White / Caucasian

Interviews were used to gain information about demographics, employment histories, income sources and amounts, clinical symptoms and other relevant information at the beginning of the study and then at 6 month intervals for 24 months. A variety of employment data was gathered such as hours worked, wages, job duties, benefits. Psychiatric symptoms were assessed using the PANSS a semi structured rating scale.
Bivariate analysis were conducted to test for significant differences in study conditions. Two vocational outcome variables were computed for every month of the 24 month follow up period: competitive employment and work for 40 or more hours in a single month. The independent variable was the local unemployment rate for the geographic area surrounding each program. Some demographic and clinical covariates that should be included in multivariate models predicting employment were excluded due to their high correlation with other variables.
Unemployment rates over times were inspected for each of the seven counties in which study sites were located. Then hierarchical random regression analysis using time varying and fixed covariates were used to determine the effect of unemployment rate on each of the two employment outcomes. Random effects logistic regression modeling, addressed issues found in longitudinal multi-site data such as missing observations, fixed versus time varying covariates and more.

Data was collected on a number of variables using the RSA 911 database. This included: disability, wages earned, hours worked, and cost of services. Vocational rehabilitation counselors are required to keep this type of documentation. The rate of employment was determined by dividing the number of individuals who had their case closed due to an employment outcome by the total number of job seekers in that group.

A survey was sent to the adult service providers of 166 people who were included in the Illinois Supported Employment Projects database. The survey include questions related to: worker characteristics, Job coach/Co-worker involvement, Benefit-cost analysis, and Job separation. Dependent variables included: cost efficiency, length of employment, job retention, hours worked per month, gross pay, IQ,level of mental retardation, gender, ethnicity, age and number of disabilities. The independent variable was collected by the coworker Involvement Instrument that assesses the Involvement of coworkers in training supported employees. There was no control. Four analysis took place. The first compared employment outcomes to the degree of coworker Involvement. The second compared the supported employee demographics to the degree of coworker Involvement. Both of these analysis were repeated using data from 1990 and 1994. In addition, an analysis of variance were performed for the nominal-scale variables and Pearsonian correlation coefficients were generated for continuous scale variables.

This included data from the NLTS2 Waves 1 to Wave 4 parent/youth phone interview and/or mail survey data (Wave 1 was collected in the 2000–2001; Wave 2 was collected in the 2002–2003; Wave 3 was collected in the 2004–2005; and Wave 4 was collected in the 2006–2007 school years) and Wave 1 to Wave 2 school program survey data (Wave 1 was collected in the 2001–2002 and Wave 2 was collected in the 2003–2004 school years). The information was gathered for the first time in the 2000–2001 school year and last time in the 2006–2007 school year. SAS 9.2 was used for statistical analyses. All the data used had no missing values. The cases containing missing values were removed. Variables were described using descriptive statistics. Continuous variables, values were reported as means ± standard deviations (SDs). Categorical variables, values were reported as percents. A three-step process was used to determine the factors significantly associated with participation in employment for students with autism.

This was a qualitative research design. Interviews were used to collect data.

Variables that predicted employment outcomes were grouped into personal variables: gender, race, disability severity, age, education, and government benefits and rehabilitation variables. Rehabilitation variables consisted of: receiving an initial assessment, medical restoration, post-secondary education, business and vocational training, adjustment training, on the job training, miscellaneous training, substantial counseling and guidance, job finding services, job placement, transportation, maintenance, personal assistive services, rehab engineering, assistive technology, and other. Data was analyzed using CHAID.

A data mining technique, exhaustive chi-squared automatic interaction detector (CHAID), was used to analyze. In the current study, exhaustive CHAID was used to build classification trees. This technique uses a systematic algorithm to detect the strongest association between predictors and the outcome variable (e.g., employment outcomes) through a comprehensive search of the predictors and the levels of predictors from the entire set that show the most differentiation on the employment outcome variable. The degree of differentiation is depicted sequentially in a decision tree format to show the optimally split predictors. Thus, homogeneous groups of vocational rehabilitation clients could be identified based on their demographic characteristics, work disincentives, and service patterns in terms of their observed levels on the outcome variable. The statistical software SPSS Answer Tree 2.0 was used to conduct the exhaustive CHAID analysis. The alpha level for all statistical tests was 0.01. This study corrected for the number of statistical tests within each predictor using a Bonferroni feature in the Answer Tree software.

The measures included the Addiction severity index-multimedia version (ASI-MV), Social adjustment scale-self report (SAS-SR), Employment questionnaire, Treatment services review and Satisfaction questionnaire. Around 75% of the sample completed 3 or more CD-ROM sessions. These participants completed a satisfaction questionnaire after each session and at the end entire course. Follow up assessment was scheduled 6 month from baseline. Subjects completed SAS-SR and Employment Questionnaire. They were also contacted by telephone to complete a follow up to the ASI.

Participants were asked to complete a shipping task that involved an average of 73 steps. The percentage of task steps completed correctly was calculated by dividing the number of relevant completed steps by the sum of relevant completed and relevant not completed steps and multiplying by 100.

Literature search included a combination of strategies, including PubMed search with keywords, examination of table of contents from major journals in related fields, and consultation with leading researchers.