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Internet use by transition-aged youths with visual impairments in the united states: assessing the impact of postsecondary predictors.

Structured abstract: Introduction: Youths and adults without disabilities have been increasing the regularity of their online connectivity at a rapid pace, as previous research has indicated. The study presented here examined the degree to which transition-aged youths with visual impairments have used the Internet and what outcomes they have achieved following their graduation from high school. Methods: The authors conducted a secondary analysis of the National Longitudinal Transition Study-2 (NLTS2) database and assessed a nationally representative sample of youths with visual impairments who were making the transition from high school to further education or work. The descriptive analysis showed estimates of Internet use among these youths during three periods (2005, 2007, and 2009). Multilevel longitudinal modeling was used to analyze the change in the prevalence of Internet use as the youths matured and to assess the impact of postsecondary opportunities on online communication. Results: Across the three waves of data collection, an average of 43% of the transition-aged youths with visual impairments used the Internet regularly for online communication. There was no significant change in the use of the Internet as these young people matured (p = .06). The youths who used the Internet for regular online communication were significantly more likely to be engaged in postsecondary work (p < .01), education or training (p < .01), or volunteering and engaging in community service (p < .01). Discussion: The study showed that transition-aged youths with visual impairments are not progressing at rates commensurate with their peers without disabilities in the vital area of Internet use. Their best performance, in Wave 5, was 49%; compared to 93% of youths without disabilities in the same year. The positive impact of participation in postsecondary opportunities on the likelihood of online connectivity is demonstrated. Implications for practitioners: Implications for lifelong success in today's technology-driven society are discussed.

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Technology is everywhere in the modem world--at home, in school, and at work. Desktop computers, laptops, tablets, cell phones, and personal digital assistants abound. Access to and use of the Internet has increased exponentially, according to the Pew Research Center's ongoing Internet and American Life project: Trend data from 2000 through 2011 indicated that in 2000, just under 50% of American adults used the Internet, while by 2011, approximately 80% of American adults were online (Pew Internet Project, 2011a, 2011b). These data are consistent with other resources, such as the Internet World Stats website (2011) and the 2009 Current Population Survey report on Internet access (U.S. Census Bureau, 2010). Teenagers are even more likely than adults to be online. In a September 2009 survey, the Pew researchers found that 93% of American teenagers were using the Internet (91% of boys and 94% of girls)--95 % of the 14-17-year-old group and 88% of the 12-13-year-old group (Pew Internet Project, 2011c).

In an online article for its Digital Divide section, the Pew Research Center reported that only 54% of Americans with disabilities use the Internet (Fox, 2011). However, these data were reported only for adults, the majority of whom (58%) were aged 50 or older. No comparable data were provided for teenagers with disabilities. According to DeBell and Chapman (2006), there are differences in Internet use between children and adolescents with and without disabilities. However, these differences are smaller than those found for Internet use by adults with and without disabilities. Comparing students with and without disabilities, DeBell and Chapman estimated that the rates of computer and Internet use for youths with disabilities were approximately 10 percentage points lower than for youths without disabilities.

The movement toward a technology-based society makes using technology an imperative for transition-aged youths with and without disabilities. Rideout, Foehr, and Roberts (2010), who assessed the use of media by a national sample of youths without disabilities, found that these youths spent an hour and 29 minutes, on average, using computers every day, 66 minutes (75%) of which was spent using the Internet to find information and communicate with others. Furthermore, this average amount of time that the youths used a computer in a typical day increased by nearly a half hour over the previous five years, that is, from approximately an hour to approximately an hour and a half (Rideout et al., 2010).

For transition-aged youths who are visually impaired (that is, are blind or have low vision), Internet use may be predicted by their postsecondary achievements, such as engagement in postsecondary education, advanced vocational training, work experience, employment, or volunteer or community service. This article explores, through a secondary analysis of data sets from the National Longitudinal Transition Study-2 (NLTS2), how young adults with visual impairments, aged 17 to 25, are using technology to access the Internet. Although assistive technology was not a direct measure in the study, it is important to consider its use and what role it plays in these young adults' use of the Internet.

Although the NLTS2 interviews with parents and youths contained a variety of questions related to the use of technology by teenagers, few questions were asked consistently across all waves of the data collection. Therefore, we chose the one variable that was consistently asked of parents and youths through the final wave of data collection, "Did the young adult engage in online activity (such as use of e-mail, instant messaging, or taking part in chat rooms)?" In our study, the term Internet use indicated the young adults' general online activity, online communication, Internet research, social networking, and other related activities. This variable provides the most current information available from the NLTS2 about the use of technology by young adults with visual impairments in the United States. To investigate the young adults' use of technology to access online services and perform online tasks, we addressed the following research questions:

1. What percentage of transition-aged youths who are visually impaired with or without additional disabilities were engaged in online activity that involved the use of technology during the three measured time periods (Wave 3 = 2005, Wave 4 = 2007, and Wave 5 = 2009)?

2. How did the prevalence of online activity by the transition-aged youths with visual impairments change as they matured?

3. How did the trajectories of online activity vary according to participation in postsecondary activities, specifically postsecondary education or training, postsecondary employment, and postsecondary volunteering or community service?

Method

DATA SOURCE

The source of data for our study was the NLTS2 federal database. This secondary analysis did not involve any direct contact with participants and was therefore exempt from approval by an institutional review board. Data from NLTS2 Wave 3 (2005), Wave 4 (2007), and Wave 5 (2009) were assessed by each of the three research questions. These three waves were the three most recent waves and included the final wave of NLST2 data collection (Wave 5).

The NLTS2 sample was nationally representative of each federal disability category, including low vision and blindness (SRI International, 2011a). Our study assessed restricted-use data and was therefore provided to the Institute of Education Sciences (IES) Data Security Office for a disclosure review. No potential disclosures were found, and the IES approved the release of all resulting data that are included in this article.

PARTICIPANTS

The term visually impaired refers to individuals with any degree of visual impairment, including those who have low vision and those who are totally blind. The participants included youths who were visually impaired with additional disabilities and youths who were visually impaired without any additional disabilities. The youths in the Wave 3 sample were aged 17 to 21 (M = 19), those in the Wave 4 sample were aged 19 to 23 (M = 21), and those in the Wave 5 sample were

The sample of youths with visual impairments who were involved in the descriptive analysis of research question 1 is provided by wave (Wave 3, n = 90; Wave 4, n = 80; and Wave 5, n = 130), and 520 were involved in the multivariate longitudinal analysis of research questions 2 and 3. The demographic characteristics of the sample as of Wave 5 (the most recent wave) are shown in Table 1.

MEASURES

All the measures assessed in the study are binary, with two possible response options (yes or no). All the variables were derived from scripted telephone interviews or mail surveys with the transition-aged youths with visual impairments and separate interviews or surveys with the youths' parents. Three predictor variables are representative of three areas of postsecondary achievement (employment, education, and volunteer or community service). The Internet-related technology outcome is described next.

Internet-related technology outcome

The youths were asked, "How frequently do you use e-mail and instant messaging or take part in chat rooms?" The parents of the youths were also asked, "How frequently does the youth use e-mail and instant messaging or take part in chat rooms?" This NLTS2 survey question was investigated to assess the Internet use of youths with visual impairments and was the NLTS2 survey question of interest for our study. The assistive technology devices and software that the youths used to access the Internet were related to this survey item but were not investigated in our study.

Response options for both questions were presented in a Likert-scale format: several times a day = 5, once a day = 4, several times a week = 3, once a week = 2, or less often than once a week = 1. These question-and-response options were asked in the same way during Waves 3, 4, and 5 of NLTS2. In each of the three waves, the measure was collapsed to include the responses of both the youths and the parents.

For the purposes of our study, these measures were recoded into dichotomous outcomes representative of youths who were communicating online regularly and those who were not. The youths were considered to be using the Internet regularly if the response was "several times a day, once a day, or several times a week." The youths were considered not to be communicating regularly online if the response option was "once a week or less often than once a week." Thus, the response options "several times a day," "once a day," or "several times a week" were recoded into one response option (1 or "yes"), and the response options "once a week" or "less often than once a week" were recoded into a second response option (0 or "no"). The rationale for recoding was based on the ease of understanding the "big picture." Likert scales fall into the ordinal measurement level and involve the interpretation of intervals between each value, ranking observations in terms of magnitude but not revealing the exact magnitude of the differences (Jamieson, 2004). In comparison, dichotomous measures have the advantage of the simplicity of interpretation (Stockburger, 1998).

Predictor variables included in the analysis

The measure of postsecondary employment indicated whether the youth had worked since high school. More specifically, this binary measure documented if the youth ever had a paid job since leaving high school = 1 or did not ever have a paid job since leaving high school = 0. This NLTS2-created variable included the responses of both the youths and parents in Wave 5.

The postsecondary education measure assessed if the youth had attended any postsecondary institution since high school. This binary predictor indicated if the youth had taken classes at a postsecondary institution since high school = 1 or had not = 0. In this instance, postsecondary institution included a two-year community college, vocational school, or four-year college or university. This variable was collapsed to include the responses of both the youths and parents in Wave 5.

The third and final predictor involved in research question 3 was participation in volunteering or community service. This measure pertained only to postsecondary youths (those who were not enrolled in high school). These youths were asked, "During the past 12 months, have you done any volunteer or community service activities?" Likewise, the parents were asked, "During the past 12 months, has the youth done any volunteer or community service activities?" Responses for both the parent and youth interview questions were "yes" and "no." This measure was collapsed to include the responses of both the parents and youths in Wave 5.

DATA ANALYSIS

Data analysis procedure for research question 1

Research question 1 involved a descriptive analysis of the Internet-related technology measure. This analysis resulted in an estimate of the proportion of transition-aged youths with visual impairments who were using the Internet during the three measured waves. Normalized weights were used to account for the complex sampling design involved in the NLTS2 database. The normalized weights addressed nonresponse, oversampling, and any other sample selection biases while retaining the original sample size.

Data analysis procedure for research questions 2 and 3

For research questions 2 and 3, binary logistic longitudinal multilevel modeling was used to assess the binary outcome across the three measured periods. There are several possible link functions when the model is binomial (Raudenbush & Bryk, 2002). The population-average model was used in the multilevel longitudinal modeling analysis to estimate average probability across the entire U.S. population of transition-aged youths with visual impairments (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2004). For all the multilevel longitudinal modeling analyses, we used a standard alpha value in the hypothesis testing ([alpha] = .05).

Results

Research question 1 assessed the proportion of transition-age youths with visual impairments in the United States who were engaged in online activity during the three measured periods (2005, 2007, and 2009). The results of the secondary analysis of this nationally representative sample showed that less than half the youths with visual impairments in the United States were using the Internet regularly to communicate with others. The results for research question 1 are presented in Table 2.

Research question 2 analyzed the change in the prevalence of online activity by transition-aged youths with visual impairments as time progressed. Although there was a measurable increase in the use of the Internet during the three measured periods (see Table 2), this increase was not significant. There was no significant change in Internet use by transition-aged youths who were visually impaired in the United States in 2005, 2007, and 2009 (p = .06). Table 3 presents the descriptive statistics and results for research question 2.

Research question 3 assessed how the trajectories of online activity varied according to participation in postsecondary activities. More specifically, it looked at how the trajectories of online activity by youths with visual impairments in the United States varied according to employment, education, and volunteering or community service during the postsecondary years. The results showed that having a paid job since high school (p < .01), participating in postsecondary education or training (p < .01), and participating in postsecondary volunteering or community service (p < .01) were all statistically significant predictors of Internet use by these youths.

Youths with visual impairments who had a paid job since leaving high school were approximately 2.24 times more likely to use technology for regular online communication than were those who did not. Those who attended postsecondary education or training were 5.32 times more likely to communicate with others online than were youths who did not, and those who participated in postsecondary volunteering or community service were 1.78 times more likely to use the Internet to interact with others than were youths who did not. The descriptive statistics and the results for research question 3 are shown in Table 4.

Discussion

The study took a close look at the degree to which transition-aged youths with visual impairments have used the Internet and achieved postsecondary outcomes (that is, engagement in employment, postsecondary education, or volunteering or community service). What we discovered was that less than half the youths who were visually impaired used the Internet for regular online communication during each of the three waves of data collection. Across the three waves of data collection, an average of 57% of transition-age youths with visual impairments did not use the Internet regularly for online communication. This finding of Internet use coincided almost exactly with the estimates of the use of assistive technology by Kapperman, Sticken, and Heinze (2002) and Kelly (2009, 2011). According to these previous studies, the majority (close to or exactly 60%) of youths with visual impairments were not using assistive technology.

With regard to youths without disabilities, the use of assistive technology is a moot point. These youths were found to be using the Internet for more than an hour every day, and their use of digital devices to access the Internet continues to grow (Rideout et al., 2010). Our study showed that transition-aged youths with visual impairments are clearly not progressing at rates that are commensurate with youths without disabilities in this vital area of online connectivity. We found that even though there was a measurable increase in Internet use as the youths with visual impairments matured, the change in Internet use over the five-year time span that we investigated was not significant.

We were especially interested in the impact of engagement in postsecondary opportunities on online communication. It was reassuring to find that those transition-aged youths with visual impairments who were engaged in postsecondary opportunities (employment, education or training, and volunteer or community service) were significantly more likely to use the Internet for regular online communication. The regularity with which these youths used the Internet was significantly predicted by their participation in each of these postsecondary activities. Transition-aged youths with visual impairments who had worked at a paid job since high school or volunteered in the community were about two times as likely to have used the Internet as were youths who did not. Those who attended postsecondary school or vocational training were more than five times as likely to have used the Internet for regular online communication than were those who did not.

Thus, the impact of engagement in postsecondary opportunities on the regularity of Internet use was clearly demonstrated. Transition-aged youths with visual impairments who were involved in postsecondary opportunities were two to five times more likely to be online regularly. This finding demonstrates the critical importance of youths with visual impairments being involved in postsecondary work, school, and community service to increase their likelihood of having the necessary technology related skills to use the Internet regularly in today's high-technology climate.

LIMITATIONS

A strength and weakness of our study was that it was a secondary analysis of an existing federal database that included a nationally representative sample of transition-age youths with many different types of disabilities, not just visual impairments. The NLTS2 database was designed to address research questions across a wide range of disability groups that included those with visual impairments. We had to work with the methods that were already in place and were unable to adjust anything about the broad survey design or methods of implementing the survey. We were able to parcel out data specific to students with visual impairments from the broader NLTS2 data that were collected.

For example, the technology outcome selected for the study was representative of both mainstream technology and assistive technology. It was likely that for the youths who were visually impaired to access the Internet for online communication, they were using assistive technology (such as screen-reading software, screen-enlargement software, accessible notetakers, or low vision devices). It was also likely that if the youths were not using assistive technology, it would have been beneficial in most instances. This indirect measure of the use of assistive technology was intended to be an underlying aspect of the study, since the actual assistive technology the youths used to access the Internet was not directly assessed by the NLTS2 survey across all waves of data collection. The use of assistive technology in and of itself was assessed in Waves 1, 2, and 3 but not in Waves 4 or 5. In addition, several other variables of interest that involved the use of technology were included in Waves 1-4 but were not included in Wave 5 (for example, "The youth knows how to use a computer for homework [yes or no], playing games [yes or no], Internet [yes or no], e-mail [yes or no], or chat [yes or no]") (SRI International, 2011b). The fact that these variables were not addressed in Wave 5 (the most recent data collection period) was a limitation of our study.

Although reporting by parents has been noted as a preferred source of reporting for youths of all ages (Pastor & Reuben, 2002), it may be an additional limitation to consider in this study. Missing data must also be considered as a possible limitation. Last, it should be noted that although the data are recent, they may still be considered somewhat dated, since Internet technology advances at a fast pace.

Conclusion

Although there is a plethora of literature on the use of technology and accessing the Internet by people without disabilities in the United States, the opposite is true about the use of technology and Internet access by people with disabilities, and notably absent from the mix is published documentation of the use of technology by youths with visual impairments (Internet World Stats, 2011; Pew Internet Project, 2011a, 2011b; Rideout et al., 2010; Roberts, Foehr, & Rideout, 2005). When such published information is available about the use of technology and the Internet by children, youths, and adults with disabilities, it tends to be less current than similar information about people without disabilities and generic rather than disability specific (DeBell & Chapman, 2006; Kaye, 2000). An exception in terms of timeliness is the succinct, online report available from the Pew Internet and American Life Project (Fox, 2011); however, that report addressed disability in general terms only.

Future research in this area should consider providing results of analyses comparing differences that may exist in Internet communication for those who are visually impaired with additional disabilities and those who have a visual impairment as their only disability. Although there may not be a current summary report that addresses this particular research question, databases and survey questions are readily available for additional analyses.

Furthermore, reports specific to the use of technology to access the Internet by adults with visual impairments in the United States are both rare and dated (Gerber, 2003; Gerber & Kirchner, 2001; Leonard, 2002). There has also been a smattering of reports on the use of technology to access the Internet by people with disabilities in other countries (Knight, Heaven, & Christie, 2002; Ommerborn & Schuemer, 2001; Pilling, Barrett, & Floyd, 2004) and specifically people with visual impairments (Armstrong & Murray, 2010; Douglas, Corcoran, & Pavey, 2006; Evans & Douglas, 2008). The latter tend to focus on individuals with visual impairments using technology to participate in training or distance education. These articles have reported similar rates of computer and Internet use in other developed countries to the rates reported in the United States. The Network 1000 report (Douglas et al., 2006) indicated that using the Internet and e-mailing are the most common computer uses among young people with visual impairments in the United Kingdom. Despite evidence of the viable relationship between engagement in postsecondary opportunities and the use of technology by people who are visually impaired (Byerley & Chambers, 2002; Crudden, 2002; Luxton, 1990; Williamson, Schauder, & Bow, 2000), there is still evidence that the majority (nearly 60%) of transition-aged youths with visual impairments in the United States are not using assistive technology (Kelly, 2011) and that less than half, according to our study, are accessing the Internet to communicate regularly with others. The latter finding is a critical point in light of previous research that indicated that in addition to being predicted by promising postsecondary goals, such as employment and postsecondary training, use of the Internet for communication purposes facilitates social engagement (Wolffe & Kelly, 2011).

Preparation for today's workforce and higher education classrooms necessitates training with the Internet and widely used high-technology tools. For youths with visual impairments, this preparation also includes training with assistive technology, such as state-of-the-art software or devices that can make visual text instantaneously accessible to people who are blind or have low vision in a variety of formats (Kelly, 2009). Confidence and skill in the use of technology and accessing the Internet are important for people who are visually impaired to participate fully in today's technology-based society. Practitioners will want to encourage youths with visual impairments to use technology, including assistive technology, to go online so that they can communicate effectively with their same-aged peers for socialization purposes. Teaching students how to navigate websites, send and read e-mail, do online research, and engage in social networking needs to be a priority for instruction in the disability-specific skills or Expanded Core Curriculum (ECC) content areas. As this study demonstrated, young adults with visual impairments who have the skills that are necessary to communicate regularly with others online have also been assimilated successfully into mainstream post-secondary environments. Youths who hone their disability-specific skills across all areas of the ECC for use in postsecondary environments (volunteering and community service, further education and training, or employment) will be that much more likely to be active participants in today's technology-driven climate. The United Nations International Telecommunication Union workshop in 2010, which brought together nearly 200 cyber experts and other stakeholders who were concerned about access to the Internet by people with visual impairments, underscored the critical need for intervention that supports equal access for this population (UN News Service, 2010). Professionals in the field of visual impairment and blindness can do no less.

[check] EARN CEUs ONLINE by answering questions on this article. For more information, visit: <http://jvib.org/CEUs>. aged 21 to 25 (M = 23). The sample sizes are rounded to the nearest 10 to comply with the IES publication policy for restricted-use data.

References

Armstrong, H. L., & Murray, I. D. (2010). Adapting advanced information technology network training for adults with visual impairments. Journal of Visual Impairment & Blindness, 104, 504-509.

Byerley, S. L., & Chambers, M. B. (2002). Accessibility and usability of Web-based library databases for non-visual users. Library Hi Tech, 20, 169-178.

Crudden, A. (2002). Employment after vision loss: Results of a collective case study. Journal of Visual Impairment & Blindness, 96, 615-621.

DeBell, M., & Chapman, C. (2006). Computer and Internet use by students in 2003 (NCES 2006-065). Washington, DC: National Center for Education Statistics.

Douglas, G., Corcoran, C., & Pavey, S. (2006). Network 1000. Birmingham, England: Visual Impairment Centre for Teaching and Research, University of Birmingham.

Evans, S., & Douglas, G. (2008). E-learning and blindness: A comparative study of the quality of an e-learning experience. Journal of Visual Impairment & Blindness, 102, 77-88.

Fox, S. (2011). Americans living with disability and their technology profile. Retrieved from http://pewinternet.org/Reports/2011/ Disability.aspx

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

Gerber, E., & Kirchner, C. (2001). Who's surfing? Internet access and computer use by visually impaired youth and adults. Journal of Visual Impairment & Blindness, 95, 176-181.

Internet World Stats. (2011). Internet world stats: Usage and population statistics. Bogota, Colombia: Miniwatts Marketing Group. Retrieved from http://www. internetworldstats.com/

Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38, 1217-1218.

Kapperman, G., Sticken, J., & Heinze, T. (2002). Survey of the use of assistive technology by Illinois students who are visually impaired. Journal of Visual Impairment & Blindness, 96, 106-108.

Kaye, H. S. (2000). Computer and Internet use among people with disabilities (Disability Statistics Report 13). Washington, DC: National Institute on Disability and Rehabilitation Research.

Kelly, S. M. (2009). Use of assistive technology by students with visual impairments: Findings from a national survey. Journal of Visual Impairment & Blindness, 103, 470-480.

Kelly, S. M. (2011). Assistive technology use by high school students with visual impairments: A second look at the current problem. Journal of Visual Impairment & Blindness, 105, 235-239.

Knight, J., Heaven, C., & Christie, I. (2002). Inclusive citizenship: Social equality for disabled people. London: Leonard Cheshire. Leonard, R. (2002). Statistics on vision impairment: A resource manual. New York: Lighthouse International.

Luxton, K. (1990). Training students in adaptive computer technology. Journal of Visual Impairment & Blindness, 84, 523-525.

Ommerborn, R., & Schuemer, R. (2001). Using computers in distance study: Results of a survey amongst disabled distance students. Hagen, Germany: FernUniversitat.

Pastor, N., & Reuben, C. A. (2002). Attention deficit disorder and learning disability: United States, 1997-98 (PHS 2002-1534). Washington, DC: U.S. Government Printing Office. Retrieved from http://www.cdc. gov/nchs/data/series/sr_ 10/sr 10_206.pdf

Pew Internet Project. (2011a). Trend data (teens): Online activities. Washington, DC: Pew Research Center. Retrieved from http://www.pewinternet.org/Static-Pages/ Trend-Data-(Teens)/Online-Activites-Total. aspx

Pew Internet Project. (2011b). Trend data (adults): Online activities. Washington, DC: Pew Research Center. Retrieved from http://www.pewinternet.org/Trend-Data(Adults)/Online-Activites-Total.aspx

Pew Internet Project. (2011c). Trend data (teens): Who's online. Washington, DC: Pew Research Center. Retrieved from http://www.pewinternet.org/Trend-Data(Teens)/Whos-Online.aspx

Pilling, D., Barrett, P., & Floyd, M. (2004). Disabled people and the Internet: Experiences, barriers and opportunities. London: City University.

Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

Raudenbush, S., Bryk, A., Cheong, Y. F., Congdon, R., & du Toit, M. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.

Rideout, V., Foehr, U. G., & Roberts, D. F. (2010). Generation M2: Media in the lives of 8- to 18-year-olds (Report 8010). Menlo Park, CA: Henry J. Kaiser Family Foundation.

Roberts, D. F., Foehr, U. G., & Rideout, V. (2005). Generation M: Media in the lives of 8-18 year-olds (Report 7251). Menlo Park, CA: Henry J. Kaiser Family Foundation.

SRI International. (2011a). NLTS2 database overview. Menlo Park, CA: Author. SRI International. (2011b). NLTS2 database structure. Menlo Park, CA: Author.

Stockburger, D. W. (1998). Multivariate statistics: Concepts, models, and applications. Springfield: Missouri State University. Retrieved from http://www.psychstat. missouristate.edu/multibook2/mlt.htm

UN News Service. (2010, February 5). Internet access for visually impaired is focus of UN workshop. Retrieved from http:/www. un.org/apps/news/printnewsAr.asp?nid= 33682

U.S. Census Bureau. (2010). Computer and Internet use in the United States: October 2009. Retrieved from http://www.census. gov/hhes/computer/publications/2009.html

Williamson, K., Schauder, D., & Bow, A. (2000). Information seeking by blind and sight impaired citizens: An ecological study. Information Research, 5(4). Retrieved from http://informationr.net/ir/5-4/ paper79.html

Wolffe, K. E., & Kelly, S. M. (2011). Instruction in areas of the Expanded Core Curriculum linked to transition outcomes for students with visual impairments. Journal of Visual Impairment & Blindness, 105, 340-349.

Stacy M. Kelly, Ed.D., COMS, assistant professor, Department of Special Education, Illinois State University, Campus Box 5910, Normal, IL 61790; e-mail: <skelly@ilstu.edu>. Karen E. Wolffe, Ph.D., consultant, Career Counseling and Consultation, LLC, 2109 Rabb Glen Street, Austin, TX 78704; e-mail: <kwolffe@austin.rr.com>.
Table 1

Demographic characteristics of the transition-
aged youths with visual impairments: Wave 5.

Variable                                     Percentage

Degree of visual impairment
  Total blindness                                12

Visual impairment other than total
  blindness                                      88

Additional disabilities (a)
  Attention deficit disorder or attention
  deficit hyperactivity disorder                 31

Cerebral palsy                                   6

Deafness, hard of hearing, or hearing
  impairment                                     4

Developmental delay                              2

Dyslexia                                         18

Emotional disability, behavior
  disorder, or having emotional
  problems                                       16

Learning disability                              47

Mental retardation                               7

Multiple disabilities                            3

Physical or orthopedic impairment                12

Speech impairment or communication
  impairment                                     5

Age
  21                                             6
  22                                             18
  23                                             18
  24                                             39
  25                                             19

Gender
  Male                                           66
  Female                                         34

Race or ethnicity
  White                                          39
  African American                               29
  Hispanic                                       15
  Asian American or Pacific Islander             2
  American Indian or Alaska Native               15

High school completion status
  Youth graduated                                89
  Youth did not graduate                         11

Note: Normalized weights were used in the
analysis of demographic characteristics.

(a) Additional disabilities were reported whenever
more than 1 % of the sample presented with a
particular additional disability.

Table 2
Percentage of transition-aged youths who used
the Internet, by wave.

Wave    Percentage    SD     SE

3          35%       0.48   0.07
4          44%       0.50   0.05
5          49%       0.50   0.11

Note: SD = standard deviation, SE = standard
error.

Table 3

Descriptive statistics and results for research
question 2.

Variable name (NLTS2                      Log-    Odds
database variable name)     M      SD     odds    ratio

Internet use              0.44    0.48    0.13    1.13
  np3Pl3b_J10 (Wave 3)
  np4Pl3b_J10 (Wave 4)
  np5Pl3b_J10 (Wave 5)

Note: M = mean; SD = standard deviation.
(a) Significant (p < .05).

Table 4
Research question 3 descriptive statistics and results.

Variable name                                           Log-    Odds
(NLTS2 database variable name)            M      SD     odds    ratio

Postsecondary employment
  np5JobSinceHS ever (Wave 5)            .71    .45    0.81 *   2.24
Postsecondary education
  np5S3a_S4a_S5a_A3a_A3e_A3i (Wave 5)    .52    .50    1.67 *   5.32
Volunteer/community service
  participation np5P8_J4 (Wave 5)        .35    .48    0.58 *   1.78

Note: M = mean; SD = standard deviation; * significant (p< .05).
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Title Annotation:CEU Article
Author:Kelly, Stacy M.; Wolffe, Karen E.
Publication:Journal of Visual Impairment & Blindness
Article Type:Report
Geographic Code:1USA
Date:Oct 1, 2012
Words:5433
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