Information technology (IT) literacy skills are increasingly important for all adolescents to learn, as the majority of post-school pursuits will require at least some amount of computer skills. For adolescents with disabilities, this urgency is perhaps more pronounced, as this subpopulation typically experiences more dismal post-school outcomes than their peers without disabilities. The purpose of this study was to examine the psychometric properties of the Envision Information Technology Literacy (EITL) scale based on pretest and posttest responses of students with and without disabilities (N = 150). Findings show promising validity and reliability of the EITL scale. Implications for practice are discussed with regard to uses in high school career courses and as an age-appropriate transition assessment.
BaroneD.WrightT. E. (2008). Literacy instruction with digital and media technologies. The Reading Teacher, 62, 292–302.
2.
BentlerP. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
3.
BentlerP. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
4.
CoiroJ. (2003). Rethinking comprehension strategies to prepare students for critical evaluation on the Internet. The New England Reading Association Journal, 39(2), 29–34.
5.
CoiroJ. (2012). Understanding dispositions toward reading on the Internet. Journal of Adolescent & Adult Literacy, 55, 645–648.
6.
ConceiçãoS. C. (2016). Competing in the world’s global education and technology arenas. New Directions for Adult & Continuing Education, 149, 53–61.
7.
De AyalaR. J. (2013). Theory and practice of item response theory. New York, NY: Guilford Press.
8.
DrewS. V. (2012). Open up the ceiling on the Common Core State Standards: Preparing students for 21st century literacy—Now. Journal of Adolescent & Adult Literacy, 56, 321–330.
9.
Every Student Succeeds Act (ESSA), S. 1177 § 1000 et seq.
10.
FitzgeraldG.KouryK.MitchemK. (2008). Research on computer-mediated instruction for students with high incidence disabilities. Journal of Educational Computing Research, 38, 201–233.
11.
HuL. T.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
12.
HutchisonA.WoodwardL. (2014). A planning cycle for integrating digital technology into literacy instruction. The Reading Teacher, 67, 455–464.
13.
IzzoM. V.YurickA.NagarajaH. N.NovakJ. A. (2010). Effects of a 21st century curriculum on students’ information technology and transition skills. Career Development for Exceptional Individuals, 33, 95–105.
14.
Kaiser Family Foundation. (2010). Generation M2: Media in the lives of 8- to 18-year-olds—A Kaiser Family Foundations Study. Menlo Park, CA: Author. Retrieved from www.kff.org/entmedia/upload/8010.pdf
15.
LeuD. J.KinzerC. K.CoiroJ. L.CammackD. W. (2004). Toward a theory of new literacies emerging from the Internet and other information and communication technologies. Theoretical Models and Processes of Reading, 5, 1570–1613.
16.
LevyF.MurnaneR. J. (2006). Why the changing American economy calls for twenty-first century learning: Answers to educators’ questions. New Directions for Youth Development, 110, 53–62.
17.
LittleT. D. (2013). Longitudinal structural equation modeling. New York, NY: Guilford Press.
18.
LombardiA. R.IzzoM. V.GelbarN.MurrayA.BuckA.JohnsonV.. . . KowittJ. (in press). Leveraging information technology literacy to enhance college and career readiness for secondary students with disabilities. Journal of Vocational Rehabilitation.
National Governors Association Center for Best Practices, Council of Chief State School Officers. (2010). Common Core State Standards. Washington, DC: Author.
24.
NorthropL.KilleenE. (2013). A framework for using iPads to build early literacy skills. The Reading Teacher, 66, 531–537.
PreacherK. J.MacCallumR. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2(1), 13–43.
27.
SanfordC.NewmanL.WagnerM.CametoR.KnokeyA. M.ShaverD. (2011). The post-high school outcomes of young adults with disabilities up to 6 years after high school. Key findings from the National Longitudinal Transition Study–2 (NLTS-2) (NCSER 2011-3004). Menlo Park, CA: SRI International. Retrieved from https://ies.ed.gov/ncser/pubs/20113004/pdf/20113004.pdf
28.
SchmittT. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304–321.
29.
SolbergV. S.PhelpsL. A.HaakensonK. A.DurhamJ. F.TimmonsJ. (2012). The nature and use of individualized learning plans as a promising career intervention strategy. Journal of Career Development, 39, 500–514.
30.
SteigerJ. H.LindJ. C. (1980, May). Statistically based tests for the number of common factors. Paper presented at the Annual Spring Meeting of the Psychometric Society, Iowa City, IA.
