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A national study of youth attitudes toward the inclusion of students with intellectual disabilities.

During the past 50 years, numerous studies have focused on the public's attitudes toward people with intellectual disabilities (ID). As the move toward educating children in the least restrictive environment gained momentum following the initial passage of Public Law 94-142 in 1975, much of this research addressed the attitudes of children and youth. The consistent findings have been, with few exceptions, that children and youth hold negative attitudes toward their peers with ID (Nowicki & Sandieson, 2002; Siperstein & Bak, 1980; Siperstein, Bak, & O'Keefe, 1988; Stainback & Stainback, 1982). Further, research demonstrates that children without ID socially reject or neglect students with ID, behavior which researchers attribute in part to negative attitudes (Baldwin, 1958; Bruininks, Rynders & Gross, 1974; Goodman, Gottlieb, & Harrison, 1972; Hughes et al., 1999; Johnson, 1950; Sabornie & Kauffman, 1987; Wolfberg, Zercher, & Lieber, 1999). In fact, Siperstein et al. (1988) were able to show a direct connection between children's attitudes and their social acceptance of students with ID in the classroom.

The reasons for these negative attitudes are complex. Some studies have demonstrated that children's negative attitudes are a result of their perception that children with ID are less able academically and socially (e.g., Siperstein & Bak, 1980). Children without ID rank children with ID lower than they rank children with physical disabilities (e.g., sensorimotor or orthopedic), and rank both groups of children with disabilities lower than they rank children without disabilities (Jones, Gottfried, & Owens, 1966; Karnilowicz, Sparrow, & Shrinkfield, 1994; Nowicki, 2006; Siperstein & Bak, 1985b; Wisely & Morgan, 1981). Further, children often hold an image of a person with ID as someone who is more severely impaired and not capable of basic adaptive behavior skills (Gottlieb & Siperstein, 1976; McCaughey & Strohmer, 2005; Siperstein & Bak, 1980; 1985b), even though actual prevalence numbers show that those with moderate to severe ID make up the smallest percentage (less than 15%) of all those characterized as having an intellectual disability.

Although many studies have attempted to understand the nature of children's negative attitudes, other studies have sought to identify ways to change those attitudes. Early on, advocates for educating students in the least restrictive environment believed that one of the main benefits of inclusion was that over time, exposure to peers with ID in schools and classrooms would result in more positive attitudes and would ultimately result in the social acceptance of these students by children without disabilities. Part of the basis for this assumption was the notion that educating students with and without disabilities in the same classrooms would provide more opportunities for social interactions. A number of studies have provided some evidence to support this belief and documented that repeated contact with students with ID within general educational settings can have a positive impact on attitudes (e.g., Bunch & Valeo, 2004; Esposito & Reed, 1986; Krajewski & Flaherty, 2000; Slininger, Sherrill, & Jankowski, 2000; Townsend, Wilton, & Vakilirad, 1993) and have also documented that students without disabilities can benefit personally from being in an inclusive environment (Fisher, 1999; Helmstetter, Peck, & Giangrego, 1994).

However, although these studies have supported the positive effects of inclusion, researchers have noted a number of exceptions. Over the years, for example, some studies have shown that contact with students with ID in general classroom environments does not necessarily promote more positive attitudes (e.g., Hastings, Sonuga-Barke, & Remington, 1993; Helmstetter, et al., 1994; Manetti, Schneider, & Siperstein, 2001; Nowicki & Sandieson, 2002). In a recent study that examined attitudes of youth attending schools with varying levels of inclusion over a 10-year span, Krajewski and colleagues (Krajewski & Hyde, 2000; Krajewski, Hyde, & O'Keefe, 2002) concluded that some small positive shifts in attitudes occurred over time. However, a careful review of the data suggests that overall attitudes stayed the same or in some instances, became more negative.

The inconsistencies in the findings may be attributable in part to the nature of the exposure that takes place in the school or classroom. All-port (1954) suggested that stereotypes can change through contact if the contact is both frequent and of high quality; specifically, if the contact works in such a manner that it breaks down existing stereotypes rather than reinforces them. Some research has supported this idea and has indicated that contact with peers with ID does little to change attitudes if these interactions, because of their nature (unstructured, hierarchical, etc.), serve to highlight the dissimilarity rather than the similarity between children with ID and those without ID (Helmstetter, et al., 1994; Siperstein & Chatillon, 1982; Strauch, 1970).

Notwithstanding this previous research, the idea that the inclusion of students with ID promotes positive attitudes continues to be a widely debated topic, both in the field of research and in the greater educational community. Moreover, although numerous policies and legislation for inclusion have been put in place during the past 30 years, what do we know about how youth feel about inclusion? Do students see their peers with ID as capable students who are able to learn in their classrooms? Are students willing to interact with a student with ID both in and out of school? Further, because the adolescent population of our country for the most part has "grown up" during this paradigmatic shift to educate students with disabilities in the general classroom environment, we might expect that youth have experienced a greater amount of contact with peers with ID in school. Yet the question remains: Has this experience made a difference in their attitudes? In short, are the attitudes of students more positive?

As numerous as the studies of attitudes toward individuals with disabilities have been, the methodologies used in these studies have varied considerably. The literature is replete with studies that focus on attitudes by using small samples, various methodologies, and various characterizations of disability--or in some instances, a lack of differentiation between types of disabilities, all of which makes drawing conclusions from the results difficult (for reviews, see Nowicki & Sandieson, 2002; Siperstein, Norins, & Mohler, 2006). For example, in a noteworthy study of youth attitudes, McDougall, DeWit, King, Miller, & Killip (2004) concluded that high school students reporting contact with peers with disabilities held more positive attitudes toward individuals with disabilities; however, the researchers failed to document the type of disability that respondents had in mind, the type of contact, and the level of contact. Further, many previous studies have not accounted for the idea that attitudes are multifaceted and therefore should be measured as such. Most studies to date have essentially provided only a glimpse of a much larger picture.

We therefore conducted a national survey that focused on multiple aspects of youth attitudes. For the purposes of this study, we conceptualized youths' attitudes in terms of their image of a student with ID, their intentions to interact with a student with ID, their expectations for inclusion (or how they believed that inclusion would affect them personally), and last, whether they believed that students with ID can take part in academic and nonacademic classes. Figure 1 presents our a priori structural model of predicted relations among variables in this study.

[FIGURE 1 OMITTED]

From the findings of previous studies, we reasoned that contact with and exposure to individuals with ID would influence how youth view their peers with ID. We also expected, on the basis of previous work by Siperstein and colleagues (Bak & Siperstein, 1987; Hemphill & Siperstein, 1990; Siperstein & Bak, 1985a), that youths' perceptions of the competence of students with ID would influence their belief about whether students with ID should be in classes with them and their willingness to interact with these students. Finally, we hypothesized that youths' expectations about the ways that inclusion could affect them personally would also influence their beliefs about inclusion. As subsequently described, we used structural equation modeling to test our a priori model.

METHOD

PARTICIPANTS

We randomly selected 47 school districts from 26 states that represented every geographic region of the country. From these identified school districts, we selected a total of 68 schools in urban (27), suburban (24), and rural (17) communities. Enrollments in the selected schools ranged from fewer than 100 students to more than 1,000 students. Most schools had combined seventh- and eighth-grade enrollments of more than 300, with 39% of the schools having a combined enrollment between 300 and 599; 24% of the schools having a combined enrollment of 600 to 1,000; and 19% of the schools having combined enrollments greater than 1,000. Only 13 schools had combined seventh- and eighth-grade enrollments of fewer than 300 students, with 4% having fewer than 100 students. We chose a minimum of two seventh-grade classes and two eighth-grade classes from each school. Of the 6,901 eligible middle school students randomly selected, 5,837 responded with permission to participate (representing a response rate of 85%). Table 1 gives demographic information about participating students.

We selected this representative sample of public middle school students (in seventh and eighth grade) in the United States in a three-stage stratified random sampling of all schools in the country. In the first stage, we stratified school districts by the number of schools in the district and then randomly selected for participation. In the second stage, we sampled a fixed number of schools from each selected district, with the number of schools drawn per district allocated on the basis of first-stage strata. In the third stage, we selected two intact classes each of seventh- and eighth-grade students from a subject in which all seventh- and eighth-grade students were enrolled.

For sampling districts and schools (Stages 1 and 2), we used probabilities proportional to size (PPS) sampling methods. The measure of size used in both stages was the total enrollment in the two target grades. The only schools that were eligible for this study were public schools in the 50 states and in Washington, D.C., that included both seventh- and eighth-grade students. We excluded vocational and alternative schools from the sampling frame, as well as schools in such outlying territories as Puerto Rico and Guam.

PROCEDURES

Teachers of academic subjects (e.g., English or mathematics) administered surveys to whole classrooms of students. The schools determined the subject that was appropriate as the setting for survey administration. Before the survey administration, the teacher sent permission forms home to parents. We provided the teachers with guidelines for distributing parent permission forms, administering the survey, and collecting and packaging all surveys. We also supplied survey materials, including optical scan response sheets and pencils for the students.

On the day of the survey, teachers read the instructions for completing the survey to students. The front page of each survey instrument also included instructions for students. The survey time was approximately 20 min.

MEASURES

Items on the survey instrument included questions assessing present and prior contact with and exposure to mental retardation, as well as five inclusion-related attitude scales that reflect the major dimensions shown in Figure 1. These scales, which we describe subsequently, are as follows:

* Perceived Capabilities Scale.

* Impact of Inclusion Scale.

* Behavioral Intentions Scale.

* Academic Inclusion Scale.

* Nonacademic Inclusion Scale.

We use the term mental retardation when referring to questionnaire items and students' responses to items, as well as in the results. We used this term in the questionnaire because it was the most accessible/understandable term for youth. In the discussion and elsewhere, we use the term intellectual disabilities (ID).

Contact With Persons With Mental Retardation. The survey asked youth to indicate their personal contact with persons with mental retardation. We coded responses to this set of questions into three variables:

* Family member: have a family member with mental retardation (1 = yes, 0 = no).

* Friend: have a friend with mental retardation (1 = yes, 0 = no).

* In school: have a current classmate with mental retardation, a current schoolmate with mental retardation, or had a schoolmate with mental retardation in elementary school (1 = yes, 0 = no). In-school scores could range from 0 to 3.

Exposure to Mental Retardation. The survey asked youth to indicate their exposure to people with mental retardation. Sample questions included the following: Have you ever read about mental retardation in a book, newspaper, or magazine? Have you ever watched a TV show that was about mental retardation? Have you ever heard about mental retardation from your parents or other adults?

For each of the eight questions, youth answered on a dichotomous yes/no scale, (1 = yes, 0 = no). Total scores across the eight items could range between 0 and 8. The coefficient alpha index of internal consistency reliability was .623 for the total score across the Exposure items.

Perceived Capabilities Scale. The first attitude scale consisted of 16 questions that assessed youths' perceptions of the capabilities of students with mental retardation. The scale, adapted from the Prognostic Belief Scale (Wolraich & Siperstein, 1983), included a list of items addressing a number of skills common to the everyday living of adolescents. Pilot testing showed that approximately 100% of students without disabilities attending middle school (in seventh and eighth grade) were capable of the skills listed. Each item began with the stem, "Do you think most seventh- or eighth-grade students with mental retardation can ..." Sample items included the following: help other students on a science project, learn the same academic subjects as students without mental retardation, understand the rules of a competitive game, use public transportation without adult supervision, handle their own money. Students answered each of the 16 items on a dichotomous yes/no scale (1 = yes, 0 = no). The total score could range between 0 and 16. The coefficient alpha index of internal consistency reliability was .824 for the Perceived Capabilities Scale.

Impact of Inclusion Scale. The second attitude scale consisted of five questions that assessed youths' expectations about the impact of inclusion on their class. For each of these questions, the survey asked youth what would happen if a new student with mental retardation joined their class. Sample items were "It would make it harder for students to concentrate on the lessons" and "It would teach students that being different is OK." For each of the five questions, youth answered on a 4-point scale, with 0 (no), 1 (probably no), 2 (probably yes), and 3 (yes). We reverse-scored three items so that higher scores on each item indicated a more positive impact on the class. Total scores across the five items could range between 0 and 15. The coefficient alpha index of internal consistency reliability was .656 for the Impact of Inclusion Scale.

Behavioral Intentions Scale. The third attitude scale, adapted from the Friendship Activity Scale (Siperstein, 1980), consisted of 12 questions to assess youths' intentions to interact with peers with mental retardation. For each question, the survey asked students whether they would do a certain activity with a peer with mental retardation. Six of the items assessed activities at school. Sample school-related items were "Choose a student with mental retardation to be on your team in a gym class" and "Lend a student with mental retardation a pencil or a pen." The remaining six items assessed activities in nonschool settings. Sample nonschool-related items were "Go to the movies with a student with mental retardation" and "Invite a student with mental retardation to your home." For each of the 12 questions, youth answered on the same 4-point scale used with the previously discussed impact of inclusion items. Total scores across the 12 items could range between 0 and 36. The coefficient alpha index of internal consistency reliability for the 12-item Behavioral Intentions Scale was .932.

A factor analysis of the 12 behavioral intentions items revealed a single major factor with an eigenvalue over 1 (first eigenvalue = 6.50, second eigenvalue = 0.84), and this general factor explained more than 54% of the variance. All 12 items loaded highly on the general factor, with loadings ranging between .54 and .82. Although the factor analysis suggested the presence of a single factor underlying the 12 items, we analyzed the School and Nonschool subscales separately because we hypothesized that average levels of endorsement of school-related intention items might differ substantially from average levels of endorsement of nonschool-related intention items. Total scores across the six items on each subscale could range between 0 and 18. The coefficient alpha index of internal consistency reliability was .872 for Behavioral Intentions-School and .891 for Behavioral Intentions-Nonschool.

Academic Inclusion Scale. To assess beliefs about academic inclusion, the survey asked youth two questions. The first question asked students to indicate whether most seventh- or eighth-grade students with mental retardation could take part in a mathematics class with students who do not have mental retardation. The second question asked about participation in an English class. For each question, youth answered simply yes or no (1 = yes, 0 = no). The total score across the two items could range from 0 to 2. The coefficient alpha for the Academic Inclusion Scale was .784.

Nonacademic Inclusion Scale. To assess beliefs about nonacademic inclusion, the survey posed two questions. The first question asked youth to indicate whether most seventh- or eighth-grade students with mental retardation could take part in an art class with students who do not have mental retardation. The second question asked about participation in a gym class. For each question, youth answered simply yes or no (1 = yes, 0 = no). The total score across the two items could range from 0 to 2. The coefficient alpha for the Nonacademic Inclusion Scale was .439. Although this internal consistency reliability is rather low, we used the two nonacademic inclusion items as separate indicators of a nonacademic inclusion latent variable in the structural equation modeling, and each had acceptable factor loadings (above .50).

STATISTICAL DESIGN

Traditional Analyses. We performed two types of traditional analyses on the study variables. First, we tested whether scores on the previously discussed attitude scales differed on the basis of the student's gender and tested whether any of the remaining demographic variables (such as community setting) were related to the attitude scores. In these analyses, we reported significance test values, as well as Cohen's d, a measure of effect size. Cohen's d is the difference between means for two groups divided by the pooled within-group standard deviation. Therefore, Cohen's d indicates the number of d units by which the means differ; Cohen (1988) identified d values of .2, .5, and .8 as small, medium, and large effect sizes, respectively. Second, we computed correlations among gender, prior contact and exposure, and the attitude scores as an initial evaluation among the variables. Where appropriate, we also report the 95% confidence interval (CI) or the standard error (SE) of a statistic. To avoid unnecessarily citing CIs for percentages, we note here that the large sample size in the current investigation (N > 5,000) ensures that the standard error for percentages reported subsequently is approximately .7, so the 95% CI for each percentage reported is the estimated proportion [+ or -] 1.96 (.7), or the estimated proportion [+ or -] 1.4. Thus, rather than a margin of error of 3%, which is common for political polls, the current results are accurate within a margin of error of 1.4%

Structural Equation Modeling. We tested our a priori model shown in Figure 1 by using structural equation modeling of the strength of relations. We performed all modeling by using the Mplus program (Muthen & Muthen, 2006), and models were fit to covariances among manifest variables. To model relations among latent variables, we needed multiple indicators for each latent variable. To develop these multiple indicators, we formed parcels of items for prior experience and the five attitude scales. Item parcels are simple sums of the items comprising a scale; we randomly assigned items to parcels so that all items for a scale were assigned to one or another of the parcels for that scale; and the sum of the parcel scores for a scale equaled the scale total score (see Kishton & Widaman, 1994; Little, Cunningham, Shahar, & Widaman, 2002). Specifically, we formed the following parcels:

* Three parcels for Exposure (each the sum of two or three items).

* Three parcels for Perceived Capabilities (each the sum of four or five items).

* Three parcels for Impact of Inclusion (2 two-item parcels and 1 one-item parcel).

* Three parcels for Behavioral Intentions-School (each the sum of two items).

We used the two items for Academic Inclusion as the two indicators for the Academic Inclusion latent variable and used the two items for Nonacademic Inclusion as the two indicators for the Nonacademic Inclusion latent variable.

We first fit an initial model with all possible paths among latent variables shown in Figure 1, which served as a baseline model. Due to the presence of some missing data, we used full information maximum likelihood (FIML) estimation of parameters. For FIML estimation, models are fit directly to the raw data matrix (rather than to a covariance matrix), with missing data identified by a missing data flag. Thus, FIML estimation fits models to the data that are present and ignores data elements identified as missing values.

Next, we fit our a priori model shown in Figure 1 and determined whether this restricted model fit the data less well. If additional, nonhypothesized paths were needed to allow the a priori model to fit the data, we added these paths. Next, we trimmed paths that were not needed. Because the sample size was very large, we had very high power to detect significant path coefficients. We therefore deleted all paths that were nonsignificant, as well as all paths associated with standardized path coefficients smaller than .10 in absolute magnitude (and therefore of trivial practical significance) to arrive at our final model.

We evaluated model fit by using three indices of fit of the model to the data. The first index of fit was the standard [chi square] index of fit. In structural equation modeling, a significant [chi square] indicates significant misfit of the model to data, implying a statistical basis for rejecting the model. However, when sample size is large (as in the present case), the [chi square] index is too powerful, often suggesting rejection of a model that has only trivial levels of misfit to the data. Thus, we also used two practical fit indexes, the Tucker-Lewis index (TLI) and the root mean square error of approximation (RMSEA; Browne & Cudeck, 1993). For the TLI, higher values indicate better fit, and values of approximately .95 indicate dose fit to the data. For the RMSEA, lower values indicate better fit; values of .05 or lower indicate close fit of a model to data, and the confidence interval for the RMSEA should either fall below .05 or include .05 to satisfy the criterion of close fit to data. Finally, because youth were nested within school classrooms and we therefore could not assume independence, we reestimated our final model to yield standard errors for parameter estimates that were corrected for nonindependence of observations.

RESULTS

PRIOR CONTACT AND EXPOSURE

Youth report little past and present contact with students with mental retardation, with fewer than 20% having had contact with a schoolmate with mental retardation in elementary school (see Table 2). We were somewhat surprised to find that in the larger middle-school setting, where greater opportunity exists for interactions with a varying peer group, youth again report very little personal contact with a schoolmate with mental retardation. In fact, only 38% report having a schoolmate with mental retardation, and much fewer (10%) report having a current classmate with mental retardation. This small percentage of youth who report having a schoolmate with mental retardation is consistent across districts. That is, results are similar for youth who are attending the only middle school in a district and for youth attending a middle school in a district with two or more middle schools. With such little reported contact in school and in the classroom, the finding that only 10% of youth report having a friend with mental retardation is not surprising.

Many more youth report being exposed to information about mental retardation through a number of different sources. As shown in Table 2, most youth (90%) report seeing a person with mental retardation in a public place, the most distant and generic form of exposure. A major source of information for youth is the media, since a majority of youth (81%) report seeing a movie about mental retardation and many (50%) report seeing a television show or reading about it (47%). Youth have also heard about mental retardation from a number of sources, including their parents (71%) and teachers (67%). Secondary sources of information (media, parents, etc.) are apparently a major way that youth learn about mental retardation.

OVERALL ATTITUDES ON INCLUSION-RELATED VARIABLES

Table 3 presents youths' responses on each attitude measure.

The mean on the Perceived Capabilities Scale, was 10.30, higher than the midpoint of 8, indicating that middle school youth, on average, have a fairly positive view of the capabilities of peers with mental retardation, endorsing approximately 64% of the capabilities items. However, the percentage agreement across the 16 items comprising the Perceived Capabilities Scale varies considerably. For school-related items, as shown in Table 4, 88% of youth believe that students with mental retardation can make friends with students without mental retardation, but they have much lower levels of confidence that students with mental retardation can easily learn academic subjects (57%) or help other students on science projects (44%). With regard to sports, youth think that peers with mental retardation can play on sports teams with others with mental retardation (88%) and can perform physical activities (71%) but are less sure that peers with mental retardation can play on sports teams with students without mental retardation (54%) or can understand the rules of games (53%). Finally, and most important, with regard to activities outside school, youth are least likely to believe that peers with mental retardation can use public transportation without supervision (36%) or handle their own money (30%). Overall, 50% to 60% of youth view students with ID as more moderately impaired, rather than mildly impaired, and not able to do all that the average adolescent can (for example, use computers, learn the same subjects, use public transportation, and handle their own money).

The complexities in youth attitudes were apparent with the Impact of Inclusion Scale. The mean of 8.51 on this scale (as shown in Table 3) was slightly above the midpoint of 7.5 and indicates that, on balance, middle school youth believe that including peers with mental retardation would have beneficial effects. However, across the five items, important variation occurred in students' responses. As shown in Table 5, youth believe that inclusion of peers with mental retardation would teach other students that being "different is OK" (M = 2.1) and believe that students might become more accepting of others through this contact (M = 2.0). However, youth express concerns that teachers would tend to focus more on the peer with mental retardation than on the rest of the class (M = 1.3) and that the presence of the peer with mental retardation would make it harder for students to concentrate (M = 1.4). Differences in youth attitudes are clear when looking at responses to individual items. (Note: Percentages represent combined yes and probably yes responses.) Many youth expect that inclusion will impede their learning and have a negative effect on them, either by making concentrating more difficult (59%) or by drawing the teacher's attention to the student with mental retardation (63%). Yet, youth also recognize that inclusion can have a positive impact on them by teaching that "differences are OK" (77%) and by promoting the acceptance of others (74%). Overall, youth believe that inclusion has both positive and negative effects.

With regard to behavioral intentions, youth are more inclined to interact with peers with mental retardation in school than outside school. In fact, youth have significantly higher levels of expected interactions with peers with mental retardation in school settings (M = 11.75) than in nonschool settings (M = 8.71). The observed difference between youths' responses on the two subscales ([M.sub.diff] = 3.01; CI = 2.93, 3.08) is highly significant, t (5269) = 75.38, p < .0001.

As with the preceding scales, mean levels vary considerably across items on the Behavioral Intentions--School, as well as on the Behavioral Intentions--Nonschool subscales. As Table 6 indicates, the highest intentions for school-related interactions are for items assessing the most nonpersonal forms of interaction, such as saying hello to a student with mental retardation (M = 2.3) or lending the student with mental retardation a pencil or pen (M = 2.6). The lowest mean ratings are for activities that require the closest interaction with the peer with mental retardation, including working together on a class project (M = 1.7) or choosing the peer with mental retardation as a teammate in gym class (M = 1.7). Still, middle school youth have mean ratings above the midpoint (1.5) of the scale on all six school-related items, indicating fairly positive levels of behavioral intentions in school settings. More specifically, the percentages indicate (percentages represent combined yes and probably yes responses) that youth report high intentions to lend a pencil to a student with mental retardation (91%) and to say hello to the student in the hall (81%) but rather low intentions to pick a student with mental retardation as a teammate in gym class (53%) or work with a student with mental retardation on a project (51%).

The mean ratings are very different for behavioral intentions in nonschool settings, as shown in Table 6. Youth have mean ratings below the midpoint of the scale (1.5) for five of the six items, indicating that youth have rather low intentions to interact with students with mental retardation in nonschool settings. Youth are reluctant to spend time with a student with mental retardation outside of school (M = 1.4), with the lowest rating for talking about personal things with a peer with mental retardation (M = 1.1). Examining the percentages of responses indicates that students report little intention to invite a student with mental retardation to their home (35%), go to the movies with a student with mental retardation (32%), or talk about personal things with a student with mental retardation (27%). Youth apparently do not see students with mental retardation as potential friends, a finding that is not surprising, given that only 10% of youth across the country report having a friend with mental retardation.

As for inclusion, youth indicate only modest support for inclusion of peers with mental retardation in English and mathematics classes. As shown in Table 3, the mean on the Academic Inclusion Scale is 0.90, slightly below the midpoint on this scale (1.0). However, youth indicate a higher level of support for the inclusion of peers with mental retardation in nonacademic classes, with a mean of 1.57, considerably above the midpoint of the scale (1.0). The difference between means on the Academic Inclusion and Nonacademic Inclusion Scales ([M.sub.diff] = 0.67; CI = 0.64, 0.69) is significant, t (5624) = 48.68, p < .0001, indicating significantly higher levels of support for the inclusion of peers with mental retardation in nonacademic classes than for their inclusion in academic classes. The percentage of youth who answered yes to the four inclusion items further highlights the difference between youths' beliefs about inclusion in nonacademic subjects versus academic subjects. More than 80% of youth believe that students with mental retardation can participate in art class, whereas fewer than 40% believe that they can participate in math class.

GROUP DIFFERENCES ON ATTITUDINAL VARIABLES

Gender. The next analysis was to determine whether males and females differ significantly in their mean levels on the attitude scales. Table 7 shows the results of these analyses. Because the sample size was quite large, analyses of five of the six scales or subscales reported in Table 7 revealed statistically significant gender differences, with females having higher scores--and therefore more positive attitudes--in each of these five instances. However, the Cohen's d values for all six statistically significant sex differences tended to be fairly small, ranging from 0.11 to 0.27. Even the largest of the Cohen's d values was only 0.27, which falls closer to the midpoint for small effect sizes (d = 0.20) than to the midpoint for medium effect sizes (d = 0.50). Thus, despite the consistency of the statistically significant differences between females and males, gender differences tend to be rather small. Furthermore, despite the large sample size and resulting high levels of power to detect a difference, the gender difference on Academic Inclusion is nonsignificant.

Contact. Similar to the results for gender--and somewhat unexpected--differences in youth attitudes as a function of contact with students with ID were quite small. Youth who reported knowing a student with ID in their class (10%) and/or school (38%) did not differ significantly from all other youth on four of the six attitude scales. We observed no differences on the Perceived Capabilities Scale, the Impact of Inclusion Scale, the Academic Inclusion Scale, and the Nonacademic Inclusion Scale. Only on the Behavioral Intentions-School and Behavioral Intentions-Nonschool subscales do significant differences occur (t = 3.65, p < .001; t = 5.66, p < .001, respectively). Given the large sample size, Cohen's d value was only 0.12 and 0.19, which suggests that these effects were quite small. Although reported contact as a variable has received much attention in attitude research, similar to gender, the present results show that it has only a marginal effect. Our subsequent analyses involving structural equation modeling reconfirm this finding.

Other Demographic Variables. We also tested to determine whether scores on the six scales shown in Table 3 varied as a function of the youth demographic variables, specifically grade in school (seventh vs. eighth), student age (ranging between 11-15 years), hr of television watching per day, and hr of Internet use per day. None of these analyses resulted in any consistent differences, and all differences found were of trivial size (i.e., Cohen's d values less than .02).

Last, we examined differences in youth responses on the attitude scales as a function of school demographics, specifically the school's community designation (urban, suburban, or rural). On three of the six scales, youth from rural communities were slightly more positive in their perceptions of capabilities, behavioral intentions in school, and expectations for the impact of inclusion (F = 7.77, 16.76, and 10.59, p < .001 respectively). As a follow-up, the effect sizes for the differences between rural versus urban/suburban communities were all very small, with a Cohen's d value range of 0.10 to 0.16. Even with these small differences as a function of community type, the question arises as to whether the observed differences are in part a function of the opportunity for youth to have contact with students with ID in their class or school. Results of chi-square analyses clearly showed that the low level of youths' contact with students with ID in the classroom and school was the same for youth attending urban, suburban, or rural middle schools.

CORRELATIONS AMONG SCALES

The correlations among gender, exposure to mental retardation, and the attitude scales are shown in Table 8. Because we were interested in attitudes regarding the inclusion of peers with mental retardation in school, we based the Behavioral Intentions variable shown in Table 8 only on the six items comprising the Behavioral Intentions--School subscale. We obtained the correlations reported in the course of the structural equation

modeling to represent correlations among latent variables. Thus, these correlations are disattenuated, with effects of error variance partialed out. As shown in Table 8, all correlations were positive. The Exposure Scale correlated significantly, but at low to moderate levels with the remaining five scales, with correlations ranging between .07 and .36. Correlations among the remaining five scales were significant and moderate to large in magnitude, ranging between .27 and .66. Interestingly, the lowest of these correlations, .27, was between Academic Inclusion and Nonacademic Inclusion, revealing a lack of close correspondence in answers by youth across these two domains. That is, youth who strongly favor inclusion in nonacademic classes do not necessarily show strong support for inclusion in academic classes.

STRUCTURAL EQUATION MODELING

Model Fitting. The first model fit to the data, Model 1, was a path model with all possible paths among latent variables estimated. With the 10 latent variables shown in Figure 1, a total of 10(9)/2, or 45, paths were possible among the latent variables; and Model 1 estimated all 45 of these paths. Model 1 had a significant [chi square] index of fit, [chi square] (129) = 849.72, p < .001, suggesting a statistical basis for rejection of the model. But the sample size was very large (N = 5,790), and the [chi square] statistic becomes too powerful in such situations, suggesting rejection of a model with trivial levels of misfit to the data. To evaluate this likelihood, we considered the TLI and RMSEA, our indexes of practical fit. The TLI was clearly adequate, at .965, and the RMSEA was very low, with a value of .031 (CI = .029, .033), indicating very close fit of the model to the data. Given the very good fit, as indexed by the TLI and RMSEA, we deemed Model 1 to be an acceptable representation of the data.

We next fit Model 2, which corresponded to our a priori model, with the restricted set of relations among latent variables shown in Figure 1. Thus, Model 2 deleted 20 paths that Model 1 had included. These paths included a total of 12 direct effects from Family Member, Friend, Know in School, and Gender (Female) to Behavioral Intentions-School, Academic Inclusion, and Nonacademic Inclusion; 4 paths from Friend and Know in School to Perceived Capability and Impact of Inclusion; 2 paths from Exposure to Academic Inclusion and Nonacademic Inclusion; and tingle paths from Family Member to Impact of Inclusion and from Gender to Perceived Capability. Model 2 had a significant [chi square] index of fit, [chi square] (149) = 1006.11, p < .001, again suggesting rejection of the model. However, the practical fit indexes changed only slightly from Model 1, with TLI = .964 and RMSEA = .032 (CI = .030, .033). The increased parsimony of Model 2--attained by deleting 20 paths contained in Model 1--and the continued very close fit of the model to the data lend support to our a priori Model 2 over the more complex Model 1.

However, three of the path coefficients in Model 2 were nonsignificant: the paths from Family Member to Perceived Capability, from Behavioral Intentions to Academic Inclusion, and from Impact of Inclusion to Nonacademic Inclusion. The nonsignificance of these path coefficients indicates that these paths are not important for representing the data. We therefore fixed these three path coefficients to 0, leading to our final model, Model 3. As with the preceding models, Model 3 had a significant [chi square] index of fit, [chi square] (152) = 1008.06, p < .001, suggesting rejection of the model; but the practical fit indexes showed improvement over Model 2 and were identical to values for Model 1, with TLI = .965 and an RMSEA = .031 (CI = .029, .033). The highly restricted form of Model 3, plus the very close fit of Model 3 to the data, suggested that Model 3 is the optimal model for the data. That is, Model 3 includes all statistically significant and practically important paths among the latent variables required to represent the relations among the variables shown in Figure 1, and the 23 paths deleted in moving from Model 1 to Model 3 do not affect the ability of the structural model to account for or explain patterns in the data.

Measurement Model Parameter Estimates. The measurement model in structural modeling includes estimates of factor loadings of parcels on their respective latent variables and unique factor variances. All factor loadings and unique factor variances were statistically significant (p < .001). All factor loadings had SEs of .03 or smaller and therefore had t-values of 16.0 or greater; and unique factor variances had SEs of .03 or smaller and had t-values of 8.0 or larger. The standardized factor loadings ranged between .51 and .88, with a median factor loading of .76 and with 11 of the 16 factor loadings greater than .70. Thus, the common factor loadings tended to fall in the substantial to large range, and even the lowest loadings were moderate in size.

Relations Among the Inclusion-Related Latent Variables. The most important outcomes of the structural modeling are the path coefficients, reflecting the strength of relations among the latent variables. Figure 2 shows these path coefficients from the final model. In the following discussion, we consider path coefficients below .20 in absolute magnitude to be relatively small, those between .20 and .35 to be moderate, and those above .35 to be relatively large. All path coefficients had SEs that ranged between .02 and .04, and t-values for parameter estimates were 6.0 or larger. Therefore, all path coefficients were significantly different from 0, p < .001, and our modeling approach ensured the retention of all path coefficients that were of practical significance, as well.

[FIGURE 2 OMITTED]

All four of the exogenous variables had significant effects on Exposure, although all path coefficients were in the small to moderate range. Family Member ([beta] = .19, SE = .01), Friend ([beta] = .17, SE = .02), and Gender ([beta] = .13, SE = .02) had rather small but significant effects on Exposure; and the effect of Know in School on Exposure was moderate ([beta] = .28, SE = .02). Combined, the four predictors explained approximately 18% ([R.sup.2] = .175) of the variance of Exposure. Moreover, all effects were in the predicted direction, with greater contact being associated with higher levels of exposure.

Only Exposure, and not the preceding four exogenous variables, had a significant direct effect on the Perceived Capability latent variable, a moderate-sized path ([beta] = .35, SE = .02) from Exposure. Thus, Exposure explained about 12% of the variance ([R.sup.2] = .122) in Perceived Capability: the greater the youths' previous exposure to mental retardation, the more positive their perceptions of the capabilities of persons with mental retardation.

Two variables--Gender of the respondent and Exposure--had significant influences on Impact of Inclusion, but both these path coefficients were rather small (both [beta]s = . 11, both SEs = .02) and explained less than 3% of the variance of the Impact of Inclusion ([R.sup.2] = .026).

Three latent variables influenced Behavioral Intentions-School, with a small effect of Exposure ([beta] = .18, SE = .02), a moderate effect of Perceived Capability ([beta] = .27, SE = .02), and a large effect of the Impact of Inclusion ([beta] = .47, SE = .03). Thus, with increased exposure to mental retardation and with more positive views regarding the competence of peers with mental retardation, youth had more positive behavioral intentions to interact with such peers. However, the strongest predictor of youths' behavioral intentions was their expectation of the likely impact that inclusion of such peers would have on their classrooms: the more positive the expected impact of inclusion, the higher the level of intentions to interact with peers with mental retardation at school. In total, these three predictors had a strong effect on behavioral intentions, explaining half of the variance on this outcome variable ([R.sup.2] = .500).

Perceived Capabilities largely and strongly influenced the Academic Inclusion latent variable ([beta] = .47, SE = .02), but Impact of Inclusion also influenced the Academic Inclusion latent variable to a minor extent ([beta] = .14, SE = .02). Although their expectations about the impact that inclusion would have on their classroom influenced youth to a minor extent, the principal importance of Perceived Capabilities demonstrates that the primary concern of youth was whether peers with mental retardation could handle the material covered in courses. The two predictors of Perceived Capability and Impact of Inclusion explained more than 30% of the variance of the Academic Inclusion latent variable ([R.sup.2] = .301).

Finally, two predictors also influenced the Nonacademic Inclusion latent variable: Perceived Capabilities influenced it primarily and strongly ([beta] = .57, SE = .04), but Behavioral Intentions ([beta] = .17, SE = .03) also influenced it to a small extent. These two predictors explained more than 45% of the variance in this outcome variable ([R.sup.2] = .454). As with Academic Inclusion, views on inclusion of peers with mental retardation in nonacademic school settings were affected primarily by whether the individual thought that these peers were capable of functioning in nonacademic activities in school. But youths' perceptions of competence were not the sole determinant of these views, since youth who had more positive behavioral intentions to interact with peers with mental retardation were also more likely to support inclusion of these peers in nonacademic school settings.

DISCUSSION

In carrying out this national survey of nearly 6,000 middle school youth in the United States, one of the assumptions made was that after years of policies supporting inclusion, youth would report high levels of contact with students with ID. Surprisingly, this outcome did not occur. Fewer than 40% of youth in the country--attending urban, suburban, and rural schools--reported having a student with ID in their previous elementary school or present middle school. Further, only 10% reported having a student with ID in their current classroom. With such limited contact with students with ID in school, on what basis do youth form their attitudes about ID? (Note that almost all students reported seeing a person with ID in their community.) Most youth gain their knowledge about people with ID predominantly from secondary sources, such as the media and from talking about ID with their teachers or parents.

When asked about their perceptions of the capabilities of students with ID, youth see their peers with ID as competent, but not as competent as the average adolescent. That is, although youth believe that students with ID can carry out the simplest tasks (e.g., engaging in physical activities), they do not believe that students with ID can carry out tasks that are more complex (e.g., using public transportation or handling money). These findings confirm the consistent findings from 30 years ago (Gottlieb & Siperstein, 1976; Siperstein & Bak, 1980; 1985b) and from the present (McCaughey & Strohmer, 2005) that a person's existing schema for ID is of those individuals who present with more moderate, rather than mild, disabilities. In the most recent of these studies (McCaughey & Strohmer) when asked to describe a person with ID, respondents most often offered traits consistent with moderate impairment (e.g., helplessness or dependence on others) rather than traits consistent with mild ID. Taken together with the findings from the present study, little appears to have changed during the past 30 years regarding people's perceptions of ID.

Given what we found about youths' perceptions about the competence of students with ID, we can understand why more than half believe that students with ID should not participate in such academic classes as English and mathematics. We also trace this lack of support for academic inclusion to youths' expectation that including students with ID in academic classes would impede their own learning or create discipline problems. It is not unreasonable to assume, then, that part of the reason that youth support the inclusion of students with ID in nonacademic classes, such as art and gym, is because this inclusion provides less chance for a negative impact on them academically. Youths' negative expectations of academic inclusion are, however, in sharp contrast to their belief that the inclusion of students with ID can also have positive consequences, as previous studies (e.g., Fisher, 1999) have found. Youth believe that including students with ID in classrooms will have a positive impact on them personally by making them more accepting of differences and teaching them that differences are acceptable. Youth may understand the moral and societal message that acceptance of diversity is important.

In addition, middle school students understand the normative social behavior of the school and thus are willing to engage in superficial activities, such as saying hello to a student with ID, lending a pencil, or standing in line next to a student with ID. In contrast, decidedly fewer youth are willing to interact with a student with ID when the activity involves more personal commitment or choice, such as talking to a student during free time or lunch or choosing a teammate in gym class. Most dramatically, youth are not willing to interact with students with ID outside school, where social norms and the pressures of the peer group drive behavior. Moreover, they are reluctant to engage in any age-appropriate, "friend-type" activity with a student with ID (e.g., going to the movies, talking about personal things, or inviting the student with ID to their homes). It is understandable then that only 10% of the respondents report having a friend with ID. The combination of youths' behavioral intentions and actual reported friendships support much of the previous literature that has documented the absence of friendships between youth with ID and those without ID (Siperstein, Leffert, & Wenz-Gross, 1997; Zetlin & Murtaugh, 1988).

The findings from our structural equation modeling underscore the key role that youths' perceptions of the competence of students with ID play in their attitudes toward inclusion, as well as their behavioral intentions to interact with students with ID in school. But what influences youths' perceptions? Their experiences shape the perceptions that youth hold about students with ID, where experience refers not only to direct contact with people with ID but also to what youths have learned from the media, peers, parents, and teachers. In the past, most researchers have assumed that contact with and exposure to people with ID directly influences youths' attitudes; however, contact and exposure do not directly influence those attitudes. In fact, students who reported having a classmate with ID were no different in their attitudes from those who did not report this type of contact.

The most important finding of the present study is that youths' perception of a person with ID is the pivotal factor. More specifically, the results strongly show that neither contact nor exposure per se leads to more positive attitudes, but rather contact and exposure that provide youth with the opportunity to witness the competence of individuals with ID. Youth who perceive students with ID as being more competent are also more positive about the inclusion of those students in academic classrooms. This finding suggests that youth primarily base their judgments on whether students with ID should be included in academic classes on their perceptions of competence.

However, the image that youth hold of students with ID is not the only factor that explains their attitudes toward inclusion. The findings also suggest that youths' expectations of how inclusion will affect them and their peers influence their attitudes. That is, those youth who expect inclusion to have more of a positive impact ("teach students that being different is OK") and less of a negative impact ("make it hard for students to focus on lessons") are the same ones who believe that students with ID can successfully function in the academic classroom. In sum, by understanding youths' perceptions of students with ID and their expectations of the ways that inclusion will affect them personally, we can begin to explain and understand their beliefs about inclusion.

Understanding youths' beliefs about whether students with ID can participate in inclusive settings is only half of the picture. Examining the social aspects--more specifically, youths' willingness to interact with students with ID--is equally important. Those youth who perceive students with ID as competent and who do not perceive them as having a negative impact on the classroom experience are more willing to interact with them in the school setting. These findings are dramatic, since youths' perceptions of competence and their expectations of the impact of inclusion can explain more than 50% of the variance of youths' willingness to interact with a student with ID. However, this willingness to interact occurs only within the realm of school-related activities and does not generalize to activities outside school, where few students are willing to interact with a peer with ID.

Gender differences were not a primary focus of this study; however, we believed that examining gender differences was important so that we could attempt to replicate the findings of numerous studies that have suggested that females have more positive attitudes toward individuals with disabilities than males (e.g., Krajewski & Flaherty, 2000; Nowicki & Sandieson, 2002; Siperstein & Chatillon, 1982; Townsend et al., 1993). Our data provide support for the conclusion that females do hold more positive attitudes overall, but they also support the further claim that differences between males and females are rather small. In fact, when placed in our model, gender predicted very little, with females only slightly more likely to see the positive impact of inclusion. Because of our national sample, we feel confident in assuming that, although gender will always be a factor to consider, its predictive value is not as important as such other factors as youths' perceptions of people with ID.

To summarize, although educators have made considerable efforts to promote inclusive practices, youth report little contact with students with ID in their classrooms and schools. Youth see students with ID as moderately impaired rather than mildly impaired, are unwilling to interact with students with ID outside school, and limit their interactions in school to those that are less personal in nature. Youth expect that inclusion will have a positive effect on them personally, but also expect a negative effect academically. Finally, although youth are supportive of inclusion in nonacademic classes, they are not supportive of inclusion in academic classes.

Even though our study presents a comprehensive picture of youth attitudes across the country, it has several limitations. Our assessment of contact with a student with ID did not include information about the nature of the contact. In addition, we can only infer through youth self-reports whether a classmate had an intellectual disability, because we have no direct information about the level of inclusionary practices in each school. However, because this study involved a national random sample of all middle schools in the United States, we can surmise that it represented all levels of inclusion. Last, the low percentage of youth who report having a classmate with ID may be an underrepresentation, because students may be unaware that a student in their classroom has an intellectual disability.

Although it is important to keep the limitations of this study in mind, we can draw some meaningful conclusions. Most significantly, the results indicate a consistent pattern in youth attitudes across the country. Furthermore, these findings provide a baseline by which researchers can monitor improvement in youth attitudes as inclusionary practices live up to the promises of the present legislative policies. To monitor future improvement, we, as researchers, must continue to be sophisticated in our approach to assessing attitudes toward people with ID. The idea that one barometer exists for gauging youth attitudes toward their peers with ID is overly simplistic. As the results of this study indicate, attitudes are complex; and by using multiple measures, we were better able to examine youth attitudes toward the inclusion of students with ID in their classrooms and schools.

In conclusion, we can view the results of this study in two ways: the glass is either half empty or it is half full. Some might interpret the results as indicating that inclusion is not working; that the policies and practices put in place have not reduced or eliminated the social barriers to inclusion for students with ID; and thus, that the social goals of including students with ID in general education environments may not be attainable or even realistic. Others might interpret the results as indicating that although some progress has been made, we have not yet done enough to promote inclusion and that we cannot rely on physical inclusion by itself to foster positive attitudes. To promote change, educators must engage in a programmatic and systematic approach to facilitate positive attitudes among youth. Our recent historical overview of attitudes and attitude change in the schools (Siperstein, Norins, & Mohler, 2006) provides strong evidence that attitudes can change--but effort, creativity, and commitment are necessary. What the results of this survey do indicate is that finding ways for youth to witness the competence of people with ID would go a long way toward fostering positive attitudes.

The authors extend their thanks to Carol Cosenza and Anthony Roman of the Center for Survey Research at the University of Massachusetts Boston, for their assistance with questionnaire development. We also thank Brenda Clark and James Ross of ORC Macro, Inc., for their help in conducting the sampling and administration of the survey. The authors would further like to acknowledge the participating schools for their willingness to take part in the study. This research was sponsored by Special Olympics, Inc., and funded through Cooperative Agreement No. U59/CCU321826-04 from CDC. The views represented in the report are those of the authors and do not necessarily represent the official views of CDC.

Manuscript received June 2006; accepted October 2006.

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Address correspondence to Gary N. Siperstein, Center for Social Development and Education, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA 02125, 617-287-7250 (e-mail: gary.siperstein@umb.edu).

GARY N. SIPERSTEIN

ROBIN C. PARKER

JENNIFER NORINS BARDON

University of Massachusetts Boston

KEITH F. WIDAMAN

University of California Davis

GARY N. SIPERSTEIN (CEC MA Federation), Professor, Director, Center for Social Development and Education; ROBIN C. PARKER, Research Associate, Center for Social Development and Education; and JENNIFER NORINS BARDON, Research Associate, Center for Social Development and Education, University of Massachusetts, Boston. KEITH F. WIDAMAN (CEC MA Federation), Professor of Psychology, University of California, Davis.
TABLE 1
Demographics

Characteristics N Percentages M (SD) Range

Gender
 Female 2937 51
 Male 2827 49
Age 5792 12.9 (.78) 11-15
Grade
 Seventh 2913 50
 Eighth 2870 50
How long in United States
 Less than one year 28 0.5
 1-4 years 134 2
 5-10 years 223 4
 11+ years 329 6
 Whole life 5066 87
Hours of television
watching per day
 0 216 4
 Less than 1 966 17
 1-3 2273 39
 3-6 1613 28
 6+ 683 12
Hours of Internet use per day
 0 1804 31
 Less than 1 1573 27
 1-3 1562 27
 3-6 605 11
 6+ 235 4

TABLE 2
Contact and Exposure

Scale Percentages

Contact
 In current middle school
 Schoolmate 38
 Classmate 10
 In elementary school 18
 Friend 10
 Family member 15

Exposure
 Read about MR 47
 Watched television show about MR 50
 Watched movie about MR 81
 Heard about people with MR from
 parents or adults 71
 Heard about people with MR from
 school 67
 Talked about MR with friends 46
 Saw a person with MR in public place 90
 Talked with person with MR 66

TABLE 3
Descriptive Statistics on Attitude Measures

Scale N M SD Range

Perceived Capabilities 5167 10.30 3.84 0-16
Impact of Inclusion 5519 8.51 3.03 0-15
Behavioral Intentions--School 5435 11.75 4.49 0-18
Behavioral Intentions--Nonschool 5428 8.71 4.77 0-18
Academic Inclusion 5658 0.90 0.90 0-2
Nonacademic Inclusion 5680 1.57 0.65 0-2

Note: Ns differ across scales because of the presence of missing data.

TABLE 4
Percentage Agreement With Items on the Perceived
Capabilities Scale

 Percentage
Item Agreement

Make friends with students without MR 88
Play on sports team with other players
 with MR 88
Do physical activities like running or
 riding a bike 71
Describe how they feel when they are
 sick to school nurse 70
Act in consideration of another's feelings 69
Talk with students without MR about
 common interests 67
Choose their own clothes 66
Recognize when someone needs help 66
Use computers 63
Act appropriately when introduced to
 strangers 59
Learn same academic subjects as students
 without MR 57
Play on sports team with other players
 without MR 54
Understand the rules of a competitive
 sports game 53
Help other students on science projects 44
Use public transportation without adult
 supervision 36
Handle their own money 30

Note. Respondents answered each item on a 0 = no,
1 = yes scale, and tabled values are the percentage of
students responding yes to the item.

TABLE 5
Descriptive Statistics for Items on the Impact of Inclusion Scale

 Percentage
Item M (SD) Agreement

Teacher would focus more on that
 student than rest of class (R) 1.3 (1.00) 63
It would be harder for students to
 concentrate on lessons (R) 1.4 (0.86) 59
It would create discipline
 problems (R) 1.7 (0.96) 42
It would teach students that being
 different is OK 2.1 (0.92) 77
It would help students be more
 accepting of others 2.0 (0.92) 74

Note. Respondents answered each item on a scale where 0 = no,
1 = probably no, 2 = probably yes, and 3 = yes. Tabled values are the
mean and SD of ratings for each item, and (R) indicates that ratings
on these items were reverse-scored. Percentages indicate combined
yes and probably yes responses.

TABLE 6
Descriptive Statistics for Items on the Behavioral Intentions Scales

 Percentage
Domain/Item M (SD) Agreement

Behavioral Intentions-School
 Lend a student with MR a
 pencil or pen 2.6 (0.79) 91
 Stand next to a student with
 MR while waiting in line 2.5 (0.85) 86
 Go up to the student with MR
 and say hello 2.3 (0.90) 81
 Talk to the student with MR
 during free time or lunch 1.8 (0.99) 60
 Choose a student with MR to
 be on your team in gym class 1.7 (0.98) 53
 Work together with student with
 MR on a project in class 1.7 (1.03) 51
Behavioral Intentions--Nonschool
 Sit next to student with MR on the
 bus for a field trip 1.7 (1.03) 53
 Spend time with a student with MR
 outside of school 1.4 (1.01) 41
 Invite a student with MR to go out with
 you and your friends 1.3 (1.02) 36
 Invite a student with MR to your home 1.3 (1.03) 35
 Go to the movies with a student with MR 1.2 (1.00) 32
 Talk about personal things with a
 student with MR 1.1 (1.00) 27

Note. Respondents answered each item on a scale where 0 = no,
1 = probably no, 2 = probably yes, and 3 = yes. Tabled values are the
mean and SD of ratings for each item. Percentages indicate combined
yes and probably yes responses.

TABLE 7
Mean Differences by Student Gender on Attitude Variables

Scale Gender N M (SD)

Perceived Capabilities Female 2597 10.51 (3.75)
 Male 2536 10.08 (3.92)

Impact of Inclusion Female 2807 8.83 (2.94)
 Male 2678 8.16 (3.08)

Beh. Intentions--School Female 2782 12.33 (4.21)
 Male 2618 11.14 (4.68)

Beh. Intentions--Nonschool Female 2764 9.24 (4.73)
 Male 2628 8.14 (4.74)

Academic Inclusion Female 2871 0.90 (0.90)
 Male 2751 0.89 (0.90)

Nonacademic inclusion Female 2888 1.62 (0.61)
 Male 2756 1.51 (0.69)

Scale t Prob Cohen's d

Perceived Capabilities 4.05 <.001 0.11

Impact of Inclusion 8.15 <.001 0.22

Beh. Intentions--School 9.85 <.001 0.27

Beh. Intentions--Nonschool 8.56 <.001 0.23

Academic Inclusion 0.54 0.58 0.01

Nonacademic inclusion 6.37 <.001 0.17

Note. Ns differ across scales due to the presence of missing data.

TABLE 8
Correlations Among Latent Variables on the Basis of Structural Modeling

 Perceived Impact of
Variable Gender Exposure Capabilities Inclusion

Gender 1.00
Exposure .16 (.02) 1.00
Perceived
 Capabilities .07 (.02) .36 (.02) 1.00
Impact of
 Inclusion .13 (.02) .13 (.02) .46 (.02) 1.00
Behavioral
 Intentions--
 School .13 (.02) .33 (.02) .55 (.02) .62 (.02)
Academic
 Inclusion .01 (.02) .07 (.02) .54 (.02) .35 (.02)
Nonacademic
 Inclusion .13 (.02) .29 (.03) .66 (.02) .39 (.03)

 Behavioral
 Intentions-- Academic Nonacademic
Variable School Inclusion Inclusion

Gender
Exposure
Perceived
 Capabilities
Impact of
 Inclusion
Behavioral
 Intentions--
 School 1.00
Academic
 Inclusion .35 (.02) 1.00
Nonacademic
 Inclusion .48 (.03) .27 (.03) 1.00

Note. Total N = 5,790; tabled correlations are based on FIML
estimation with standard errors in parentheses. Gender = respondent
gender (female = 1, male = 0). Exposure = exposure to information
about MR. Perceived Capabilities = perceived capabilities of
students with MR. Impact of Inclusion = impact of inclusion of
students with MR. Behavioral Intentions-School = behavioral
intentions to interact in school with students with MR. Academic
Inclusion = positive evaluation of inclusion of students with MR in
academic settings. Nonacademic Inclusion = positive evaluation of
inclusion of students with MR in nonacademic settings.
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Author:Siperstein, Gary N.; Parker, Robin C.; Bardon, Jennifer Norins; Widaman, Keith F.
Publication:Exceptional Children
Geographic Code:1USA
Date:Jun 22, 2007
Words:11976
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