WBU-ICEVI Joint General Assemblies The Impact of the Expanded Core Curriculum on Select Quality of Life Indicators for Students with Visual Impairment ICEVI Day, 22 August 2016 WBU-ICEVI Joint General Assemblies Orlando, FL

The NLTS2 – Dataset National Longitudinal Transition Study 2 Funded by the United States Department of Education, authorized by IDEA amendments Gathered data from school districts, parents of students with disabilities, the educators of students with disabilities, and from students with disabilities themselves. Began in the 2000-2001 school year with students who were in at least grade 7 or between the ages of 13 and 16, continued through the 2009-2010 school year. Results can be generalized to students with disabilities in general, as well as to each of the individual disability categories (Levine, Marder, & Wagner, 2004). Captured information related to each of the ECC areas, with the exception of sensory efficiency, while students were in school.

Project Information Institute for Education Sciences Grant #R32409288 $232,581 2009-2010 Used data from Waves 1 and 4

There are three types of lies: Keep in Mind … There are three types of lies: Lies Damn Lies, and Statistics Mark Twain

Factor Analysis 188 submitted for Exploratory Factor Analysis with 9-factor solution, using Principal Components method with Varimax Rotation, Factor Loadings > .5 Resulted in different factors in each educational setting

So, What? 8 of the 9 ECC components appeared to be addressed in this sample of youth and adults with visual impairments But little distinct pattern Not discrete components Failed to find a relationship between identified factors and composite variable of “adult success”

So What, continued For those adults engaged in competitive employment, best predictor variables were: Social behaviors and opportunities Career and transition services and instruction Alternative access (braille & large print), but in opposite direction More braille and large print = less chance of employment? In general education settings, no factors emerged as predictors

So What, continued When general education and special education settings are considered together, more related services received seems to predict less opportunity for competitive employment But, which came first?

Preliminary Analyses Wave 5 MANOVA to investigate SEM results (difference in means with several dependent variables) Simplified from the full model to 3 outcomes of interest: Any Paid Employment Outside the Home Lodging (living independently from parent[s]) Post-Secondary Education Use a MANOVA analyses to investigate why the SEM results acted in unexpected ways. Steps: Simplified the outcome from the full QOL model to the 3 area that have been identified in Gen Sped literature as indicators to support post-secondary transition Repeated the factor analysis that Dr. Ferrell used for Wave 4, One change—Co-Varied receiving “Related Services” with the assumption that high school students who are receiving more services are actually students who have additional exceptionality that were not necessarily identified on their IEPs in addition to their vision impairment.

One Change Co-varied receiving “Related Services” Assumption that high school students who are receiving more services are students who have additional disabilities Not necessarily identified multiple disability on IEPs

Sample Wave 5 (2009-2010) Youth with visual impairments only No additional exceptionality Sample Size Unweighted n = 230 Weighted n = 26,975

Factors Independent Variables: Covariate: Factor 1: Categorical: Career Factor 2: Dichotomous: Career, Transition Factor 3: Orientation and Mobility Skills Factor 4: Social Recreation & Leisure Factor 5: Alternative—Technology Factor 6: Alternative—Accommodations (i.e., braille) Factor 7: Parent Expectations Factor 8: Independence Covariate: Factor 9: Related Services

Model 1: Any Paid Work Outside of Home Factor Factor Description Tests of Main Effects-Wald F  F1 Categorical-Career Transition/ Job Experience 4.439 F6 Alternative Accommodations (i.e., Braille) 10.158 F8 Independence 6.126

Model 2: Living Independently Factor Factor Description Tests of Main Effects-Wald F F1 Categorical-Career Transition/Job Experience 8.548 F2 Dichotomous-Career Educational Supports 4.558 F3 Orientation & Mobility 8.439 F5 Compensatory Technology 5.114 F6 Alternative Accommodations (i.e., Braille) 5.756 F7 Parental Expectations 4.836 F8 Independence 9.170

Model 2: Living Independently Note: at alpha = .05 F4 Social, Recreation, & Leisure (p = .047; Wald F = 2.705) is also statistically significant and could be retained.

MANOVA 3 Findings Outcome Variable: Model F1+F2+F3+F4+F5+F6+F7+F8 Transition To Post-Secondary Education n = 230; df = 174 Model F1+F2+F3+F4+F5+F6+F7+F8 Covariate F9 (Related Services) Accounted for 29.4% of variance in employment outcomes.

Model 3: Post-Secondary Education Factor Factor Description Tests of Main Effects-Wald F F1 Categorical-Career: Transition/Job Experience 7.114 F2 Dichotomous-Career: Educational Supports 5.933 F3 Orientation & Mobility 3.964 F4 Social, Recreational, & Leisure 11.666 F6 Compensatory Accommodations (i.e., Braille) 7.477 F7 Parental Expectations 3.165 F8 Independence 7.224

Model 3: Post-Secondary Education Note: at alpha = .05 F5 Compensatory Technology (p = .006; Wald F = 3.756) is also statistically significant and could be retained.

Limitations? Same as before: > 10,000 variables Much missing data, especially from teachers Number of dichotomous variables required recoding of all variables into dichotomous values Only 11.4% of sample competitively employed at Wave 4, even though out of school > two years New: Wave 5 (n = 230) 30.4% reported full-time employment in last job

Send-Offs For the sample of youth with visual impairments only, up to 8 of the 9 ECC components appeared to be addressed Consistently significant in all .001 models: Independence Alternative accommodations Categorical Career (job experience) Adjust alpha to .05 and Alternative Technology also significant

Send-Offs, continued For the Wave 5 sample of young adults with only visual impairments best predictor variables for employment, independent living, and post-secondary education were: Career and transition services and instruction Alternative access (braille & large print) Independence skills

Send-Offs, continued Outcomes for participation in paid employment, post-secondary education, and living independently differ within the total population of young adults identified as having visual impairment.

Questions To Ask Ourselves Where is the disconnect? Why is this sample of youth as unemployed as blind adults were 50 years ago? Are we expending our energy in the right place at the right time?

Lessons Challenge your assumptions! Don’t stop looking/investigating Ask questions

Thank you, merci, gracias, grazie, danke, cпасибо, obrigado, 谢谢 Kay Alicyn Ferrell, PhD Kay.Ferrell@unco.edu Kathryn D Botsford, PhD kdbotsford@gmail.com