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Exploring the relationship between access technology and standardized test scores for youths with visual impairments: secondary analysis of the National Longitudinal Transition Study 2.

Abstract: This article presents the findings of a secondary analysis of the National Longitudinal Transition Study 2 that explored the predictive association between training in access technology and performance on the Woodcock-Johnson Tests of Academic Achievement: III. The results indicated that the use of access technology had a limited predictive relationship to performance on tests. Practical implications are discussed.

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In recent years, much legislative attention has been given to ensure that accessibility to all aspects of the educational system for all children, including those with disabilities, will improve (Individuals with Disabilities Education Act, IDEA, 2004; National Instructional Materials Accessibility Standard, 2006; No Child Left Behind Act, 2002). Along with such provisions as the assurance of a free and appropriate education (IDEA, 2004), these legislative actions also ensure the inclusion of access to information through the use of accommodations and access technologies. Access technologies include environmental adaptations; technological and optical devices; and strategies that allow individuals with disabilities to expand their ability to perform specific tasks, such as reading from a computer screen. The ultimate goal of these efforts is to provide an efficient and equitable education for all youths, regardless of their disability status (Gamble, Dowler, & Hirsch, 2003; IDEA, 2004). For youths with visual impairments (that is, those who are blind or have low vision), access technologies play a significant role in the acquisition of information, but their effectiveness in attaining academic achievement is not known.

Both quantitative and qualitative studies have found that youths with visual impairments gain proficiency in nonacademic skill sets differently from sighted youths (American Foundation for the Blind, n.d.; Hatlen, 1996). Whereas sighted youths learn many important skills and gain vital educational information incidentally through visual observation, youths with visual impairments must gain the same information through systematic, organized, and deliberate educational means (Corn, Hatlen, Huebner, Ryan, & Siller, 1995; Hatlen, 1996; Huebner, Merk-Adam, Stryker, & Wolffe, 2004; Willoughby & Duffy, 1989). This access to information is often achieved through technology that is based on individual needs. The American Printing House for the Blind (2007) estimated that as of 2007, nearly 58,000 youths were receiving specialized services for visual impairments. This number reflects only those youths with legal blindness who were receiving specialized educational support. As a result, it may be a gross underestimation of the actual number of youths with visual impairments who were included in the National Longitudinal Transition Study 2 (NLTS2), since those youths needed only to demonstrate an educational need for accommodations, regardless of their legal-blindness status. For youths with visual impairments, having access to educational information at the same time as their sighted peers increases the likelihood of improved independence, higher self-esteem, and competitive academic success, all of which lead to greater preparation for postsecondary activities (Alper & Raharinirina, 2006; Duhaney & Duhaney, 2000; Gerber, 2003; IDEA, 2004; Kapperman & Sticken, 2003; Michaels & McDermott, 2003). However, few, if any, studies have quantitatively examined the extent to which access technology is being used nationally and its predictive relationship to scores on standardized tests for youths who are receiving specialized services for their visual impairments. The study presented here explored data collected for the NLTS2 on behalf of the Office of Special Education Programs of the U.S. Department of Education.

The NLTS2, an ongoing multiwave study that began in 2000, focuses on the experiences of youths with disabilities who are transitioning from high school to postsecondary activities. The intent is to identify effective instructional strategies, situations, and modifications for youths with disabilities and to find areas that need improvement. A preliminary analysis of the data indicated that youths who were receiving a variety of specialized services for visual impairments consistently performed better on standardized tests than did those in other disability groups; however, researchers have not looked specifically at the impact of access technology on the performance of youths with visual impairments on these tests (Wagner, Newman, Cameto, & Levine, 2006). The current study examined the NLTS2 data, exploring the relationship between the use of access technology and scores on standardized tests, specifically the research edition of the Woodcock-Johnson Tests of Academic Achievement: l]I (WJIII) (Woodcock, McGrew, & Mather, 2001).

Research questions

This study analyzed the NLTS2 data to explore the frequency with which youths with visual impairments used access technology and the relationship between the timing of that use and the youths' performance on standardized tests. Therefore, we sought to answer the following research questions:

1. How many youths used access technology, and did the rates of use vary by demographic factors (gender, age, race or ethnicity, urbanization, family income or socioeconomic status, and degree of vision loss)?

2. What is the predictive relationship among the degree of vision loss, use of access technology, and the combined effect of visual impairment and the use of access technology on the youths' performance on the six stand-alone subtests of the WJIII?

Methods

DESIGN OF THE STUDY

The NLTS2, an ongoing study with data collected biennially in waves, includes a large, nationally representative sample of all 13- to 21-year-old students who receive special education from the following disability categories: visual impairment, emotional disturbance, learning disability, speech or language impairment, orthopedic impairment, hearing impairment, traumatic brain injury, autism, deaf-blindness, multiple disabilities, mental retardation, and "other health impairments" (Wagner et al., 2006). The current study focused on the data that were collected during Wave 1 (2002) and Wave 2 (2004). Information for NLTS2 is collected from a variety of sources, including parents or guardians, teachers, principals, school records, and the students themselves; however, this study focused only on information gathered in interviews with the students and their parents and direct assessments.

PARTICIPANTS

Approval for the study was obtained from Western Michigan University's Institutional Review Board. Students were included in the study if they were receiving specialized services for a primary disability of visual impairment and had answered survey questions about their use of access technology (N = 280). The racial or ethnic groups Asian or Pacific Islander (n = 7) and American Indian or Alaska Native (n = 4) both contained too few participants to include in the multiple linear regression analyses, so these groups were excluded from the study.

MEASURES

The demographic variables that were included in the study had been identified in previous studies as having a significant impact on academic performance: age, gender, and race or ethnicity (Blackorby, Chorost, Garza, & Guzman, 2003, 2004; National Center for Education Statistics, 2005; Wagner, Blackorby, & Hebbeler, 1993; Wagner et al., 2006). Information on urbanicity and family income was also collected, but because of significant missing data (more than 20%), the variables urbanicity (n = 121, 43.2%) and family income (n = 215, 76.8%) were not included in the multivariate analyses. Also, a variable describing the youths' visual functioning (diagnosed with total blindness, yes or no) was included, since youths with low vision may have accessibility options that do not include access technology, such as large print and bright lighting. The predictor and demographic variables in the study were retrieved from the interviews with the parents and youths for Waves 1 and 2.

The predictor variable for the study was the use of access technology by the youths who were receiving specialized services for visual impairment. Visual impairment was identified using two possible responses--"(blindness) complete blindness" or "visual impairment/partial sight"--on the parent survey for Waves 1 and 2 to the question, "Which of the disabilities or problems you told me is [youth's] main problem or disability?" The response "blindness, complete blindness" is referred to in these analyses as "total blindness," and the response "visual impairment/partial sight" is referred to in these analyses as "low vision." In both Wave 1 (collected in 2002) and Wave 2 (collected in 2004), the interviews with the parents and youths in NLTS2 included the question, "In the previous 12 months, has this youth received assistive technology services/devices?" The answer to that question was either yes or no. Original NLTS2 methodologists defined access technology as "high-tech" technology, such as text-to-speech devices and programs, as well as magnification programs (Wagner et. al, 2006). Since the survey question asked the participants about their receipt of "services/ devices," the term utilization in this study refers to either training in or the use of access technology devices. This predictor variable from Waves 1 and 2 was combined and recoded into a new variable to account for the amount of utilization on the basis of whether a youth did not use access technology at all over the course of the two waves (n = 63), used it only during one wave (n = 69), or used it during both waves (n = 136).

The variables for degree of vision loss (low vision or total blindness) and access technology utilization group (none, one wave only, or both waves) were combined into one six-level variable to explore the combined effect on the outcome variables. The six levels of the new variable (Visual Impairment x Utilization of Access Technology) were low vision with no use of access technology, low vision with the use of access technology in one wave, low vision with the use of access technology in both waves, total blindness with no use of access technology, total blindness with the use of access technology in one wave, and total blindness with the use of access technology in both waves. Dummy variables were created for nominal predictor variables with more than two groups, specifically for the variables race or ethnicity (referent: white) and Visual Impairment x Utilization of Access Technology (partial or none).

The outcome variables included all six subtests from the research edition of the WJIII standardized test, which were retrieved from the Direct Assessment data set that was available only in Wave 2. The NLTS2 methodologists consulted with experts on assessment to determine the most appropriate method to assess current academic achievement while predicting future academic success (SRI International, 2000). It was determined that the most appropriate direct assessment tool was the six subtests of the WJIII. These stand-alone subtests and descriptions are Passage Comprehension, which evaluates students' ability to read a narrative passage and provide a missing key word that makes sense in the context of the passage; Synonyms/Antonyms, which evaluates students' ability to read a word (through print, large print, or braille) and to provide either a synonym or antonym; Mathematics Calculation, which evaluates students' computation skills, ranging in difficulty from elementary to advanced, through the completion of mathematical problems provided in print, large print, or the Nemeth Code; Applied Problems, which evaluates students' ability to solve practical mathematical problems that are provided via dictation; Science, which evaluates students' knowledge of biology and the physical sciences and is completed orally; and Social Studies, which evaluates students' knowledge of history, geography, government, economics, and other aspects of social studies and is completed orally (Wagner et al., 2006). Scores for all the subtests were standardized with a mean of 100 and a standard deviation of 15 for the general population.

DATA ANALYSIS

In addition to descriptive analyses of the data, forced-entry multiple linear regression analyses were conducted using SPSS 17.0 to examine the predictive relationship between the utilization of access technology and a student's performance on the six WJIII subtests. Diagnostic tests were conducted to explore bivariate relationships among the predictor variables to assess for potential confounding. Assumptions were tested and met using the Durbin-Watson statistic (a test of independent errors), variance-inflation factor (collinearity), and P-P plots (a test for homoscedasticity). To account for possible interaction effects because of the combined effect of vision loss and the resulting need for access technology, predictor variables in each model included the combination of vision loss and access technology utilization group (low vision-no wave, low vision-one wave, low vision-both waves, total blindness-no wave, total blindness-one wave, or total blindness-both waves) (referent group: low vision-no wave) and degree of vision loss (low vision or total blindness) while accounting for age, gender, and race or ethnicity.

Results

Descriptive analyses were conducted to determine how many youths used access technology during the study period. Table 1 shows the number of youths in each training group by reported degree of vision loss (low vision or total blindness), gender, age, and race or ethnicity. The majority of youths who were totally blind (63%) received training in both Waves 1 and 2, with 12% having not used access technology during either wave, and 25% using access technology in one wave or the other but not in both. Of the youths with low vision, 45% received training in access technology during both waves, and 29% received no training during either wave.

Table 2 shows the means and standard deviations of the scores for each of the six WJIII subtests by the demographic and utilization-group variables. The older youths generally performed more poorly on all the subtests than did the younger youths with as much as a 20-point difference (for example, for calculations, the 13 year olds scored 90 points on average, while the 17 year olds scored 67 points on average). The boys outscored the girls by approximately 3 to 5 points on all the subtests except Science, on which the girls outscored the boys by less than 1 point. Compared with the test scores for the white youths, those for those in other racial or ethnic groups were markedly lower on Passage Comprehension, Science, SocialStudies, and Synonyms/Antonyms subtests. The Hispanic youths outscored those in the other racial or ethnic groups on the Applied Problems (89 points) and Mathematics Calculation (88 points) subtests; however, all scores fell within a 9-point range. It is perhaps surprising that the youths who had received no training in access technology in either wave outscored those who had received training in one or both waves on all the subtests. However, the greatest difference in means between the groups was only 5.4 points. Bivariate associations for gender, age, degree of vision loss, and race (using chisquare tests) showed no statistically significant correlations.

The data were then analyzed using multiple linear regression analysis to examine whether the use of access technology and the degree of vision loss predicted test scores for each of the six subtests. Other variables that were included in the regression model were gender, age, and race or ethnicity. These results are reported in Tables 3 and 4, which include regression coefficients for access technology and visual impairment.

APPLIED PROBLEMS SUBTEST

The Applied Problems subtest contained data for 193 participants. No statistically significant difference was found in the test scores on the basis of the degree of visual impairment and utilization of access technology separately (see Table 3) or for the combined effect of visual impairment and utilization of access technology (see Table 4). Gender ([beta] = -6.7, -12.5 to -.0) was a statistically significant predictor of test performance, with the female students scoring 6.7 points lower than the male students. This regression model accounted for 7.1% of the variance ([[gamma].sup.2] = .071). An analysis of variance (ANOVA) showed that this model was a poor goodness-of-fit, with an alpha level of .05" F(9,192 = 1.56, p = .13).

MATHEMATICS CALCULATION SUBTEST

The Mathematics Calculation subtest contained data for 263 participants. No statistically significant difference in test scores was found on the basis of visual impairment or utilization of access technology separately (see Table 3) or for the combined effect of visual impairment and utilization of access technology (see Table 4). Age ([beta] = -4.4, -7.1 to -1.6) was a statistically significant predictor of test performance, with a 4.4-point decrease in test scores for each year's increase in age. This regression model accounted for 6.9% of the variance ([[gamma].sup.2] = .069). An ANOVA showed that this model was a good fit with these data, with an alpha level of .05: F(9,253 = 2.107, p = .03).

PASSAGE COMPREHENSION SUBTEST

The Passage Comprehension subtest contained data for 267 participants. Neither visual impairment nor utilization of access technology individually were statistically significant predictors of test performance (see Table 3) or for the combined effect of Visual Impairment x Utilization of Access Technology (see Table 4). Being older ([beta] = -3.4, -6.3 to -0.5) and being Hispanic ([beta] = 10.4, -19.4 to -1.4) were statistically significant predictors of test performance; older youths scored 3.4 points lower with each increase in year of age, and Hispanic youths scored 10.4 points lower than did the white students. This regression model accounted for 6.2% of the variance ([[gamma].sup.2] = .062). An ANOVA showed that this model has a poor goodness-of-fit, with an alpha level of .05: F(9, 266 = 1.880, p = .06).

SCIENCE SUBTEST

The Science subtest contained data for 266 participants. No statistically significant effect was found for visual impairment or the utilization of access technology separately (see Table 3), but the youths who were totally blind and used access technology in only one wave scored statistically significantly lower (10.4 points lower) on the subtest than did the youths with low vision who did not use access technology at all ([beta] = -10.4, -20.6 to -0.1) (see Table 4). Also, the results revealed that being African American ([beta] = -9.5, -16.0 to -3.0), or Hispanic ([beta] = -10.0, -17.0 to -3.0) were statistically significant negative predictors of test performance. Both the African American and the Hispanic youths scored lower than did the white youths (9.5 points and 10 points, respectively). This regression model accounted for 6.1% of the variance ([[gamma].sup.2] = .061). An ANOVA showed that this model had a poor goodness-of-fit, with an alpha level of .05: F(9, 265 = 1.861, p = .06).

SOCIAL STUDIES SUBTEST

The Social Studies subtest contained data for 266 participants. Total blindness was found to be a statistically significant negative predictor of test performance ([beta] = -7.3, -13.4 to -1.5), while low vision and the use of access technology were not (see Table 3). In addition, there was no statistically significant difference between combined visual impairment and the access technology utilization groups except for youths with total blindness who used access technology in only one wave. These youths scored statistically significantly lower on the standardized test than did those with low vision who did not use access technology at all ([beta] = -13.7, -25.1 to -2.4), scoring 13.7 points lower (see Table 4). Also, the results indicated that being older ([beta] = -2.6, -5.1 to -0.1), female ([beta] = -6.0, - 11.5 to -0.5), or African American ([beta] = -10.2, -17.4 to -3.0) was negatively correlated with performance on tests. The older students scored 2.6 points lower than did the younger students, the female students scored 6 points lower than did the male students, and the African American students scored 10.2 points lower than did the white students. This regression model accounted for 9.7% of the variance ([[gamma].sup.2] = .097). An ANOVA showed that this model was a good fit of the data, with an alpha level of .05: F(9, 265 = 3.054, p = 00).

SYNONYMS/ANTONYMS SUBTEST

The Synonyms/Antonyms subtest contained data for 266 participants. The results indicated that neither visual impairment nor the utilization of access technology were statistically significant predictors of test performance (see Table 3). However, the youths with total blindness who used access technology in only one wave scored statistically significantly lower than did the youths with low vision who did not use access technology at all ([beta] = -10.2, -20.1 to -0.3), with the former scoring 10.2 points lower (see Table 4). In addition, the African American youths ([beta] = -7.7, -13.9 to - 1.4) and the Hispanic youths ([beta] = - 8.4, - 15.1 to -1.6) scored statistically significantly lower than did the white youths (7.7 points and 8.4 points, respectively). This regression model accounted for 6.4% of the variance ([[gamma].sup.2] = .064). An ANOVA showed that this model was a good fit of the data, with an alpha level of .05: F(9, 265 = 1.95, p = .04).

Discussion

The findings of this study suggest that the individual and combined association of the utilization of assistive technology and vision status during the study period had a limited predictive relationship to performance on the standardized tests that were administered in the original study. However, the youths with total blindness who used access technology during only one wave scored statistically significantly lower on several of the subtests than did the youths with low vision who did not use access technology at all. Furthermore, for certain subtests of the standardized test, age, gender, and race or ethnicity were statistically significant predictors of performance, yet little of the variation in test scores was accounted for by these factors (less than 10% for each model in each subtest).

These results seem to contradict those of previous studies (Alper & Raharinirina, 2006; Duhaney & Duhaney, 2000; Gerber, 2003; Kapperman & Sticken, 2003; Michaels & McDermott, 2003), which reported that the use of assistive technology services and devices is vital to the successful acquisition of educational materials and thus is essential to high academic achievement and competitive performance with nondisabled peers. Nevertheless, it is important to note that few of those studies were quantitative, and none had a sample size as large as the NLTS2 data set. In addition to the combined effect of visual impairment and the use of access technology, the analysis revealed a statistically significant relationship between demographic confounders and the standardized tests, which is consistent with previous analyses of the NLTS2 data set by the original study methodologists for the examination of disability groups in general (Blackorby et al., 2003, 2004; Wagner et al., 1993).

The results of the study may have alternative explanations that were not explained by the current statistical analysis. Previous studies (Blackorby et al., 2003, 2004; Wagner et al., 1993) indicated three broad categories of interrelated variables that may help explain variations in performance on standardized tests for youths with disabilities in general. These categories included individual characteristics of youths, characteristics of the youths' households, and school experiences, in addition to test accommodations as directed by the youths' Individualized Educational Programs. Extensive examination of these possible confounding variables was limited because of the large number of missing variables, which is a limitation of the study. It should also be noted that the findings reported here are unweighted results and are thus not generalizable to the entire population of youths who receive specialized services for visual impairments.

Conclusion

The results of the study seem to indicate that for youths with visual impairments, access technology is not as effective as was previously thought. According to the results, assistive technology does not level the playing field in the acquisition of educational information, at least as measured by the participants' performance on standardized tests. There are several potential reasons for these results, including the ambiguity of the access technology variables that were included in the analyses. It is unclear if the participants understood the definition of access technology or details regarding the quality, timing, extent, and quantity of the access technology services reported in the survey, all of which are important for assessing the use of access technology fully. It is also important to note that the built-in accessibility features in most contemporary computer operating systems give youths with low vision additional "high-tech" options for accessibility without using specialized access technology software or devices. The results seem to support those of previous studies that reported variability in test performance on the basis of age, race, and gender, with older students falling behind over time and nonwhite students using access technology less frequently.

These findings should be carefully considered by educational researchers, program planners, and policy makers who advocate for access technology to ensure equity in the classroom. Future research should clearly define access technology to examine fully the quality, quantity, and specific access technology that is utilized and its impact on academic performance. Access technology services should be standardized to ensure that equitable reception and attention are focused on assuring that youths who rely on access technology exclusively to access textbooks and other educational materials are given ample opportunities for it. Finally, academic outcomes for youths with visual impairments should be monitored in the coming years as access technologies advance, further leveling the academic playing field for youths with visual impairments.

References

Alper, S., & Raharinirina, S. (2006). Assistive technology for individuals with disabilities: A review and synthesis of the literature. Journal of Special Education Technology, 21(21), 47-64.

American Foundation for the Blind. (n.d.). What the national agenda means for visually impaired children. Retrieved from http:// www.afb.org/Section.asp?SectionID= 56&DocumentID = 2465

American Printing House for the Blind. (2007). Distribution of eligible students, based on the federal quota census of January 2, 2006 (fiscal year 2007). In 2007 annual report, October 1, 2006-September 30, 2007, American Printing House for the Blind. Retrieved from http://www.aph.org/ fedquotpgm/dist07.html

Blackorby, J., Chorost, M., Garza, N., & Guzman, A. (2003). The academic performance of secondary school students with disabilities. In M. Wagner, C. Marder, J. Blackorby, R. Cameto, L. Newman, P. Levine, & E. Davies-Mercier (with M. Chorost, N. Garza, A. Guzman, & C. Sumi), The achievements of youth with disabilities during secondary school (pp. 4.1-4.15). Menlo Park, CA: SRI International.

Blackorby, J., Chorost, M., Garza, N., & Guzman, A. (2004). The academic performance of elementary and middle school students with disabilities. In J. Blackorby, M. Wagner, R. Cameto, E. Davies, P. Levine, L. Newman, C. Marder, & C. Sumi (with M. Chorost, N. Garza, & A. Guzman), Engagement, academics, social adjustment, and independence: The achievements of elementary and middle school students with disabilities (pp. 4.1-4.22). Menlo Park, CA: SRI International.

Corn, A., Hatlen, P., Huebner, K., Ryan, F., & Siller, M. A. (1995). The national agenda for the education of children and youths with visual impairments, including those with multiple disabilities. New York: American Foundation for the Blind.

Duhaney, D., & Duhaney, L. (2000). Assistive technology: Meeting the needs of learners with disabilities. International Journal of Instructional Media, 27, 393-401.

Gamble, M. J., Dowler, D. L., & Hirsch, A. E. (2003). Informed decision making on assistive technology workplace accommodations for people with visual impairments. Work, 22, 123-130.

Gerber, E. (2003). The benefits and barriers of computer use for individuals who are visually impaired. Journal of Visual Impairment & Blindness, 97, 536-550.

Hatlen, P. (1996). The core curriculum for blind and visually impaired students, including those with additional disabilities. RE:view, 28, 25-32.

Huebner, K. E., Merk-Adam, B., Stryker, D., & Wolffe, K. E. (2004). The national agenda for the education of children and youths with visual impairments, including those with multiple disabilities--revised. New York: American Foundation for the Blind.

Individuals with Disabilities Education Act, 20 U.S.C. [section] 1400 et seq. (2004).

Kapperman, G., & Sticken, J. (2003). Using the Braille Lite to produce mathematical expressions in print. Journal of Visual Impairment & Blindness, 97, 710-719.

Michaels, C. A., & McDermott, J. (2003). Assistive technology integration in special education teacher preparation: Program coordinators' perceptions of current attainment and importance. Journal of Special Education Technology, 18(3), 29-41.

National Center for Education Statistics. (2005). NAEP inclusion policy. Retrieved from http://nces.ed.gov/nationsreportcard/ about/inclusion.asp

National Instructional Materials Accessibility Standard (71 FR 41084) Appendix C, Part 300. (2006). Retrieved from http:// nimas.cast.org/system/files/OSEP.NIMAS_ .Summary.pdf

No Child Left Behind Act of 2001, [section] 6301 et seq. (2002).

SRI International. (2000). National Longitudinal Transition Study 2: Planned direct assessment content and process. Menlo Park, CA: Author.

Wagner, M., Blackorby, J., & Hebbeler, K. (1993). Beyond the report card: The multiple dimensions of secondary school performances of students with disabilities. Menlo Park, CA: SRI International.

Wagner, M., Newman, L., Cameto, R., & Levine, P. (2006). The academic achievement and functional performance of youth with disabilities. A final report from the National Longitudinal Transition Study-2 (NLTS2) (NCSER 2006-3000). Menlo Park, CA: SRI International.

Willoughby, D., & Duffy, S. (1989). Handbook for itinerant and resource teachers of blind and visually impaired students. Baltimore, MD: National Federation of the Blind.

Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson Tests of Academic Achievement--Research edition. Itasca, IL: Riverside.

Amy L. Freeland, Ph.D., former doctoral student, Department of Blindness and Low Vision Studies, Western Michigan University, 1903 West Michigan Avenue, Mail Stop 5218, Kalamazoo, MI 49008; e-mail: <afreeland@cdc.gov>. Robert Wall Emerson, Ph.D., associate professor, Department of Blindness and Low Vision Studies, Western Michigan University; e-mail: <robert. wall@umich.edu>. Amy B. Curtis, Ph.D., MPH, associate professor, Interdisciplinary Health Sciences Program, College of Health and Human Services, Western Michigan University, 1903 West Michigan Avenue, Mail Stop 5254, Kalamazoo, MI 49008; e-mail: <amy.curtis@ wmich.edu>. Kieran Fogarty, Ph.D., MPH, associate professor, Interdisciplinary Health Sciences Program, College of Health and Human Services, Western Michigan University; e-mail: <kieran. fogarty@wmich.edu>.
Table 1
Sizes of access technology utilization groups, by visual
status, gender, age, and race or ethnicity (numbers;
percentages in parentheses).

                                   Utilization   Utilization
                         No        during one     in both      Total
Characteristic       utilization    wave only       waves        N

Vision status
  Total blindness     11 (12.1)     23 (25.3)     57 (62.6)       91
  Low vision          52 (29.4)     46 (26.0)     79 (44.6)      177
Gender
  Male                38 (26.4)     36 (25.0)     70 (48.6)      144
  Female              25 (20.2)     33 (26.6)     66 (53.2)      124
Age (in years)
  13                    6(28.6)       5(23.8)     10 (47.6)       21
  14                  18 (22.8)     20 (25.3)     41 (51.9)       79
  15                  26 (36.1)     14 (19.4)     32 (44.4)       72
  16                  11 (14.9)     25 (33.8)     38 (51.4)       74
  17                     2(9.1)       5(22.7)     15 (68.2)       22
Race or ethnicity
  White               30 (18.0)     39 (23.4)     98 (58.7)      167
  African American    14 (25.5)     17 (30.9)     24 (43.6)       55
  Hispanic            19 (41.3)     13 (28.3)     14 (30.4)       46

Table 2
Means and standard deviations for the Woodcock Johnson III
subtests, by various characteristics of the participants
(numbers; percentages in parentheses).

                                  Applied     Mathematic
                                 Problems     Calculation
Characteristic                   (n = 193)     (n = 263)

Access technology
 utilization group
  None                          88.3 (13.8)   91.1 (16.3)
  One                           84.7 (23.3)   85.7 (30.1)
  Both                          84.7 (21.8)   86.4 (26.0)
Visual status
  Low vision                    87.4 (19.2)   89.4 (23.9)
  Total blindness               80.3 (22.0)   83.3 (27.5)
Visual impairment x
 utilization
  Low vision: No wave           89.7 (12.9)   91.4 (16.7)
  Low vision: One wave          86.8 (19.4)   88.5 (29.5)
  Low vision: Both waves        85.9 (23.2)   88.6 (24.4)
  Total blindness: No wave      82.1 (16.4)   90.0 (15.3)
  Total blindness: One wave     76.8 (34.2)   79.6 (31.2)
  Total blindness: Both waves   81.1 (16.8)   83.4 (28.0)
Gender
  Male                          89.0 (17.0)   89.6 (24.0)
  Female                        82.2 (22.5)   84.8 (26.5)
Age (in years)
  13                            80.5 (19.5)   90.4 (21.7)
  14                            85.3 (21.6)   90.8 (23.6)
  15                            88.4 (18.9)   91.4 (22.1)
  16                            85.9 (19.8)   84.6 (27.0)
  17                            80.0 (19.6)   66.9 (29.8)
Race or ethnicity
  White                         87.7 (20.4)   87.9 (26.5)
  African American              82.1 (22.1)   85.0 (25.2)
  Hispanic                      88.6 (14.0)   88.2 (20.5)

                                   Passage
                                Comprehension     Science
Characteristic                    (n = 267)      (n = 266)

Access technology
 utilization group
  None                           86.4 (19.2)    89.5 (18.1)
  One                            82.1 (30.4)    85.9 (23.1)
  Both                           82.6 (27.8)    88.9 (20.5)
Visual status
  Low vision                     83.4 (27.0)    89.1 (18.9)
  Total blindness                83.3 (26.3)    86.7 (23.7)
Visual impairment x
 utilization
  Low vision: No wave            86.8 (20.3)    89.2 (18.9)
  Low vision: One wave           84.9 (29.1)    88.4 (19.8)
  Low vision: Both waves         80.4 (29.4)    89.4 (18.7)
  Total blindness: No wave       84.5 (13.7)    90.9 (14.2)
  Total blindness: One wave      76.2 (32.9)    80.8 (28.5)
  Total blindness: Both waves    85.8 (25.2)    88.3 (22.9)
Gender
  Male                           84.3 (26.4)    88.0 (23.6)
  Female                         82.3 (27.2)    88.6 (16.6)
Age (in years)
  13                             89.4 (22.4)    85.8 (26.4)
  14                             87.2 (24.6)    88.0 (19.4)
  15                             84.9 (22.7)    91.4 (17.2)
  16                             78.4 (30.8)    88.3 (21.6)
  17                             75.8 (32.4)    81.2 (25.3)
Race or ethnicity
  White                          86.4 (27.9)    91.5 (20.4)
  African American               79.2 (24.9)    83.1 (21.4)
  Hispanic                       77.4 (23.0)    82.6 (18.5)

                                  Social       Synonyms/
                                  Studies      Antonyms
Characteristic                   (n = 266)     (n = 266)

Access technology
 utilization group
  None                          88.7 (17.0)   94.3 (15.7)
  One                           86.6 (27.3)   93.2 (22.2)
  Both                          86.7 (23.8)   92.7 (20.7)
Visual status
  Low vision                    89.0 (21.6)   94.2 (19.8)
  Total blindness               83.5 (26.2)   91.2 (20.3)
Visual impairment x
 utilization
  Low vision: No wave           89.9 (17.7)   94.5 (16.6)
  Low vision: One wave          92.2 (19.3)   97.2 (20.8)
  Low vision: Both waves        86.5 (24.8)   92.2 (21.7)
  Total blindness: No wave      82.8 (12.2)   93.5 (16.8)
  Total blindness: One wave     75.2 (36.7)   85.2 (23.1)
  Total blindness: Both waves   78.0 (22.4)   93.0 (19.5)
Gender
  Male                          90.0 (22.3)   93.4 (20.6)
  Female                        83.3 (24.3)   92.9 (19.4)
Age (in years)
  13                            89.4 (17.7)   94.0 (21.0)
  14                            89.7 (20.9)   94.9 (19.6)
  15                            90.3 (19.2)   94.7 (17.5)
  16                            83.8 (26.4)   92.1 (20.6)
  17                            76.8 (33.0)   85.2 (25.0)
Race or ethnicity
  White                         89.6 (23.1)   95.8 (19.7)
  African American              80.6 (22.6)   89.1 (21.1)
  Hispanic                      85.8 (24.0)   88.7 (18.4)

Note: 95% confidence interval.

Table 3
Regression coefficients for the individual effect of visual
status and utilization of access technology on test
performance.

Visual impairment and
utilization of access
technology by subtests                n    [beta]   SE [beta]

Applied Problems
  Degree of vision
    Low vision (referent)            157    --         --
    Total blindness                   45   -6.6        3.6
  Utilization of access technology
    No wave (referent)                65    --         --
    One wave                          54    0.5        3.8
    Both waves                        83    0.0        3.5
Mathematics Calculation
  Degree of vision
    Low vision (referent)            182    --         --
    Total blindness                   93   -5.8        3.4
  Access technology utilization
    No wave (referent)                66    --         --
    One wave                          71   -3.3        4.3
    Both waves                       138   -0.8        3.9
Passage Comprehension
  Degree of vision
    Low vision (referent)            184    --         --
    Total blindness                   95   -0.5        3.5
  Utilization of access technology
    No wave (referent)                67    --         --
    One wave                          72   -2.0        4.5
    Both waves                       140   -2.5        4.1
Science
  Degree of vision
    Low vision (referent)            183    --         --
    Total blindness                   95   -3.4        2.7
  Utilization of access technology
    No wave (referent)                67    --         --
    One wave                          72   -2.4        3.5
    Both waves                       140    0.2        3.2
Social Studies
  Degree of vision
    Low vision (referent)            183    --         --
    Total blindness                   95   -7.3        3.1
  Utilization of access technology
    No wave (referent)                67    --         --
    One wave                          72    0.9        4.0
    Both waves                       140    1.7        3.6
Synonyms/Antonyms
  Degree of vision
    Low vision (referent)            183    --         --
    Total blindness                   95   -3.6        2.6
  Utilization of access technology
    No wave (referent)                67    --         --
    One wave                          72    0.4        3.4
    Both waves                       140   -0.6        3.1

Visual impairment and
utilization of access
technology by subtests                   95% CI

Applied Problems
  Degree of vision
    Low vision (referent)                  --
    Total blindness                   -13.6 to 0.4
  Utilization of access technology
    No wave (referent)                     --
    One wave                           -7.1 to 8.0
    Both waves                         -6.9 to 6.9
Mathematics Calculation
  Degree of vision
    Low vision (referent)                  --
    Total blindness                   -12.4 to 0.8
  Access technology utilization
    No wave (referent)                     --
    One wave                          -11.8 to 5.2
    Both waves                         -8.5 to 6.9
Passage Comprehension
  Degree of vision
    Low vision (referent)                  --
    Total blindness                    -7.4 to 6.4
  Utilization of access technology
    No wave (referent)                     --
    One wave                          -10.9 to -6.9
    Both waves                        -10.6 to 5.5
Science
  Degree of vision
    Low vision (referent)                  --
    Total blindness                    -8.7 to 2.0
  Utilization of access technology
    No wave (referent)                     --
    One wave                           -9.4 to 4.5
    Both waves                         -6.1 to 6.5
Social Studies
  Degree of vision
    Low vision (referent)                  --
    Total blindness                  -13.4 to -1.5 *
  Utilization of access technology
    No wave (referent)                     --
    One wave                           -7.0 to 8.8
    Both waves                         -5.5 to 8.8
Synonyms/Antonyms
  Degree of vision
    Low vision (referent)                  --
    Total blindness                    -8.7 to 1.6
  Utilization of access technology
    No wave (referent)                     --
    One wave                           -6.3 to 7.1
    Both waves                         -6.7 to 5.5

Note: [beta] = beta; SE [beta] = standard error beta; and
95% CI = 95 percent confidence interval.

* Statistically significant at a = .05; CI does not
include 0.

Table 4
Regression coefficients for the combined effect of visual
status and utilization of access technology on test
performance: National Longitudinal Transition Study 2, 2002
and 2004.

Visual impairment and
utilization of access                            SE
technology by subtests             n   [beta]  [beta]      95% CI

Applied Problems
  Low vision: No wave (referent)   53     --     --          --
  Low vision: One wave             43    -1.9   4.3     -10.4 to 6.6
  Low vision: Both waves           61    -2.5   4.0     -10.3 to 5.3
  Total blindness: No wave         12    -6.5   6.7      19.7 to 6.7
  Total blindness: One wave        11   -11.0   6.8     -24.4 to 2.4
  Total: Both waves                22   -10.5   5.5     -21.3 to 0.3
Mathematics Calculation
  Low vision: No wave (referent)   55     --     --          --
  Low vision: One wave             49    -1.5   5.1     -11.6 to 8.6
  Low vision: Both waves           80    -0.7   4.6      -9.8 to 8.4
  Total blindness: No wave         12    -0.1   8.3     -16.4 to 16.4
  Total blindness: One wave        23    -9.5   6.5     -22.4 to 3.4
  Total blindness: Both waves      60    -7.8   5.0     -17.7 to 2.1
Passage Comprehension
  Low vision: No wave (referent)   54     --     --          --
  Low vision: One wave             49    -1.9   5.4     -12.5 to 8.7
  Low vision: Both waves           79    -6.8   4.9     -16.3 to 2.8
  Total blindness: No wave         12    -3.4   8.8     -20.8 to 13.9
  Total blindness: One wave        22   -10.5   6.8     -24.0 to 2.9
  Total blindness: Both waves      59    -3.9   5.3     -14.4 to 6.6
Science
  Low vision: No wave (referent)   55     --     --          --
  Low vision: One wave             49    -1.6   4.2      -9.8 to 6.6
  Low vision: Both waves           79    -1.5   3.8      -8.9 to 5.9
  Total blindness: No wave         12    -0.3   6.8     -13.7 to 13,2
  Total blindness: One wave        24   -10.4   5.2    -20.6 to -0.1 *
  Total blindness: Both waves      59    -5.1   4.1     -13.2 to 3.0
Social Studies
  Low vision: No wave (referent)   55     --     --          --
  Low vision: One wave             49    3.20   4.6     -5.9 to 12.3
  Low vision: Both waves           79    -2.4   4.2     -10.6 to 5.8
  Total blindness: No wave         12    -6.5   7.6     -21.4 to 8.4
  Total blindness: One wave        24   -13.7   5.8    -25.1 to -2.4 *
  Total blindness: Both waves      59    -5.0   4.6     -14.0 to 4.0
Synonyms/Antonyms
  Low vision: No wave (referent)   55     --     --          --
  Low vision: One wave             49     2.3   4.0     -5.6 to 10.2
  Low vision: Both waves           70    -3.2   3.6     -10.3 to 4.0
  Total blindness: No wave         12    -2.3   6.6     -15.3 to 10.7
  Total blindness: One wave        24   -10.2   5.0    -20.1 to -0.3 *
  Total blindness: Both waves      59    -4.3   4.0     -12.1 to 3.6

Note: [beta] = beta; SE [beta] = standard error beta; and 95% CI = 95
percent confidence interval.

* Statistically significant at [alpha] = .05; CI does not include 0.
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Author:Freeland, Amy L.; Emerson, Robert Wall; Curtis, Amy B.; Fogarty, Kieran
Publication:Journal of Visual Impairment & Blindness
Article Type:Report
Geographic Code:1USA
Date:Mar 1, 2010
Words:6782
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