1034-912X (print)/ISSN 1465-346X (online)/02/030243-23 Ó 2002 Taylor & Francis LtdDOI: 10.1080/1034912022000007270

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244 E. A. Nowicki & R. Sandieson

by their classmates. Students with intellectual disabilities are less likely to beaccepted by their peers than classmates who have physical disabilities (Townsend,Wilton, & Vakilirad, 1993). Children with physical disabilities can also � nd thesocial aspect of schooling dif� cult. Llewellyn (1995) asserted that children andadolescents who had physical disabilities and attended inclusive schools were oftenbullied by their classmates.

Classrooms are social communities in their own right. Students who are alreadycoping with academic dif� culties may have the additional burden of dealing withpeer acceptance issues. Attitudes research has revealed prejudice directed at childrenwith special educational requirements (e.g., Abrams, Jackson, & St. Claire, 1990;Bracegirdle, 1995; Cassidy & Sims, 1991; Cohen & Lopatto, 1995; Harper, 1997;Kratzer & Nelson Le Gal, 1990; Nabors & Keyes, 1995). Children with intellectualdisabilities often experience social isolation, social neglect, or rejection by theirpeers, and may also receive low evaluations of social skills by their teachers (Bear,Clever, & Proctor, 1991; Elliot & McKinnie, 1994; Haager & Vaughn, 1995;Nabuzoka & Smith, 1993; Roberts & Zubrick, 1993; Santich & Kavanagh, 1997).In contrast, several studies have revealed a more optimistic side to the inclusiondebate. Several researchers have reported that children are as accepting of peers withphysical disabilities as they are of peers without disabilities (e.g., Cohen, Nabors, &Pierce, 1994; Colwell, 1998; Woodard, 1995).

There is a need to collectively summarise research � ndings so that educators canbetter understand the social rami� cations associated with inclusion. Researchershave used a variety of paradigms to investigate attitudes towards individuals withdisabilities. Thus, one of the concerns in drawing collective conclusions from pastresearch is that qualitative comparisons between studies are dif� cult due to thevariety of measures. A meta-analysis is a quantitative method of reviewing studyresults. It provides a statistical summary across a given area of research. Quantitativeresults from each study are converted to an effect size or a standardised value. Abene� t associated with this approach is that results can be numerically comparedfrom a variety of instruments that measure similar constructs. These standard valuesare then averaged to provide a quantitative summary estimate of the overall effectsize (Hedges & Olkin, 1985). The purpose of this meta-analysis is to summariseschool-age children’s attitudes towards individuals with physical and intellectualdisabilities. Research published between 1990 and 2000 will be examined to achievea current overview. Factors of interest are nature of the measure, type of disability,age and gender of respondents, and the role of inclusion.

Method

Literature Search

Original journal articles were selected from PsychINFO, MEDLINE, and ERICdatabases. The keyword attitudes was entered in a search limited to English languagejournal articles published from 1990 onwards. This time span was selected so thatlater, rather than earlier, research could be examined. Thus, these studies may be

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Meta-Analysis of School-Age Children’s Attitudes 245

more re� ective of present day attitudes than of earlier research conducted wheninclusive education policies and practices were relatively novel and perhaps in needof further development. A further restriction narrowed the search to articles focusingon children between 3 and 12 years of age. This age range was selected toencompass the youngest possible age for school entry through to the end ofchildhood. Searches were conducted for the keywords handicap$, disabilit$, disable$,impair$, retard$, and challenge$, where the symbol $ denotes a wild character. Eachof these additional searches were combined with the conjunction OR to form aninclusive list. This list was then combined with the attitudes search using theconjunction AND. Thus, the search was restricted to articles containing the keywordattitudes and at least one of the terms denoting disability.

The abstracts of the selected citations were examined. Studies conducted onschool-age children were retained. Intervention studies that required special pro-gramming, such as special friends or buddies, were eliminated. The rationale behindimposing this selection criterion was based on the assumption that attitudes mea-sured after an intervention are expected to be more positive than those held bychildren who have not been exposed to these speci� c programs. Thus, the resultsfrom these studies may not be indicative of attitudes in regular inclusive classrooms.For statistical purposes, single case reports and studies with sample sizes of � ve orfewer were also eliminated. One further selection criterion addressed study design.Studies within the domain have typically used a repeated measures, or withinparticipants design, allowing participants to make comparisons between targets withand without disabilities. One of the problems inherent in an independent betweenparticipants design is that participants are not able to make comparisons acrossconditions. Responses are non-comparative and experimental groups do not havethe bene� t of anchoring their responses to a non-disability condition. Thus, studiesnot using a repeated measures design were eliminated.

Variables of Interest

Several categories of variables were selected so that commonalities and differencesbetween studies could be examined. One category that has not received muchattention in the relevant literature is the nature of the measure. The formation ofattitudes towards disabilities may be multi-faceted in nature and more than one classof dependent variables may be needed. Eagly and Chaiken (1993) discussed theformation of attitudes in regards to three classes of evaluative responses: cognitive,affective, and behavioural. The cognitive aspect of an attitude re� ects an individual’sknowledge about an attitude object. The affective component addresses theemotional reaction that may be elicited by the attitude object. The third component,behavioural, is identi� ed as behaviour directed at the attitude object or as anintention to behave in a particular manner. Eagly and Chaiken emphasised thatcognitive, affective, and behavioural processes and responses can be importantfactors in attitude formation. This perspective is sometimes neglected by researchinvestigating children’s attitudes towards disabilities. Although the majority ofstudies in the current analysis have used measures re� ecting one or more of the

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three components, many authors do not explicitly categorise dependent measuresin this manner. Thus, in the current meta-analysis, dependent measures wereclassi� ed as affective, cognitive, or behavioural in content. If a measure consistedof more than one of the components, it was classi� ed as a general attitudemeasure.

Age of participants was also of interest. Researchers have reported age-relateddifferences in attitudes towards individuals with physical disabilities (e.g., Roberts &Smith, 1999) or intellectual disabilities (e.g., Townsend et al., 1993). Other factorsof interest were the type of disability (i.e., physical or intellectual), gender of theparticipant, and gender of the individual with the disability.

Estimation of Effect Size from a Single Experiment

Effect size was calculated for studies that provided adequate information. For themajority of the studies, an unweighted effect size (d) was calculated by

where MC and ME are the means for the control and experimental conditions,respectively, and sC and sE are the standard deviations for the control and experimen-tal groups (Glass, McGaw, & Smith, 1981). According to Cohen (1988) a value of0.20 is a small effect size, a medium effect is .50, and effect sizes of .80 or more areconsidered to be large.

Cohen’s (1988) nonoverlap measure U provides a practical interpretation of effectsize. Effect sizes are standard scores and indicate extent of group differentiation oramount of overlap between groups on a dependent variable. For example, an effectsize of 1.00 indicates that two groups differ by 1 standard deviation. Therefore, 84%of one group can be differentiated from the other with only 16% of overlap.Nonoverlap U is determined by entering the effect size value into a z table andlocating the corresponding proportion of area under the normal curve. This value isequivalent to the amount of differentiation.

F-ratios and proportions. Several studies (e.g., Abrams et al., 1990; Bickett &Milich, 1990; Gash & Coffey, 1995) did not provide values for standard deviationsalthough group means and F-ratios were reported. Estimates of sC were calculated byprocedures described by Glass et al. (1981). An estimate of sC

2, which is equivalentto the mean squared error (MSW) was calculated from the F-ratio and the meansquared between conditions term (MSB):

MSB is derived by estimating the sums of squares between conditions (SSB) anddividing this term by the relevant degrees of freedom (df). Not all studies provided

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a summary table of analysis of variance terms. Therefore, SSB was calculated fromgroup means:

where ni is the number of participants in a condition, Xi is the mean for a condition,and XG is the grand mean. Due to the fact that sE cannot be derived in suchinstances, the denominator used to calculate effect size is sC.

Effect sizes for dichotomous or percent data were determined by calculatingproportions for each condition and entering this information into Glass et al.’s(1981) table for probit transformations of difference in proportion to effect size.Several additional studies did not report suf� cient data for the calculation of effectsize. These studies were not eliminated as they provided information of interest andare included in the tabular and descriptive summaries.

Studies with More than One Effect Size

Often, it is possible to calculate more than one effect size for a given study. This maybe the case when mean responses for each item in a questionnaire are listed acrossexperimental and control conditions. For such cases, Cooper, Valentine, and Charl-ton (2000) suggested that one of two alternative strategies be used. For the � rst, theeffect size is calculated for each item within a measure and entered into the overalldatabase. A problem with this method is that numerous effect size estimates fromone measure in a given study could bias the overall effect size estimate acrossstudies. Alternately, the mean effect size, calculated across items within a measurefor each experimental condition, can be used so that one value per measure percondition is recorded. The latter strategy was employed in the current analysis. Aneffect size was determined for each measure within a condition.

Calculating Average Effect Sizes

Unweighted and weighted. To determine the overall effect size across studies,unweighted and weighted effect sizes were calculated. An average unweighted effectsize is simply the mean of each effect size entered into the data base. Each effect sizeis given equal weight. However, it has been suggested that the weighted effect size(d 9 ) is preferable to the unweighted effect size as it gives greater weight to effect sizesdetermined from large samples (Cooper et al., 2000; Hedges & Olkin, 1985).According to this procedure, each effect size was multiplied by its weight (wi):

The resulting products were then summed to determine the weighted average effectsize.

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Con� dence intervals. Con� dence intervals for the average weighted effect size werecalculated according to Hedges and Olkin (1985). The estimated variance (sd

2) foreach unweighted effect size was calculated by:

Next, the 95% con� dence interval (d) was determined for each unweighted effectsize:

where Ca/2 is the two-tailed critical value of the standard normal distribution.To calculate the 95% con� dence interval for the overall average weighted effect

size, the variance of the weighted effect size was � rst determined by:

Then the con� dence interval was obtained with:

If the interval did not contain 0, then it was concluded that the mean weighted effectsize was reliably different from 0.

Homogeneity of variance. Homogeneity analysis compares the amount of variance inan observed set of effect sizes with the amount of variance that would be expecteddue to sampling error (Hedges & Olkin, 1985). If the variance is greater than whatwould be expected by chance, then it is necessary to examine the experiments forpotential moderating factors (Cooper et al., 2000). In the current meta-analysis,homogeneity of variance was determined with the Q statistic:

Vote Counting Method

The vote counting method provides an alternative and informative complement tothe aforementioned procedures. One of the bene� ts of this approach is that both theweighted and unweighted effect size calculations require information that is notalways reported. Hedges and Olkin (1985) described a vote counting method wherethe size of an overall treatment effect can be estimated from the proportion ofstudies showing positive and negative outcomes. The direction of the outcome foreach dependent measure and the corresponding sample sizes for each comparisonare required. The direction of each signi� cant comparison is recorded and thisinformation is used to calculate the proportion of positive results (i.e., the number

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of comparisons yielding positive results divided by the total number of compari-sons). An overall mean sample size across outcomes must also be calculated forcomparison groups. Proportion and mean sample size are then entered into a table(Hedges & Olkin) to determine the effect size.

Results

A total of 494 abstracts were found with PsychINFO, 40 were located withMEDLINE, and the ERIC search yielded 294 abstracts. After reviewing the ab-stracts, a total of 20 studies met the � nal selection criteria, allowing for 65comparisons across 2,240 participants. The average sample size was 112.0 partici-pants with a standard deviation of 124.8. A notable � nding was that only threestudies focused solely on intellectual disabilities compared to 11 studies addressingphysical disabilities. Six studies examined both physical and intellectual disabilities.Of these six studies, Abrams et al. (1990) and Nabors and Keyes (1995) comparedattitudes towards physical disabilities with attitudes towards intellectual disabilities,whereas the other four studies combined the disabilities into one general category(i.e., Block & Malloy, 1998; Cassidy & Sims, 1991; Tripp, French, & Sherrill,1995). Cohen and Lopatto (1995) did not specify if the disability was physical orintellectual, thus it was categorised as a general disability. Table I provides a generaloverview of the studies included in the current meta-analysis.

Attitudinal Components

As stated earlier, the majority of studies did not directly classify measures ascognitive, behavioural, or affective in nature. In the current meta-analysis, aclassi� cation scheme was imposed so that such measures could be adequatelycategorised (see Table II). Measures assessing knowledge about persons with dis-abilities were classi� ed as cognitive in nature. Semantic differentials, trait descrip-tors, and adjective checklists are examples. Measures were classi� ed as affective ifparticipants were requested to indicate their feelings towards the attitude object. Anexample would be a Likert scale rating of how much a participant would like tointeract with the target. Measures focusing on the behavioural component consistedof direct observation of behaviours in relation to the attitude object, or consisted ofrating scales assessing intent to interact. For example, a participant might be askedto state the likelihood that they would invite the target to play with them after school(Roberts & Lindsell, 1997). Classi� cation of measures according to attitudinalcomponent were made by the � rst author and veri� ed by the second author.

Table II provides an overview of each study including the nature of the target, thedependent measure, and outcomes. Summary statistical information for unweightedand weighted means, nonoverlap U, homogeneity of variance (Q), and the 95%con� dence interval (C. I.) for the weighted means are presented in Table III for eachof the attitudinal components. Vote counting summaries are shown in Table IV.

Cognitive. Five studies included cognitive measures and all of them revealed that

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TABLE I. General overview of studies included in the meta-analysis

Study Agenumber Authors n (years) Ka Type of disability

1 Abrams, Jackson, & St Claire (1990) 48 11 4 intellectual &physical

2 Bickett & Milich (1990) 201 9 to 11 2 intellectual3 Block & Malloy (1998) 88 10 to 12 1 intellectual &

physical4 Bracegirdle (1995) 25 5 to 11 1 physical5 Cassidy & Sims (1991) 60 10 to 13 1 intellectual &

physical6 Cohen & Lopatto (1995) 30 3 to 4 4 general7 Cohen, Nabors, & Pierce (1994) 75 3 to 5.5 5 physical8 Colwell (1998) 49 10 to 12 2 physical9 Diamond, Hestenes, Carpenter, & 60 3 to 6 4 physical

Innes (1997)10 Gash & Coffey (1995) 124 6 to 9 4 intellectual11 Harper (1997) 48 10 to 12 1 physical12 Kratzer & Nelson Le Gal (1990) 168 5 to 8 4 physical13 Nabors & Keyes (1995) 32 3 to 5 4 intellectual &

physical14 Nabuzoka & Rønning (1997) 40 8 to 12 3 intellectual15 Okagaki, Diamond, Kontos, & 36 3 to 5 3 physical

Hestenes (1998)16 Roberts & Lindsell (1997) 143 8 to 10 2 physical17 Townsend, Wilton, & Vakilirad 563 8 to 13 8 intellectual

(1993)18 Tripp, French, & Sherrill (1995) 255 9 to 12 8 intellectual &

physical19 Weirserbs & Gottlieb (1992) 162 8 to 11 3 physical20 Woodard (1995) 33 4 to 9 1 physical

aK 5 number of comparisons.

children had more favourable attitudes towards a target child without disabilitiescompared to a child with either a physical or intellectual disability. Effect sizesindicating a preference for the target child without disabilities ranged from 0.36 to0.91. The overall unweighted effect size was 0.54, suggesting a moderate effect. Theweighted effect size was larger at 0.71 and the vote counting method yielded asimilar effect size estimated to be at least 0.70. The Q statistic associated with theweighted effect size was signi� cant and thus indicated that there was considerableheterogeneity of variances.

Affective. Five studies reported suf� cient data so that effect sizes associated with sixaffective measures could be determined. Nabors and Keyes (1995) included twoaffective measures. Five of the comparisons reported a more favourable perceptionof persons without disabilities. Effect sizes for these comparisons ranged from 0.26

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to 1.30. Woodard (1995) did not � nd any differences for targets with physicaldisabilities compared to a target without disabilities. The mean unweighted effectsize for the six comparisons was of moderate magnitude (d 5 0.52). The votecounting procedure across eight comparisons yielded a comparably moderate effectsize of 0.50. The weighted effect size was smaller at 0.41, and was associated witha lack of homogeneity of variance.

Behavioural intent. For behavioural intent, effect sizes could be calculated for onlythree studies. Values ranged from 0.14 to 1.22. Cassidy and Sims (1991) andNabors and Keyes (1995) both reported effect sizes greater than 1.0, whereasOkagaki et al. (1998) reported the small, negative value. The mean unweightedeffect size was 0.73 with a similar value for the weighted effect mean of 0.79. Again,homogeneity of variance was not found, and the small number of comparisonsshould be taken into consideration. Conversely, the effect size associated with thevote counting method was smaller (d 5 0.30) and was based on eight comparisons.

General attitude measures. Only two studies in this category provided enoughinformation to allow for the calculation of an effect size. Bickett and Milich’s (1990)research resulted in an effect size of 0.18 and a value of 0.24 was found for Colwell’s(1998) study. Due to the fact that only two values were obtained, it would not bemeaningful to calculate the weighted mean effect size. Vote counting methodologyallowed for one additional study, one that reported a positive outcome (Block &Malloy, 1998). Vote counting results for these three studies gave an effect size of0.15.

Type of Disability

Due to the fact that there was not suf� cient data to compare type of disability acrosseach of the attitudinal components, the latter category was collapsed into onecomposite attitude factor. Tables III and IV provide summary data for studiesexamining attitudes towards individuals with intellectual and physical disabilities.Unweighted mean effect sizes for the two categories were moderate at 0.59 forintellectual disabilities and 0.58 for physical disabilities.

The weighted effect sizes for the intellectual and physical disability categoriesdiffered (d9 5 0.35 and d 9 5 0.68, respectively), and both displayed heterogeneity ofvariance. The vote counting method gave moderate effect sizes for the intellectualcategory (d 9 5 0.60) and for the physical category (d 9 5 0.40).

Gender

Eleven studies addressed gender differences in attitudes towards persons withdisabilities. Effect sizes were calculated for seven comparisons and summary data arepresented in Table V. Results of the vote counting method are presented in TableIV. Resulting effect sizes across the three methods were small and did not exceed0.24. However, the fact that Q was large indicates that there may be somemoderating variables in� uencing the results.

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The Role of Inclusion

Table VI provides a summary of studies addressing the effect of inclusion onattitudes towards disabilities. The majority of studies in this category indicated thatchildren in inclusive classrooms were more accepting of children with disabilitiesthan were children attending non-inclusive classrooms. The unweighted mean effectsize of 0.58 (see Table III) con� rms this conclusion. However, the exception was alarge study conducted by Tripp et al. (1995). The effect size for the physicaldisability subscale was negative and of moderate magnitude. In comparison, theeffect size for the learning subscale (i.e., attitudes towards peers with intellectualdisabilities) was negligible. The � ndings of Tripp et al. (1995), in combination withthe study’s large sample size, brought the weighted mean effect size down to 0.32.Similarly, the vote counting method resulted in an effect size of 0.30. The large Qvalue indicated that moderating variables may be contributing to the heterogeneityof responses.

Age

Table VII provides a summary of studies addressing the effect of age on attitudestowards disabilities. Between studies, there was a range of nine years with a lowerbound of three years and an upper value of 12. The range of ages within studies wasgenerally small, but the variability between studies was large. Furthermore, due to

TABLE IV. Interpretation of summary data using the vote counting method

EstimatedCategory Studiesa Kb Mean nc Proportiond effect size

Attitudinal componentsCognitive 1, 4, 6, 12, 15 5 61.4 1.00 . 0.70Affective 6, 7, 11, 13, 20 7 40.0 0.75 0.50Behavioural 1, 5, 7, 13, 15 6 47.8 0.33 0.30General 2, 3, 8 3 112.7 0.33 0.15

Type of disabilityIntellectual 1, 2, 3, 13 6 65.5 0.83 0.60Physical 1, 3, 4, 5, 7, 8, 9, 11, 15 54.4 0.53 0.40

12, 13, 15, 16, 18, 19,20

Gender 1, 2, 6, 7, 8, 12, 13, 14, 22 235.5 0.50 0.2017, 18, 20

Inclusive vs. 9, 10, 14, 16, 17, 18 11 250.0 0.82 0.30non-inclusiveAge 9, 10, 12, 17, 18, 19 15 216.2 0.27 0.11

aStudy numbers used in calculations (refer to Table I).bK 5 number of comparisons.cMean group size per comparison.dProportion of positive comparisons.

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the lack of statistical information regarding this variable in the majority of studies,only the vote counting method was used to calculate effect sizes for age (Table IV).Four of the 15 comparisons revealed a positive correlation for age and attitudes (i.e.,as age increases attitudes towards persons with disabilities are more positive), threeyielded a negative correlation, and the remainder indicated no relation. The result-ing effect size was negligible.

Discussion

Several factors associated with children’s attitudes towards individuals with disabil-ities were examined in a collection of studies published between 1990 and 2000.Attitudinal components, type of disability, gender of the target and participants,inclusion, and age were examined. Three methods (unweighted means, weightedmeans, and vote counting) were used to determine mean effect sizes for each of thefactors. Outcomes varied as a function of methodology. Furthermore, considerableheterogeneity of variance was found for mean weighted effect size associated witheach factor.

The three component theory (Eagly & Chaiken, 1993) provided a framework toclassify attitude measures. Effect sizes for cognitive, affective, and behaviouralcomponents were generally of moderate magnitude, but there was considerableheterogeneity of variance among the measures within each attitudinal com-ponent. One possible explanation is the lack of consistency in targets across studies.Targets were presented by video, written descriptions, drawings or dolls, or wereclassmates of participants. Another source of variability across studies was thedependent measures. Measures were generally not used in more than onestudy and the majority of the measures had been previously unpublished. Further-more, the age of participants also varied across studies and it is possible thatchildren vary in attitudes as a function of maturity. Due to the wide variations intargets, measures, and age of participants, it is dif� cult to discern the presence ofany particular patterns that might account for the variability in effect sizes. Hadmore research been available, it would be possible to determine if, for example,speci� c kinds of targets, measures, or age groups might be associated with smallversus large effect sizes. Furthermore, interactions between these factors mightexist, but a much larger body of research would be needed to make meaningfulcomparisons.

Similarly, considerable heterogeneity of variance was found for outcomes focusingon type of disability. Differences between studies regarding targets, measures, andage of participants may be contributing factors. Also, the fact that only three studiesfocused solely on intellectual disabilities not only precludes a reliable overall effectsize, but it also fails to provide an adequate overview of children’s attitudes. Giventhese limitations, moderate effect sizes were found regardless of type of disability ormethodology. It should be noted that the weighted mean effect size revealed thatchildren preferred persons with physical disabilities over persons with intellectualdisabilities. In comparison, the vote counting method would lead one to believe thatthe opposite was true, whereas the unweighted effect size method showed very little

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difference between the two categories. Thus, choice of methodology can in� uenceoutcomes of meta-analysis research.

Evidence suggested that girls were generally more accepting of individuals withdisabilities than were boys, but only if (a) the target was of the same gender as theparticipant or (b) if male and female targets were presented to each participant. Anegative bias on the part of girls emerged only when male targets were presented.Kratzer and Nelson Le Gal (1990) matched the gender of the participant with thetarget child and presented story vignettes depicting a target child of the same ageand gender as the participant. Their results showed similar attitudes for girls andboys. Other studies exposed participants to both male and female targets in the formof toys (Nabors & Keyes, 1995), videos of a choir (Colwell, 1998), or classmates(Nabuzoka & Rønning, 1997; Townsend et al. 1993; Tripp et al., 1995). Cohen andLopatto (1995) used the general term “children” to describe their � ctional targetsand Cohen et al. (1994) provided participants with drawings of “adults”. Of the 15comparisons using both male and female targets, three showed no gender differ-ences, 11 revealed that girls were more positive than boys, and one indicated thatboys were more positive than girls. In contrast, � ve comparisons provided male andfemale participants with only a male target child (Abrams et al., 1990; Bickett &Milich, 1990; Woodard, 1995). In three of these comparisons (i.e., Bickett &Milich; Woodard), girls were more biased against the male target child than were theboys. In the other two comparisons (Abrams et al.) there were no gender differencesin attitudes.

It would appear, then, that girls may be more biased towards targets withdisabilities only when the target is male. Researchers need to develop a greaterawareness of this potential confounding factor. Furthermore, it is interesting to notethat only one study included a sole female target, an adult with a physical disabilitywho read stories to participants (Cohen et al., 1994). One could conclude thatperhaps gender biases existed not only in the participants, but in researchers as well.

The role of inclusion was also examined and unweighted effect sizes indicated thatinclusive classrooms have a medium sized effect on facilitating positive attitudes.However, Tripp et al. (1995) provided evidence that context may be an importantfactor in shaping these attitudes. They administered a questionnaire to participantsduring physical education classes to assess attitudes towards hypothetical childrenwith physical or intellectual disabilities. The moderate and negative effect size forthe physical disability suggests that children in the non-inclusive physical educationclasses had more favourable attitudes towards a hypothetical target child with aphysical disability than did children attending an inclusive physical education class.The fact that the data was collected during physical education classes may havebiased the results. Children who had previously engaged in physical activities withclassmates with physical disabilities may have been providing responses thatre� ected an understanding of the limitations such a classmate might experience.Children in the non-inclusive classes may have lacked this knowledge. In compari-son, the effect size for the learning subscale (i.e., attitudes towards peers withintellectual disabilities) was negligible, suggesting a child’s intellectual ability maynot be of importance in a physical education class.

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This is an interesting issue, and it may be that children’s attitudes towardsindividuals with disabilities vary as a function of the nature of the assessmentcontext, the context in which individuals with disabilities have been observed, andtype of disability. For example, children may be more willing to express positiveattitudes towards a target child with a physical disability with regard to academicactivities, but such a disability may be a hindrance in a context that requires grossand/or � ne motor control.

The results for age were inconclusive. One possibility for this outcome is that therange of ages within studies was small and the variability between studies was large.For instance, Bracegirdle (1995) had the widest range at six years, with the majorityof studies focusing on a three to four year age span. Weirserbs and Gottlieb (1992)did cover a much wider age range that included participants from Grades 3 to 12(although due to selection criteria only the data for the youngest group was includedin the current meta-analysis). They found a negative correlation for age and thenumber of positive descriptors used to describe a target with a physical disability. Inorder to investigate age-related differences in attitudes towards persons with disabil-ities, it would be necessary to include participants representing a wider range of agesthan the research included in the current meta-analysis. Restriction of range appearsto be a common shortcoming of research in this domain and future investigationsshould address this concern.

It must be emphasised that mean effect sizes can also vary as a function ofstatistical procedures. In the current meta-analysis, the majority of studies revealedthat children are biased against targets with disabilities. However, this pattern can beobscured in mean effect sizes, regardless of the methodolgy. One or two studies withsubstantial and discrepant � ndings can substantially in� uence the unweighted meaneffect size. Calculations of weighted effect sizes and vote counting methods are evenmore sensitive to large and discrepant results especially when the discrepant studieshave comparably large sample sizes.

In conclusion, there is evidence that children’s attitudes towards individuals withdisabilities are often negatively biased. Current educational policies are pro-in-clusion and educators need to be aware of children’s responses towards disabilitiesso that effective programs can be encouraged. Continued research is required tocomprehensively address potential in� uencing factors with psychometrically soundmeasures administered throughout the childhood years. Educators will then bebetter equipped to design age-appropriate inclusion strategies that target speci� cdisabilities. Schools with successful inclusion programs are essential so that allchildren have the opportunity to achieve to the best of their abilities.

The pool of comparisons is in need of further expansion as several key issuesrequire continued investigation. Furthermore, individual study differences and sum-mary statistical techniques need to be considered. The psychometric properties ofmeasures used in this domain are generally under-emphasised, thus it is not alwayspossible to discern if dependent variables are derived from instruments with soundreliability and validity. A variety of targets, measures, and ages of participants mayfurther contribute to variability in effect sizes across studies, and currently, there isnot enough research to untangle all of the potential interactions of these factors.

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However, in general, children’s attitudes towards persons with disabilities are inneed of improvement. This analysis underscores the need for future research intofactors associated with attitudes in order to develop effective intervention programsthat are based on a substantive body of research. Otherwise, educators may bedesigning and implementing programs to facilitate positive attitudes before assur-ances are in place regarding the identi� cation of the critical issues linked to suchchanges.

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Documents

A Meta-Analysis of School-Age Children's Attitudes Towards Persons with Physical or Intellectual Disabilities